Abstract:
A method and system for enhancing engagement and accountability of students are provided. The method includes posting at least one question to a plurality of user devices associated with the students; receiving, responsive to at least one question, answers from the plurality of user devices; analyzing the received plurality of answers to at least compute statistical information respective of the plurality of answers; displaying the statistical information on a display; receiving a selection of at least a portion of the statistical information; and causing a display of at least one visual representation that includes at least an identifier representative of each user represented in the selection.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit of U.S. Provisional Application 62/031,866 filed on Aug. 1, 2014, which is herein incorporated by reference for all that it contains. 
     
    
     TECHNICAL FIELD 
       [0002]    The disclosure generally relates to electronic learning (e-learning) systems and more particularly to systems and methods for enhancing students&#39; engagement. 
       BACKGROUND 
       [0003]    Earning a higher education degree has become an essential step in building a career. In many developed countries, a high proportion of the population now enters higher education at some time in their lives. Higher education is therefore very important to national economies, both as a significant industry in its own right and as a source of trained and educated personnel for the rest of the economy. College educated workers command a significant wage premium and are much less likely to become unemployed than less educated workers. 
         [0004]    Students are often expected to learn in exactly the same way even though students are different by nature and usually require customized methods in order to learn new materials. Higher education institutions will employ traditional methods of teaching to all students. Therefore, higher education is not working for every student and students often find themselves lost among large classes of students. 
         [0005]    Traditionally the primary means to engage a student is interaction with an instructor. Identifying which students require more engagement, i.e. more interaction with the instructor becomes increasingly difficult as the size of a class grows. Instructors in higher education often employ teaching assistants to help review assignments by hand. Employing teaching assistants further increases the amount of man-hours to identify students in need of engagement, typically at cost to the academic institution. Currently, there is no efficient method of aggregating all the submitted answers to assignments to identify individual students who require increased engagement or topics the class as a whole requires more instruction with. 
         [0006]    Furthermore, lack of engagement of students results due to the fact that some students do not have access to the classes that interest them most. Others do not feel challenged, and thus do not engage themselves in classroom discussions are may not be held accountable for the lack of participation. All of these factors result in a major number of drop outs due to a lack of students&#39; engagement. 
         [0007]    It would be advantageous to overcome these limitations by providing a solution that increases students&#39; engagement and accountability. 
       SUMMARY 
       [0008]    A summary of several exemplary embodiments of the disclosure follows. This summary is provided for the convenience of the reader to provide a basic understanding of such embodiments and does not wholly define the breadth of the disclosure. This summary is not an extensive overview of all contemplated embodiments, and is intended to neither identify key or critical elements of all embodiments nor delineate the scope of any or all embodiments. Its sole purpose is to present some concepts of one or more embodiments in a simplified form as a prelude to the more detailed description that is presented later. For convenience, the term some embodiments may be used herein to refer to a single embodiment or multiple embodiments of the disclosure. 
         [0009]    Certain exemplary embodiments disclosed herein include a method for enhancing engagement and accountability of students. The method comprises posting at least one question to a plurality of user devices associated with the students; receiving, responsive to at least one question, answers from the plurality of user devices; analyzing the received plurality of answers to at least compute statistical information respective of the plurality of answers; displaying the statistical information on a display; receiving a selection of at least a portion of the statistical information; and causing a display of at least one visual representation that includes at least an identifier representative of each user represented in the selection. 
         [0010]    Certain exemplary embodiments disclosed herein include a system for enhancing engagement and accountability of at least one user. The system comprises a processing unit; a memory coupled to the processing unit, memory containing instructions therein that when executed by the processing unit configures the system to: post at least one question to a plurality of user devices associated with the students; receive, responsive to at least one question, answers from the plurality of user devices; analyze the received plurality of answers to at least compute statistical information respective of the plurality of answers; display the statistical information on a display; receive a selection of at least a portion of the statistical information; and cause a display of at least one visual representation that includes at least an identifier representative of each user represented in the selection. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]    The subject matter disclosed herein is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other objects, features, and advantages of the disclosed embodiments will be apparent from the following detailed description taken in conjunction with the accompanying drawings. 
           [0012]      FIG. 1  is a schematic block diagram of a networked system utilized to describe the various disclosed embodiments. 
           [0013]      FIG. 2  is a flowchart describing the operation of a method for generating a visual representation in accordance with one embodiment. 
           [0014]      FIG. 3A  is a simulation of a display of statistical information according to one embodiment. 
           [0015]      FIG. 3B  is a simulation of a display of statistical information according to one embodiment. 
           [0016]      FIG. 3C  is a simulation of a display of statistical information according to one embodiment. 
           [0017]      FIG. 4  is a simulation of a visual representation that includes an identifier representative of a student by which an answer was received according to an embodiment. 
       
    
    
     DETAILED DESCRIPTION 
       [0018]    The embodiments disclosed herein are only examples of the many possible advantageous uses and implementations of the innovative teachings presented herein. In general, statements made in the specification of the present application do not necessarily limit any of the various claimed embodiments. Moreover, some statements may apply to some inventive features but not to others. In general, unless otherwise indicated, singular elements may be in plural and vice versa with no loss of generality. In the drawings, like numerals refer to like parts through several views. 
         [0019]      FIG. 1  depicts an exemplary and non-limiting schematic diagram of a networked system  100  utilized to describe the disclosed embodiments. 
         [0020]    As shown in  FIG. 1 , a learning server (LS)  110  is communicatively connected to a network  120 . The network  120  can be wired or wireless, a local area network (LAN), a wide area network (WAN), a metro area network (MAN), the Internet, the worldwide web (WWW), the likes, any combinations thereof, and other networks capable of enabling communication between the elements of the system  100 . 
         [0021]    The system  100  further includes user devices  130 - 1  through  130 -N (collectively referred hereinafter as user devices  130  or individually as a user device  130 , merely for simplicity purposes) where N is an integer equal to or greater than 1. The user devices  130  are communicatively connected to the network  120 . Each user device  130  is operated by a student (or any user attending a class). The system  100  further comprises at least one instructor device (ID)  140 . The instructor device  140  is typically operated by an instructor, such as a teacher, professor, and the like. The instructor device (ID)  140  and a user device  130  and may be, for example, a personal computer (PC), a personal digital assistant (PDA), a mobile phone, a smartphone, a tablet computer, a wearable computing device, and the like. 
         [0022]    According to one embodiment, the learning server  110  is configured to receive a plurality of answers from a plurality of students via the plurality of user devices  130  responsive to at least one question. The question(s) may be provided by the learning server  110  or by the instructor device  140 . In an embodiment, questions may be selected from a database  150  communicatively connected to the network  120 . The database  150  is configured to store academic content. Such academic content may be, but is not limited to, textbooks, lectures, case studies, exams, and the like. In an embodiment, questions sent to the user devices  130  may be retrieved from the academic content in the database  150 . 
         [0023]    The learning server  110  is configured to generate statistical information respective of one, some, all answers received in response to a posted question. According to an embodiment, the statistical information may include an amount of answers received, a type of each of the answers received, historical data associated with identical answers and/or questions, analysis of terms within the answers, a combination thereof, and the like. 
         [0024]    According to a further embodiment, the statistical information may be generated respective of individual students. Statistical information associated with individual students may include, for example, past answers received from individual students, the percentage of correct answers received from individual students, the percentage of questions answered by individual students, a combination thereof, and the like. It should be appreciated that the learning server  110  and the operations of the disclosed embodiments save the instructor a great deal of time by generating the statistical information in a more efficient manner than capable by teaching staff. 
         [0025]    Furthermore, one or more thresholds respective of answers received from students may be preconfigured in the learning server  110 . As an example, upon reaching a threshold of wrong answers received by a student via a user device  130  an alert is provided by the learning server  110  to the instructor device  140 . The learning server  110  is configured to then display the statistical information generated on, for example, a display unit of the instructor device  140 . Exemplary embodiments of a display of statistical information on the instructor device  140  are shown herein below with respect of  FIGS. 3A to 3C . 
         [0026]    In an embodiment, the statistical information may include an indication of the similarity of answers between user devices  130 . Similarities over a certain threshold may indicate plagiarism or cheating. This embodiment is particularly effective when the answers are text. 
         [0027]    The learning server  110  is further configured to receive a selection of at least a portion of the statistical information. In an embodiment, the selection may be received from the instructor device  140 . In a further embodiment, the selection may be received as a user&#39;s gesture or a user query from the instructor device  140 . The selection indicates an intent of the instructor of the instructor device  140  to receive additional metadata respective of the selected portion. The selected portion may be a specific set of data within the statistical information. Additional metadata may refer to any type of data associated with any one of the answers, for example, data related to one or more students associated with one or more user devices of the user devices  130  from which an answer was received, past instances related to such student(s), additional statistical information related to an answer, etc., or a combination thereof. 
         [0028]    According to another embodiment, the learning server  110  is configured to provide recommendations to the instructor device  140  respective of the type of portion selected. The recommendations may further include at least one topic for discussions with respect of the statistical information and/or the at least one portion thereof. The at least one topic may include, for example, a proposed study plan, a need for tutoring, identification of a common mistake, suggested follow-up lectures and/or questions, a suggestion to discuss an answer in class, identification of plagiarism or cheating, a combination thereof, and the like. 
         [0029]    For example, a professor (instructor) using the instructor device  140  may select a portion of answers for one particular homework assignment submitted by one user device  130 - 1 . The learning server  110  is configured to provide additional metadata in response, such as the percentage of correct answers submitted by the user device  130 - 1  for the same subject material as the homework assignment. The learning server  110  may also provide a recommendation for discussing a study plan with the student using user device  130 - 1  if the percentage of correct answers submitted is below a certain threshold. 
         [0030]    In an embodiment, the learning server  110  is configured to cause a display of at least one visual representation respective of the selection. The visual representations may include at least an identifier representative of a user of each associated user device  130  respective of the selection. The display may be made on a display unit of the instructor device  140 . The identifier may be, for example, a name of a user, a picture of a user, demographic information related to a user, past answers received from the user device(s)  130 , a combination thereof, and so on. The identifier may be retrieved from the user device  130  or database  150 . The visual representation displays the selection in such a manner as to enable the instructor to conduct further analysis of the selection relative to individual users of the user devices  130 . 
         [0031]    In an embodiment the learning server  110  is installed in a cloud-computing platform, such as a public cloud, a private cloud, or a hybrid cloud. The processes performed by the learning server  110  can be realized by a cloud-based application (as known as SaaS application). It should be noted that the instructor device  140  are the user device  130  are connected to the learning server  110  during a class. This allows the instructor to post questions during the class and receive the answers and the computed statistical information in real-time (during the class) as the students answer the posted question. The statistical information can be displayed by the instructor. This increases the engagement of the interactions of the students with the instructor. 
         [0032]    In certain embodiment, the questions can be provided to the user devices  130  by the learning server  110  prior to the class or after the class. The questions (or any other class material) can pushed to the user devices  130  or downloaded to the user devices  130  having the users&#39; access the learning server  110 . 
         [0033]    In certain configurations, the learning server  110  comprises a processing unit  112  which is coupled to an internal memory  114 . The processing unit  112  may include one or more processors. The one or more processors may be implemented with any combination of general-purpose microprocessors, multi-core processors, microcontrollers, digital signal processors (DSPs), field programmable gate array (FPGAs), programmable logic devices (PLDs), controllers, state machines, gated logic, discrete hardware components, dedicated hardware finite state machines, or any other suitable entities that can perform calculations or other manipulations of information. 
         [0034]    In an embodiment, the memory  114  contains instructions that when executed by the processing unit  112  results in the performance of the methods and processes described herein below. Specifically, the processing unit  112  may include machine-readable media for storing software. Software shall be construed broadly to mean any type of instructions, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. Instructions may include code (e.g., in source code format, binary code format, executable code format, or any other suitable format of code). The instructions, when executed by the one or more processors, cause the processing unit  112  to perform the various functions described herein. 
         [0035]      FIG. 2  depicts an exemplary and non-limiting flowchart  200  describing a method for generating a visual representation in accordance with an embodiment. The method may be performed by the learning server  110 . Without limiting the scope of the disclosed embodiment, the method will be discussed with reference to the various elements shown in  FIG. 1 . 
         [0036]    In S 210 , a plurality of answers responsive to at least one question are received from users via their respective user devices  130 . In S 220 , statistical information respective of each of the answers is computed. According to an embodiment, the statistical information may include an amount of answers received, a type of each of the answers received, historical data associated with identical answers and/or questions, analysis of terms within the answers, a combination thereof, and the like. 
         [0037]    In an embodiment, the statistical information or measure is computed for each individual student. Examples for such measures include, for example, past answers received from individual students, the percentage of correct answers received from individual students, the percentage of questions answered by individual students, a combination thereof, and the like. It should be noted that the individual computed statistical measures allow tracking of the students&#39; performance over time, compare between students, and so on. 
         [0038]    In S 230 , the statistical information is displayed on the display of the instructor device  140 . In S 240 , a selection of at least a portion of the statistical information is received. In an embodiment, the selection is performed as described herein above. 
         [0039]    In S 250 , at least one visual representation that includes at least one identifier representative of the users is displayed on the display of the instructor device  140 . In an embodiment, the at least one visual representation includes data selected in S 240 . In S 260 , it is checked whether additional selections are received and if so, execution continues with S 250 ; otherwise, execution terminates. 
         [0040]      FIG. 3A  shows an exemplary and non-limiting simulation  300 A of statistical information respective of yes/no answers as displayed on the instructor device  140 .  FIG. 3B  shows an exemplary and non-limiting simulation  300 B of statistical information respective of mathematical answers as displayed on the instructor device  140 . 
         [0041]      FIG. 3C  shows an exemplary and non-limiting simulation  300 C of statistical information respective of open text answers as displayed on the instructor device  140 . According to this embodiment, the size of each word represents the amount of mentions of that word in answers sent by the user devices  130 . The size of the words may indicate the amount of times each word was mentioned generally within all of the answers, or only the mentions made by specified user devices  130 . This embodiment of portraying statistical information highlights trends in submitted answers by analyzing the text in such a manner that would be infeasible for an instructor unaided by the system  100 . 
         [0042]      FIG. 4  shows an exemplary and non-limiting simulation of a visual representation  400  with an identifier representative of students from which answer was received according to an embodiment. A selection  410  of the statistical information generated respective of a first answer is received from the instructor device  140 . Responsive thereto, a visual representation  420  of images of students associated with the user devices  130  from which the first answer received is display on a display unit of the instructor device  140 . 
         [0043]    The various embodiments may be implemented as hardware, firmware, software, or any combination thereof. Moreover, the software is preferably implemented as an application program tangibly embodied on a program storage unit or tangible computer readable medium consisting of parts, or of certain devices and/or a combination of devices. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture. Preferably, the machine is implemented on a computer platform having hardware such as one or more central processing units (“CPUs”), a memory, and input/output interfaces. The computer platform may also include an operating system and microinstruction code. The various processes and functions described herein may be either part of the microinstruction code or part of the application program, or any combination thereof, which may be executed by a CPU, whether or not such computer or processor is explicitly shown. In addition, various other peripheral units may be connected to the computer platform such as an additional data storage unit and a printing unit. All or some of the servers maybe combined into one or more integrated servers. Furthermore, a non-transitory computer readable medium is any computer readable medium except for a transitory propagating signal. The display segments and mini-display segments may be shown on a display area that can be a browser or another other appropriate graphical user interface of an internet mobile application, either generic or tailored for the purposes described in detail hereinabove. 
         [0044]    All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the principles and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure.