Patent Publication Number: US-2017358230-A1

Title: System and method for providing group learning via computerized student group assignments conducted based on student attributes and student-viable-related criteria

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. patent application Ser. No. 14/658,997, entitled “S YSTEM AND  M ETHOD FOR  P ROVIDING  G ROUP  L EARNING VIA  C OMPUTERIZED  S TUDENT  G ROUP  A SSIGNMENTS  C ONDUCTED  B ASED ON  S TUDENT  A TTRIBUTES AND  S TUDENT -V ARIABLE -R ELATED  C RITERIA ”, filed Mar. 16, 2015, which is hereby incorporated herein by reference in its entirety. 
    
    
     FIELD OF THE INVENTION 
     The invention relates to providing group learning via computerized student group assignments conducted based on student attributes and student-variable-related criteria. 
     BACKGROUND OF THE INVENTION 
     In recent years, with the advent of new technologies, eLearning has become an increasingly popular alternative to traditional classroom instruction and training. Benefits of eLearning may, for example, comprise lower costs and efficient learning as a result of reduced overhead and reoccurring costs, the ability for students to learn at their own pace (as opposed to the pace of the slowest member of their class), the option for students to skip elements of a program that they&#39;ve already mastered, decreased student commuting time, etc. 
     However, in an effort to keep costs low, eLearning programs generally have large class sizes, which typically results in less student engagement, and gives the appearance of a lack of attention to individual students. Although some eLearning programs may offer small group learning, the composition of each group may not facilitate effective learning (e.g., if group members are geographically far from one another, if group members do not have backgrounds, skills, or interests that complement or supplement one another, etc.). Courses offered via eLearning programs are also typically managed by an institution, limiting individual instructors (e.g., instructors that are not employed by specific institutions) from creating and managing their own courses. These and other drawbacks exist. 
     SUMMARY OF THE INVENTION 
     The invention addressing these and other drawbacks relates to methods, apparatuses, and/or systems for providing group learning via computerized student group assignments conducted based on student attributes and student-variable-related criteria. 
     In accordance with one aspect of the invention, an electronic learning platform that allows any instructor to create, offer, and teach their own course may be provided. As an example, an instructor need not be limited to those instructors belonging to a specific institution (e.g., such as a university system comprising a set of affiliated universities or other higher education systems) to create, offer, and teach a course via the electronic learning platform. In an implementation, student records, instructor records, or other information on the electronic learning platform may be managed across the electronic learning platform (as opposed to being managed by a specific institution). The records may, for example, be used to match students of current or prior courses to instructors or their courses, to suggest new future courses of interest for instructors to create, offer, and teach, to suggest created courses to students with a potential interest in such courses, or for other purposes—even if the suggested courses will not be taught by instructors of the current or prior courses (or by instructors within a specific institution that offered the current or prior courses). 
     In an implementation, students may be provided with suggestions for a course for which they can register based on instructor information (comprising attributes of an instructor of the course), course information (comprising attributes of the course), student information (comprising attributes of the students), or other information. As an example, the suggestions may be determined based on a determination of which instructors match the students in their courses of interest or need (e.g., in addition to which available courses match the student&#39;s interest). 
     In an implementation, suggestions to register for a course may be provided to students not registered for the course to supplement a composition of student already registered to take a course. As an example, if the current composition of registered students does not satisfy a preferred composition of students for the course (e.g., user-selected preferred composition, automated selection of the preferred composition, etc.), non-registered students may be provided with such suggestions based on a determination that the addition of those non-registered students to the registered composition would create a composition that satisfies the preferred composition. In one use case, if the current composition is lacking registered students with a particular attribute (e.g., a certain achievement level, a certain race, or other attribute), students that have an interest in the course and have the particular attribute (but have not registered for the course) may be determined. Those students may then be provided with a suggestion to register for the course in an effort to end up with a composition of registered students for the course that satisfies the preferred composition. 
     In an implementation, students registered to take a course may be assigned to student groups associated with the course based on student information comprising attributes of the students and group criteria information comprising criteria for a student group associated with the course. Individual student groups for the same course may, for example, be limited to a predetermined maximum number of students, and each student group may be “walled off” from the other student groups such that the existence of the other student groups are not indicated to any of the student groups. In this way, although a large number of students may register for the course, a single (or few) instructors may nevertheless teach the course to all of the students without the disadvantages typically associated with large class size (e.g., appearance of a lack of attention to individual students, less student engagement, etc.). 
     In an implementation, student information (on which the student assignments may be based) may comprise attributes of students, such as the students&#39; names (e.g., first name, last name, etc.), residential/mailing information (e.g., residential/mailing address or zip code), biological information (e.g., sex, age, race, etc.), cultural background information (e.g., race, ethnicity, social economic status, religion, etc.), career information (e.g., current profession or experience, areas of expertise, professional interests, etc.), education information (e.g., current or previous schools attended, current grade level or high grade level completed at the respective schools, graduation years at the respective schools, grade point averages at the respective schools, school majors or degrees received, etc.), personal interest information (e.g., hobbies, favorites, etc.), ability information (e.g., skills, languages known, etc.), legal status information (e.g., military status, marital status, etc.), or other information. 
     Group criteria information (on which the student assignments may be based) may comprise criteria indicating that a student group associated with a course is to be diverse with respect to a student variable, criteria indicating that a student group associated with a course be similar with respect to a student variable, criteria indicating respective preferred amounts of students (e.g., percentages of students, numbers of students) having certain attributes (for a student variable) to be assigned to a student group associated with a course, or other criteria to be considered when assigning students to student groups associated with a course. 
     In an implementation, students may be assigned to student groups associated with a course based on attributes of the students, first criteria indicating that a student group associated with the course is to be diverse with respect to a first variable of the students, and second criteria indicating that a student group associated with the course is to be similar with respect a second variable of the students. The student assignment may, for example, be such that each of the student groups comprises a set of students that, as a whole, is diverse with respect to the first variable and similar with respect to the second variable. As an example, the first variable may relate to at least one of residential or mailing information, biological information, cultural background information, career information, education information, personal interest information, ability information, or legal status information, and the second variable may relate to at least another one of the residential or mailing information, the biological information, the cultural background information, the career information, the education information, the personal interest information, the ability information, or the legal status information. Other criteria with respect to other student variables may be utilized for assignment of the students to the student group, for example, to produce student groups (e.g., of 30 or less students, of other sizes, etc.) that are demographically and intellectually balanced, and comprise age, interest, and experienced appropriate, like-minded students. 
     In an implementation, access to a communication channel (e.g., a chatroom or other communication channel) associated with a student group for a course may be enabled for a student based on the student being assigned to the student group. As an example, responsive to the student being assigned to a particular student group, the student may have access to post messages (e.g., text, multimedia, or other information) on a communication channel associated with the particular student group, view messages posted by others on the communication channel, view a list of participants currently accessing the communication channel, receive automated messages posted on the communication channel, etc. The students may be encouraged (e.g., through automated bot prompts) to communicate with one another via the communication channel before a class session begins, during the class session, or after the class session. As such, peer-to-peer cohort learning may be encouraged and promoted via the communication channel to facilitate learning—as many education and learning studies demonstrate, students can learn as much (or more) from their classmates, as they can from their instructors. 
     In an implementation, other students that have not been assigned to the particular student group (e.g., but are instead assigned to other student groups) may not be given access to the communication channel. In some implementations, access to view the list of participants currently accessing the communication channel may not be enabled for those other students (that are not assigned to the particular student group). As an example, no indication of the existence of the particular student group may be provided to those other students (e.g., to “wall off” student groups from one another). 
     In an implementation, access to communication channels associated with student groups for a course may be enabled for an instructor of the course. As an example, the instructor may have the ability to select a set of the student groups (e.g., by selecting the associated set of communication channels) and send a message to the students assigned to the student groups of the selected set (e.g., by providing the message on the communication channels accessible to the students assigned to those student groups). In one use case, the message may be provided on a first communication channel accessible to the students assigned to a first student group (of the selected set) without indicating to the students assigned to the first student group that the message is accessible to the second student group (or other student groups). Similarly, the message may be provided on a second communication channel accessible to the students assigned to the second student group (of the selected set) without indicating to the students assigned to the second student group that the message is accessible to the first student group. In this way, among other benefits, the student groups may remain “walled off” from one another to give the appearance of a small class size while taking advantage of the benefits of a large class. 
     In an implementation, access to interface windows representing different communication channels may be enabled for students, instructors, or other users. In an implementation, access to multiple interface windows, each of which represent a communication channel associated with a particular student group for a course, that are presented on a user interface may be enabled for an instructor of the course (or other user, e.g., an administrator, a student, etc.). As an example, when a message is provided on a specific one of the communication channels, the message may be provided for presentation on the interface window that represents the specific communication channel. 
     In an implementation, access to an interface window (e.g., on a chat interface) that represents multiple communication channels, each of which is associated with a particular student group for a course, may be enabled for an instructor of the course (or other user, e.g., an administrator, a student, etc.). The interface window may represent two or more of the communication channels associated with the student groups for the course (e.g., some or all of the student groups for the course). As an example, messages provided on any one of those communication channels may be presented on the interface window. As another example, duplicate messages provided on those communication channels may be presented as only one message on the interface window. 
     Various other aspects, features, and advantages of the invention will be apparent through the detailed description of the invention and the drawings attached hereto. It is also to be understood that both the foregoing general description and the following detailed description are exemplary and not restrictive of the scope of the invention. As used in the specification and in the claims, the singular forms of “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. In addition, as used in the specification and the claims, the term “or” means “and/or” unless the context clearly dictates otherwise. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is an exemplary illustration of a system for providing group learning via computerized student group assignments conducted based on student attributes and student-variable-related criteria, in accordance with an aspect of the invention. 
         FIG. 2  is an exemplary illustration of a user interface that enables a user to provide information to create a student account, in accordance with an aspect of the invention. 
         FIG. 3  is an exemplary illustration of a user interface that enables a user to register for a course, in accordance with an aspect of the invention. 
         FIG. 4  is an exemplary illustration of a student user interface that enables a user to access content associated with a course and to communicate with an instructor of the course and other students in the user&#39;s student group, in accordance with an aspect of the invention. 
         FIG. 5  is an exemplary illustration of an instructor user interface that enables a user to manage content distribution to students of a course and to communicate with the students based on their respective student groups, in accordance with an aspect of the invention. 
         FIG. 6  is an exemplary illustration of a user interface that enables a user to provide information to create an instructor account, in accordance with an aspect of the invention. 
         FIG. 7  is an exemplary illustration of a user interface that enables a user to provide information to create a course, in accordance with an aspect of the invention. 
         FIG. 8  is an exemplary illustration of a flowchart of a method of providing group learning via computerized student group assignments conducted based on student attributes and student-variable-related criteria, in accordance with an aspect of the invention. 
         FIG. 9  is an exemplary illustration of a flowchart of a method of providing communication between an instructor of a course and students of the course based on student groups to which the students are assigned, in accordance with an aspect of the invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the implementations of the invention. It will be appreciated, however, by those having skill in the art that the implementations of the invention may be practiced without these specific details or with an equivalent arrangement. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the implementations of the invention. 
     Exemplary System Description 
       FIG. 1  is an exemplary illustration of a system  100  for providing group learning via computerized student group assignments conducted based on student attributes and student-variable-related criteria, in accordance with an aspect of the invention. As shown in  FIG. 1 , system  100  may comprise server  102  (or multiple servers  102 ). Server  102  may comprise student management subsystem  112 , registration subsystem  114 , group segmentation subsystem  116 , communication management subsystem  118 , instructor/student matching subsystem  120 , content management subsystem  122 , or other components. 
     System  100  may further comprise user device  104  (or multiple user devices  104 ). User device  104  may comprise any type of mobile terminal, fixed terminal, or other device. By way of example, user device  104  may comprise a desktop computer, a notebook computer, a netbook computer, a tablet computer, a smartphone, a navigation device, an electronic book device, a gaming device, or other user device. Users may, for instance, utilize one or more user devices  104  to interact with server  102  or other components of system  100 . It should be noted that, while one or more operations are described herein as being performed by components of server  102 , those operations may, in some implementations, be performed by components of user device  104 . 
     In some implementations, the various computers and subsystems illustrated in  FIG. 1  may comprise one or more computing devices that are programmed to perform the functions described herein. The computing devices may include one or more electronic storages (e.g., learning management database  132 , or other electric storages), one or more physical processors programmed with one or more computer program instructions, and/or other components. The computing devices may include communication lines, or ports to enable the exchange of information with a network or other computing platforms. The computing devices may include a plurality of hardware, software, and/or firmware components operating together to provide the functionality attributed herein to the servers. For example, the computing devices may be implemented by a cloud of computing platforms operating together as the computing devices. 
     The electronic storages may comprise non-transitory storage media that electronically stores information. The electronic storage media of the electronic storages may include one or both of system storage that is provided integrally (e.g., substantially non-removable) with the servers or removable storage that is removably connectable to the servers via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic storages may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. The electronic storages may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). The electronic storage may store software algorithms, information determined by the processors, information received from the servers, information received from client computing platforms, or other information that enables the servers to function as described herein. 
     The processors may be programmed to provide information processing capabilities in the servers. As such, the processors may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. In some implementations, the processors may include a plurality of processing units. These processing units may be physically located within the same device, or the processors may represent processing functionality of a plurality of devices operating in coordination. The processors may be programmed to execute computer program instructions to perform functions described herein of subsystems  110 - 120  or other subsystems. The processors may be programmed to execute computer program instructions by software; hardware; firmware; some combination of software, hardware, or firmware; and/or other mechanisms for configuring processing capabilities on the processors. 
     It should be appreciated that the description of the functionality provided by the different subsystems  110 - 120  described herein is for illustrative purposes, and is not intended to be limiting, as any of subsystems  110 - 120  may provide more or less functionality than is described. For example, one or more of subsystems  110 - 120  may be eliminated, and some or all of its functionality may be provided by other ones of subsystems  110 - 120 . As another example, additional subsystems may be programmed to perform some or all of the functionality attributed herein to one of subsystems  110 - 120 . 
     Attention will now be turned to a more detailed description of various implementations comprising one or more features related to providing group learning via computerized student group assignments. It should be noted that features described herein may be implemented separately or in combination with one another. 
     Computerized Student Assignment Based on Student-Variable-Related Criteria 
     In an implementation, students registered to take a course may be assigned to student groups associated with the course based on student information comprising attributes of the students and group criteria information comprising criteria for a student group associated with the course. Individual student groups for the same course may, for example, be limited to a predetermined maximum number of students (e.g., 30 or less, or other limit), and each student group may be “walled off” from the other student groups such that the existence of the other student groups are not indicated to any of the student groups. In this way, although a large number of students may register for the course, a single (or few) instructors may nevertheless teach the course to all of the students without the disadvantages typically associated with large class size (e.g., appearance of a lack of attention to individual students, less student engagement, etc.). 
     Student management subsystem  112  may manage student information of students (e.g., current students, past students, etc.), and enable authorized users to view, update, or otherwise access the student information in accordance with permissions granted to the authorized users. Student information may comprise attributes of students, such as the students&#39; names (e.g., first name, last name, etc.), residential/mailing information (e.g., residential/mailing address or zip code), biological information (e.g., sex, age, race, etc.), cultural background information (e.g., race, ethnicity, social economic status, religion, etc.), career information (e.g., current profession or experience, areas of expertise, professional interests, etc.), education information (e.g., current or previous schools attended, current grade level or high grade level completed at the respective schools, graduation years at the respective schools, grade point averages at the respective schools, school majors or degrees received, etc.), personal interest information (e.g., hobbies, favorites, etc.), ability information (e.g., skills, languages known, etc.), legal status information (e.g., military status, marital status, etc.), or other information. 
     As an example, registration subsystem  114  may initially obtain student information from students during a registration process (e.g., to register as a potential student, to register for a course, etc.). In one use case, with respect to  FIG. 2 , a user may provide one or more inputs to create a student account with an eLearning platform via user interface  200 . The inputs may, for instance, comprise the user&#39;s name (e.g., first name, last name, etc.), a desired password, residential/mailing address (e.g., street address, city, state, zip code, country, etc.), contact information (e.g., phone number, email address, etc.), biological information (e.g., sex, birthdate/age, race, etc.), cultural background information (e.g., race, ethnicity, social economic status, religion, etc.), career information (e.g., current profession or experience, areas of expertise, professional interests, etc.), education information (e.g., current or previous schools attended, current grade level or high grade level completed at the respective schools, graduation years at the respective schools, grade point averages at the respective schools, school majors or degrees received, etc.), personal interest information (e.g., hobbies, favorites, etc.), ability information (e.g., skills, languages known, etc.), legal status information (e.g., military status, marital status, etc.), or other information. The student information may then be stored in one or more databases (e.g., learning management database  132 ). 
     As another example, registration subsystem  114  may obtain course information associated with one or more courses, provide the course information to users (e.g., potential students, current students, etc.), enable users to register for courses or modify their registered courses, or perform other operations related to registration. In one scenario, with respect to  FIG. 3 , a user (e.g., a student) may view information about available courses (e.g., course title, class dates/times, instructor&#39;s name, course price, course pre-requisites, etc.) and register for one or more of the courses via user interface  300 . Upon registration and/or payment for a course, the user may be asked (e.g., via a floating window) if the user would like to be reminded of the course at one or more predetermined times before the course begins (e.g., whether the student wants a reminder of the course two weeks, one day, or some other predetermined time before the course begins via text, email, push notification, or other methods of contact). 
     Group segmentation subsystem  116  may assign students registered to take a course to student groups associated with the course based on student information (e.g., student information  134 ) comprising attributes of the students and group criteria information (e.g., group criteria information  136 ) comprising criteria for a student group associated with the course. As an example, student management subsystem  112  may obtain the attributes of the students, and group segmentation subsystem  116  may obtain the criteria for a student group associated with the course. Group segmentation subsystem  116  may then process the attributes of the students and the criteria to assign the students to respective student groups associated with the course. 
     The criteria (on which the student assignment is based) may comprise user-selected criteria (e.g., criteria selected by an instructor of the course) or system-selected criteria (e.g., criteria defined by a developer). The criteria may comprise criteria indicating that a student group associated with a course is to be diverse with respect to a student variable, criteria indicating that a student group associated with a course be similar with respect to a student variable, criteria indicating respective preferred amounts of students (e.g., percentages of students, numbers of students) having certain attributes (for a student variable) to be assigned to a student group associated with a course, or other criteria. As an example, the criteria may be selected/defined to produce student groups (e.g., of 30 or less students, of other sizes, etc.) that are demographically and intellectually balanced, and comprise age, interest, and experienced appropriate, like-minded students. 
     In an implementation, criteria (on which student assignment is based) may comprise a first criteria indicating that a student group associated with the course is to be diverse with respect to a first variable of the students, second criteria indicating that a student group associated with the course is to be similar with respect a second variable of the students, or other criteria. As an example, group segmentation subsystem  116  may assign students to student groups associated with a course based on attributes of the students, the first criteria, and the second criteria such that each of the student groups associated with the course comprises a set of students that, as a whole, is diverse with respect to the first variable and similar with respect to the second variable. 
     As an example, Table 1 below provides exemplary preferred amounts of students having particular attributes corresponding to a student&#39;s achiever status (e.g., high achievers, mid-level achievers, and low achievers statuses determined based on residential information, education information, or other attributes of the students) for a student group associated with a course. The indicated percentages may, for example, represent maximum levels of each student attribute for each student group associated with the course to create student groups that are diverse with respect to the “achiever” variable. 
     
       
         
           
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                   
                 Preferred Percentage of Students Having The Student 
               
               
                 Student Attribute 
                 Attribute in Each Student Group for a Course 
               
               
                   
               
             
            
               
                 High Achievers 
                 30% 
               
               
                 Mid-level 
                 48% 
               
               
                 Achievers 
               
               
                 Low Achievers 
                 22% 
               
               
                   
               
            
           
         
       
     
     In an implementation, one or more attributes of a student may be explicitly provided by the student (e.g., during registration, subsequent account modification, etc.). In another implementation, one or more attributes of a student may be inferred for the student (e.g., inferred attributes determined based on attributes explicitly provided by the student). As an example, residential information provided by a student (e.g., or “private” information) may be “matrixed” with related “public” information (e.g., via public bot lookup) to determine whether the student is to be inferred as a High Achiever, a Mid-level Achiever, or a Low Achiever. Public information related to a student&#39;s residential address or zip code may comprise purchase price of the house associated with the address, assessed value of the house, local court house, state delegate or senator information (e.g., who they are, how much they raised, etc.), congressional precinct information, information about nearby public or private schools (e.g., quality rating of schools, types of courses offered at the respective schools, etc.), demographic information (e.g., racial/ethnic demographic, free/reduced lunch demographic, income demographic, etc.), or other information. In one use case, a student may be inferred to be a High Achiever based on the fact that his/her house is associated with a high purchase or assessed price and that the student lives in an area with high quality schools (e.g., based on public quality rating, advanced course offerings, etc.). Other information, such as education information about the student, may also be taken into account to modify the student&#39;s achiever status. 
     In an implementation, user-provided “private” information (e.g., provided by a student) may be matched and matrixed to “public” information as follows in Table 2A below: 
     
       
         
           
               
               
             
               
                 TABLE 2A 
               
               
                   
               
               
                 Private 
                 Public 
               
               
                   
               
             
            
               
                 Residential Address 
                 Location (e.g., Global Position System (GPS) data) 
               
               
                   
                 Local Court House 
               
               
                   
                 Purchase Price of House Associated with Residential Address 
               
               
                   
                 Current Assessed Value of the House 
               
               
                   
                 State Delegate and Senators 
               
               
                   
                 Congressional Precincts 
               
               
                 Zip Code 
                 Nearby Public and Private Schools 
               
               
                   
                 Demographic Information 
               
               
                 Sex 
                 County and Precinct Demographics “Matrixed” with Age (e.g., 
               
               
                   
                 calculated from birthdate) and Education (e.g., Current School, 
               
               
                   
                 Grade Level, Major, Highest GPA, Graduation Year, etc.) 
               
               
                 Race 
                 “Matrixed” with Address, Zip Code, Age, and Education 
               
               
                 Education (e.g., 
                 School Demographics 
               
               
                 Current School, 
                 Test Scores “Matrixed” with Address 
               
               
                 Grade Level, Major, 
                 Job Market 
               
               
                 Highest GPA, 
                 Internal Revenue Service (IRS) Salary Ranges “Matrixed” with Age, 
               
               
                 Graduation Year, 
                 Address, and Demographic Information 
               
               
                 etc.) 
               
               
                 Highest Grade Point 
                 “Matrixed” with Address, Zip Code, Age, and Education 
               
               
                 Average (GPA) 
               
               
                 Graduation Year 
                 School Ranking 
               
               
                   
                 Tuition Rates 
               
               
                   
                 Mascot 
               
               
                   
                 Sports Teams 
               
               
                 Birthdate 
                 “Matrixed” with Education, Address, Zip Code, Interests, and 
               
               
                   
                 Hobbies 
               
               
                 Email 
                 Service Provider 
               
               
                   
                 IP Address (at the time of the student&#39;s data input, e.g., during 
               
               
                   
                 registration or other process) 
               
               
                 Mobile Phone 
                 Service Provider 
               
               
                   
                 Geo-Location 
               
               
                 Profession 
                 IRS Code and Job Salary Range “Matrixed” with Age, Education, 
               
               
                   
                 and Address 
               
               
                 Veteran (Brand, 
                 “Matrixed” with Address, Zip Code, Age, and Education 
               
               
                 Active, Reserve, 
               
               
                 Dates, etc.) 
               
               
                 Professional 
                 “Matrixed” with Address, Zip Code, Age, and Education 
               
               
                 Interest/Activities 
               
               
                 Hobbies 
                 “Matrixed” with Address, Zip Code, Age, and Education 
               
               
                 Languages 
                 “Matrixed” with Address, Zip Code, Age, and Education 
               
               
                 Favorite City 
                 “Matrixed” with Profession, Professional Interests/Activities, 
               
               
                   
                 Address, Zip Code, Age, and Education 
               
               
                 Favorite Vacation 
                 “Matrixed” with Favorite City, Profession, Professional 
               
               
                 Spot 
                 Interests/Activities, Hobbies, Address, Zip Code, Age, and 
               
               
                   
                 Education 
               
               
                   
               
            
           
         
       
     
     As an example, each of the data sets above may be divided into three variable groups: Basic (r), Education (d), and Personal (z) where r comprises Address, Birthdate, Sex, Race, Name, etc., d comprises Education (e.g., School, Grade Level, Major, Highest GPA, Graduation Year, etc.), and z comprises Profession, Professional Interest/Activities, Hobbies, Languages, Favorite City, Favorite Vacation Spot, etc. Based on the three variable groups, distinct variable group IDs (r, d, z) may be associated with each student. These IDs may be compared and matrixed with the IDs of other students that have registered for the same course to determine student assignments to student groups. 
     As a further example, the r, d, and z variables (or Matrix variables) may be utilized to determine the similarity (or difference) between students. In one use case, factors that may be part of the r-Matrix variable may comprise Address, Birthday, Sex, Race, and Name, and associated weights of the r-Matrix variable factors may comprise A, B, C, D, and E, respectively. Similarity (or difference) between two students (e.g., Student 1 and Student 2) may be calculated as follows in Table 2B below. 
     
       
         
           
               
             
               
                 TABLE 2B 
               
               
                   
               
             
            
               
                 Dbetween = Distance Between Student 1 and Student 2 
               
               
                 Abetween = Age Difference Between Student 1 and Student 2 
               
               
                 Csex = 1 if the sex (or gender) of the Student 1 and Student 2 are the  
               
               
                 same, 0 if the sex is different 
               
               
                 Drace = 1 if the race of the Student 1 and Student 2 are the same  
               
               
                 (e.g., 1 = 100%), 0.5 if partially the same, 0 if no similarity, etc. 
               
               
                 Whoareyou = 1 if they are the same person (e.g., same name), 0 if not 
               
               
                 Basic_Similarity = A * (1/(Dbetween) + B * (1/(Abetween) +  
               
               
                 C * (Csex) + D * (Drace) + E * (Whoareyou) 
               
               
                   
               
            
           
         
       
     
     In another use case, factors that may be part of the d-Matrix variable may comprise School, Grade Level, Major, and Highest GPA, and associated weights of the d-Matrix variable factors may comprise F, G, H, and I, respectively. Similarity (or difference) between two students (e.g., Student 1 and Student 2) may be calculated as follows in Table 2C below. 
     
       
         
           
               
             
               
                 TABLE 2C 
               
               
                   
               
             
            
               
                 WSchool = 1 if Student 1 and Student 2 went/goes to the same school,  
               
               
                 0 if not 
               
               
                 GLvl = 1 if the grade levels of Student 1 and Student 2 are equal,  
               
               
                 0 if not 
               
               
                 WMajor = 1 if the majors of Student 1 and Student 2 are the same,  
               
               
                 0.5 if in the same school, 0 if neither 
               
               
                 MyGPA = GPA of Student 2 − GPA of Student 1 
               
               
                 Education_Similarity = F * (1/(WSchool) + G * (1/(Glvl) +  
               
               
                 H * (WMajor) + I * (1/(MyGPA)) 
               
               
                   
               
            
           
         
       
     
     In a further use case, factors that may be part of the z-Matrix variable may comprise Profession, Professional Interest/Activities, Hobbies, Languages, Favorite City, Favorite Vacation Spot, and associated weights of the z-Matrix variable factors may comprise J, K, L, M, N, and O, respectively. Similarity (or difference) between two students (e.g., Student 1 and Student 2) may be calculated as follows in Table 2D below. 
     
       
         
           
               
             
               
                 TABLE 2D 
               
               
                   
               
             
            
               
                 WProfession = 1 if Student 1 and Student 2 are in the same profession,  
               
               
                 0 if not, 0.5 if similar 
               
               
                 WInterests = 1 if Student 1 and Student 2 have the same interests, 0 if not,  
               
               
                 .5 if similar 
               
               
                 WHobbies = 1 if Student 1 and Student 2 have the same hobbies, 0 if not,  
               
               
                 .5 if similar 
               
               
                 WLanguage = 1 if Student 1 and Student 2 are fluent in the same  
               
               
                 language, 0 if not 
               
               
                 FCity = 1 if the favorite city of Student 1 and Student 2 are the same,  
               
               
                 0 if not, 0.5 if similar 
               
               
                 FVS = 1 if the favorite vacation spot of Student 1 and Student 2 are the  
               
               
                 same, 0 if not, 0.5 if similar 
               
               
                 Personal_Similarity = J * (1/(WProfession) + K * (1/(WInterests) +  
               
               
                 L * (WHobbies) + M * (WLanguage) + N * (FCity) + O * (FVS) 
               
               
                   
               
            
           
         
       
     
     In an implementation, student assignments may be performed prior to a designated registration end period (e.g., the last day to register for a course), prior to the start date of the course, etc. In an implementation, suggestions to register for a course may be provided to students not registered for the course to supplement a composition of students already registered to take a course so that each of the student groups that are created may satisfy group criteria for the course (e.g., so that each group may have close to the preferred percentages shown in Table 1 above). As an example, if student groups that satisfy group criteria for a course cannot be created from the current composition of registered students, non-registered students may be provided with such suggestions based on a determination that the addition of those non-registered students to the registered composition would allow student groups that satisfy the group criteria to be created. In one use case, for instance, if student groups created from the current composition are lacking registered students with a particular attribute (e.g., a certain achievement level, a certain race, or other attribute), students that have an interest in the course and have the particular attribute (but have not registered for the course) may be determined, and those students may be provided with a suggestion to register for the course in an effort to end up with a composition of registered students for the course that would allow the “ideal” student groups to be created. 
     Communication Between an Instructor and Students Based on Student Groups 
     In an implementation, access to communication channels (e.g., chatrooms or other communication channels) may be enabled for students based on their respective assigned student groups. As an example, communication management subsystem  118  may enable access to a communication channel associated with a student group for a course may be enabled for a student based on the student being assigned to the student group. Responsive to the access being enabled, the student may have access to post messages (e.g., text, multimedia, or other information) on the communication channel, view messages posted by others on the communication channel, view a list of participants currently accessing the communication channel, receive automated messages posted on the communication channel, etc. Such access may, for example, be available to the student before a class session begins, during the class session, or after the class session (e.g., during a predetermined time period before the class session, during a predetermined portion of the class session, during a predetermined time period after the class session, during any time, etc.). 
     In an implementation, a student that has not been assigned to a particular student group associated with a course may not be given access to a communication channel associated with the student group. In an implementation, no indication of the existence of the particular student group (or other student groups of the same course that the student is not assigned) may be provided by an eLearning platform through which the course is taught. As an example, communication management subsystem  118  may disable, for students not assigned to the student group, access to view a list of participants of the communication channel. 
     In one use case, with respect to  FIG. 4 , a student user interface  400  may be provided for a student registered to take a course. The student user interface may comprise an area in which content streamed from an instructor is rendered (e.g., live face-to-face interactions, broadcasted class material, etc.). The student user interface may also comprise an area in which a chat interface is provided. The chat interface may comprise an input area and an area in which messages from other users (e.g., the instructor of the course and other students assigned to the same student group as the student viewing the message area) are presented. The student utilizing the chat interface may, for example, see a list of participants (e.g., the instructor and other students in the same student group), but other students of the course that are not assigned to the student group may not be able to view the list of participants. As shown in  FIG. 4 , the student user interface may further present student information (e.g., student&#39;s name), course information (e.g., course title), class session information (e.g., time until the class session begins), a link to course material (e.g., a course library), a link to upload content, a link to view students in the same group for the course, a link to contact the instructor, or other items. 
     In an implementation, communication management subsystem  118  may enable access to communication channels associated with a course for an instructor of the course. Responsive to receiving a request to send a message to a user-selected set of student groups associated with the course, communication management subsystem  118  may provide the message on one or more communication channels accessible to the students assigned to the user-selected set of the student groups. As an example, the instructor may select the set of the student groups (e.g., by selecting the associated set of communication channels), input the message, and submit the message for placement on the communication channels associated with the student groups of the selected set. 
     In an implementation, communication management subsystem  118  may provide a message (e.g., from an instructor of a course) on multiple communication channels such that students assigned to individual ones of the student groups (to which the communication channels are associated) are presented with the message, but without indicating to any of the student groups that the message is presented (or otherwise accessible) to the other student groups. In this way, among other benefits, each of the student groups may remain “walled off” from the other ones of the student groups associated with the same course to give the appearance of a small class size while taking advantage of the benefits of a large class. 
     In an implementation, access to interface windows representing different communication channels may be enabled for students, instructors, or other users. In an implementation, communication management subsystem  118  may enable, for an instructor of a course, access to multiple interface windows (e.g., on a chat interface) that each represents a communication channel associated with a particular student group for the course. When a message is provided on a specific one of the communication channels, the message may be provided for presentation on the interface window that represents the specific communication channel. As an example, with respect to  FIG. 5 , messages provided on the “Group C” chat channel may be presented on the “Group C” interface window of the instructor chat interface (without being presented on the other interface windows that only represent a single chat channel) on user interface  500 . An instructor may, for example, select one of the chat interface window tabs (e.g., “Chat with All Groups,” “Group A,” “Group B,” “Group C,” “Group D,” etc.) to specify on which chat channel(s) a message is to be posted (and, thus, to which student group the message is to be presented), and utilize the input field of the instructor chat interface or one of option buttons (e.g., “Post Media Link,” “Post Assignment,” “Open for Questions,” etc.) to provide the message that is to be posted. If, for example, the instructor selects the “Group C” tab, the message will be posted on the “Group C” chat channel such that the message will be presented on the “Group C” interface window (and on corresponding chat interfaces of students assigned to “Group C”). On the other hand, if the instructor selects the “Chat with All Groups” tab, the message will be posted on all four of the chat channels (e.g., Groups A-D) such that the message will be presented on the “Chat with All Groups,” “Group A,” “Group B,” “Group C,” and “Group D” interface windows (and the corresponding chat interfaces of the students in the course). 
     In an implementation, communication management subsystem  118  may enable, for an instructor of a course, access to an interface window (e.g., on a chat interface) that represents multiple communication channels, each of which is associated with a particular student group for the course. The interface window may represent two or more of the communication channels associated with the student groups for the course (e.g., some or all of the student groups for the course). As an example, messages provided on any one of those communication channels may be presented on the interface window. As another example, duplicate messages provided on those communication channels may be presented as only one message on the interface window. 
     In one scenario, with respect to  FIG. 5 , the instructor may select the “Chat with All Groups” chat interface window tab to send a message to the students in all the student groups (e.g., Groups A-D) associated with the course. Upon inputting and submitting the message via the chat interface, the message may be provided as a first message on the “Group A” chat channel, as a second message on the “Group B” chat channel, as a third message on the “Group C” chat channel, and as a fourth message on the “Group D” chat channel. As such, the respective messages (albeit, the same content) will be provided for presentation to the students in all the student groups associated with the course via their corresponding chat interface windows representing the respective chat channels. Although the “Chat with All Groups” chat interface window represents the four chat channels, communication management subsystem  118  may recognize the first, second, third, and fourth messages provided on the four chat channels as being duplicates of one another, and provide only one of the four messages for presentation on the “Chat with All Groups” chat interface window. 
     Matching of Instructors with Potential Students for a Course 
     In an implementation, students may be provided with suggestions for a course for which they can register based on instructor information (e.g., instructor/course information  138 ) comprising attributes of an instructor of the course, course information (e.g., instructor/course information  138 ) comprising attributes of the course, student information comprising attributes of the students, or other information. As an example, the suggestions may be determined based on a determination of which instructors match the students in their courses of interest or need (e.g., in addition to which available courses match the student&#39;s interest). 
     As an example, registration subsystem  114  may initially obtain instructor information from instructors during a registration process (e.g., to register as an instructor). In one scenario, with respect to  FIG. 6 , a user may provide one or more inputs to create an instructor account with an eLearning platform via user interface  600 . The inputs may, for instance, comprise the user&#39;s name (e.g., first name, last name, etc.), a desired password, residential/mailing address (e.g., street address, city, state, zip code, country, etc.), contact information (e.g., phone number, email address, etc.), biological information (e.g., sex, birthdate/age, race, etc.), cultural background information (e.g., race, ethnicity, social economic status, religion, etc.), career information (e.g., current profession or experience, areas of expertise, professional interests, etc.), education information (e.g., current or previous schools attended, current grade level or high grade level completed at the respective schools, graduation years at the respective schools, grade point averages at the respective schools, school majors or degrees received, etc.), personal interest information (e.g., hobbies, favorites, etc.), ability information (e.g., skills, languages known, etc.), legal status information (e.g., military status, marital status, etc.), or other information. The instructor information may then be stored in one or more databases (e.g., learning management database  132 ). 
     In a further scenario, with respect to  FIGS. 6 and 7 , during registration, the instructor may provide inputs related to a course (or courses) that the instructor would like to offer (e.g., via user interfaces  600  and  700 ). The course-related inputs may, for instance, comprise the course name/title, recommended/preferred student attributes for the course (e.g., course age group, profession, etc.), class session information (e.g., course length, course days/times, etc.), course cost, course requirements (e.g., software requirements, hardware requirements, class/knowledge prerequisites, etc.), course description (e.g., topics covered, goals of the course, etc.), or other information. The course information may then be stored in one or more databases (e.g., learning management database  132 ). 
     In an implementation, user-provided “private” information (e.g., provided by the instructor) may be matched and matrixed to “public” information as follows in Table 4A below: 
     
       
         
           
               
               
             
               
                 TABLE 3A 
               
               
                   
               
               
                 Private 
                 Public 
               
               
                   
               
             
            
               
                 Residential Address 
                 Location (e.g., Global Position System (GPS) data) 
               
               
                   
                 Local Court House 
               
               
                   
                 Purchase Price of House Associated with Residential Address 
               
               
                   
                 Current Assessed Value of the House 
               
               
                   
                 State Delegate and Senators 
               
               
                   
                 Congressional Precincts 
               
               
                 Zip Code 
                 Nearby Public and Private Schools 
               
               
                   
                 Demographic Information 
               
               
                 Sex 
                 County and Precinct Demographics “Matrixed” with Age (e.g., 
               
               
                   
                 calculated from birthdate) and Education (e.g., Current School, 
               
               
                   
                 Grade Level, Major, Highest GPA, Graduation Year, etc.) 
               
               
                 Race 
                 “Matrixed” with Address, Zip Code, Age, Education, and 
               
               
                   
                 Employment 
               
               
                 Employment 
                 Job Market 
               
               
                   
                 IRS Salary Ranges 
               
               
                   
                 “Matrixed” with Age, Address, Demographic Information, and 
               
               
                   
                 Education 
               
               
                 Education (e.g., 
                 “Matrixed” with Address, Zip Code, Age, Sex, Race, and 
               
               
                 Current School, 
                 Employment 
               
               
                 Grade Level, Major, 
               
               
                 Highest GPA, 
               
               
                 Graduation Year, 
               
               
                 etc.) 
               
               
                 School or College of 
                 School Ranking 
               
               
                 Graduation 
                 Tuition Rates 
               
               
                   
                 Mascot 
               
               
                   
                 Sports Teams 
               
               
                 Graduation Year 
                 “Matrixed” with Age, Address, and Employment 
               
               
                 Birthdate 
                 “Matrixed” with Education, Address, Zip Code, Professional 
               
               
                   
                 Interests, and Hobbies 
               
               
                 Email 
                 Service Provider 
               
               
                   
                 IP Address (at the time of the instructor&#39;s data input, e.g., during 
               
               
                   
                 registration or other process) 
               
               
                 Mobile Phone 
                 Service Provider 
               
               
                 Veteran (Brand, 
                 “Matrixed” with Address, Zip Code, Age, Education, and 
               
               
                 Active, Reserve, 
                 Employment 
               
               
                 Dates, etc.) 
               
               
                 Professional 
                 “Matrixed” with Address, Zip Code, Age, and Education 
               
               
                 Interest/Activities 
               
               
                 Hobbies 
                 “Matrixed” with Address, Zip Code, Age, and Education 
               
               
                 Teaching Experience 
                 Years “Matrixed” with Address, Age, Education, Graduation Year, 
               
               
                   
                 and Employment 
               
               
                 Area of Expertise 
                 “Matrixed” with Age, Education, School, Graduation Year, and 
               
               
                   
                 Employment 
               
               
                 Course Name 
                 “Matrixed” with Education, School, Employment, and Experience 
               
               
                 Course Age Group 
                 “Matrixed” with Age, Sex, Education, Employment, and Experience 
               
               
                 Course Days/Time 
                 “Matrixed” with Address, Zip Code, Employment, and Experience 
               
               
                 Course Cost 
                 “Matrixed” with Education, School, Graduation Year, and 
               
               
                   
                 Experience 
               
               
                   
               
            
           
         
       
     
     As an example, each of the data sets above may be divided into three variable groups: Basic (v), Education (t), and Experience (y) where v comprises Address, Birthdate, Sex, Race, Name, etc., t comprises Education, School (or School of Graduation), Graduation Year, etc., and y comprises Teaching Experience, Areas of Expertise, etc. Based on the three variable groups, distinct variable group IDs (v, t, y) may be associated with each instructor. These IDs may be compared with the IDs of students to match instructors with students. 
     As a further example, the v, t, and y variables (or Matrix variables) may be utilized to determine the similarity (or difference) between instructors and students. In one use case, factors that may be part of the v-Matrix variable may comprise Address, Birthday, Sex, Race, and Name, and associated weights of the r-Matrix variable factors may comprise A, B, C, D, and E, respectively. Similarity (or difference) between an instructor and a student may be calculated as follows in Table 3B below. 
     
       
         
           
               
             
               
                 TABLE 3B 
               
               
                   
               
             
            
               
                 Dbetween = Distance Between Instructor and Student 
               
               
                 Abetween = Age Difference Between Instructor and Student 
               
               
                 Csex = 1 if the sex (or gender) of Instructor and Student are the same,  
               
               
                 0 if the sex is different 
               
               
                 Drace = 1 if the race of Instructor and Student are the same (e.g., 1 =  
               
               
                 100%), 0.5 if partially the same, 0 if no similarity, etc. 
               
               
                 Whoareyou = 1 if they are the same person (e.g., same name), 0 if not 
               
               
                 Basic_Similarity = A * (1/(Dbetween) + B * (1/(Abetween) +  
               
               
                 C * (Csex) + D * (Drace) + E * (Whoareyou) 
               
               
                   
               
            
           
         
       
     
     In another use case, factors that may be part of the t-Matrix variable may comprise Education, School (or School of Graduation), and Graduation Year, and associated weights of the t-Matrix variable factors may comprise F, G, and H, respectively. Similarity (or difference) between an instructor and a student may be calculated as follows in Table 3C below. 
     
       
         
           
               
             
               
                 TABLE 3C 
               
               
                   
               
             
            
               
                 WEducation = 1 if Instructor and Student went/goes the same school,  
               
               
                 0 if not 
               
               
                 WSchool = 1 if the grade levels of Student 1 and Student 2 are equal,  
               
               
                 0 if not 
               
               
                 WYoG = 1 
               
               
                 Education = F * (1/(WEducation) + G * (1/(WSchool) + H * (WYoG) 
               
               
                   
               
            
           
         
       
     
     In a further use case, factors that may be part of the y-Matrix variable may comprise Teaching Experience and Expertise, and associated weights of the y-Matrix variable factors may comprise I and J, respectively. Similarity (or difference) between an instructor and a student may be calculated as follows in Table 3D below. 
     
       
         
           
               
             
               
                 TABLE 3D 
               
               
                   
               
             
            
               
                 WTeachExp = 1 if the Instructor and the Student have the same profession,  
               
               
                 0 if not, 0.5 if similar 
               
               
                 WExpertise = 1 if Instructor and Student have the same interests, 0 if not,  
               
               
                 0.5 if similar 
               
               
                 Experience = I * (1/(WTeachExp) + J * (1/(WExpertise) 
               
               
                   
               
            
           
         
       
     
     In an implementation, instructor/student matching subsystem  120  may match instructors (or their respective courses) to students based on student information and the foregoing obtained instructor or course information. As an example, instructor/student matching subsystem  120  may match an instructor&#39;s location (e.g., determined time zone based on the instructor&#39;s residential/mailing information), education or professional experience, and areas of expertise to students that reside at a location with a similar time zone, that have at least tangentially similar interest and/or skill sets, or that have other attributes similar to that of the instructor. Upon determining matches, instructor/student matching subsystem  120  may provide suggestions to the students to register for courses offered/taught by instructors with which they match. 
     In an implementation, suggestions to register for a course may be provided to students not registered for the course to supplement a composition of student already registered to take a course. As an example, if the current composition of registered students does not satisfy a preferred composition of students for the course (e.g., user-selected preferred composition, automated selection of the preferred composition, etc.), non-registered students may be provided with such suggestions based on a determination that the addition of those non-registered students to the registered composition would create a composition that satisfies the preferred composition. In one use case, for instance, if the current composition is lacking registered students with a particular attribute (e.g., a certain achievement level, a certain race, or other attribute), students that have an interest in the course and have the particular attribute (but have not registered for the course) may be determined. Those students may then be provided with a suggestion to register for the course in an effort to end up with a composition of registered students for the course that satisfies the preferred composition. 
     As an example, Table 4 below provides exemplary preferred compositions of students for a course based on the instructor attributes. The percentages may, for example, represent maximum levels of each student attribute (e.g., high achievers, mid-level achievers, and low achievers statuses determined based on residential information, education information, or other attributes of the students) for a preferred composition in accordance with the instructor&#39;s attribute corresponding to the instructor&#39;s teaching experience level. 
     
       
         
           
               
               
             
               
                 TABLE 4 
               
               
                   
               
               
                 Instructors 
                 Preferred Student Composition for a Course 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                 Most Experienced 
                 High Achievers: 25% 
                 Mid. Achievers: 40% 
                 Low Achievers: 35% 
               
               
                 Mid. Experience 
                 High Achievers: 35% 
                 Mid. Achievers: 30% 
                 Low Achievers: 35% 
               
               
                 New Instructors 
                 High Achievers: 15% 
                 Mid. Achievers: 50% 
                 Low Achievers: 35% 
               
               
                   
               
            
           
         
       
     
     In one scenario, if an instructor has a “most experienced” status, instructor/student matching subsystem  120  may provide suggestions (to register for a course) based on the preferred composition associated with the “most experienced” status. The suggestions may be provided such that the suggestions are initially provided to a composition of students that match the course and/or the instructor of the course and that reflect the preferred composition (e.g., the composition of students to which the suggestions are initially provided may comprise about 25% that are high achievers, 40% that are mid-level achievers, and 35% that are low achievers). In another scenario, if the current composition of students registered to take a course is lacking high achievers (e.g., less than 25% of the total composition), instructor/student matching subsystem  120  may provide suggestions to students that have not registered for the course and that are high achievers. 
     Exemplary Flowcharts 
       FIGS. 8 and 9  comprise exemplary illustrations of flowcharts of processing operations of methods that enable the various features and functionality of the system as described in detail above. The processing operations of each method presented below are intended to be illustrative and non-limiting. In some implementations, for example, the methods may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the processing operations of the methods are illustrated (and described below) is not intended to be limiting. 
     In some implementations, the methods may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of the methods in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of the methods. 
       FIG. 8  is an exemplary illustration of a flowchart of a method of providing group learning via computerized student group assignments conducted based on student attributes and student-variable-related criteria, in accordance with an aspect of the invention. 
     In an operation  802 , student information about students registered to take a course may be obtained. The student information may comprise attributes of the students that correspond to variables of the students. Operation  802  may be performed by a student management subsystem that is the same as or similar to student management subsystem  112 , in accordance with one or more implementations. 
     In an operation  804 , group criteria information associated with the course may be obtained. The group criteria information may comprise first criteria indicating that a student group associated with the course is to be diverse with respect to a first variable of the students, second criteria indicating that a student group associated with the course is to be similar with respect a second variable of the students, or other criteria. Operation  804  may be performed by a group segmentation subsystem that is the same as or similar to group segmentation subsystem  116 , in accordance with one or more implementations. 
     In an operation  806 , the students may be assigned to student groups associated with the course based on the attributes, the first criteria, and the second criteria. The students may be assigned such that a student group associated with the course comprises a set of students that, as a whole, is diverse with respect to the first variable and similar with respect to the second variable. Operation  806  may be performed by a group segmentation subsystem that is the same as or similar to group segmentation subsystem  116 , in accordance with one or more implementations. 
     In an operation  808 , information for the course may be provided such that the information is available to the students in accordance with the student groups to which the students are assigned. Operation  808  may be performed by a communication management subsystem that is the same as or similar to communication management subsystem  118 , in accordance with one or more implementations. 
       FIG. 9  is an exemplary illustration of a flowchart of a method of providing communication between an instructor of a course and students of the course based on student groups to which the students are assigned, in accordance with an aspect of the invention. 
     In an operation  902 , a request to send a message to a user-selected set of student groups associated with a course may be received from a user device of an instructor of the course. Operation  902  may be performed by a communication management subsystem that is the same as or similar to communication management subsystem  118 , in accordance with one or more implementations. 
     In an operation  904 , the message may be provided as a first message on a first communication channel accessible to students assigned to a first student group (of the selected set) without indicating to the first student group that the message is accessible to a second student group (of the selected set). Operation  904  may be performed by a communication management subsystem that is the same as or similar to communication management subsystem  118 , in accordance with one or more implementations. 
     In an operation  906 , the message may be provided as a second message on a second communication channel accessible to students assigned to the second student group without indicating to the second student group that the message is accessible to the first student group. Operation  906  may be performed by a communication management subsystem that is the same as or similar to communication management subsystem  118 , in accordance with one or more implementations. 
     In an operation  908 , access to a multi-channel interface window representing the first communication channel and the second communication channel may be enabled for the instructor. Operation  908  may be performed by a communication management subsystem that is the same as or similar to communication management subsystem  118 , in accordance with one or more implementations. 
     In an operation  910 , the message may be provided for presentation to the instructor such that only one of the first message or the second message is presented on the multi-channel interface window. As an example, the other one of the first message or the second message may be excluded from presentation on the multi-channel interface window based on a determination that the other one of the first message or the second message is a duplicate message. Operation  910  may be performed by a communication management subsystem that is the same as or similar to communication management subsystem  118 , in accordance with one or more implementations. 
     Although the present invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred implementations, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed implementations, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any implementation can be combined with one or more features of any other implementation.