Patent Publication Number: US-2022238033-A1

Title: Integrated Student-Growth Platform

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 17/086,733, titled “Integrated Student-Grown Platform,” filed on Nov. 2, 2020, which is a continuation of U.S. Patent Application No. 15/483,976, titled “Integrated Student-Growth Platform,” filed on Apr. 10, 2017, which claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application No. 62/320,595, titled “System and Method for the Discovery and Organization of Heterogeneous Instructional Resources to Optimize Student Growth,” filed on Apr. 10, 2016, the contents of all of which are incorporated herein by reference in their entireties. 
    
    
     BACKGROUND 
     1. Field of the Invention 
     The present invention relates to systems and methods for use in learning in an educational environment. More particularly, the present invention relates to an integrated architecture for a student-growth platform for use in an educational environment for the discovery and organization of heterogeneous instructional resources to optimize student growth and development in an online, cloud-based environment. This student-growth platform provides the ability to surround the instructional process, at any level (district, state, national, or global) with growth-optimizing options and integrates assessment, teaching, and learning-solution operations for students and educators. 
     2. Description of the Related Art 
     Teachers are continually faced with the problem and challenges of planning lessons before teaching students. It is estimated that teachers today can spend as much as seven to ten hours per week assembling lesson plans. 
     With digital instruction on the rise, within the last twenty years, this lesson-planning exercise has become quite an ordeal for teachers. In the past, teachers often relied on a printed textbook to impart from and organize student learning. Yet, with the increasing use of digital resources, teachers have an increasing desire to build lesson plans typically involving a combination of core curriculum, supplemental resources, and ad hoc internet-based resources (OERs). However, searching for, finding and organizing instructional resources into an efficacious and differentiated lesson plan that varies and is tailored to individual students has become a daunting task. As the digital universe of educational content expands and the complexity of digital curriculums increases, the lesson-planning exercise only becomes more challenging and time consuming every day. 
     Research reveals that the lesson-planning exercise and ordeal consumes at least seven to ten hours weekly. In many teaching scenarios, teachers build lesson plans by using ad hoc variations of a seven-step workflow with each step typically relying on a distinct tool such as MS Excel, Google, and pen &amp; paper. The typical seven-step workflow includes: 1) review student data (50 minutes); 2) identify learning objectives using a pacing guide (20 minutes); 3) prepare to teach this week&#39;s skills or concepts (25 minutes); 4) search for instructional resources (45 minutes); 5) create homework (50 minutes); 6) find or build assessments (90 minutes); and 7) create assignments (50 minutes). 
     Existing digital instruction technology is unable to help teachers, bridge the gap from use of assessment data to identifying particular skills that a particular student or student group is ready to learn. Nor can it use those target skills to first identify relevant instructional resources and then, to organize those resources to optimize and accelerate student learning, growth, and performance toward achieving academic excellence. Previous solutions were inadequate or deficient and are solved by the present technology. 
     SUMMARY 
     The present technology created overcomes the deficiencies and limitations of prior systems and methods, at least in part by, providing an integrated learning and student-growth platform with improved systems and methods for discovery, organization and prescription of heterogeneous instructional resources for student learning and development. 
     The student-growth platform instantiates a closed-loop system accepting teacher context (including a chosen curriculum), and student context (including assessment) and automatically generating an output with a digital lesson plan. In some embodiments, the closed-loop system, global in scope, comprises five fundamental elements: 1) a growth-projection engine or platform; 2) a universal-skills-pool engine or platform; 3) a curriculum-to-skills-mapper engine or platform; 4) a growth-optimizing-instructional-resource-recommendation engine or platform; and 5) a lesson-planning platform. 
     In accordance with one aspect of the invention, the student-growth platform uses a student growth percentile (SGP) algorithm to uniquely position a target student or a target group of students into a scaled learning progression scheme based on the amount of time elapsed since the last set of assessments for the target student or the target group of students. In some implementations, this SGP algorithm may be implemented by a scaled learning-progression engine, which determines if particular students fall into a particular group, a class, a group of classes, a school, a group of schools, a district, or a state. 
     In accordance with another aspect of the invention, the student-growth platform has a system and method adapted to establish the best possible set of skills to teach a group of students on a particular day. This system and method combines computer-adapted testing (CAT)+student-growth-percentile (SGP)+time-based projection+entry points to establish curriculum entry point. 
     In accordance with yet another aspect, the student growth system has a mastery-model system that is adapted to combine a student&#39;s CAT scores with practice scores and evaluate the combined score against a learning progression scheme. This innovation focuses on normalizing computer-adapted testing outcomes (GOMs) with practice assignments to create an integrated model of mastery, which may be evaluated against a learning progression scheme. 
     In accordance with yet another aspect, the student-growth platform has a mastery model, extended by an inferencing capability. The integrated mastery model extends actual testing, by using intelligent inferencing capability, based on determining the relationship of objects within the learning progression scheme. 
     In accordance with yet another aspect, the student-growth platform utilizes a universal-skills-pool to enable curriculum mapping. This feature, system, or method facilitates lesson planning by mapping to the teacher&#39;s chosen curriculum, pacing guide, or text book. Skills may be divided by domains. In some embodiments skills may be divided into four domains including foundational skills, language, literature, informational text. 
     In accordance with yet another aspect, the student-platform has a seven step planning engine, which is a lesson-planning engine that mirrors or simulates a teacher&#39;s complete process by providing a score-to-skill-to-resource-to-assignment-to-plan-management process. 
     In accordance with yet another aspect, the student-growth platform has a multi-dimensional-response-item (MIRT) model that is utilized to bind assignments from assessment, instruction, and practice assignments into a unified scale that supports the other aspects of the student-growth platform. 
     In accordance with yet another aspect, the student-growth platform has a four level mastery model supporting lesson planning that has the ability to view mastery by assignment source, by probed assessment, by general outcome measurement (GOM) assessment, and by an integrated model. 
     In accordance with yet another aspect, the student-growth platform uses a universal skills pool to bridge from a GOM to a range of curriculums. This feature or system enables the lesson planning engine to act as a Rosetta Stone or like language capability for automatically linking multiple assessments to multiple government-created learning standards. 
     In accordance with yet another aspect, the student growth system or platform has a mechanism for probabilistic mastery, by which probabilistic algorithms are incorporated into the student-growth platform. 
     In accordance with yet another aspect, the student-growth platform has an open-lesson planning eco-system, by which the lesson planning process weaves into many different assessment sources with many different standards, curriculums, instructional resources, and assignment delivery systems. 
     The system and methods disclosed here are advantageous in a number of respects. They provide a significant improvement over existing systems and other solutions that exist. The integrated student-growth platform enables successful competition with “Assessment” vendors that lack the ability to create a complete feedback loop, and “Practice” vendors, who lack assessment functionality. The student-growth platform is global in scope and enables any education company or organization in the world to build a network-effect based category that spans separate markets in “ed-tech” today. The innovative approach of the instant technology is combined with unique access to student data to provide significant competitive advantages. This cutting edge approach of the student-growth platform may be implemented by a single source implementation, an implementation with assignment generation, and/or an implementation with curriculum mapping. 
     The student-growth platform in accordance with the present invention advantageously solves four significant problems that have in the past defeated other attempts to create a system for organizing resources into growth-optimizing lesson plans. The first is management of heterogeneous resources. Lesson planning solutions so far have typically focused on utilizing resources from one or a small number of sources. By using a recommendation engine coupled to a universal-skills-pool engine, the student-learning-and growth platform allows teachers or educators to employ almost any IR in their lesson plan. 
     The second is that teachers must bring their own instruction. Teachers use a wide range of curriculums (text books, core instructional software, and pacing guides) to provide the frame for lesson planning. Most solutions either ignore the teacher&#39;s chosen curriculum or require the teacher to abandon that solution and employ a different scope &amp; sequence of skills instruction embedded into the solution. The student-learning-and-growth platform advantageously allows teachers to utilize their chosen curriculum in the planning process, regardless of what curriculum they&#39;ve chosen. 
     The third advantage is planning from projected growth data. Most lesson planning proceeds from a point of either no data available or from observation of assessment information that is not current. Lesson planning is typically performed weekly and often with even greater frequency. The student-learning-and-growth platform has a growth-projection engine that translates assessment data into an actionable planning template, building a valid bridge from historical assessments on a student to a student&#39;s zone of proximal development (ZPD) in a short time. 
     The fourth advantage is the automated workflow. Lesson planning solutions so far have failed to mirror the full end-to-end process employed by teachers. As a consequence, they have not delivered meaningful reduction in teacher time spent in planning lessons. 
     Other innovative aspects include corresponding systems, methods, apparatus, and computer program products. 
     It should be noted that the language used in the present disclosure has been principally selected for readability and instructional purposes, and not to limit the scope of the subject matter disclosed herein. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention is illustrated by way of example, and not by way of limitation in the figures of the accompanying drawings in which like reference numerals are used to refer to the same or similar elements. 
         FIG. 1A  is a high-level block diagram illustrating a networked distributed environment including user/client devices for use by users (e.g., students, teachers, etc.) coupled to an integrated student-growth platform including different platforms with specific functions and dedicated to specific operations for educational use. 
         FIG. 1B  is a high-level block diagram illustrating an alternative embodiment of the distributed environment illustrated in  FIG. 1A . 
         FIG. 2A  is a block diagram illustrating example assessment and planning platforms within the integrated student-growth platform illustrated in  FIG. 1A . 
         FIG. 2B  is a block diagram illustrating example assignment and reporting platforms within the integrated student-growth platform. 
         FIG. 3  is a block diagram illustrating an example integrated student-growth platform with integrated assessment, planning, assignment, reporting coupled to the learning-progression engine, the master-maker engine and other elements of the platform. 
         FIG. 4  is a block diagram of the example integrated student-growth platform illustrating its example hardware units including data storage. 
         FIG. 5  is an example hardware configuration of an example user device that interacts with the student-growth platform. 
         FIG. 6  is a flow chart illustrating an example overall method of student growth according to a prescribed plan generated by the student-growth platform. 
         FIG. 7  is a flow chart illustrating an example method of assessment. 
         FIG. 8  is a flow chart illustrating an example method of a prescribed plan generated by the student-growth platform. 
         FIG. 9  is a flow chart illustrating an example method of prescribing assignments. 
         FIGS. 10 and 11  are a flow chart illustrating an example method of reporting by the student-growth platform. 
         FIG. 12  is a flow chart illustrating an example method of determining electronic resources and associating them with the assignment. 
         FIG. 13  is a flow chart illustrating an example method for reporting. 
         FIG. 14  is a observational graphic interface generated by the student-growth platform. 
     
    
    
     DETAILED DESCRIPTION 
     The systems and methods of this technology are directed to system architecture, technical platforms (e.g., to facilitate and link educational activities involving input and output by different users) and methods configured to facilitate student growth and development, by providing an integrated student-growth platform with technical tools for use in an online, cloud-based environment, for improving the educational process. This integrated student-growth platform provides a comprehensive view of student growth and mastery available for educators, by its observational interface, accessible to users or clients. This integrated student-growth platform addresses the problems that educators have faced so far, on how to accurately measure and monitor student growth and performance on a daily basis, as well as, differentiate and personalize instruction to each student. The integrated student-growth platform in accordance with the present invention permits educators to set goals and monitor student progress with greater efficiency. This integrated student-growth platform includes a workflow engine that allows educators (e.g., teachers) to manage and deliver all assignments from assignment engines to subject practices through a simple-to-use student inbox. This student-growth platform provides educators with a comprehensive view of student growth and mastery while giving them more time to focus on students. The student-growth platform fully integrates learning analytics to make decisions and lay the groundwork for increased interoperability with existing school systems and instructional partners. 
       FIG. 1A  illustrates a general distributed environment (e.g., cloud-based) as designated generally by reference numeral  100   a,  with users  114   a,    114   b,  through  114   n,  using user/client devices,  106   a,    106   b,  through  106   n,  and interacting with an integrated student-learning-and-growth platform  118 , via a network  102 . Each of the user devices may have a user application  108   a.  User/client communications flow via lines  112   a,    112   b,  through  112   n,  respectively, to the user devices,  106   a,    106   b,  through  106   n,  and through lines  104   a,    104   b,  through  104   n,  to the network  102  and through line  116  to the student-learning-and-growth platform  118 . The integrated student-learning-growth platform  118  integrates functionalities of various platforms, including but not limited to, an assessment platform  220 , a planning platform  222 , a learning-progression platform  224 , an assignment platform  226 , a mastery-maker platform  228 , a MIRT (multi-dimensional-response) platform  230 , and a reporting platform  232 . Access to each of these platforms is accomplished via an observation engine  221 , which is a part of the user interface of the student-growth platform  118 . Such platforms facilitate digital reading and enable collaboration in and with the pages of digital books, articles, and documents, enabling users to embed materials and assignments in the text itself or attach them and provide them separately. They facilitate attaching highlight and tag functions to a piece or portion of text in a single action or activity. In some implementations, the text of interest or display for use in assessment, lesson planning, or any other task described here may be a digital book, an electronic article, or any other available text content presented by a suitable electronic device. In some examples, the text may be the text content on a page of a digital book available on the web or downloaded as an ePub (electronic publication) or PDF (portable document format). 
     The integrated student-growth platform  118  may include one or more servers with one or more processors and one or more storage devices storing data or instructions executable by the one or more processors. For example, the integrated student-growth platform  118  may be a server, a server array or any other computing device, or group of computing devices, having data processing, storing and communication capabilities. The integrated student-growth platform  118  may be a virtual server (i.e., a virtual machine) implemented via software. For example, the virtual server operates in a host server environment and accesses the physical hardware of the host server including, for example, a processor, memory, storage, network interfaces, etc., via an abstraction layer (e.g., a virtual machine manager). It should be understood that the integrated student-growth platform  118  may be made up of any combination of devices and servers, or only one device or server. The integrated student-growth platform  118  may interact with the user devices  106   a - 106   n  or other third-party servers  117  or media-distribution servers  115 , media store  111 , or data stores  113   a  through  113   n,  of the distributed system  100   a,  via the network  102 , or may be coupled to and interact with any of these entities via a direct data connection. 
     In some embodiments, the entities of the distributed system  100   a  including the integrated student-growth platform  118  and the media-distribution server  115  may be implemented using cloud-based architectures where one or more computer functions are performed by remote computing systems and devices at the request of a local computer device. For example, a user/client device  106   a  may be a computing device having a limited set of hardware and/or software resources and may access hardware and/or software resources provided across the network  102  by other computer devices and resources, such as other user devices  106   b,  the third-party server  117 , the integrated student-growth platform  118 , or any other computing resources. The user/client device  106   a  may access these resources through a user application  108   a,  such as a web browser or customized application, and the results of any computer functions or resources may be delivered through the user application  108   a  to the user/client by the user device  106   a,  such as those described. The integrated student-growth platform  118  may be a cloud-based distributed computing system having dynamically scalable and virtualizable resources, and various functionality of the integrated student-growth platform  118 , including the functionality of the assessment platform  220 , the planning platform  222 , the learning-progression platform  224 , the assignment platform  226 , the mastery-maker platform  228 , the multi-dimensional-response platform  230 , and the reporting platform  232  and/or the media-distribution server  115  may be carried out and supplemented by computing systems and devices distributed over the network  102 . Although only one integrated student-learning-and-growth platform  118  is shown, multiple servers/platforms  118  may be included in the system  100   a  for regional or global reach or for specific purposes. 
     The media-distribution server  115  is a computing device and/or system for transmitting electronic resources stored in or available through the media data store  111  to the other entities of the environment  100   a.  In some embodiments, the media-distribution server  11  cooperates with the integrated student-growth platform  118  to provide an electronic resource to a user (e.g., teacher or student) for consumption. For example, the assessment platform  220  or the assignment platform  228  of the integrated student-growth platform may transmit a file (e.g., a webpage) to a user/client device  106  for display to the user/client  114 . In some instances, the file may include code (e.g., a video player) executable to receive a video and/or audio stream (e.g., an electronic resource) from the media distribution server  115  and render it for display to the user/client. In other embodiments, the integrated student-growth platform  118  performs the function of the media-distribution server  115 . In the depicted embodiment, the media-distribution server  115  is coupled to the network  102  via signal line  123  for communication with the other entities of the environment  100 . The media-distribution server  115  is also coupled to the media store  111  to access electronic resources and other data stored in the media store  111 . In some embodiments, the media-distribution server  115  is a hardware server including a processor, memory and network communication capabilities. In other embodiments, the media-distribution server  115  is a virtual server. 
     In some embodiments, the media-distribution server  115  transmits video and audio streams to one or more user/client devices  106   a - n.  The video and audio streams may be live feeds or may be previously recorded, stored as media objects in the media store  111 , and transmitted to the one or more user/client devices  106   a - n  on demand, via delayed broadcast, etc. In some embodiments, the audio and video are streamed from the media-distribution server  115  via the network  102 . In other embodiments, a user/client can download an instance of the video and audio media objects from the media-distribution server  115  to a local repository for storage and local playback. 
     The media-distribution server  115  and/or the integrated student-growth platform  118  is/are capable of transmitting any number of electronic resources to any number of user/client devices  106   a - n  simultaneously. While in the illustrated embodiment, only one media-distribution server  115  is shown, any number of media-distribution servers  115  and/or media stores  111  may be included in the distributed environment. For example, the media-distribution server  115  and the media store  111  may be a distributed server and storage system with local instances strategically located in locations where spikes in demand for the electronic resources are likely to occur. For example, if a cluster of user/client devices  106   a - n  are located in a particular geographic region, local instances of the media-distribution server  115  and the media store  111  may be coupled to the network  102  in that geographic region such that the media objects stored in the media store  111  may be served locally and at a faster data rate to that cluster of user/client devices  106   a - n.    
     It should be understood that, in some embodiments, the media-distribution server  115  and/or the third-party server  117  have the same or similar architecture (e.g., memory, processor, communication unit, bus, etc.) as the integrated student-growth platform  118  illustrated in  FIG. 2 , and thus the description of those components applies to the media-distribution server  11  and/or the third-party server  117 . 
     The media store  111  is an information source for storing data and providing access to stored data. The stored data may include the electronic resources described above, such as media objects including video, audio, vector-based files, electronic books, documents, etc. In some embodiments, the media store  111  is included in the memory (not shown) of the media-distribution server  115 . In other embodiments the media store  111  is included in the memory  404  (see  FIG. 4 ) of the integrated student-learning-and-growth platform. In yet other embodiments, the media store  111  is included in a server or storage system distinct from but accessible by the media-distribution server  115  and the integrated student-learning-and-growth platform. In some embodiments, the media store  111  includes a database management system (DBMS) executable by a processor to manage a collection of records, files, and objects including the media objects. For example, the database could be a structured query language (SQL) DBMS. In these embodiments, the integrated student-learning-and-growth platform  118  and/or the media-distribution server  115  are coupled to a data store  113   a  through  113   n,  via the bus  406  to store data in multi-dimensional tables having rows and columns, and manipulate, i.e., insert, query, update and/or delete, rows of data using programmatic operations (e.g., SQL queries and statements). 
     The third-party server  117  is a server hosting a network-based software application operable to provide various services or functionalities, and to send data to and receive data from the integrated student-learning-and-growth platform  118 , the media-distribution server  115 , and the client devices  106   a  . . .  106   n  via the network  102 . In the depicted embodiment, the third-party server  1117  is coupled to the network  102  via signal line  125  for communication with the other entities of the system  100 . The third-party server  117  is also coupled to the data stores  113   a - 113   n  by signal lines  121   a  and  121   n  for accessing and storing data. In some embodiments, the third-party server  117  is a server, server array or any other computing device, or group of computing devices, having data processing, storing and communication capabilities. In other embodiments, third-party server  117  is a virtual server. 
     The third-party server  117  can provide access to data stored in the data store  113   a - 113   n  that is associated with users of the integrated student-learning-and-growth platform  118 . In some embodiments, the data stored in the data store  113   a - 113   n  may include demographics data, achievement data, student data, teacher data, standards data, inter-rater reliability data, etc., and the third-party server  117  may include a software application for providing secure access to this data to the integrated student-learning-and-growth platform  118  over the network  102  via an API. For example, in an educational setting, the demographics data may include instructor and pupil demographics data, and may be segmented across school district, school, classroom, grade, etc.; the achievement data may include standardized test scores for educators and pupils; the student data may include student assessments of teachers (e.g., aggregated from surveys, reviews, etc.), biographical data describing the students, social graph data (e.g., aggregated from third-party social networking services), etc.; the teacher data may include biographical data describing the teachers, social graph data (e.g., aggregated from third-party social networking services), teacher preferences, teacher assessments of students (e.g., aggregated from surveys, reviews, etc.), etc.; and the standards data may include standards compiled and approved by a governing organization or institution which define the levels of attainment pupils much reach to be considered acceptably educated. It should be recognized that the fifty states in the U.S. may have unique needs and standards for education. The standards may require a varying range of skills. In some embodiments, a local instance of the data stored in the data store  113   a - 113   n  may be included in the data store  113   a - 113   n.  For example, a batch program operating periodically (every few minutes, hours, days, weeks, etc.) may retrieve a refreshed version of the data stored in the data store  113   a - 113   n.    
     In  FIG. 1A , the integrated student-learning-and-growth platform  118  includes an user-interface unit  119 , an observation engine  221 , an assessment platform  220 , a planning platform  222 , a learning-progression platform  224 , an assignment platform  226 , a mastery-maker platform  228 , a multi-dimensional response platform  230 , and a reporting platform  232 . The assessment engine  220  is software including routines for providing network-based assessment of students. 
     In some embodiments, the integrated student-learning-and-growth platform  118  may collect and store mapping information (i.e., social graphs) in the data store  113   a - 113   n  mapping how all users  106   a - 106   n  of the integrated student-learning-and-growth platform  118  are associated. For example, the social graph of each user may describe that user&#39;s  114   a  relationships with other users  114   n,  based at least in part on shared attributes, etc. All users  114   a - 114   n  may be associated by school, school district, subject matter taught, amount of experience, etc. Users may also define their own connections and sets of users using functionality provided by the client application  108  in cooperation with the integrated student-learning-and-growth platform  118 . For example, users  114   a - 114   n  sharing a similar subject matter may add one another to their community by using functionality provided by the client application  108   a  in cooperation with the integrated student-learning-and-growth platform  118 . The integrated student-learning-and-growth platform  118  may also generate and maintain a user profile in the data store  113   a - 113   n  for each user of the integrated student-learning-and-growth platform  118 . A user profile is a collection of personal and student/teacher/administrator data that is unique to a specific user. In some embodiments, the user profile is a digital representation of that person on a student/teacher/administrator development service and includes a user&#39;s customized settings and preferences, biographical information, schooling information, personal interests, teacher/administrator information, lesson-plan development information, social connection information, etc. 
     In some embodiments, access to the integrated student-learning-and-growth platform  118  via the network  102  may be provided to teachers and administrators in an academic environment or other educational setting, such as a school district. Instruction may be provided by electronic resources. 
     An electronic resource may be any electronic media for conveying information. For example, an electronic resource can be instructional in nature, and can convey knowledge, information, and resources to a user who interacts with or views it. As a further example, an electronic resources may include an instructional audio or video segment, a publication, an interactive instructional reference, a lesson plan, a planning tool, a community forum, a sharing tool, an industry standard, a portfolio tool, a progress monitoring tool, a reporting tool, etc. In some embodiments, an electronic resource can include any of textural data, graphical data, video data, audio data, etc. For example, the electronic resource may be a webpage including one or more of text, graphics, video, audio, etc. In another example, the electronic resource may be or include a downloadable or streamable media object, including, for example, an electronic document (e.g., portable document format (PDF) document), electronic book (e-book), digital video, digital audio file, vector graphics file, etc. In these or other examples, the electronic resource may include a dataset/electronic file with text, graphics, video, audio, etc. embedded therein. 
     In some embodiments, these electronic resources may convey information on various topics, such as student training, teaching skills, and similar subjects of consequence and importance to the growth and development of the users. For instance, for teachers an electronic resource may be an instructional video about an aspect of teaching, and a teacher may view the video by streaming it using his/her client device  106 . In another example, the electronic resource may be a web-based interactive reference including text, audio, video, etc., and the teacher may study the reference by interacting with it via a client application  106  such as a web browser before determining that it is appropriate for a particular student, student group or a particular lesson plan. 
       FIG. 1B  illustrates an alternative embodiment including a growth-projection engine  101  connected through the network  102  to a universal-skills pool  103 , a curriculum-to-skills mapper  105 , an instructional-resource-recommendation engine  107 , a lesson-planning engine  109 , and the media store  111 , a data store  113   a - 113   n,  media-distribution server  115  and the third-party server  117 . As illustrated here, the student-growth platform instantiates a closed-loop system accepting an input of teacher context (including a chosen curriculum) and an input of student context (including assessment) and automatically generating an output with a digital lesson plan. The closed-loop system, global in scope, may be tailored by institution or educational intent and comprises at least five fundamental components: 1) a growth-projection engine  101 ; 2) a universal-skills-pool engine  103 ; 3) a curriculum-to-skills-mapper engine  105 ; 4) an instructional-resource recommendation engine  107 ; and 5) a lesson-planning engine  109 . 
     The student-growth system has a unique student growth percentile algorithm (SGP) to selectively position a student or a group of students into a scaled learning progression scheme, based on the amount of time that has elapsed since observation of the last set of assessments on a particular student. In some implementations, this unique SGP algorithm may be implemented by a scaled learning progression schemes (similar to those used by the learning-progression platform in  FIG. 1A ), which may determine if students fall into a particular group, a class, a group of classes, a school, a group of schools, a district, or a state. The learning-progression schemes are adapted to establish the best possible set of skills to teach a group of students on a particular day. This combines the CAT (Computer Adapted Testing)+SGP (Student Growth Percentile)+time-based projection+entry points to establish curriculum entry point. The universal-skills pool engine  103  bridges from a GOM to a range of curriculums. This feature enables the lesson-planning engine  109  to act as a Rosetta Stone or like language capability for linking many assessments to many government-created learning standards. 
     After a user (e.g., educator or teacher) selects the learning objectives (skills) and chooses to build a lesson plan, by the lesson-planning engine  109 , the user/client  106  may choose the student resources and assessments to include in the lesson plan for each group. Resources may include sample items, worked examples, videos, lessons, definitions, or activities. Assessments include assessment probes designed to evaluate a level of skills. As a user selects resources and assessments, they may be assigned to student groups. 
     When a teacher generates a lesson plan, students automatically see the resources and assessments in the assignments list on their home page once the lesson plan begins. At the top of the add resources and assessments page, the teacher sees the learning objectives (skills) that was selected. If the teacher wants to concentrate on resources and assessments for one skill at a time, only that skill may be checked. The teacher may easily change which skills are checked as the teacher adds resources and assessments. If the teacher choses more than three skills, the teacher may use the scroll bar to see the rest of the skills. Resources and assessments that are related to the checked skills are already listed on the page. The template may be configured to show colored squares for each resource or assessment to show the viewer which of the skills it relates to. For example, in one example, the colors show the viewer that the resource is for the second skill. 
     For the purposes of this disclosure, it should be recognized that education has many standards and preferences that must be met in a particular country, state, or district. For example, the common core state standards initiative in the U.S. is an educational initiative that details what K-12 students should know in English language arts and mathematics at the end of each grade. This initiative seeks to establish consistent educational standards across the states as well as ensure that students graduating from high school are prepared to enter credit-bearing courses at two or four-year college programs to enter the workforce. The student-growth platform  118  approaches student development based on a universal-skills pool approach that is made available through learning progression schemes. This approach is based on selecting a range of skills that are appropriate for a specific scale score accorded to a student or group of students. A scale score delivers or specifies an entry point into the learning progression schemes that represents the student&#39;s “zone of engagement.” This zone of engagement includes a range of skills that the student is more likely to be ready to learn. This improves the accuracy of the data and insights that are provided to teachers to inform their instruction. The planner (e.g. teacher) permits aligning learning progressions to pacing guides, district curriculum, and textbooks. This makes the learning progression (by subject) more useful to teachers and administrators focused on a curriculum and not just a standard. 
     Referring now to  FIGS. 2A and 3 , the assessment platform  220  of the student-growth platform  118  includes a computer-adapted-testing engine  203 , a fixed-form engine  205 , an assessment-importer module  207 , and a universal-scale module  209 . The assessment platform  220  as illustrated may be accessed by district administration of an institution to obtain previous or old assessments for a particular student or group of students that may be imported by the assessment-importer module  207  from sources that have these assessments. The universal-scale module  209  receives inputs from the computer-adapted-testing engine  203 , fixed-form engine  205  and the assessment-importer  207 . The universal-scale module  209  positions a target student into a standard scale (e.g., mandated by a governing body) based on testing data obtained from the computer-testing engine  203 , prior data provided by a student determined by the fixed-form engine  205 , and prior assessment data imported by the assessment-importer module  207 . 
     The planning platform  202  includes an expected-score engine  244 , a pacing-guide manager  246 , a skills-selection engine  248 , a recommendations engine  250 , a resource-finder engine  252 , and lesson-organizer engine  254 .  FIG. 2A  also illustrates a learning-progression engine  236 , a mastery-maker engine  238 , and a multi-dimensional-response-item engine  240 . In some embodiments, the planning platform  202  is coupled to a single-source implementation  211 , a multiple-source implementation  213 , an assignment generation unit  215 , and a curriculum mapping unit  217 . 
     The planning platform  202  computes and expected score for a target student based on where the target student is positioned (e.g., by comparing within a range of scores for the level where the target student is positioned). The pacing-guide manager  246  is software including routines for prescribing and managing the pace at which the target student should learn. The skills-selection engine  248  is software including routines for selecting skills appropriate for the level and pace prescribed for the target student. The recommendation engine  250  is software including routines for recommending instructional resources for the target student that are consistent and appropriate for the level determined and pace prescribed for the target student. The resource-finder engine  252  is software including routines for managing and providing resources and content for students. In some embodiments, the resource-finder engine  252  catalogs the electronic resources, provides for the addition or removal of electronic resources, transmits the electronic resources to students for consumption, tracks user consumption and interaction with the of electronic resources, etc. 
     The resource-finder engine  252  is coupled to the data store  410  ( FIG. 4 ) and the media data store  111 , either directly or via the media-distribution server  115 , to access the electronic resources stored therein. In some embodiments, the resource-finder engine  252  can search the data store  410  and the media data store  111  to generate and collect information about the electronic resources. For instance, the resource-finder engine  252  can aggregate attributes of the electronic resources, such as the author, publisher, file size, creation date, publication date, a thumbnail of the resource, etc., and store them in a resource library database. In various embodiments, the resource-finder engine  252  can access the electronic resources in the data store  410  and the media data store  111  to transmit or stream copies of those resources to the client devices  106  of the users  114  requesting to interact with them. 
     The resource-finder engine  252  can also receive and store new electronic resources in the media data store  111  or the data store  410 . In some embodiments, the resource-finder engine  252  may interact with the media-distribution server  118  to store information in the media data store  111 . In other embodiments, the resource-finder engine  252  may store information in the media store  111  directly. In some embodiments, the resource-finder engine  252  may receive resource addition requests via the network  102 , requesting the addition of electronic resources accessible to the student-growth platform  118 . For example, the re-source finder engine  252  is capable of serving a webpage to a user/client device  106  that provides functionality for the user of the client device  106  to author or upload an electronic resource along with metadata characterizing it. The electronic resource may be an interactive electronic book, a video file, an audio file, a document, a dataset, an electronic link, or any other electronic resource that can be accessed and viewed by the observational engine  221  of via the student-growth platform. The resource-finder engine  252  may receive the additional electronic resource, store the metadata about the resource in the resource library database, and store the electronic resource in the data store  410  and/or media data store  111 . Thus, the resource-finder engine  252  can update the resource library database, either periodically or real-time, with any new electronic resources that have been added to or removed from the student-growth platform  118 . 
     The resource-finder engine  252  is capable of receiving requests for electronic resources from users  106  and fulfilling those requests by transmitting the electronic resources to the corresponding client devices  106  of the users  114 . In one example, upon logging in to the student-growth platform, a user  106  may be presented with an interface by the user application  108  that shows any outstanding assignments that the user  114  must complete, the dates by the assignments must be completed, a description of what the assignments are, etc. Using this interface, the user  114  may select an assignment, in response to which the user application  108  transmits a request to the resource-finder engine  252  for the electronic resource associated with the assignment. In yet another example, an observer, upon logging in, may be provided with electronic resources (e.g., video, audio, etc.) by the resource-finder engine  252  in cooperation with the client application  108 , which describes what to focus on, observe, evaluate, during an upcoming/pending observational assessment of a target subject. In these or other examples, electronic resources can be identified and served to the users based on the users&#39; social graphs and/or preferences. The resource-finder engine  252 , upon receiving this request, may locate the electronic resource in the data store  410  and provide it to the user application  108  via the network  102  for presentation to the user  114 . As discussed elsewhere herein, the resource-finder engine  252  may, in some embodiments, cooperate with the media-distribution server  116  to provide the electronic resources for consumption and/or interaction by the users  114  requesting them. 
     When users consume or interact with the electronic resources provided by the resource-finder engine  252 , the resource-finder engine  202  is capable of logging the consumption and interaction in the data store  410  in association with those users. In some embodiments, the resource-finder engine  252  cooperates with the user application  108  to monitor user interactions with the electronic resources. For example, when user interacts with a user interface generated and displayed by the user application  108 , the user application  108  sends interaction data via the network  102  to the resource-finder engine  252  informing the resource-finder engine  252  of the interaction, and the resource-finder engine  252  stores this interaction data. In a further example, if a user interacts with a media player embedded in a user interface of the user application  108 , interaction data describing the user&#39;s interactions, such which actions the user took (e.g., clicked a pause button, a play button, a scrubbing dial, volume dial; maximized the viewing field of the media player; added a comment about the video using an associated interface element; etc.) are sent by the user application  108  to the resource-finder engine  252  and the resource-finder engine  252  may log those interactions. The interaction data may also include or be associated with data identifying which electronic resource was interacted with, the user who interacted with the resource, the time and date of the interaction, etc. In another example, if a user is accessing an interactive electronic book, the user application can send interaction data describing when the user begins interacting with the electronic book, pages through the electronic book, downloads files included with or embedded in the electronic book, completes surveys included with the electronic book, views videos embedded in the electronic book, comments on passages of the electronic book, or otherwise uses any other functionality provided by the user application  108  for interaction with the electronic book or the corresponding components of the student-growth platform  118 . 
     In some embodiments, the resource-finder engine  252  may provide the electronic resource to the user/client devices  106  with presentational information and the client application  108  may use the presentational information to form the look and feel of the user interfaces. For example, the electronic file(s) or data stream(s) may be formatted using a markup language (e.g., HTML, XML, etc.), style sheets (e.g., CSS, XSL, etc.), graphics, and/or scripts (e.g., JavaScript, ActionScript, etc.), and the client application  108  may interpret the interface instructions and render an interactive Web User Interface (WUI) for display on a user device  106  based thereon. 
     In other implementations, the user/client application  108  may determine the formatting and look and feel of the user interfaces independently. Using the user interfaces presented by the client application  108 , the user can input commands selecting various actions. 
     Referring now to  FIG. 2B and 3 , the assignment platform  222  illustrated with an instructional bridge  242   a  and an instructional bridge  242   b.  The first instructional bridge  222   a  includes a print engine  253  adapted to print assignments as needed, a scan engine  256  for scanning documents with assignments as needed, and an assignment-importer module  258  for importing or downloading assignments from other sources. The second instructional bridge  222   b  has an assignment manager  260  for managing assignments given to students, a grading framework  262 , by which grading of assignments is accomplished, and an assignment player  264 , by which assignments are conveyed to target students (e.g., by audio, video, or other forms of media). The reporting platform  232  includes dashboards/dashboard services  233 , alerts/alert manager  235 , and an exporter  237 , by which completed assignments may be exported or sent for further consideration or storing. 
     In some embodiments, the user-interface unit  119  ( FIG. 1 ), in cooperation with the observation engine  221  ( FIG. 1 ), may generate a report dashboard/interface for viewing reports generated and provided by the reporting platform  232  and received by the observation engine  221 . In some instances, the reporting platform  232  may provide diagnostic reports. This dashboard provides numerous advantages including providing an observer (e.g. teacher) or administrator with detailed information about a given target student&#39;s performance (e.g., execution, effectiveness, compliance, etc.) over time. For example, the observer may be a teacher using the dashboard, to access any previous observational assessments of that student or student group; view an overall performance (e.g., execution, effectiveness, compliance, etc.) view statistics across all observational assessments of that student or a subset, such as the observational assessments performed for that academic year; may quickly ascertain the areas a student has had problems with or has been working on, or the areas the student has been improving on; review the test scores for the student, view the electronic training resources the student has consumed/interacted with; view any work-product, lesson plans, videos, presentation, etc., the student has uploaded, the groups the student has interacted with, etc. Using this information, the teacher may quickly get up-to-speed on where the student is at, thus provide pertinent and relevant observations (e.g., evaluations, ratings, suggestions, comments, etc.) and assignments, etc., during the observation session to be performed. The alerts  235  may be adapted to generate and provide alerts depending upon certain criteria that are specified. 
     Referring specifically to  FIG. 3 , is should be recognized that in some example scenarios, a district administrator  332  may have access to the assessment platform  220  to generate the assessments required. A teacher  336  may have access to the planning platform  222  to plan and generate lessons and the student  334  may have access to the assignment platform  226  to receive and complete assignments. 
     The learning progression engine  316  drives information that is conveyed in the instructional planning and diagnostic reports that are generated. Learning progressions are descriptions of how learning typically advances in a subject area. Empirically based learning progressions can visually and verbally articulate a hypothesis, or an anticipated path, of how student learning will typically move toward increased understanding over time with good instruction. The learning-progressions engine  316  has an organizational structure, separated into domains, skill areas, and core skills. For example, a core progress scenario for mathematics has four domains, which form the base of the learning progression for that subject: 1) numbers and operations, 2) algebra, 3) geometry and measurement, and 4) data analysis, statistics and probability. The skills areas (e.g., whole numbers, place value, symbols and expressions, time etc.) represent the various skills and concepts students acquire as they progress in the development of mathematics at the level they are prescribed. The core progress learning progression is an interconnected web of prerequisite skills. For increased understanding over time, progress requires continually building up and building on a solid foundation of knowledge, concepts, and skills. The core progress learning progression is a map of skills created, where new learning is built on previous, foundational understanding of the subject. A core progress learning progression for a subject is defined in terms of a number of skills. Each skill is represented by a separate data point. The difficulty value may be derived from the calibrated difficulty of the test items from standard or existing tests to assess the skill level. There are several assessment items per skill, called an item-set. 
     Common to these perspectives is the idea that the development of learning progressions is an iterative process. It begins with a hypothesis, informed by what is known about student learning, which undergoes empirical testing and subsequent refinement based on the data. As another example, a core progress learning progression for reading was developed according to this iterative model. To reflect the organization of the standards, a core progress reading learning progression may have four domains, including 1) foundational skills, 2) language, 3) literature, and 4) informational text. The learning progression is comprised of five (sub) domains: 1) word knowledge and skills; 2) comprehension strategies and constructing meaning; 3) analyzing literary text; 4) understanding author&#39;s craft; and 5) analyzing argument and evaluating text. For each student group, grade-level domain expectations may be identified to describe the desired level of student understanding by the end of the year. These expectations form the foundation of the learning progression. The learning progression then goes a step further to identify the intermediate skills and concepts necessary for students to move toward those expectations. Learning progressions are a progression of cognitive states that move from simple to complex and, while not necessarily linear, the progression is not random, but rather is sequenced and ordered as “expected tendencies” or “likely probabilities” of how learning develops. Inherent in these views of progressions is the idea of a coherent and continuous pathway along which students move incrementally through states of increasing competence in a domain. Every incremental state builds on and integrates the previous one as students accrue new levels of expertise with each successive step in the progression. It is important to note, however, that while progressions may provide clear descriptions of how learning develops in a domain, they are not developmentally inevitable. Rather, they are dependent on good curriculum and instruction. The skill areas represent the various skills and understandings that students gain as they progress in their reading development. For example, the grade-level skill statements identify the incremental steps students take as they progress in acquiring specific skills and understandings. It should be recognized that the grade-level skill statements provide specific examples of relevant words and texts, but do not specify reading content or identify the activities students should be able to perform to reflect attainment of a skill. They are intended as statements of the skill itself, which serve to advance subject (e.g., reading or math) competence. The skill statements reflect levels of relative difficulty of skills and understandings identified in the progression from their most basic, foundational states through increasingly sophisticated states of competency. For example, in the learning progression for a student in grade two, a domain defined for comprehension strategies and constructing meaning may require a skill (defined in a particular area) identifying the author&#39;s purpose, based on an understanding that authors write texts for different purposes. Having established this basic understanding, students may move incrementally through successive steps of increasing competence so that by the middle-level grades they are able to evaluate the appropriateness of the form chosen by the author in light of the author&#39;s purpose. These focus skills and prerequisites act as building blocks; each representing a specific level of competency of a skill or understanding that rests on prior development and that also provides a foundation for the next level of learning. The learning progression engine  236  identifies for each focus skill, the associated prerequisites necessary to understand that skill, and provides these criteria across grades, skill areas, and domains. To continue with the example for reading, by the 10 th  grade, the focus skill may require analyzing the cumulative impact of figurative language on wider themes and meanings of the text, from the domain defining an understanding of the author&#39;s craft. This domain may have five prerequisite skills that span two grades and three domains. The learning progress engine  236  may be further adapted to perform a quantitative analysis to determine where skills fall on an assessment scale (e.g., standard ones used by educators). This analysis may compare empirically observed order of skills (i.e., where skill difficulty falls on a measurement scale) to the pedagogically determined order of skills (i.e., the most productive order of skills for learning a particular skill). 
     Information and data flows from the Assessment Platform  220 , to the expected-score engine  244 , and from that point to the pacing-guider manager  246 . Based on the assessment results (e.g. old or previous) for a particular student or group, the expected-score engine  244  designates an expected score for that student or group of students. Based on the expected score for a student, the pacing-guide manager  246  determines a pace appropriate for the student or group of students, and the skills-selection engine  248  matches the skills required for the pace determined for the student and student group. The recommendation engine  250  discovers and finds resources from the resource-finder engine  252 . The lesson-organizer engine  254  organizes the lessons for teacher to use. In some embodiments, a teacher may provide input at any stage of the planning process, for example, either to the resource-finder engine  252 , or to other sections or portions of the planning platform  222 , in other instances (as indicated by the arrows (e.g. to the skills-selection engine  248 ). The lesson-organizer engine  254  may provide information and data to assessment platform  220  or the assignment platform  226 . 
     The learning-progression engine  236  may be adapted to provide input to the assessment platform  220 , the planning platform  222 , or alternatively, to the assignment platform  226 , or the reporting platform  232 . 
     The grading-framework  262  is adapted to receive information from the mastery-maker engine  238  and to the MIRT engine  240 . The Grading Framework 4e is also adapted to provide information and data to the Assignment Manager 4d. 
     The mastery-maker engine  238  is software including routines for providing information and data to the dashboard services  333  of the reporting platform  232 . The mastery-maker engine  238  prescribes practice tests ( FIG. 14, 1438 ) and assignments in a particular subject for a target student to assist the target student with mastering a particular subject. Some standards for mastery measurement may be used to track either long-term progress or short-term progress. In some instances, the mastery-maker engine  238  may use general outcome measures (e.g. SAT, ACT etc.) to assess high-level skills (e.g., reading, math, or preparedness for college) or skill mastery measurement that measures more granular sub-skills (e.g. fluent recall of division involving single digit numbers with 8 and 9 as divisors). The mastery-maker engine  238  may be adapted to define clear pass/fail criteria, present a multiple equivalent valid forms that measure the same sub-skill, an ability to measure improvement even if the mastery criteria is not met, a valid underlying skill sequence, and an opportunity to test whether mastered skills are retained at a later date. The mastery-maker engine  238  in some embodiments is adapted to test depth of knowledge. The mastery-maker engine  238  tests a scale of cognitive demand and aligns assessments with standards. For example, Webb&#39;s four levels of cognitive complexity include recall and reproduction (level 1), skills and concepts (level 2), strategic thinking (level 3), and extended thinking (level 4). In some embodiments, mastery may be computed by a combination of assessments, instruction, and practice inputs. Assignment forms that contribute to mastery may include practice (contributing to probed consideration), formative assessment (contributing to probed consideration), instruction (contributing to probed mastery), summative (imported data contributing to assessed mastery), and CAT assessment (contributing to assessed mastery). In some embodiments, there are two tiers to probed mastery, including a system tier and an item tier. In some instances, the system tier may include the requisite items, forms, skills sequences, and mastery criteria. The items may include items worked for sub-skills and third-party tagged imported data. In some scenarios, an item % attainment by a student may be computed by an item score/highest possible score for the item. A probed % mastery designation for a student may be computed by a weighted mean for all item % attainments known to the system. The mastery-maker engine  238  in some embodiments may normalize the outcomes form computer-adapted testing with scores from practice tests ( FIG. 14, 1438 ) to create an integrated model that reflects mastery by the target student, which may be compared or positioned within the learning progression data for the target student. The mastery-maker engine  238  may extend actual testing data by intelligent inferencing schemes by deriving relationships of objects within the learning progression. 
     The MIRT engine  240  is software including routines for binding responses from multiple activities (or assignments or results) received from assessment, instruction, and practice tests into a unified scale, which contributes to determining overall mastery. The MIRT engine  240  may receive information and data from the mastery-maker  338 . Both the mastery-maker engine  238  and the MIRT engine  240  provide data flow to the reporting platform  232 , specifically, the alert manager  335 . The dashboard services  333  and the alert manager  335  provide information and data to the teacher and the exporter engine exports data out of the reporting platform  337 . 
       FIG. 4  illustrates the various components of the student-growth platform  118  coupled by a bus  406  to a communication unit  408 , a processor  402 , a memory  404 , and a data store  410 . The integrated student-growth platform  118  includes the assessment platform  220 , the planning platform  222 , learning-progression services  224 , the assignment platform  226 , the master-maker engine  228 , the MIRT engine  230 , and reporting platform  232 . 
     The processor  202  processes data signals and program instructions received from the memory  204  and the data storage  210 . The processor  202  may comprise an arithmetic logic unit, a microprocessor, a general or special purpose controller or some other processor array to perform computations and provide electronic display signals to a display device (e.g., on a user device  106   a ). The processor  202  is coupled to the bus  206  for communication with the other components. The processor  202  may comprise various computing architectures including a complex instruction set computer (CISC) architecture, a reduced instruction set computer (RISC) architecture, or an architecture implementing a combination of instruction sets. Although only a single processor is shown in  FIG. 4 , multiple processors may be included. It will be obvious to one skilled in the art that other processors, operating systems, sensors, displays, and physical configurations than those that are illustrated may be used to perform the operations described in this specification. 
     The memory  204  may be a non-transitory storage medium. The memory  204  stores the instructions and/or data for operating the student growth platform  118 , which may be executed by the processor  202 . In one implementation, the instructions and/or data stored in the memory  204  comprises code for performing any and/or all of the techniques or functionalities that are described in this specification. The memory  204  may be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory or some other memory device known in the art. 
     The data storage  210  stores the data and program instructions that may be executed by the processor  202 . In one implementation, the data storage  210  may store the data of various types of users in the web forum. The data storage  210  may include a variety of non-volatile memory permanent storage device and media such as a hard disk drive, a floppy disk drive, a CD-ROM device, a DVD-ROM device, a DVD-RAM device, a DVD-RW device, a flash memory device, or some other non-volatile storage device known in the art. 
     The communication unit  208  facilitates the communication between the user device  106  (in  FIG. 1 ) and the student-growth platform  118  over the network  102  (in  FIG. 1 ). For example, a user  114   a,  via the user device  106   a,  may access the student-growth platform  118  to view or read electronic content and otherwise interact with the student-growth platform  118  and receive information from the student-growth platform  118 , via the communication unit  208 . The communication unit  208  also displays the content or information either received from or hosted on the student-growth platform  118  to any of the users  114   a  through  114   n.    
     The communication unit  208  couples the student-growth platform  118  to the network  102  by the signal line  116  (in  FIG. 1 ) and via the bus  206 . The communication unit  208  may include network interface modules, which include ports for wired connectivity such as but not limited to USB, SD, or CAT-5, etc. The network interface modules are configured to link the processor  202  to the network  102  that may in turn be coupled to other processing systems. The network  102  ( FIG. 1 ) may comprise a local area network (LAN), a wide area network (WAN) (e.g., the Internet), and/or any other interconnected data path across which multiple devices may communicate. The network interface modules are configured to provide conventional connections to the network  102  using standard network protocols such as TCP/IP, HTTP, HTTPS and SMTP as well as any others that are understood to those skilled in the art. The network interface modules include a transceiver for sending and receiving signals using WIFI, Bluetooth® or cellular communications for wireless communication. Each of the platforms, modules, and/or engines described above may include software or program instructions configured to perform the functionalities described here. 
     Example Student-Growth Platform  118   
     The example student-growth platform  118  depicted in  FIGS. 4  (and  1 A) is provided by way of example and it should be understood that it may take other forms and include additional or fewer components without departing from the scope of the present disclosure. For example, while not shown, in some implementations, the student-growth platform  118  may include input and output devices (e.g., a computer display, a keyboard and mouse, etc.). Additionally, it should be understood that the computer architecture depicted in  FIG. 4  is applicable to the other entities of the system  100   a  ( FIG. 1A ), such as the media-distribution server  115  and/or the third-party server  117  with various modifications. 
     The processor  416  includes an arithmetic logic unit, a microprocessor, a general purpose controller, or some other processor array to perform computations and provide electronic display signals to a display device (not shown). The processor  402  may be coupled to the bus  406  for communication with the other components of the student-growth platform  118 . The processor  416  may process data signals and may have various computing architectures including a complex instruction set computer (CISC) architecture, a reduced instruction set computer (RISC) architecture, or an architecture implementing a combination of instruction sets. Although only a single processor  216  is shown in  FIG. 4 , multiple processors may be included. The processor  402  may be capable of supporting the display of images and the capture and transmission of images, performance of complex tasks, including various types of feature extraction and sampling, etc. It should be understood that the student-growth platform  118  could include various operating systems, sensors, displays, additional processors, and other physical configurations. 
     The memory  404  stores instructions and/or data that may be executed by the processor  402 . The memory  404  is coupled to the bus  406  for communication with the processor  402  and the other components of the student-growth platform  118 . The instructions and/or data may comprise code for performing any and/or all of the techniques described herein. In particular, the memory  404  includes a non-transitory computer-usable (e.g., readable, writeable, etc.) medium, which can be any apparatus or device that can contain, store, communicate, propagate or transport instructions, data, computer programs, software, code, routines, etc., for processing by or in connection with the processor  402 . A non-transitory computer-usable storage medium may include any and/or all computer-usable storage media. In some implementations, the memory  404  may include volatile memory, non-volatile memory, or both. 
     For example, the memory  404  may include a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory, a hard disk drive, a floppy disk drive, a CD ROM device, a DVD ROM device, a DVD RAM device, a DVD RW device, a Blue-Ray™ storage device, a flash memory device, or any other mass storage device known for storing information on a more permanent basis. It should be understood that the memory  404  may be a single device or may include multiple types of devices and configurations. 
     The communication unit  408  is an interface for sending to and receiving data from other computing devices. In the depicted embodiment, the communication unit  408  is coupled to the network  102  by the signal line  116  and coupled to the bus  406 . In some embodiments, the communication unit  408  includes a network interface device (I/F) having ports for wired connectivity. For example, the communication unit  408  includes a CAT-5/6/7 interface, USB interface, or SD interface, etc. The communication unit  408  may also include a transceiver for sending and receiving signals using Wi-Fi, Bluetooth® or cellular communications for wireless communication. The communication unit  408  can link the processor  402  to the network  102  that may in turn be coupled to other processing systems. The communication unit  408  can provide connections to the network  102  and to other entities of the system  100  using standard communication protocols including, for example, TCP/IP, HTTP, HTTPS, etc. 
     The student-growth platform  118  includes the assessment platform  220 , the planning platform  222 , the learning progression services  224 , the assignment platform  226 , the mastery-maker engine  228 , and the MIRT engine  230 , and reporting engine  232 . 
     In some embodiments, the student-growth platform  118  and/or the assessment platform  220  are sets of instructions executable by the processor  402  to provide their respective functionality. In other embodiments, the student-growth platform  118  and/or the assessment platform  220  are stored in the memory  404  of the student-growth platform and are accessible and executable by the processor  402  to provide their respective functionality. In any of these embodiments, the student-growth platform  118  and the assessment platform  220  may be adapted for cooperation and communication with the processor  402  and other components of the student-growth platform  118 . 
     Example Observation Engine  221   
     The observation engine  221  is software including routines for facilitating student growth based on observational assessments received from the assessment platform  220 . In particular, the observation engine  221  may send, receive and store observation-related data, such as observation data, templates and files including questions and answers tied to performance standards (e.g., standards related to execution, compliance, effectiveness, personalized learning plans, etc.), identify and suggest electronic learning resources (in cooperation with the resource-finder  252 ) based on observation-related data received, generate reports including analytics and diagnostics about the students and their learning progress, generate performance (e.g., execution, evaluation, compliance, effectiveness, etc.) assessments of the students based on demographics data, observation-related data, achievement data, standards data, student data, teacher-oversight data, interaction data, inter-rater reliability data, observer comparison data, or any other data described herein. 
     In the illustrated embodiment, the observation engine  221  cooperates with the planning platform  222  including the recommendation engine  250 , an assignment engine  208 , and a reporting platform  232 . The observation engine  221  is coupled for communication with the other components of the student-growth platform  118 . The observation engine  221  is also coupled to the network  102  via the communication unit  408  for communication with the other entities of the system  100   a  (and  100   b ). 
     In some embodiments, the user-interface  119 , the observation engine  221 , the assessment platform  220 , the planning platform  222 , the learning-progression platform  224 , the assignment platform  226 , the mastery-maker platform  228 , the multi-dimensional response platform  230 , and the reporting platform  232  are sets of instructions executable by the processor  402  to provide their respective functionality. In other embodiments, the user-interface  119 , the observation engine  221 , the assessment platform  220 , the planning platform  222 , the learning-progression platform  224 , the assignment platform  226 , the mastery-maker platform  228 , the multi-dimensional response platform  230 , and the reporting platform  232  are stored in the memory  404  of the student-growth platform  118  and are accessible and executable by the processor  408  to provide their respective functionality. In any of these embodiments, the user-interface  119 , the observation engine  221 , the assessment platform  220 , the planning platform  222 , the learning-progression platform  224 , the assignment platform  226 , the mastery-maker platform  228 , the multi-dimensional response platform  230 , and the reporting platform  232  may be adapted for cooperation and communication with the processor  402  and other components  408 ,  404 , and  410  of the student-growth platform  118 . 
     The observation engine  221  is software including routines for sending, receiving, processing, and storing observation-related data. In some embodiments, the observation engine  221  may provide observation templates to observers for use in observing and assessing other users (e.g., students, also referred to as the targets), receive observation files including observation data reflecting the assessments for particular students, and store the observation files in the data store  113   a  ( FIGS. 1A &amp; 1B ) in association with the targets being observed. In some embodiments, the observation engine  221  interacts and cooperates with the user/client application  108   a  ( FIG. 5 ) to provide the above-noted functionality. 
     In the illustrated embodiment, the observation engine  221  is coupled to one or more user/client devices  106  ( FIGS. 1A &amp; 5 ) to provide one or more observation templates ( FIG. 14 ) to the user/client devices  106  and to receive observation-related data from the user/client devices  106 . In some embodiments, an observation template is an electronic form for assessing the performance of a target student (e.g., generated by the fixed-form engine  205  in  FIG. 2A ). The observation template may include different header fields for describing the circumstances of an observation session. For example, the observation template may include fields for describing the identity of a target student, the date the observation was performed by an observer (e.g. teacher or administrator), and how the results of the observation should be distributed (e.g., by reports) and stored (e.g., for later use), etc. Additionally or alternatively, the observation template may include assessment fields for describing the performance (e.g., execution, compliance, effectiveness, and/or other qualities) of the target student during the observation, data about prior observational assessments of the target students, data about other observers, etc. In some embodiments, the assessment fields may include data describing predefined questions and user-selectable or user-definable answers; fields for user-definable questions and/or answers; comment fields for providing a description of the target student; rubrics, etc. In these or other embodiments, the assessment fields may state a goal, objective (e.g., for mastery), effectiveness expectation (e.g., projections based on scores), or other metric, and include one or more indicators assessing how the target student is meeting that goal, objective, effectiveness expectation, or other metric. For example, the objective might be “students develop to meet the vision, mission, values, beliefs and goals of the organization (e.g. school), collaboratively determining the processes used to establish these attributes, and facilitating their integration into the life of the organization community,” and the selectable indicators assessing whether the student is partially proficient at meeting this goal may state that the vision, mission and values are: “developed through collaborative process,” “publically available,” part of routine,” and “routinely updated” by the target student (i.e., from a particular grade). In this example, if only some of these indicators are met, then the target student is deemed partially proficient at the goal. If all are met, additional indicators evaluating whether a target subject is proficient (as attained a high level beyond that expected), accomplished, or exemplary at meeting this goal are considered and selected if appropriate. The observation templates may also include assignment fields for recommending, assigning and/or integrating electronic resources (e.g., video) by a teacher; and fields for defining assignment parameters for the electronic resources (e.g., task timers, wait times, etc.), as described in further detail below. In some embodiments, suggestions for the assignment fields may be populated in real-time by the assignment platform  226  (particularly, the assignment manager  260 ) in response to sending the observation data. 
     The content of the observation templates may be displayed to users via user interfaces generated ( FIG. 14 ) and displayed by the user/client application  108 . The user interfaces displaying the content of an observation template to a user (e.g., student, teacher, or administrator) may also provide functionality for completing the various fields of the template. For example, while observing a target subject in the field, an observer or user  114  may interact with interface elements presented by the user/client application  108  to input information about the circumstances of the observation and the target&#39;s performance. For example, the observer or user  114  may input the location where the observation session took place; the date and time of the observation session; the identity of the target student&#39;s audience (if any); information about the identity of the observer (e.g. teacher or administrator); information about the observer&#39;s position and/or relationship to the target student (e.g., subject teacher); options for storing and distributing the results of the observation; etc. The observer or user  114  may also provide input describing the performance of the target student (e.g., teacher comments), such as inputting answers to questions about various aspects of the target student&#39;s performance, etc. 
     In some embodiments, an observation template may include predefined questions and answers for assessing the compliance of a target student with various predetermined requirements. For example, the requirements may be based on institutional policy, compliance with requirements, legislated practices, or industry standards, and the questions may be directed to whether or not a target student is meeting those requirements/standards. In these embodiments, the same template may be used repeatedly by an observer to record his/her observations of a target student over time or of a number of different target subjects. In other embodiments, various different templates may be used for the observational assessments of a target student. The structure and content of the observation templates, or portions thereof, may be user-defined or may be automatically generated by the observation engine  221  using standards data stored in the data store  113   a  or received from another entity of the system  100   a,  such as the third-party server  117 . 
     The user/client application  108  may transmit observation-related data including input provided by the observer (e.g., teacher or administrator) during the assessment of the target student to the observation engine  221  for storage. For example, the observer (e.g., teacher or administrator) may instruct the client application  108  to save a completed observation template as an observation file in a local repository, and then transmit it to the observation engine  221  via the network or cloud platform  102  for storage in the data store  113   a.  The observation file includes the information from the template upon which it is based along with the observations (e.g., evaluations, ratings, compliance assessments, and comments), assignments, and/or other information input by observer (e.g., teacher or administrator) during the observation. 
     In the illustrated embodiment, the observation engine  221  is coupled via the bus  116  (through the network  102  and bus  121   a ) to the data store  113   a  to store and retrieve observation-related data. For example, the observation engine  221  can store and retrieve the observation templates and the observation files received from the user/client application  108 . The observation engine  221  can also store, retrieve, and provide organization information associated with observers and target subjects. For example, in the educational setting, the observation engine  221  may access information associated with the organization of the school districts of a state or region; a school district; the schools of a school district; the teachers and administrators of a school district, a school, a subject, etc.; the classes in a district or school; the students of a school district, a school, a class, a subject, a teacher, an administrator, etc., from the data store  113   a.    
     The assessment platform  220  is software including routines for providing the assessments as described above with reference to  FIG. 3 . The planning platform  222  is software including routines for planning a lesson for a teacher according to the assessments. As illustrated in  FIG. 3 , the planning platform  222  has the recommendation engine  250 , which is software including routines for receiving observation data related to a target student, identifying one or more electronic resources that correspond to the observation data, and for providing data representing the one or more electronic resources for display. In some embodiments, the recommendation engine  250  is coupled via the network or cloud platform  102  to receive observation data from one or more user/client devices  106 . The observation data may characterize one or more aspects of a target student&#39;s performance during an observation session performed by an observer (e.g. teacher or administrator). In the illustrated embodiment, the recommendation engine  250  is coupled to the data store  113   a  via the bus  116  (and  121   a ) to store and retrieve data, and is coupled to the media data store  111  via signal line  127  and the network  102  to store and retrieve data. 
     In some embodiments, the observation data may accompany a resource request for a list of electronic resources that correspond to the observation data. The recommendation engine  250  may receive the request from a client device  106 , and may satisfy the request by identifying one or more electronic resources that correspond to the request, and provide a resource response including a summary of the one or more resources to the user/client device  106  for display to the user  114  of the client device  106 . For example, an observer of a target student may provide input reflecting observation data assessing the performance of the target subject, and the client application  108 , upon receiving that input, may transmit a request for recommended electronic instructional resources that can be assigned by the observer to the target student to help the target student improve his or her skills in a given area. 
     In some embodiments, to identify one or more electronic resources that correspond to the observation data accompanying the resource request, the recommendation engine  250  can compare the observation data to metadata associated with electronic resources to identify resources that match the observation data. For example, the recommendation engine  250  can search a resource library database that includes an index or catalog of the electronic resources that are available. For instance, the resource library database can include metadata for each of the electronic resources describing each resource. The metadata can include tags describing various characteristics of an electronic resource, a graphical image of the resource (e.g., a thumbnail), a description of the topic or subject matter that the resources is directed to, an author or authors of the resource, the publisher of the resource, the popularity of the resource including, for example, the number of users who have consumed the resource and the level of their interactivity with the resource, etc. The recommendation engine  250  can query the resource library database using the observation data or aspects thereof to identify resources that have corresponding metadata that match the observation data, either loosely or strictly. 
     The electronic resources may be distributed among several data stores located across the network or cloud platform  102  or may be stored in a single data store. In the illustrated embodiment, the media store  111  and the data store  113   a  work cooperatively to store the electronic resources. For example, media objects such as video, audio, e-books, vector-based files, documents, datasets, learning objects, etc., may be stored in the media store  111  and lesson plans, learning progressions, curriculum maps, publications, portfolios, industry standards, etc., may be stored in the data store  113   a.  In other embodiments, all of the electronic resources may be stored in and accessible from a single information source, such as the media store  111 , the data store  113   a - n , etc. In any of the foregoing embodiments, the resources stored in the data store may be cataloged, for example, by the recommendation engine  250 , in a single resource library database or in resource library databases distributed over the network  102 , and the recommendation engine  250  can query the resource library database or resource library databases for information matching various criteria or for information about the resources. In other embodiments, the electronic resources may be prescribed or predetermined in advance and pushed out by the student-growth platform  118  to the observer of a target student for assignment or to the target student directly for consumption. 
     In some embodiments, the observation data includes data quantifying an observer&#39;s assessment of a target student&#39;s performance. For example, the observation data may include an answer input by an observer in response to a question about the target student&#39;s performance in a particular area, and the answer may quantify how well a target subject is performing. In some embodiments, the answers to questions may be based on predefined performance scales that are defined to the recommendation engine  250  and the recommendation engine  250  may use the answer to determine where the target student lies within that performance scale. For example, a target student&#39;s performance in a particular area may be assessed from worst to best using the following identifiers: “unsatisfactory,” “needs improvement,” “developing,” “proficient,” and “distinguished,” or other such method for scaling a student and if the observation data includes data identifying “unsatisfactory” as the answer to a particular question about a target student&#39;s performance in that area, the recommendation engine  250  may use this assessment to identify one or more electronic resources that provide foundational or basic learning in that particular subject area. 
     If multiple electronic resources are identified by the recommendation engine  250  as corresponding to the observation data, the recommendation engine  250  can rank them based on one or more criteria. A criterion may be any attribute associated with the electronic resources. For example, the criterion may include a topic; the number of times an electronic resource has been interacted with, viewed, listened to, etc.; an author; a publisher; a date of the electronic resource; the number of users connected (or at the same level) to the target student who have interacted with the electronic resource previously; the number of times an electronic resource has been assigned to users having a similar assessment; etc. The recommendation engine  250  can generate the summary of electronic resources based on the ranking performed by it. For example, the top-ranked electronic resource may be listed first in the summary and the lowest-ranked resource may be listed last. In another example, the recommendation engine  250  may limit the summary to a certain number of top-ranked resources. In yet another example, the list of electronic resources may be sorted in order of rank and provided incrementally as needed by the user application  108 . In a further example, the recommendation engine  250  may rank the resources by those who have been most impactful/effective for students similar to the target student. For example, the recommendation engine  250  may use demographics, observation, achievement, interaction, standards, student, and/or teacher data, etc. to identify the resources that were the most effective at helping a set of similar target subjects develop professionally. For example, a target student may be a fourth grader who is struggling with maintaining a level appropriate for the grade. The recommendation engine  250 , using demographic data and/or profile data, may identify other fourth graders who, based on their respective observation data and/or achievement data, also initially struggled with maintaining the level and who later became proficient at that level, as reflected by their respective observation data and/or achievement data, by watching a learning video(s) on particular subject areas (e.g., math); and the recommendation engine  250  and may recommend this/these videos for assignment/consumption. 
     The learning-progression platform  224  is software for placing the student in a learning-progression scheme and for following the learning-progression scheme prescribed for a target student. The assignment platform  226  is software including routines for receiving an assignment request requesting an assignment of one or more electronic resources to the target student for completion, and for assigning the one or more electronic resources to the target student based at least in part on the assignment request. In some embodiments, the assignment platform  226  is coupled via the network  102  to receive the assignment request from one or more client devices  106 . 
     The assignment platform  226  may interact with the user/client application  108  to assign various electronic resources to a target student. For example, during an observation of the target student, the observer inputs observational data indicating that the target student is in need of training on a particular skillset, and the recommendation engine  250  provides a summary of electronic instructional/training resources that are accessible via the student-growth platform  118 . The observer, using an interface rendered and displayed by the user/client application  108 , may assign one or more of the electronic resources to the target student. In response to the assignment, the assignment unit  518  ( FIG. 5 ) of the user application  108  generates and sends and assignment request to the assignment platform  226 , which identifies the electronic resource or resources that have been assigned, as further discussed below with reference to at least  FIG. 3 . The assignment platform  226  then records the assignment of the electronic resources in the data store  113  in association with a user profile for the target student. In some embodiments, an assignment is not activated by the assignment platform  226  until the corresponding observation file including the assignment is finalized and uploaded by the observation unit  516  ( FIG. 5 ) of the user/client application  108 . In other embodiments, one or more assignment requests are provided and recorded by virtue of the observation file being uploaded for storage by the user/client application  108  to the student-growth platform  118 . For example, upon receipt of the observation file, the assignment platform  226  extracts any assignments from the observation file and records them as described above. In some embodiments, to complete the assignment, the target student, who is a user of the student-growth platform, may be required to access the service and interact with the electronic resource. In other embodiments, to complete the assignment, the target student may be required to consume the electronic resource and then report on his/her implementation of the learning provided by the resource and/or provide his/her reflections on the learning provided by the resource, etc., via the user/client application  108 . For example, the target student may be required to submit, via the user/client application  108 , input describing his/her experience with trying-out/implementing the principles taught by the assigned resource (e.g., an online learning video). Once this input has been received, the assignment platform  226  may flag the assignment as being completed in the data store  113 . Other configurations for completing an assignment are also contemplated. 
     In some embodiments, the assignment request includes one or more assignment parameters or particulars. Each assignment parameter sets a condition that must be met in order to complete the assignment. For example, an assignment parameter includes a due date, a level of interaction with the electronic resource that is required to complete the assignment, an additional requirement that must be satisfied for completion of the assignment, etc. For instance, the observer may assign a video to the target student to view and may require the target student to write his/her thoughts or reflections about the video by inputting and transmitting them via an interface associated with the student-growth. In the illustrated embodiment, the assignment platform  226  is coupled to the data store  113   a - n  via the bus  116  to store the one or more assignment parameters in association with assignment to which they pertain. In these or other embodiments, one or more assignment parameters can be predefined and stored in the data store  113   a - n . A predefined assignment parameter can be applicable to all users who are assigned electronic resources, or may be customized for a particular group of users, such as those belonging to a particular school or grade or being observed by a particular observer (e.g., teacher). For example, for all videos that are assigned, a predefined assignment parameter can be set (e.g., by an observer via an associated interface of the student-growth platform  118 ) requiring that the videos must be viewed to completion in order for the assignments of those videos to be considered satisfied. In another example, predefined assignment parameters can require videos to be viewed to completion in full screen mode with the sound of the video being set at an audible level in order for the assignments for the videos to be considered satisfied. 
     In some embodiments, the assignment engine  226  generates and sends an electronic notification to the users associated with the assignment request. For example, the assignment engine  226  may send an email to the target subject and/or the observer(s) summarizing the assignment. The email may include a description of the electronic resource and an electronic link (e.g., a hyperlink including the uniform resource locator (URL) of the electronic resource) for directing the reader directly to the electronic resource. The email may also describe any assignment parameters, such as when the assignment must be completed by. In another example, the assignment platform  226  may send a similar message to the user via an internal messaging system, an instant-messaging system, a text-messaging system, or any other electronic-messaging system. In these embodiments, the assignment platform  226  is coupled to the data store  113   a - n  to access information about the electronic resource and to store a copy of the electronic notification that was sent. 
     The mastery-maker platform  228  is software including routines for enabling mastery of a particular subject. The details are described above with respect to  FIG. 3 . The multi-dimensional response platform  230  is software including routines for binding various responses. 
     The reporting platform  232  is software including routines for generating and sending reports. The reporting platform  232  may use the data stored/and or aggregated by the student-growth platform such as achievement data, demographics data, student data, teacher data, observation-related data, interaction data, standards data, or any other data described herein, to generate the reports. For example, the reporting platform  232 , using the data aggregated and stored by the observation engine  221  and/or student-growth platform  118 , may generate/segment/organize a report by region, district, school, class, teacher, student(s), class-size, gender, ethnicity, public policy, legislation, standards, requirements, etc. In a further example, the reporting platform  232  may process this data to make macro and/or micro qualitative assessments for inclusion in one or more reports. For instance, the reporting platform  232 , based on the observation-related data, demographics data, achievement data, student data, teacher data, interaction data, and/or standards data, etc., may generate an aggregate effectiveness score for a region, body, or group, and/or individual effectiveness scores for each of the students/teachers of that region, body, or group. The reports may be generated by the reporting platform  232  to include any type of data including textual, graphical, video, audio, and vector-based data to provide rich, qualitative and quantitative analysis of the target subject(s), observer(s), and associated organization(s) or businesses(s), including their performance (e.g., execution, effectiveness, compliance, problem-areas, etc.). 
     In some embodiments, the reporting platform  232  may analyze two or more data types, such as observation-related data, achievement data, and/or student data related to the target subject, to generate an effectiveness rating for that target subject. Analyzing two more data types to generate an effectiveness rating is advantageous as it can provide a more reliable effectiveness rating for a target subject compared to an effectiveness rating generated from a single data type. For instance, the observation data for a given teacher may reflect, for a particular evaluation period, that the teacher received a rating of “proficient” for four of the metrics evaluated and a “needs improvement” rating for three of the metrics. However, during this same evaluation period, the student data may reflect that the students of this teacher gave the teacher a “proficient” or “excellent” rating in every category surveyed, and the achievement data for these students may reflect standardized test scores, which meet or exceed legislative requirements. As a result, the effectiveness rating generated by the reporting module  210  can balance the “needs improvement” ratings against the positive survey and test score results to produce a more accurate overall “effectiveness” rating for the teacher. In other examples, the reporting platform  232  may determine the assessments of the target subject described by each data type as being consistent, and as providing further evidence/support for a particular effectiveness rating. 
     In some embodiments, the reporting platform can generate a report based at least in part on the receipt of interaction data describing an interaction between the target subject and the at least one electronic resource that was assigned. The reporting platform  232  may be coupled to the resource-finer engine  252  ( FIG. 3 ), the memory  404 , and/or the data store  113   a - n  to receive the interaction data. For example, to generate a report, the reporting platform  230  may analyze user behavior in interacting with one or more electronic resources provided by the resource-finder engine  252 , and generate a report summarizing and/or detailing this analysis. In particular, when a user consumes an electronic resource, the resource-finder engine  252  of the student-growth platform  118  may receive and store interaction data describing the interaction in the data store  113   a - n  in association with a user profile associated of the user, and the reporting platform  232  may access the interaction data to analyze the user interaction and results and generate a report describing the user interaction and results. 
     For example, when a user accesses an electronic resource, pages through an electronic book, downloads files included with or embedded in a webpage, complete a survey associated with any electronic resource, views a video file, listens to an audio file, comments on passages of an interactive electronic book, submits lesson plans, submits curriculum maps, downloads documents, uploads files including video, audio, text, graphics, etc., participates in communities, groups defined by his/her social connections, or otherwise uses any other functionality provided by the user/client application  108  (e.g., see  FIG. 5 ) to interact with an electronic resource. The student-growth platform  118  the receives interaction data describing these interactions from the user/client application  108  or another entity of the system, such as the media-distribution server  117 , and stores interaction data describing the interaction in the data store  113   a - n . In another example, if an observer assigns a target student the task of watching a video on achieving effective scores via the student-growth platform  118 , the reporting platform  232  can generate status updates about the target student&#39;s progress on watching the video and sending them to the observer (e.g. teacher). The reporting platform  232  can also report on the target student&#39;s additional efforts to develop his/her skills by reporting on what other electronic learning resources the target student has consumed since the observer made the assignment, provided the target student provides his/her consent for doing so via an associated privacy settings interface. 
     In some embodiments, the reporting platform  232  generates a report in response to receiving a trigger signal. In some embodiments, the trigger signal may be generated by the student-growth platform  118  upon the completion of an assignment by a target user and transmitted to the reporting module  232 . In other embodiments, the trigger signal may be generated in response to a request for a report, for example, from a user of the student-growth platform via an associated user interface. For example, an observer who observed a target student and assigned the target student one or more electronic resources may input a command into his/her user device  106  via the user application  108  commanding that a report be generated describing the target student&#39;s progress on completing the assignment. Responsive to receiving the command, the user application  108  may generate and send a report request via the network  102  to the reporting platform  232 , thus triggering the reporting platform  232  to generate and send the report for display to the target student, observer, an administrator, a combination of the foregoing, etc. 
     In other embodiments, the reporting platform  232  may automatically generate the report at certain intervals, times, etc. For example, the reporting platform  232  may automatically generate reports for all outstanding assignments and send them to the administrator and/or observer users  114  who oversee the target students that the outstanding assignments correspond to. In some embodiments, the reporting platform  232  may transmit the report to the user application  108  for display to the user  114 , provide the report for download as a portable document, transmit the report via electronic message (e.g., via email) to one or more other users  114  associated with or responsible for the target subject, etc. 
     The reporting module  232  is also capable of analyzing the performance/effectiveness of an observer/student, and generating and providing a report describing the observer&#39;s/student&#39;s effectiveness/performance to the observer and other users  114 , such as an administrator of the observer. In some embodiments, to analyze the effectiveness/performance of the observer/student, the reporting platform  232  compares achievement data and observation-related data associated with the target to determine if the performance assessment of the target reflected by the observation-related is accurate and consistent. The achievement data can include any type of achievement data associated with the target. For example, depending on the target student&#39;s performance, the achievement data may include test scores for the target, reviews by teachers, performance reviews, compliance with requirements/standards, etc. The observation data can include any data associated with the performance assessments made by an observer, such as the observation files associated with the observer and/or target students observed by the observer. In these or other embodiments, the reporting platform  232  can track the observational assessments performed for the target student and compare them for consistency based on substance, frequency, etc. 
     Based on the observation-related and achievement data, the reporting module  232  can determine the accuracy and consistency of a performance assessment (e.g., execution, effectiveness, compliance, performance, trending, and other metrics, etc.) of the target students. In some embodiments, the reporting module  232  can analyze the achievement data to determine an achievement-based performance assessment for the target student; can analyze the observation-related to determine an observation-based performance assessment for the target student; and compare the achievement-based and the observation-based performance assessments to further determine if the observation-based performance assessment of the target student is accurate/consistent. In other embodiments, the reporting platform  232  may compare the observational assessments by one observer of a target student to the observational assessments of the same target student by other previous observers to determine the accuracy of the observer&#39;s assessments. For example, if an observational assessment of a target student by a first observer is grossly inconsistent with the observational assessments of that target student by other observers on the same or similar subject matter, the observational assessment of the first observer may be flagged and reported to an administrator of the observer for further review/scrutiny. 
     In some embodiments, the accuracy of the observation-based performance assessment can be determined based on whether the achievement-based and the observation-based performance assessments are consistent. For example, the reporting platform  232  may determine the observation-based performance assessment to be inaccurate if the observation-based performance assessment is negative and the achievement-based performance assessment is positive, or conversely, if the observation-based performance assessment is positive and the achievement-based performance assessment is negative. 
     Further, the reporting platform  232  may determine the observation-based performance assessment to be accurate if both the observation-based performance and achievement-based performance assessments were negative or positive. However, if the both the observation-based performance and the achievement-based performance assessments were neutral, the reporting platform  232  may report that the accuracy of the performance assessment made the by the observer could not be verified. 
     The reporting platform  232  can generate a report describing the determination it made about the accuracy of the observer&#39;s performance assessment of a target subject and provide the report for display to the observer(s) or one or more other users, such as an administrator of the observer(s). In some embodiments, the reporting platform  232  can generate the report in response to receiving a request from a client device  106  of an administrator/user  114  who oversees the observer. In other embodiments, the reporting platform  232  can automatically generate and send the report to the administrator via an electronic message, such as an email, an internal messaging application provided by the student development application, a text message, etc. 
     In some embodiments, the accuracy of all of the observer&#39;s performance assessments of a particular target student or multiple target students may be determined by the reporting platform  232  and included in the report. For example, the observer&#39;s overall accuracy in performing the observational assessments may be computed over time by the reporting platform  232  to determine if the observer is consistently inaccurate with his/her observations. Additionally, the reporting platform  232  may compare the accuracy of one or more of an observer&#39;s assessments of a target student to the assessments of that target student by other observers to determine whether they are consistent. If not, information describing the inconsistencies may be included in the report. 
     The reporting platform  232  may also determine whether an observer is properly performing the observational assessments and can include this determination in the report. In some embodiments, the reporting platform  232  may analyze the observation files for some or all target students observed by the observer to determine the level and quality of feedback provided by the observer about those students. For example, if the reporting platform  232  determines that the assessments (e.g., answers, ratings, comments, etc.) for the target students made by the observer in the observation files are all the same or substantially similar, the reporting platform  232  may determine that the observer is simply making the same assessments for each target student and is not performing the assessments as required. The reporting platform  232  may also make a determination as to the quality of one or more assessments performed by an observer based on the level and/or variety of feedback included in the observation file(s) for one or more target students. 
     The reporting platform  232  may store any reports and/or data generated by it in the data store  113   a - n  for later access by the reporting platform  232  or any other component of the student-growth platform  118 , such as an administrative module (not shown) of the student-growth platform  118  that provides administrator/users access via the client application  108  to statistics and reports about the users  114  of the student-growth platform  118  that the administrator oversees. 
     In the depicted embodiment, the reporting platform  232  is coupled to the data store  113   a - n  via the bus  116  via the network/cloud platform  102  and the third-party server  117  to receive the achievement data. For example, the reporting platform  232  can periodically retrieve the achievement data from the third-party server  117  via an API and store it locally in the data store  113   a - n  for later access or use. In another example, the reporting platform  232  can retrieve the achievement data real-time via the API for analysis and compare it to the observation-related data from the observation file. However, in other embodiments, the reporting platform  232  may retrieve the achievement data from any information source communicatively coupled to the student-growth platform  118  or network  102  via the network. 
     The reporting platform  232  provides numerous additional advantages including providing the target student a mechanism for reporting on the completion of an assignment, providing an observer/user a mechanism to monitor whether the target student(s) he/she observes completes the assignments assign to them, analyzing and reporting on an student&#39;s performance and work quality, determining/rating effectiveness of target students, etc. 
     Additional functionality of the student-growth platform  118  and its observation engine  221 , and their corresponding components are further described below. 
     Example Client Device  108   
       FIG. 5  is a block diagram of an example user/client device  106 . In the depicted embodiment, the client device  106  includes a client application  108 . The client device  106  also includes a communication unit  308 , a processor  302 , a memory  304 , a display device  310  with a graphics adapter  320 , a display  318 , and an input device  312 , which are communicatively coupled via the bus  306 . In some embodiments, the functionality of the bus  306  may be provided by an interconnecting chipset. 
     The communication unit  308  includes interfaces for interacting with other devices/networks of devices. In some embodiments, the communication unit  308  includes transceivers for sending and receiving wireless signals. For example, the communication unit  308  includes radio transceivers (4G, 3G, 2G, etc.) for mobile network connectivity, and radio transceivers for WiFi and Bluetooth® connectivity. In these or other embodiments, the communication unit  308  may include a network interface device (I/F), which includes ports for wired connectivity. For example, the communication unit  308  may include a CAT-type interface, USB interface, or SD interface, etc. In the depicted embodiment, the communication unit  308  is coupled to the network  105  ( FIG. 1 ) by the signal line  104   a - n.    
     The processor  302  comprises an arithmetic logic unit, a microprocessor, a general purpose controller, or some other processor array to perform computations and optionally provide electronic display signals to the display device  310 . The processor  302  may communicate with the other components via the bus  306 . Processor  302  processes data signals and may comprise various computing architectures including a complex instruction set computer (CISC) architecture, a reduced instruction set computer (RISC) architecture, or an architecture implementing a combination of instruction sets. Although only a single processor is shown in  FIG. 5 , multiple processors may be included. The client device  106  also includes an operating system executable by the processor  302  as discussed elsewhere herein, for example, with reference to  FIG. 1 . 
     The memory  304  stores instructions and/or data that may be executed by processor  302 . The memory  304  communicates with the other components of client device  106  via the bus  308 . The instructions and/or data comprise code for performing any and/or all of the techniques described herein. In particular, the memory  304  includes a non-transitory computer-usable (e.g., readable, writeable, etc.) medium, which can be any apparatus or device that can contain, store, communicate, propagate or transport instructions, data, computer programs, software, code, routines, etc., for processing by or in connection with the processor  302 . A non-transitory computer-usable storage medium may include any and/or all computer-usable storage media. In some implementations, the memory  304  may include volatile memory, non-volatile memory, or both. For example, the memory  304  may include a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory, a hard disk drive, a floppy disk drive, a CD ROM device, a DVD ROM device, a DVD RAM device, a DVD RW device, a flash memory device, or any other mass storage device known for storing information on a more permanent basis. It should be understood that the memory  304  may be a single device or may include multiple types of devices and configurations. In some embodiments, the user/client application  108  is stored in the memory  304  and executable by the processor  302 . 
     The display device  310  represents any device equipped to present output signals generated and provided by the user/client device  106 . In some embodiments, the display device  310  displays electronic images and data including, for example, user interfaces and formatted information. For example the display device  310  may be any conventional display device, monitor or screen, such as an organic light-emitting diode (OLED) display, a liquid crystal display (LCD), an e-ink display, etc. In some embodiments, the display device  310  is a touch-screen display capable of receiving input from one or more fingers of a user/client  106 . For example, the display device  310  may be a capacitive touch-screen display capable of detecting and interpreting multiple points of contact with the display surface. In some embodiments, the display device  310  may be coupled to the bus  306  via a graphics adapter  320  (shown within the display device  310 , but also may be configured outside), which generates and provides display signals to the display device  310 . The graphics adapter  320  may be a separate processing device including a separate processor and memory (not shown) or may be integrated with the processor  302  and memory  304 . 
     The input device  312  represents any device for inputting data on the client device  106 . In some embodiments, the input device  312  is a touch-screen display capable of receiving input from the one or more fingers of the client/user  106 . The functionality of the input device  312  and the display device  310  may be integrated, and a user/client  106  of the client device  106  may interact with the client device  106  by contacting a surface of the display device  310  using one or more fingers. For example, the user/client  114   a - n  may interact with an emulated (i.e., virtual or soft) keyboard displayed on the touch-screen display by using fingers to contacting the display device  310  in the keyboard regions. In other embodiments, the input device  312  is a separate peripheral device or combination of devices. For example, the input device  312  includes a keyboard (e.g., a QWERTY keyboard) and a pointing device (e.g., a mouse or touchpad). The input device  312  may also include a microphone (e.g., for voice input) or other known peripheral devices. 
     Example User/Client Application  108   
     Referring now to  FIG. 5 , the user/client application  108  is software including routines for sending and receiving data to the other entities of the system, including, for example, the student-growth platform  118 , the media-distribution server  115 , and the third-party server  117 . In some embodiments, the user/client application  108   a  is a web browser application for accessing the resources provided by the student-growth platform  118  and the media-distribution server  115 . For example, the student-growth platform  118  operated by the in cooperation with the media-distribution server  115  may be a web-based service and the user/client application  108  may access various electronic resources provided by the service via uniform resource locators (URLs). In other embodiments, the user/client application  108   a  is an application customized specifically for accessing the student-growth platform  118 , and more particularly, for cooperating and interacting with the observation engine  119 . 
     In the depicted embodiment, the user/client application  108  provides a user  114   a - n  (e.g., an observer) interacting with the client device  106  mechanisms for inputting viewing, adding, modifying, deleting observation-related data related to one or more other users/clients  114   a - n . The user/client application  108  may cooperate with the observation engine  221  ( FIG. 1A ) to conveniently store and retrieve observation templates and files for viewing by the user. The user/client application  108  may, in some embodiments, send a resource request to the observation engine  221  to identify and provide recommended electronic resources that can be assigned to a user. The user/client application  108  may also send a request to the reporting module  232  ( FIG. 1A ) to provide observation-related statistics and reports for display to the user  114   a - 114   n  via a report interface generated by the user-interface module  514  of the user/client application  108 . 
     In the illustrated embodiment, the user/client application  108  includes a user-interface module  514 , an observation unit  516 , and an assignment unit  518 . The observation unit  516 , the assignment unit  518 , and the user-interface module  514  are communicatively coupled with each other and the other components  502 ,  504 ,  508 ,  510 , and  512  of the client device  106 . The components are also coupled to the network  102  via the communication unit  508  (and line  104 ) for communication with the other entities of the system  100   a.  While not shown, in some embodiments, the user/client application  108  may include an authentication or verification module for authenticating the user  114   a - n  to access the student-growth platform  118 . 
     In some embodiments, the user/client application  108 , the user-interface module  514 , the observation unit  516 , and/or the assignment unit  518  are sets of instructions executable by the processor  502  to provide their respective functionality. In other embodiments, the user/client application  108 , the user-interface module  514 , the observation unit  516 , and/or the assignment unit  518  are stored in the memory  504  of the client device  106  and are accessible and executable by the processor  502  to provide this functionality. In any of these embodiments, the user/client application  108 , the observation unit  516 , the assignment unit  518 , and/or the user-interface module  514  may be adapted for cooperation and communication with the processor  502  and other components of the user/client device  106 . 
     In some embodiments, the observation-related data managed by the user/client application  108  may be locally stored in the memory  504 , remotely stored in any of the data stores  113   a - 113   n  (via signal lines  121   a - 121   n ), the third-party server  117 , or may be stored in any combination of the forgoing thereof. For example, an instance of the observation-related data may be stored locally on the user/client device  106  and remotely on the student-growth platform  118 , and the client/user application  108  may synchronize the information via the network  105 , either continuously or periodically, as the information changes. In some embodiments, the user/client application  108  may be a stand-alone application or may be integrated into another application operable on the user/client device  106 . 
     The observation unit  516  is software including routines for sending and receiving observation-related data to the observation engine  221  ( FIG. 1A ), cooperating with the interface engine  306  to display observation-related information to a user, and cooperating with the user-interface unit  119  to receive observation-related input from the user/client. In some embodiments, the observation unit  516  interacts with the observation engine  221  to receive observation templates and observation files for display to the user  114   a - n  of the user/client device  106  and to send observation files to the observation engine  221  for processing and/or storage in the data store  113   a - n , as discussed above with reference to at least  FIG. 1A . 
     In some embodiments, the observation unit  516  can cooperate with the observation engine  221  via the network  102  to provide information about target students to an observer and provide functionality to the observer for assessing and tracking the performance and development of the target students. The observation unit  516  may also interact with the user-interface module  514  to provide administrative tools such as a reporting tool for viewing statistics and other analytical data, and/or an observational tool for assessing the performance of students, assigning instructional resources to students, and tracking completion of the assignments given to them. In some embodiments, the observation unit  516  interacts with the user-interface module  514  to display observation templates and files to a particular user, as discussed with reference to at least  FIG. 14  below. 
     The observation unit  516  may be coupled to the user-interface module  314  to receive user input and display the information to the user  1114   a - 114   n  via user interfaces generated by the user-interface module  514 , such as the observation interface discussed with reference to  FIG. 14  below. For example, the observation unit  514  may send interface signals to the user-interface module  314 , and responsive to receiving these signals, the user-interface module  314  may generate and display user interfaces that correspond to the instructions carried by the interface signals. In another example, the user-interface module  314  may receive input signals from a user via the input device  312  and send those signals to the observation unit  314  for processing. In some embodiments, in cooperation with the user-interface module  314 , the observation unit  314  can receive user-related and observation-related information and display the data to the user, display observation templates to the user, populate observation templates with user input, save observation files based on the observation templates, transmit observation-related data such as observation files to the observation engine  221  or storage, receive observation-related statistics and reports and organize and display them to the user, receive electronic resources for assignment, consumption, etc., by the user, receive electronic communications from other users via the network  102  and display them to the user, etc. In some embodiments, an observer may, via a user interface rendered by the user-interface module, preselect options and/or be guided similarly in designing observation templates and appropriate follow-up activities. 
     In some embodiments, the user-interface module  514 , in cooperation with the observation unit  316 , may generate a report dashboard/interface for viewing reports generated and provided by the reporting module  232  ( FIG. 1A ) and received by the observation unit  514 . This dashboard provides numerous advantages including providing an observer or administrator with detailed information about a given target student&#39;s performance (e.g., execution, effectiveness, compliance, etc.) over time. For example, the observer may be a teacher and may need to interact with a number of students to perform observational assessments of each of them. For each student, the teacher may, using the dashboard, access any previous observational assessments of that student; view an overall performance (e.g., execution, effectiveness, compliance, etc.) rating/summary of that student (scores assigned); view the performance (e.g., execution, effectiveness, compliance, etc.) ratings/summaries of that student over time; view statistics across all observational assessments of that student or a subset, such as the observational assessments performed for that academic year; may quickly ascertain the areas a student has had problems with or has been working on, or the areas the student has been improving on; review the test scores for the students, evaluations of the student; view the electronic training resources the student has consumed/interacted with; view any work-product, lesson plans, videos, presentation, etc., the student has uploaded, the learning communities and groups the student has interacted with, any mentors the student has been working with, etc. Using this information, the teacher may quickly get up-to-speed on where the student is at, thus provide pertinent and relevant observations (e.g., evaluations, ratings, suggestions, comments, etc.) and assignments, etc., during the observation session to be performed. 
     The assignment unit  518  is software including routines for generating and sending resource requests, receiving resource responses including one or more electronic resources identified by the assignment platform  226 , and assigning one or more electronics resources to a user. In some embodiments, the assignment unit  518  cooperates and interacts with the assignment platform  226  to identify one or more electronic resources that can be assigned to a user, as discussed above with reference to at least  FIG. 1A . 
     The assignment unit  518  is coupled to the user-interface module  514  to receive user input and provide information to the user/client  114   a - n  via user interfaces generated by the user-interface module  514 . In some embodiments, responsive to receiving user input signals, the assignment unit  518  can generate a resource request or an assignment request. In some embodiments, the input signals may specify which electronic resource(s) is/are being assigned and the user the resource(s) is/are being assigned to. For example, an observer performing and observation of a target student, may select one or more of the videos identified by the recommendation engine  250  ( FIG. 2A ) and displayed via the user-interface module  514 , such as the observation interface  1400  illustrated in  FIG. 14 . The assignment unit  518  may also assign supplemental instructional, prescriptive and/or discipline-related resources in response to one or more of these resources being assigned by an observer (e.g., after receiving a report about an initial assignment). In some embodiments, the assignment unit  518  assigns one or more of these resources by generating and sending an assignment request and receiving an assignment confirmation as discussed elsewhere herein. In addition, the assignment unit  518  may provide tools/functionality to the observer to provide the target student with feedback, follow-up with the target student about an assignment or an aspect observational assessment performed, provide recommendations of additional electronic resources to assign to the target subject upon completion of an initial assignment by the target student, etc. 
     The user-interface module  514  is software including routines for rendering user interfaces and for receiving user input. The user-interface module  514  may be coupled to the input device  512  via the bus  506  to receive input signals from the user  114   a - n . For example, an observer/user  114   a - n  can select an answer to an observation-related question using the input device  512 , and the user-interface module  514  receives signals describing the answer. The user-interface module  514  may store the input signals in the memory  504  for retrieval by the other elements of the client application  508 , such as the assignment unit  518 , or may provide the signals directly to the other elements of the user/client application  108 . 
     The user interfaces generated by the user-interface module  108  include interfaces for inputting, modifying, and deleting information, displaying notifications, rendering video, displaying images and text, displaying vector-based content, sending and storing information, etc. In some embodiments, the user interfaces include user interface elements that allow users/clients  114   a - n  to interact with the user/client device  106  and input information and commands, such as text entry fields, selection boxes, drop-down menus, buttons, virtual keyboards and numeric pads, etc., as further discussed below with reference to  FIG. 14 . 
     EXAMPLE METHODS 
     Referring now to  FIG. 4 , an example method  400  for prescribing electronic resources based on observational assessments is described. The method  400  begins by identifying  402  one or more electronic resources based on observation data. In some embodiments, the recommendation engine  206  identifies  402  the one or more electronic resources by querying a library of electronic resources for resources that match one or more aspects of the observation data. If a plurality of electronic resources is identified, the recommendation engine  206  can rank and filter the electronic resources and thus recommend which electronic resources are the most suitable for a target subject. Next, the method  400  provides  404  a summary of the one or more electronic resources to an observer, such as a supervisor or evaluator, for assignment to subject that he/she is observing. For example, the client device  126  of the observer may receive a summary of training videos or other resources identified and ranked by the recommendation engine  206  and may display the summary to the observer via a user interface. The observer may use the interface to preview the videos or other resources and/or assign one or more of the videos or other resources to the target subject. 
     Next, the method receives  406  an assignment of one or more electronic resources. In some embodiments, the assignment engine  208  receives an assignment request describing the one or more electronic resources that are to be assigned to the target subject by the assignment engine  208 . The method  400  continues by associating  408  the assignment of the one or more electronic resources with the target subject. In some embodiments, to associate the assignment, the assignment engine  208  stores the assignment request or information therefrom in the data store  210  in association with the a user profile of the target subject. The method  400  is then complete and ends. 
       FIG. 6  describes an example method  600  for the developing student growth. The method  600  begins at  601 , including one or more operations for evaluating the time elapsed since a last set of assessments for a student were generated. The method  600  proceeds to  602 , including one or more operations for utilizing the smart-gradient project (SGP) algorithm to assess and position a student (or student group) into the scaled learning-progression platform based on the time elapsed. The method  600  proceeds to  603 , including one or more operations for combining the computer-adapted-testing (CAT)+SGP+time-based projection+entry points to establish the entry point to curriculum. The method  600  proceeds to  604 , including one or more operations for normalizing the CAT outcomes with practice assignments to create an integrated model of mastery against the learning progression. The method  600  proceeds to  605 , including one or more operations for extending actual testing using intelligent inferencing based on the relationship of objects within the learning progression. The method proceeds to  606 , including one or more operations for utilizing universal skills pool to enable curriculum mapping to facilitate lesson planning by mapping to the teacher&#39;s chosen curriculum, pacing guide or text book. The method  600  proceeds to  607  utilizing a multi-dimensional response item model to bind assignments from assessment, instruction, and practice assignments into a unified scale. The method  600  proceeds to  608 , including one or more operations for viewing mastery level of students by assignment score, by probed assessment, by general-outcome-measurement (GOM) assessment, and by integrated models. The method  600  proceeds to  609 , including one or more operations for enabling the lesson-planning engine to link assessment to curriculum. The method  600  proceeds to  610 , including one or more operations for enabling the lesson-planning engine to link assessments to government-created learning standards. The method  600  proceeds to  611 , including one or more operations for including probabilistic algorithms. The method  600  proceeds to  612 , including one or more operations for enabling the lesson-planning engine to link assessments to instruction resources from many sources. The method  600  proceeds to  613 , including one or more operations for enabling the lesson planning-engine to combine assessment sources with standards, curriculums, instructional resources, and assignment delivery systems. The instructions resources include metadata for electronic resources, such as audio files, video files, vector-based files, electronic books, electronic publications, spreadsheets, word processing documents, presentational slides, etc. In some embodiments, the electronic resources may be derived from storage in the data store  410  and/or the media data store  111  along with metadata describing the contents and characteristics of the electronic resources. In other embodiments, metadata for the electronic resources are derived from the electronic resources themselves, for example by parsing header information included in the electronic resources. In some embodiments, the instructional materials may be retrieved from a resource library database updated to include the metadata for the electronic resources, including for example, data describing the content and characteristics of the electronic resources and their stored location. 
     At  612 , the lesson-planning engine receives observation data reflecting an observational assessment generated for a target student. In some embodiments, the observation data reflects an answer to a question from an observation template. For example, the observation data can describe how the target subject is performing with reference to a particular skill, requirement, standard, etc. Using the metadata associated with the electronic resources, the method  600  queries for one or more electronic resources that match the observation data. The match can be loose and allow electronic resources that generally pertain to the observation data to be identified, or may be strict and require that the electronic resources be precisely directed to the assessment reflected in the observation data. For example, if the target student is identified as lacking in his or her ability in a particular area, a loose match may identify resources generally related to what is lacking, and a strict match may identify resources that specifically relate to what is lacking. 
       FIG. 7  describes an example method of assessing target students. The example method  700  begins at  702  with determining the time elapsed since a last observation assessment for a target student. The method  700  proceeds to  704  for conducting computer-adapted testing for the student determine the educational level of the target student. The method  700  proceeds to  706  for determining the student growth percentile. It should be recognized that a student growth percentile, or SGP, compares a student&#39;s growth to that of his or her academic peers nationwide. Academic peers are students in the same grade with similar achievement history on standardized assessments. The SGP is reported on a 1-99 scale, with lower numbers indicating lower relative growth and higher numbers indicating higher relative growth. For example, a SGP score of 90 means the student has shown more growth than 90 percent of students. The percentile rank (PR) and student growth percentile (SGP) are very different metrics has a PR is an achievement score that describes a single point in time and a SGP is a growth measure that explains student growth between different points in time. Both measures are norm-referenced, but they have different norming groups. The norming group for PR is all students in a particular grade level. The norming group for SGP is each student&#39;s own academic peer group. Percentile rank (PR) and student growth percentile (SGP) are based on scale of 1-99. At least two tests are typically required to report a SGP. The method  700  proceeds to  708  to integrate all the scores into a unified score for placement of the target student by the learning-progression engine. 
       FIG. 8  describes an example method  800  for generating a lesson plan. The method  800  begins and proceeds to  802 , including one or more operations for generating an expected score for a target (student). The example method  800  proceeds to  804 , including one or more operations for generating an estimated pace for the target (student). The example method  800  proceeds to  806 , including one or more operations for, selecting skills appropriate for the estimated pace for the target student. The example method  800  proceeds to  808 , including one or more operations for recommending a curriculum for the target student. The example method  800  proceeds to the  810 , includes one or more operations for finding resources that match the curriculum. The example method  800  includes one or more operations for generating a lesson plan for the target student. 
       FIG. 9  describes an example method for creating and assigning assignments in accordance with the lesson plan. The example method  900  begins and proceeds to  902 , at which point the example method receives an assignment request, generated with by the teacher or the student. The example process  900 , at  904 , includes a preview request for previewing the resource. If so, the method  900  provides  906  the selected resource for the assignment indicated in the preview request for presentation to the observer. In some embodiments, the electronic resource is provided by the student-growth platform  118  and/or media-distribution server  117  via the network  102  to a user/client device  106  of the observer (e.g., student or teacher). In other embodiments, other entities coupled to the network  102  may provide the electronic resource. By way of example, an observer who received a list of electronic resources from the recommendation engine  250  via the client application  108  can preview one or more of the electronic resources to learn more about the resource or resources, determine whether the subject matter of the resource is appropriate for the target subject, etc. 
     If the method  900  determines at  904  that the request does not include a preview request, the method  900  then determines at  908  whether the request includes an assignment request that require one or more electronic resources for a target subject for completion. If so, the method  900  determines  910  if any assignment particulars or parameters are associated with the assignment request. In some embodiments, an assignment particular places a condition on how the assignment of an electronic resource is to be completed. For example, the assignment particular may be a due date by which the target must interact with the electronic resource by. As a further example, if electronic resource is a video, the assignment particular may be a due date by which the target must watch the video by using an interface associated with the student-growth platform  118 . If it is determined at  908  that the request does not include an assignment request for resources, the method  900  is then complete and ends. 
     Next, the method  900 , at  912 , merges the one or more electronic resources with the assignment for the target based on the one or more assignment particulars or parameters. In some embodiments, the method  900  may assign  912  the one or more electronics resources by storing a record of the assignment in the data store  410  ( FIG. 4 ) in association with a user profile of the target. The record can include information describing the one or more electronic resources and the one or more assignment parameters. The method  900  is then complete and ends. 
       FIGS. 10 and 11  describe an example method  1000  for monitoring and reporting on assignments. The method  1000  begins by monitoring  1002  the progress of an assignment. The assignment may include the assignment of one or more electronic resources to a target for completion/interaction by the target subject. The assignment may also include one or more assignment parameters that dictate how the assignment should be completed by the target, and the method  1000  can analyze the assignment parameters to determine if the assignment has been completed. In some embodiments, the reporting platform  232  ( FIG. 3 ) is configured to monitor the status of the assignment, including whether the assignment has been fully completed, is in progress, or has not begun. 
     The method  1000  continues by exchanging  1004  communications between the target and the observer of the target. In some embodiments, the method  1000  facilitates the exchange by providing the contact information (e.g., an electronic messaging address) of the target to the observer and vice versa. In other embodiments, the method  1000  exchanges communication by relaying electronic messages between messaging accounts of the target and the observer using an internal messaging service. Exchanging communication using other messaging services, such as email, instant messaging, SMS, etc., is also contemplated. In these embodiments, the method  1000  may store record of any communications exchanged between the target and the observer for later reference and retrieval. Exchanging communication between the observer and the target is advantageous in a number of respects including that it provides a feedback loop between the target and the observer. For example, the target may communicate questions to the observer about what specific areas the target should focus on improving when interacting with an electronic resource assigned to him/her by the observer, and the observer may provide feedback to the target. In some embodiments, the communications exchanged by the method  1000  may be included in a report generated by the reporting module  232  to summarize the interaction between a target and an observer. 
     Next, the method  1000  determines at  1006  the completion of the assignment. For example, the method  1000  can determine whether the assignment was successfully completed, was never begun, or was in progress at the conclusion of the time set for completing the assignment. The method  1000  then provides at  1008  the grading frame to the observer and updates the target profile to reflect the completion. In some embodiments, the reporting module  232  updates a record stored in the data store  410  with data reflecting the completion. 
     The method  1000  continues by generating  1010  a report describing the status of the assignment and providing it to the observer  1012  and/or other users. The report may include the completion determined by the method in block  1006 , any electronic communication exchanged between the target subject in the observer in block  1004 , and any other information about the assignment, including a description of the electronic resource(s), information from the observation file associated with the assignment, statistics and results from other observational assessments performed previously of the target subject, any related industry standards, performance benchmarks, requirements, etc. 
     The method  1000  then determines at  1014  whether the assignment was successfully completed. In some embodiments, this determination is based on the conclusion from block  1006 . If the method  1000  determines at  1014  the assignment to have been successfully completed, the method  1000  continues by updating the assignment list for the target at  1016  and then at  1118  generating a status report for the target. 
     If the method  1000  determines at  1114  the assignment to have not been successfully completed, the method  1000  continues by updating the target status report and then proceeds to generate alerts and report on the target. The method  1000  is then complete and ends. 
       FIG. 12  describes an example method  1200  for the learning-progression scenario. The method  1200  begins by presenting  1202  an observation template including learning progression questions and associated user-selectable/definable options to an observer of a target. In some embodiments, the user interface unit  119  ( FIG. 1A ) displays the observation template upon receiving interface signals from the observation engine  221 . Next, the method  1200  receives at  1204  user input providing answers to a question and, based on the answers, the method  1200  determines at  1206  one or more electronic resources that relate to the answer. For example, the user interface unit  119  receives input signals providing observation data from the observer via the input device  512  and the assignment manager  260  generates an assignment request based on the observation data and transmits it to the assignment player  264 . The assignment manager  260 , in reply, identifies one or more electronic resources and sends them to the assignment player  264  and the assignment unit  264  instructs the display device  510  to display the one or more electronic resources to the observer. 
     The one or more electronic resources are then displayed  1208  by the method  1200  to the observer. Next, the method  1200  receives  1210  user input selecting one of the electronic resources, and determines  1212  whether the user input includes an instruction to present the resource for review for a lesson plan or assignment. If so, the method  1200  requests  1214  the electronic resource for presentation. In some embodiments, the method  800  sends a presentation request to the server hosting the resource requesting the server provide the electronic resource for presentation. For example, the electronic resource is a video and the assignment player  264  receives a video stream from the media-distribution server  115  responsive to sending a preview request to the resource-finder engine  252 . If the user input does not include an instruction to present the resource, the method  1200  continues by determining  1216  whether the user input includes an instruction to assign the electronic resource to the target subject for completion. If so, the method  1200  requests  1218  the assignment of the electronic resource to the target. In some embodiments, an assignment request is sent by the assignment manager  260  to the assignment player  264  via the network  102  requesting the electronic resource be assigned to the target for completion. If the method  1200  determines  1216  that the user input does not include an instruction to assign the electronic resource, the method  1200  is then complete and ends. 
       FIG. 13  describes an example method  1300  for assessing performance of a target. The method  1300  begins by receiving  1302  achievement data for at target student and comparing, at  1304 , the achievement data to assessment data and scores associated with the target student. For example, the reporting platform  232  may access achievement data from the data store  113  (by exporting the data by the exporter engine  337 ) or from the third-party server  117  and compare it to observation data also accessed from the data store  113 . In some embodiments, the observation data may be pulled from an associated observation file stored in the data store  113 . Based on the comparison, the method  1300  determines at  1306  whether a performance assessment of a target student meets goals for the target student and generates at  1308  a report describing the performance of the target student describing the performance of the target student. For example, the reporting platform  232  can generate a report describing the determination it made about the target student&#39;s performance. The method  1300  provides at  1310  the report for access and display to an administrator, teacher, or other entity, and then completes and ends. 
     It should be understood that the methods  600 - 1300  are provided by way of example, and the variations and combinations of these methods, as well as other methods, are contemplated. For example, in some embodiments, at least a portion of the methods  600 - 1300  represent various segments of one or more larger methods and may be concatenated or various steps of these methods may be combined to produce other methods which are encompassed by the present disclosure. Additionally, it should be understood that the assignments of electronic resources and reporting on the conclusions of the assignments, as described with reference to at least the methods  600 - 1300 , could be iterative, and thus repeated as many times as necessary to assist a target student with his or her growth and development. 
     To illustrate various aspects of the system  100   a  and the methods  600 - 1300 , the following non-limiting example is provided. A school or district administrator or such third party may visit the classrooms of each teacher in his/her school to observe. The third party may launch the client application  108  on his/her wireless client device  106 , and once launched, the observation unit  516  ( FIG. 5 ) of the client application  108  may refresh a local repository with updated target information and observation templates received from the observation engine  221  via the network  102 . The third party, using an interface generated by the user-interface module  514 , may select previously completed observation files for a given student to view how the student performed during previous observation sessions. 
     Example User Interface 
     Referring now to  FIG. 14 , an example observation or user interface (or dashboard display)  1400  for the functionalities of the student-growth platform  118  is described. It should be understood that the example observation or user interface (or dashboard display) illustrated in  FIG. 14  is provided merely by way of example, and that other user interface displays (with different criteria or user interactions) may be generated and displayed by the user/client application  108  to allow users  114  to interact with the system  100  (a and b) and to allow the system  100  to present information to the users. For example, various user interfaces may be produced, to display reports and statistics, display dialogs among the users (by a chat feature), set parameters and settings, send electronic communications, view, listen to and/or interact with the electronic resources provided by the student-growth platform, etc. 
     As depicted in  FIG. 14 , the observation interface  1400  includes a menu region  1402  and an observation region  1404 . The menu region  1402  includes a listing of students belonging to a particular school, class, or group  1406 . The menu region  1402  also includes a button  1408  for viewing suggested skills, which may be divided into domains (e.g., four domains including foundational skills, language, literature, informational text). The illustrated dashboard shows an example domain literature. Selecting a student selector  1406  displays a corresponding observation file created/being created for that particular student. For example, in the depicted embodiment, the student selector  1404  for Jim Brown has been selected and a corresponding observation file for Jim Brown is being populated with assessment information by the observer in the observation region  1004 . Selecting the view suggested skills button  1408  creates a new observation file for a student from an observation template. In some embodiments, in response to the selection of the observation creation button, a dialog (not shown) displaying a list of users may be presented to the observer. In some embodiments, the list of users represents all of the students that are associated with a particular school, class, or group within a school. For example, in the educational setting, the list of users may include all of the students in a particular grade in a school, or may be a segmented list of students selected from all of the schools within a school district and their corresponding teachers and administrators. In some embodiments, this list is provided on demand to the observation unit  516  by the observation engine  221  via the network  102  and rendered for display by the user-interface module  514 . In other embodiments, the observation unit  516  may retrieve the list from a local repository and provide it to the user-interface module  514  for display. Using the user interface, the observer may then select who the target student is from the list of users, and responsive to receiving this input, the user-interface module  514  may render the observation interface  1400  for the target student similar to the one displayed in  FIG. 14 . 
     Variation of this observational interface is possible. An observational interface may display a dashboard and screenshots that may be specific to a particular subject. In some embodiments, hovering over a standards bar once a skill is selected displays the standard code and text. Changing the selection to standards view displays the state-specific standards code; hovering over the code displays the standard&#39;s text. 
     The observation region may include a header region  1410  and a body region  1412 . The header region  1010  includes fields for displaying who the target student of the observation is (e.g., Jim Brown) and which observation template is being used for the observation, and for inputting the date and time the observation session was started and completed. The header region  1410  also includes an options dialogue box for configuring settings, such as generating and sending a report and updating a user profile. For example, the observer may check a checkbox to set an option for generating and sending a report and for updating a user profile for storage in the data store  113  for later access. 
     The body region  1412  includes elements for the observer to input his/her assessments made during the observation. For example, as depicted, the body region  1412  includes a region  1414  indicating the following: create instructional groups, find instructional resources, and indicate a performance level. There is a window (which may appear as a pop-up) for teacher activity indicating teacher objectives and lesson, with indicating a sample item. 
     As depicted, the body region  1412  also includes a resource region  1434  for displaying one or more electronic resources. In some embodiments, the electronic resources displayed in the resources region  1434  are received from the recommendation engine  250  and displayed in the resource region  1434  responsive to the observer inputting information into the answer elements  1416 . For example, upon receiving the input from the observer, the observation unit  516  transmits a resource request to the recommendation engine  250  requesting a list of related electronic resources be provided based on the input (e.g., observation data). 
     The resource region  1434 , as depicted, includes a resource scrolling region  1418 , a scrollbar  1424 , one or more electronic resources  1420 , a resource description region  1422 , an assignment button  1428 , a preview button  1430 , and a due date button  1432 . The resource scrolling region  1418  provides the user with functionality to scroll through and select one or more of the various electronic resources displayed therein. The scrolling can be performed by interacting with the scrollbar  1024  or the resource scrolling region  1018  (e.g., swiping the resource scrolling region  1018  via a touch-sensitive display with an input element, such as a finger). The selecting can be performed by interacting with the representations of the electronic resources in the resource scrolling region. For example, selecting on an electronic resource once selects the resource, and selecting it again unselects the resource. Multiple selection is also possible using known selection methods. 
     Once one or more resources have been selected by the observer, they can be previewed or assigned using the corresponding preview and assignment buttons  1430  and  1428 . In some embodiments, selecting the preview button transmits a request for a selected electronic resource, and once received, displays the selected electronic resource(s) in a preview interface with interface elements allowing the user to view and interact with the electronic resource. For example, the selected electronic resource is a video and the selecting the preview button displays a media player for viewing the video. 
     In some embodiments, selecting the assignment button  1428  sends an assignment request to the assignment unit  518  requesting the assignment of the one or more selected electronic resources to the target student. In reply, the assignment unit  518  may send a confirmation response to the assignment unit  518  indicating that the one or more resources were successfully assigned. Once this response has been received, the scrollable resource region may be refreshed to only display the resources that were assigned and the assignment button  1428  may change to an unassign button to indicate that the displayed resources have been assigned and provide functionality for the observer to unassign them if desired. The due date button is an example of an input element for setting an assignment parameter. As depicted, when the due date button is selected, a calendar dialog is displayed for selecting a date for when the assignment of the one or more electronic resources should be completed. It should be understood that the observation interface  1400  could include any number of interface elements for setting assignment parameters. 
     In some embodiments, the resource region  1434  may initially be hidden from display until the user inputs observation data into one or more of the answer elements  1416 . In other embodiments, the resource region  1434  may always be displayed, or may be hidden or displayed by selecting a corresponding expansion/contraction button (not shown). While only one assessment region  1414  and resource region  1434  are displayed in the depicted embodiment, it should be understood that numerous assessment regions  1414  and corresponding resource regions  1434  could be included. For example, there could be numerous standards and associated questions/indicators for measuring the target subject&#39;s performance during observation, and thus numerous corresponding resource regions for displaying electronic resources that correspond to the various assessments that have been made by the observer during the observation session. 
     An example system and methods for prescribing electronic resources based on observational assessments have been described. In the above description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It should be understood that the technology described in the various example embodiments can be practiced without these specific details. In other instances, structures and devices are shown in block diagram form in order to avoid obscuring the description. 
     Reference in the present disclosure to “some embodiments,” “an embodiment,” “an example embodiment,” “other embodiments,” etc., means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the description. The appearances of the phrase “in some embodiments” in various places in the present disclosure are not necessarily all referring to the same embodiments. 
     Some portions of the detailed descriptions that follow are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. 
     It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms including, for example, “processing” or “computing” or “calculating” or “ranking” or “identifying” or “determining” or “displaying” or “receiving” or “conducting” or “collecting” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     The present embodiment of the present disclosure also relates to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may include a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium including, for example, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, flash memories including USB keys with non-volatile memory or any type of media suitable for storing electronic instructions, each coupled to a computer system bus. 
     The present disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In a preferred embodiment, the present disclosure is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc. 
     Furthermore, the description can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. 
     A data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. 
     Input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers. 
     Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modems, wireless adapters, and Ethernet cards are just a few of the currently available types of network adapters. 
     Finally, the algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear from the description. In addition, the present disclosure is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present disclosure as described herein. 
     It is intended that the scope of the disclosure be limited not by this detailed description, but rather by the claims of this application. As will be understood by those familiar with the art, the present disclosure may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. Likewise, the particular naming and division of the modules, routines, features, attributes, methodologies and other aspects are not mandatory or significant, and the mechanisms that implement the present disclosure or its features may have different names, divisions and/or formats. Furthermore, as will be apparent to one of ordinary skill in the relevant art, the modules, routines, features, attributes, methodologies and other aspects of the disclosure can be implemented as software, hardware, firmware or any combination of the three. Also, wherever a component, an example of which is a module, of the present disclosure is implemented as software, the component can be implemented as a standalone program, as part of a larger program, as a plurality of separate programs, as a statically or dynamically linked library, as a kernel loadable module, as a device driver, and/or in every and any other way. Additionally, the disclosure is in no way limited to implementation in any specific programming language, or for any specific operating system or environment. Accordingly, the disclosure is intended to be illustrative, but not limiting, of the scope of the subject matter set forth in the following claims.