A system and method to assist an instructor in managing, understanding, and drawing conclusions about the learning behavior. The system includes methods for processing data collected about users as they interact with the various modalities of learning that may be integrated into the course, and rendering this information into visualizations that are displayed on an instructor dashboard and updated in real time. This instructor interface includes a plurality of modules targeted towards the management of and interaction with users, and the analysis and visualization of a different aspect of their learning

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This patent application claims benefit under 35 U.S.C. §119 to U.S. Provisional Patent Application No. 62/041,655, filed Aug. 26, 2014, which is hereby incorporated by reference in its entirety.


The present invention relates to a system and method for managing, analyzing, and visualizing learning and content within a course where the respective course, such as a college or a corporate training course, has been designed to individualize the content, even during a student's use of a course, based on a user model, and in particular where there are various types of content that are integrated into the course.


Courses that integrate a wide set of learning modes and materials in various learning and education settings are well known. These courses are increasingly being made adaptive, such that their content can change automatically and/or dynamically based on a specific user's responses and/or consumption of the course content. For purposes of adaptation, these courses generally define, and continually update, a user model based on different inputs that are collected regarding the user and the inputs subsequently analyzed.

When deployed in a learning setting, the instructor of such a course will likely not be the same person who authored the learning materials and defined the adaptation for the course. This can make it difficult for the instructor to manage and understand the learning process for the course, especially if there are a plurality of learning materials and if the adaptation structure created by the author is complex. Further, if the course is delivered online without any face-to-face interaction between the instructor and the learners, it becomes difficult for the instructor to determine when and how his/her assistance to individual learners is most beneficial.

The systems that deliver these courses typically have the ability to track a user's activities as the user interacts with both the various learning materials and with other users. Such tracked data, when collected, are usually captured in fine granularity. These systems can also provide usage information about specific course adaptations that were assigned user-to-user. However, known systems do not have the ability to process, analyze, or visualize the tracked data in real time, nor do they provide methods of depicting the real time automated adaptivity user-to-user or of recommending how adjustments should be made by the instructor to benefit individual users. Such developments would greatly assist an instructor in gaining a better understanding of the learning process of the students, as well as of the methods used by the course for content adaptation.


The present invention is directed to a plurality of systems and methods that can overcome the above limitations by providing an instructor with real time data, real time analysis of the data, and real time visualization of the data, as well as recommended and/or implemented adjustments to a course directed to the instructor through an interface, where the data are directed to student usage.

The present invention further comprises an interface that can be used by an instructor to manage, facilitate, edit, analyze, visualize, and ultimately draw conclusions about the course that he/she is instructing related to the class as a whole or a subset of students. In an embodiment, a course can be integrated (i.e., it can contain various types of content, such as in an online course for which an author has provided both recorded lecture videos and excerpts from a textbook for the students to learn from) or individualized (i.e., the content from the author is adapted to each individual user, either through machine or human intelligence or a combination thereof). In the present invention, the interface can transform data to an aggregated and easy to comprehend form, deliver conclusions based on the data, and deliver specific suggestions for course adjustments by the instructor, among other items. Such data and its analysis can be delivered based on individual students or an aggregation of students.

The instructor interface is preferably organized as an interrelated plurality of modules. These modules include, but are not limited to, the interaction and scheduling of course material for end users (i.e., the learners, or the consumers of the content). They also include modules that relate to real time processing, analysis, and visualization of various aspects of the user learning experience collected as a user interacts with the course material. Such modules include user concept proficiency of a set of author-specified course concepts, user learning behavior of the various forms of material (i.e., video, text) integrated into the course, user learning paths traversed as a result of an adaptation, and one or more networks related to social learning networks formed by the users as they interact with each other and the instructor on the various forms of discussion media integrated into the course. The present invention also includes methods to process user inputs in real time, and a multitude of methods by which outputs of each module can be visualized.

By providing an instructor with real time analysis and visualizations of user interaction and responses to course material, the present invention streamlines the review, analysis and interaction between an instructor and course users such that an instructor can draw conclusions about where and when modifications to a course for a user or a collection of users would be beneficial.


FIG. 1 is a schematic diagram of system components integrated with an instructor interface to generate real time analysis and visuals and support communication between an instructor and end users;

FIGS. 2A and 2B are exemplary displays of an embodiment of the Concept Proficiency Tracker module integrated as part of the instructor interface;

FIGS. 3A-3D are visual displays of an embodiment of the Learning Behavior Tracker module that are integrated as part of the instructor interface to track user video-watching behavior;

FIGS. 4A and 4B are embodiments of visuals of the Social Learning Network Tracker module displaying user interaction; and

FIGS. 5A and 5B are embodiments of visuals of the Learning Path Tracker module that can be available for display to an instructor.


The present invention relates to a plurality of systems and methods for assisting an instructor in generally managing a course and particularly for individual students. In the context of the present invention, is it assumed that most if not all of the material for the course has already been created by a course author, and that an instructor is using this material as the basis for his/her own course teaching. The particular content and its delivery sequence may be determined by an instructor, and in the online environment of the present invention, may differ by student. This is analogous to how a teacher may use a standard textbook to teach a course being hosted at an educational institution and whereby the teacher can provide remedial assistance to selected students or whereby the teacher can suggest alternative materials for an excelling student.

A course in the context of this disclosure can be deployed in any learning setting, such as, but not limited to, corporate training, professional certification, private tutoring, primary, secondary, or higher education, and open learning/continuing education (i.e., those taken for leisure). The term “user” refers generally to any consumer of course content, which could be an employee, student, or more generally a learner, depending on the course setting. The term “instructor” refers to one or more individuals who deliver and manage the training, tutoring, teaching, or more generally the instruction for the course.

In an embodiment, a prepared course is delivered directly to end users. In the preferred embodiment, the course is delivered online such that each student can self-pace and the student's clicks, including the precise screen location and clock time of each click, are captured for analysis. Such clicks include clicks related to progress in the course, progress in one or more course elements such as videos, and clicks related to course-associated materials such as blogs or web searches. In addition to the clicks, mouse movements and other similar activities may be treated and characterized similarly.

Further, the course delivery may be individualized for each student based on any number of parameters such as but not limited to known student skills or an assessment of the class as a whole. These courses may integrate a wide range of learning modes. Such learning modes can include different ways by which the users can learn the material, including both the course material and forums for social interaction with the instructor or other students. The course material can include, but is not limited to, videos, texts, assessments, presentations (with both static images and interactive animations), and audio files, chat rooms of prior administrations of the course, as well as links to external content. Student social interactions can occur through media including, but not limited to, discussion forums and public, private, or group note sharing.

The baseline course may also contain automated individualization, meaning that the various modes are adapted to each specific user. The specific adaptation for a user is generally determined through the application of rules based on a user model. A user model is, generally speaking, a collection of information associated with a particular user that serves as an internal representation of the user, and may evolve over time. In the context of the present invention, the user model captures a learner's usage and proficiency with the course content, and tracks his/her behavior with the various learning modes in the course, in terms of both the use of the modes and performance in relation to use. One can think of a user model as encompassing demographic information about a user, prior course results, the process by which the user traverses a course, the material presented to the user for the course, and external material the user may rely upon during the course, as well as any user's test results obtained during the course. The test results may be compared with the user's use of the modes offered to her/him, including clicks in the modes and time spent.

The course material intended to be delivered to a user may then vary from person to person based on the user models. In particular, the initial individualization for a course will specify a set of rules developed and implemented within the system of the present invention (using either an authoring tool or having worked with the platform provider of the course), which are used to determine precisely how the user model is mapped to decisions on how to adapt the content for particular users. With individualization, each user traverses one of multiple potential learning paths, each of which contains some combination of different and/or modified content with which the user is presented. Many users can traverse the same learning path. However, in the context of the present invention, the initial individualization may be adjusted in at least two fundamental ways tracking user behavior can result in automatic changes to material presented to the user, and tracked data can be presented to an instructor, together with recommendations, whereby the instructor can adjust the subsequent material to be presented to the user.

Examples of learning paths are shown in FIG. 5. In this illustration, the paths are depicted as different ways of traversing through a set of units that form the course. As an alternate representation, one can imagine all of the content in a course forming a single “linear array” containing all of the material, with the adaptation rules choosing whether to show or not show certain subsets of this material based on the user model. This is analogous to an instructor in a course working from a comprehensive textbook, and choosing only to cover the most important parts of the information in class; in this case, what is ‘important’(i.e., what is shown) is different for each user.

In an alternate example, one can envision a non-linear approach, also evident in FIG. 5, whereby the determination of which next module to undertake is determined based on the user's recent history, where the history may include duration of portions of previous modules, clicks, locations of clicks, interaction with an instructor, review of ancillary material, interim test results, and so on. Importantly, the rules encompass a syllabus of the course to assure all requisite elements of the course are covered.

In short, the present invention is directed in part to selection of materials for presentation to a student such that the likelihood of the student's success on a final examination or assessment is optimized, where that optimization is determined based on student progress.

As a result of the potential for integration and individualization in these courses, they are sometimes referred to as Integrated and Individualized Courses (IICs). In an embodiment, the present invention provides an instructor become aware of changes and helps the instructor with insights into the learning process of a course, which helps the instructor make decisions and draw conclusions about the users, the course and its materials, and where and when intervention would be most beneficial. When the course is an IIC, the instructor may be far removed from the process of creating the various learning modes and the individualization aspects of the content, and may also be far removed geographically from the learners. As a result, the present invention helps the instructor to manually differentiate learning, by providing them with visuals of the learning process to help them make decisions, and also by making recommendations to them as to how to adjust the learning process for different end users. Both of these can be seen as methods to supplement machine-based individualization with human intelligence in the case where the baseline IIC is already individualized.

FIG. 1 shows a schematic diagram of an embodiment of a system in which an instructor is hosting a course for a group of users. The instructor accesses a corresponding interface from a workstation, which could be any computing device (e.g., laptop, desktop, tablet, etc.). External assessments created by the instructor can be stored locally on the workstation shown, but could be stored remotely as well. The tracking information stored on a workstation can include data collected about users as they interact with a course.

In an embodiment, this tracking information is obtained from a web server, which is connected to the instructor workstation over an appropriate and known network interface, such as one using the IP protocol. The web server hosts and stores information about the course, including the content and user modeling. This includes instructor presets for the course, user assignment submissions, and user behavior, and more generally provides end to end connectivity between the instructor workstation and the end user devices.

The end user devices depicted in FIG. 1 are connected to the web server over an appropriate and known network interface. As with the instructor workstation, these devices can be any known types of computing device such as laptop computers or tablets running Windows or IOS operating systems (or equivalent). In an embodiment, as shown in FIG. 1, the end user devices can store a course application that contains all modes of learning for the course. For an IIC, this preferably will be a single application which integrates all forms of learning, but in general the course could be delivered as a series of applications. These devices also may store some of the course content itself, while some larger files (e.g., videos) may be streamed directly from the web server.

In an embodiment, each end user device has an interaction recorder (IR) loaded into memory, to monitor user interaction with the various learning modalities. For example, in a video, the time interval between two successive click actions (e.g., play, pause, jump, end of video, switching away from the video view, or closing the course application) is measured by the IR, as well as the UNIX Epoch time, starting position, and interval duration for each case. The specific type of click is captured as well including, for example, clicks away from the course material. As another example, for textual content, the time the user has spent viewing a page will be recorded by the IR each time she flips the page or switches away from the current text view.

Once collected, these behavioral measurements are preferably sent to the web server over the network connection. More preferably, they are sent as they are collected, which also is the time at which they are processed for adaptation. In the context of the present invention, the measurements are in turn sent to and stored at the instructor workstation, where they are processed by the plurality of modules that constitute the instructor interface application, for both visualization and recommendation purposes. Six of these modules are depicted in FIG. 1 and will be elaborated on here.

In an embodiment, a set of inputs collected about each user includes, but the inputs are not limited to, the following:

    • a. Play, pause, stop, fast forward, rewind, playback rate change, exit, and any other video player events, as well as corresponding timestamps, durations, and any other information that specifies user interaction with the video player.
    • b. Page, font size, exit, and other text viewer events, as well as corresponding timestamps and durations that specifies user interaction with the text viewer.
    • c. Slide change, completion, button press, and other events triggered from viewing a set of slides, as well as corresponding timestamps and durations that specify user interaction with the presentation viewer.
    • d. Position and length of highlights placed on video or text at specific locations, or on a particular slide, where the video length is measured in time of video and the text length in number of objects from the starting position.
    • e. Position and content of bookmarks placed on video or text at specific locations, or on a particular slide.
    • f. Position and content of notes taken on video or text at specific locations, or on a slide, as well as whether these notes were either shared publically, shared with a specific set of users, or not shared.
    • g. Information on each post made in discussion forums, including its content, whether it was meant as a question, answer, or comment, and the number of up-votes it received from other users or the instructor.
    • h. Submission, time spent, and number of attempts made for each assessment submitted, as well as the points rewarded if the assessment was machine gradable.
    • i. Individualization structure, both from automated and human intelligence, including the learning paths for the course and the paths traversed by each user, and the user modeling dimensions (concepts).

There are other examples of user data collection and use as well. The method of the present invention may involve one or more of the following non-exclusive approaches for tracking student behavior and making recommendations based on the tracked behavior. Described below are a series of tracked behaviors, ranging from clicks, durations between clicks, clicks in a series, duration at particular videos, clicks of varying types during video play, and so on. In some cases, tracked behaviors may be analyzed as individual behaviors, as collections of behaviors, or a sequence of behaviors, any or all of which can be used to generate recommendations.

Behaviors can correlate to potential test results, particularly correct on first attempt (CFA) results. More specifically, CFA is a binary measure, equal to 1 if the user answered a question correctly on the first attempt, and 0 otherwise. A goal of the present invention is to improve each user's overall percentage of CFA results. As such, recommendations for implementation are based on improving such results.

Consequently, the present invention tracks behaviors, compares behaviors to those of a known population, and identifies adjustments (recommendations) for a user based on a combination of behaviors of populations with high and low average CFA scores so as to determine how to adjust material delivered to a student. Of course, at least some of those changes are automatically implemented and the instructor is given indication of those changes as well as recommendations for other changes.

For instance, we have identified motifs, i.e., sequences of events that form recurring patterns of user behavior, which are significantly associated with CFA (i.e., correct answer) or non-CFA (i.e., incorrect answer) submissions on questions corresponding to the material. The events that form a motif can consist of any combination of behavioral action collected from a learner as he/she interacts with the course application, such as, but not limited to, play, pause, skip backwards, skip forward, rate change faster, rate change slower on a video or interactive slide presentation, scrolling up or down in an article or resizing the view, creating or sharing a note, and mouse movements. In this way, a motif can be based on recurring patterns either within one particular learning mode (e.g., sequences of actions in a video), or across multiple modes (e.g. sequences of actions in a video, followed by a switch to an article). One example is a series of behaviors which are indicative of students reflecting on material, which are significantly associated with the CFA sequences in at least one course we tested. As another example, we have identified motifs that are consistent with rapid-paced skimming through the material, and have revealed that these are discriminatory in favor of non-CFA (i.e., submitted incorrect answer) in different courses. Incorporating the lengths (e.g., duration of play before the next event is fired) in addition to the events themselves was essential to these findings, because motif extraction with the events alone does not reveal these insights. These findings can further be used by an instructor to determine which patterns in behavior are associated with successful results in his/her course; without the data to back up the correlations, it is unclear whether a given motif would be associated with CFA, non-CFA, or neither.

Specifically with respect to video, we have determined that clickstreams may be analyzed so as to determine a likelihood of CFA performance. Certain clickstreams are more indicative of improved understanding and other clickstreams are more indicative of less understanding of content. As stated, clickstream logs may be generated as one of four types: play, pause, rate change, and skip. Each time one of these events is fired, a data entry is recorded that specifies the user and video IDs, event type, playback position, playback speed, and timestamp for the event. In general, we define each of these in a particular way and use collected data to determine recommendations toward improving CFA.

In addition, the analysis may be performed relative to each user, to a collection of users, or all users.

To result in a properly usable set of data, it is important to denoise clickstreams. In order to remove noise associated with unintentional user behavior, we preferably denoise in two ways. First, we consider combining repeated, sequential events such as those that occur within a short duration (5sec) of one another, since this indicates that the user was adjusting to a final state. For example, if a series of skip back or skip forward events occur within a few seconds of each other, then likely the user was simply looking for the final position, so it should be treated as a single skip to that final location. Similarly, if a series of rate change faster or rate change slower events occur in close proximity, then the user was likely in the process of adjusting the rate to the final value. Second, we consider discounting certain unnecessary intervals between events, when the elapsed time between the two events is extremely long (e.g., greater than 20 minutes), which indicates the user was engaged in off-task behavior.

In one embodiment of the present invention, we fit collected data to a variety of known or determined motifs, e.g., reflecting, revising, and skimming behaviors. Each motif has a known set of methodology for user improvement and, based on the motifs that a user exhibits while interacting with the course material, conclusions may be drawn relative to recommendations. For example, a reflecting motif within a segment of content will consist of a series of plays interspersed with long pauses; an instructor may be recommended to divide content into chunks according to the play events where this motif occurs, and then create additional content within these chunks, because an instructor-generated summary may be more efficient than a user spending a longer time to recap the same content (as would be dictated by the motif).

As stated, at least some of these motifs are significantly associated with performance, which can similarly be used to generate recommendations about how content can be modified to create a more effective learning experience. Some basic analysis, as an example, shows that pausing to reflect on material (including play back) repeatedly is the most commonly recurring behavior. If the time spent reflecting is not too long, but longer than the time spent watching, then a positive outcome is most likely.

In another example, the present invention factors in a position-based sequence representation, which factors in the location in videos that a user visited. These data may be used to better define the student's motif, and lead to recommendations within specific video intervals, rather than at the level of a single video.

In addition, transitions between clickstream events may be tracked and modeled as well.

Measurements collected about user interaction with a specific learning mode can also be translated into intuitive quantities that summarize behavior, such as the fraction completed and time spent (relative to the length of the content). Particularly with respect to video, we have computed the following non-exclusive list of nine summary quantities (behaviors) of interest:

1. Fraction spent (fracSpent): The fraction of (real) time the user spent playing the video, relative to its length.

2. Fraction completed (fracComp): The percentage of the video that the user played, not counting repeated play position intervals; hence, it must be between 0 and 1.

3. Fraction played (fracPlayed): The amount of the video that the user played, with repetition, relative to its length.

4. Number of pauses (numPaused): The number of times the user paused the video.

5. Fraction paused (fracPaused): The fraction of time the user spent paused on the video, relative to its length.

6. Average playback rate (avgPBR): The time-average of the playback rates selected by the user.

7. Standard deviation of playback rate (stdPBR): The standard deviation of the playback rates selected over time.

8. Number of rewinds (numRWs): The number of times the user jumped backward in the video.

9. Number of fast forwards (numFFs): The number of times the user jumped forward in the video.

These quantities can also form a special type of motif, where each “action” becomes a summary of actions on a specific learning mode; e.g., completing 50% of a video, followed by fast forwarding on the video twice, followed by skipping over 20% of an article.

Machine learning algorithms, among others, are implemented on these inputs so as to discern and categorize the types of human interaction. Machine learning is a branch of artificial intelligence (i.e., intelligence exhibited by software) where there is an inductive step in which the algorithm learns from and is augmented by the data. In this context, the algorithms for the interface include both those required to process the data for visualization, and those to recognize patterns within, make predictions about, and generate recommendations from the data to assist the instructor.

Since each course will typically be instructed in terms of a set of learning concepts (e.g., in an algebra course, some concepts may be “factoring polynomials,” “solving quadratic equations,” and/or “simplifying expressions”), the machine learning algorithms may also be applied on a concept-by-concept basis, and may leverage similarities detected between these concepts in monitoring user interaction, which can further improve the quality of the interface outputs. These concepts could either be pre-defined and labeled by the author of the course, or in turn extracted through machine learning to find the set of concepts that are optimal in the sense of identifying the key factors affecting user performance. At times, the system of the present invention may suggest recommendations to the instructor. For example, the data collected regarding a particular student might be inconsistent with known patterns or might not yield sufficient confidence to implement a change. In such circumstances, the system of the present invention might present data regarding a student or regarding an entire class, or something in between, indicating confidence intervals around various options. For example, if students are spending an inordinate amount of time on one lecture, the system may recommend a number of alternatives.

The output of the instructor interface is then a processed, analyzed, and visualized version of the inputs described above, with recommendations made to the instructor as appropriate. These include, but are not limited to, the following:

    • a. Depictions of video-watching quantities, such as percent completion (i.e., percent played), time spent, and frequency of different events for each user, both in aggregate across the video and for individual intervals.
    • b. Depictions of text-viewing quantities, such as percent completion, time spent, and frequency of different events for each user, both in aggregate across the text document and for individual segments of the text.
    • c. Visualizations of similar quantities of behavior collected on other forms of media, such as audio and presentations.
    • d. Recommendations to revisit specific portions of a learning mode where the level of focus, as dictated by the quantities in (a)-(c), is exceedingly high or low.
    • e. Depictions of learning style preferences, including the percentage of focus placed on each of the different modes (video, text, audio, and/or social learning), clusters of users based on these preferences.
    • f. Depictions of progress or proficiency on each of the learning concepts for the course for each user, measured by performance on the corresponding assessments, considering all assessments up to the present, all through a current time, or even future predictions.
    • g. Recommendations as to which users and/or course concepts are in need of intervention by the instructor, through the quantities in (f) that identify particularly weak users or challenging material.
    • h. Early detection of users and/or content that may prove particularly challenging in the future, based on proficiency prediction and forecasting.
    • i. Depictions of the social network of users, obtained from their post and comment relations on the discussion forums, and their sharing of notes, both in aggregate across all material and for individual sections of content.
    • j. Recommendations as to which users can be suggested to form study groups, based on their frequency of interaction determined in (i), and their set of proficiencies in (f), which should be mutually reinforcing.
    • k. Depictions of user learning paths, the level of mastery and/or learning style preference required for each of the paths, the specific users traversing each path, and aggregate information about behavior and performance of users on the respective paths.
    • I. Depiction of the identified motifs (e.g., reflecting, revising, speeding, skimming), which users/content modes have exhibited these motifs, and how often they occur.

The output can be customized by/for an instructor so as to, for example, provide further granularity. That is, an instructor may pre-set displays.

The present invention includes a plurality of ways to visualize these outputs on the instructor interface. These include, but are not limited to, the following:

    • a. Scatterplot of points, in 2 or 3 dimensions, where the dimensions of interest are selected by the instructor.
    • b. Time-series plots of a quantity, where the time interval and granularity of measurement are selected by the instructor.
    • c. Histogram plots, which are a graphical representation of the distribution of a quantity of interest. There can be one or two independent variables on top of which this variation is measured, and they must take continuous values (e.g., intervals of a video).
    • d. Bar graphs, which are representations of how a quantity of interest varies over one or two discrete sets (e.g., set of students).
    • e. Box and whisker plots, which show the distribution of a set of points and emphasize the median, quartiles, and outliers of the dataset. They are typically depicted side-by-side for multiple datasets, to show the difference in distributions.
    • f. Network graph structures, consisting of nodes, links between the nodes (either directed or undirected), and possibly weights on the links, which may be color coded to represent different ranges of values. These graphs can emphasize various network substructures, such as clusters, cliques, or the most central nodes.
    • g. Popups and notifications, which are included in the various modules for recommendations and early detection as appropriate.
    • h. Heat maps, which indicate the level of focus of learners at specific points within the content modes, and annotations on top of these heat maps to depict motifs.

In an embodiment, each of these visuals is interactive, meaning that the instructor can select the quantities, dimensions, datasets, and graph plotting properties specified above. They are also real-time in two senses: the displays may update instantaneously when the instructor makes a new selection, and any new input data will be processed immediately and the corresponding display re-rendered.

Returning now to the instructor interface in FIG. 1, the six modules included here are: (1) Assessment Manager; (2) Interaction and Office Hours; (3) Concept Proficiency; (4) Learning Behavior; (5) Learning Paths; and (6) Social Learning Networks. The latter four are tracking modules, and each of them implements some combination of input, output, and visualization discussed above. Hence, they make extensive use of the tracking information collected about the users.

In an embodiment, Assessment Manager relates to the creation and management of a course assessment, including an evaluation of user knowledge of the material at different points during the course. The assessment course material can include, for example, weekly assignments, comprehensive exams, or quizzes appearing within or at the end of a course module. In an embodiment, the Assessment Manager can allow instructors to create, import, edit, schedule, assign, and distribute customized assessments to users, as well as view and grade the submissions as needed.

To facilitate assessment creation, the Assessment Manager module includes a graphical user interface (GUI), which can be used to author multiple choice (e.g., radio response or checkmark selection) or free response questions. Additionally, instructors can import documents that include questions created with other software that also reside on the instructor workstation, and include the answers in an assessment for the course as needed. In an embodiment, the instructor can edit assessments embedded in the baseline course by the author. The module will also provide recommendations for these edits, which are based on concept proficiencies identified for individuals or groups of users (methods for determining these proficiencies will be explained in the context of the Concept Proficiency module), identifying which of the concepts specific users require more practice with.

In an embodiment, assessments (such as exams) can be scheduled for deployment to all users, a specific group of users, or a single user to differentiate learning either at a certain date and time or once the user has completed a certain portion of the material. A group of users to which an assessment is deployed can be specified in a number of ways, such as those users who are following a specific learning path or one in a specific set of paths, those who meet proficiency on a certain set of features, or those in a list of names specified by the instructor.

From time to time, users can be delivered assessment questions as a means for, at least in part, measuring proficiency. For assessment questions that are machine graded (e.g., multiple choice or programming assignments), the instructor need not review user submissions but may review results. However, other assessment questions, including those created by the instructor and those in the baseline course, may require manual grading. In an embodiment, a method allows an instructor to grade assignments and attach scores to assignments as needed (e.g., manner similar to that provided by a Portable Document Format (PDF) editor), with functions such as adding comment boxes, highlighting, replacing, inserting, and underlining text, equations, or images. The instructor can also select a specific time at which grades and markups should be viewable by the users.

In an embodiment, the Interaction and Office Hour module supports a plurality of methods by which the instructor can interact with the users. These include, but are not limited to, one-way communication mechanisms of posting announcements, sending emails, and including comments in the course material (i.e., video or text documents) for users to read, and two-way communication mechanisms for posting on discussion forums included in the course, sending private messages, and holding Virtual Office Hour (VOH) sessions. In an embodiment, the instructor has the ability to handle this communication on a per-user or per-group basis.

VOHs require the support of real-time streaming from the instructor workstation to the target devices running the course application. In an embodiment, the instructor can have video and audio streaming support and the end users can write comments in a chat box that exists for the VOH sessions, similar to the interface used by Ustream. In another embodiment, the end users may have video and/or audio support as well, similar to a group chat like on Skype or Google Hangout except that (i) many more users can be supported, and (ii) the instructor has a master control over the users' ability to speak and show video at different times, with his/her audio and video output taking precedence over the others. In either embodiment, the web server in FIG. 1 acts as the proxy for streaming data between the instructor and the students.

Information about user participation during these sessions, such as how long they spent logged into the VOH and their activity level in terms of the number of asked and answered questions, can be recorded, for use, if desired, by an instructor, for example, as another factor in the grade given for the class and to help differentiate instructions based on those seeming to struggle during the sessions. In an embodiment, these sessions may also be held on a per-group or even per-user basis, such as scheduling an extra help or advanced material discussion session for users on a given learning path. As with the Assignment Manager module, recommendations are made based on concept proficiency for different users, which will help guide the decision as to which VOH sessions should be held.

The remaining four modules depicted for the instructor interface in FIG. 1, Concept Proficiency, Learning Behavior, Social Learning Networks, and Learning Paths, each track a different aspect of user learning. A description of each module is given first, followed by an example of (1) how an instructor can use these modules to make useful conclusions about learning behavior in their respective courses, and (2) the recommendation and early detection aspects of the system, as appropriate.

In an embodiment, the Concept Proficiency tracker module reveals information about the proficiency levels of individual users, groups of users, and/or a class of users as a whole. Concept proficiency here refers to the level of mastery a user has obtained with, or his/her tendency towards, a given course concept. Recall that these concepts may be pre-defined and labeled by the author, or may be discovered through machine learning.

With these concepts in hand, and the association between these concepts and the content learning material and assessments, the proficiency levels can be determined through a variety of methods. For example, the average score that a given user obtained on all the assessments related to a given concept can be taken as a measure of proficiency on that concept. This method can be enhanced by the application of a number of machine learning algorithms as well. For one, since a user may not have filled out all assessments related to a concept, an algorithm can be applied to predict the score that a user would have achieved on those assessments. This algorithm could be of collaborative filtering in nature, where similarities between users and assessments (e.g., quizzes) are extracted from the available data, and in turn used to build models on a per-user and per-quiz basis, the combination of which leads to the desired prediction. This method could also leverage correlations identified between behavioral information (e.g., fraction of time or number of pauses registered for that user on a video related to the assessment) to enhance the proficiency determination, by applying a supervised learning algorithm such as a Support Vector Machine (SVM) that can readily identify such correlations and apply them to prediction when they exist; this is especially useful early on in a course where there is not yet much information about specific users or quizzes for standard collaborative filtering to be effective. Behavioral motifs can also be used as machine learning features the enhance prediction quality as well.

Note that these concepts will typically be the same as the dimensions by which the course is individualized, but in an embodiment the instructor will have the ability to define his/her own dimensions to monitor as well, especially if the baseline course is not individualized already.

In an embodiment of the Concept Proficiency tracker, an instructor can choose the type and time interval of the visualization, depending on preference and the conclusions that need to be drawn. The possible visualization types for this module include, but are not limited to, scatterplots of users in which each dimension corresponds to a different concept, boxplots of users in which there is a separate box for each concept, and a time series plot of how the proficiency of different users vary for a given concept. The time intervals could be up to and including the present, and in a preferred embodiment, through some point in the future, in which case a sophisticated prediction algorithm (which could leverage the collaborative filtering and SVM techniques, or those similar, given above) would be applied that analyzes trends in user proficiency over time and for different concepts (both individually and collectively) up to the present to give a forecast of the future.

With the proficiencies and predictions computed concept-by-concept for different users, an embodiment of the Concept Proficiency tracker will provide recommendations to the instructor about where intervening in the learning process will be most useful and how that intervention may be deployed. This can be accomplished in a number of ways. For example, the instructor may input percentages for each of the concepts that define a level to be obtained in order for a user to be considered “proficient.” Then, users who have not met this level on one or more concepts would be flagged, ranked in descending order by the sum of their deviation from the proficiency mark on each of the concepts that they are not proficient in (users proficient on all concepts would get a score of 0). These are the users that the instructor would be recommended to focus his/her attention on. By the same logic, this could be applied over all course concepts by finding the total deviation across different users from the proficiency marks, in order to generate recommendations as to which concepts the instructor should give attention to.

On the other hand, if the instructor did not have set proficiency levels, then these rankings could be generated on a relative scale, considering the distribution of proficiencies obtained by all users. For example, the proficiency on a concept could be taken as the current average of all user performance on the concepts, and the deviations computed accordingly; in this case, the instructor would be recommended to focus on those users who have the lowest overall performances relative to the sample.

These recommendations, made early in the course, also provide a method for early detection of users and/or concepts requiring attention before proceeding with the remainder of the course.

In an embodiment, the Learning Behavior tracker module relates to tracking and visualizing how users of a course interact with the content. Similar to the Concept Proficiency module, in an embodiment the instructor will have the ability to see the results for individual users or groups of them (e.g., those on a given learning path), and for any time interval, which can include a future point if appropriate prediction schemes are included. This module leverages the inputs and visualizations for all of the course material aside from the assessments. The possible visualizations here include, but are not limited to, histograms of the number of times an event occurs (e.g., pauses or plays for videos) within an interval of length for a video, number of lines for text, and so on; boxplots of the completion rates of users for different types of material; and scatter plots of learning style tendencies (e.g., visual, verbal, auditory) for each user. There are a number of options for specifying the intervals of lengths at which some of these plots (e.g., the histograms) are generated. For example, they can simply be uniform across the full length of the content, where the instructor inputs the increment (e.g., 15 second chunks of a 3 minute video, for a total of 12 chunks), as depicted in FIGS. 3b and 3d. Another possibility is to have these intervals preset by the instructor in advance.

Recommendations generated from the Learning Behavior tracker module serve to direct instructors to specific intervals of content that are either too simple or may require additional explanation, as opposed to the Concept Proficiency tracker which seeks to make recommendations on a per-user or per-concept level. In an embodiment, this is accomplished by analyzing the distributions of the time spent at different intervals of the content, averaged across users, and comparing these times across each of the intervals. These distributions can be created for each separate content file, can combine all content files of a given mode (e.g., all intervals across video files), or can combine all modes within a given learning unit (e.g., all intervals across the files in a given unit). Those intervals that qualify statistically as outliers with respect to a distribution would be the ones of interest; those on the low end (i.e., below the first quartile of the data) would be those that the instructor is recommended to check for purposes of identifying whether the content was too simple and perhaps not necessary to include in the course, while those on the high end (i.e., above the fourth quartile) would be those that the instructor is recommended to check for purposes of identifying particularly confusing or difficult content.

In an embodiment, the Social Learning Network tracker module analyzes and displays information about users' interaction with one another through various social networking functions that are integrated into the course. As with the other tracking modules, there are numerous ways to depict this behavior and relate it to projected positive assessments, and in an embodiment, the instructor can choose from a wide variety of display types and time interval ranges, updated in real time as new information is collected about user interaction. In visualizing the network of interaction among users, many of the display types in this module will take the form of a network graph structure, with users as nodes and links indicating that some level of interaction has taken place among them. These graphs can have directed edges, indicating the flow of information, i.e., a link from A to B means that A sent a message to B, or can be undirected, simply to indicate some communication has taken place. The links can also be weighted, to indicate the frequency of these interactions, i.e., a link from A to B with weight of 3 could indicate that A commented on a post made by B a total of three times.

In an embodiment, analysis will also be performed on the graphs in the Social Learning Network tracker in order to generate recommendations for the instructor. For example, centrality measures can be computed on each of the users forming a graph, which are direct functions of the graph structure, to identify those who are the most influential. Then, the top-K (e.g., top 10) most influential users can be divided into three groups: those who are information seekers (i.e., those with highest in-degree, which is the number of incoming links to a node), those who are information providers (i.e., those with highest out-degree, or the number of outgoing links), and those who are both (i.e., those with highest total degree). Those who have the highest information provider scores can be recommended to be rewarded for their participation.

Further, the system can recommend pairs or groups of users to work together based on combining the information about their seeking and providing scores with information from the Concept Proficiency tracker module. Ideally, a group would consist of a complimentary set of information providers and seekers, with the providers having high proficiency in certain learning concepts, and the seekers needing to obtain proficiency in those same concepts. In an embodiment, this is accomplished as follows, for different concepts separately. First, the provider and seeker scores are normalized across users, as a percentage of the maximum in each case. Second, for each concept, the proficiency level is subtracted from each user's performance (if the user is proficient, this will be positive, if not, it will be negative). Third, each user's provider score and seeker score are multiplied by the relative proficiency, to get two numbers. Finally, those users with highest provider product (i.e., most positive) are grouped with those that have lowest seeker product (i.e., most negative), so that the former can teach the latter about the given concepts. The recommended groups are displayed to the instructor, who can in turn choose to create these groups, or modify them as desired.

In an embodiment, the Learning Path Tracker module focuses on visualizing the learning paths that are taken by users, and is only applicable where the course is individualized, either by machine or human intelligence or some combination. In an embodiment, the visualizations available to the instructor will include a description of the different paths that are traversed, the number of users who traversed each path, and the exact paths traversed by each individual user. Note that in general, an individualized course supports two types of adaptation: navigation-based, which is concerned with determining the segment of content to display next based on the current user model, and presentation-based, which is concerned with lower-level adaptation of the individual content within the current segment. The logic within the web server in FIG. 1 will enumerate all combinations of these potential variations to determine the set of learning paths, and associate users with them accordingly.

Below is an example of a trainer using the present invention to host a compliance course at a company for a number of users where the course is individualized and the primary course material is a set of lecture videos, and discussion forums are the primary mode of communications between the users. The set of learning concepts for this course may correspond to key compliance topics that all users must be proficient in, and the course author (e.g., someone from a compliance board) may have set a number of competency values for each of these concepts that the users are required to meet. The trainer can use the Concept Proficiency tracker module to chart user progress towards meeting these goals. In doing so, he/she is also given a number of recommendations, both in terms of users, such as who would benefit from intervention from the instructor and which groups may benefit from studying together, and in terms of the course content, such as which concepts may need to be explained more thoroughly, from which the trainer can draw conclusions accordingly.

FIGS. 2A and 2B show a 3D scatterplot (FIG. 2A) and boxplots of proficiency visualizations (FIG. 2B). In each of these figures, the instructor has selected exactly which concepts to show, and which users to highlight for each visual. As shown, users Bob and Alice were selected, allowing the instructor to see that Bob has a proficiency of 10, 3, and 3 on concepts A, C, and D, respectively. In the example, Bob is proficient in A, surpassing the competency value of 7 required, but is lagging behind in C and D. On the other hand, Alice is proficient with C, but is lagging behind in A and D, and is actually at the lowest point for A (judging by the corresponding boxplot). One conclusion that the instructor could make from this is that Alice and Bob may benefit from working together as study partners, since Bob could explain concept A to Alice, and Alice could explain concept C to Bob. The instructor may then send a private message (e.g., email) to both Bob and Alice through the Interaction and Office Hour module and make this recommendation. Moreover, the instructor can clearly see from the boxplot of concept D and a class average (3) that many of the users need more assistance in learning the corresponding compliance material for concept C. As a result, the instructor could prepare supplementary material for that concept. This same recommendation would also be made to the instructor through a popup notification on the interface, for not only this concept, but also any of those in the course for which the average scores were below proficiency. The same would also be done on a per-user basis, with those lagging behind in the most concepts recommended to be reached out to first.

Upon analyzing the class scores across each concept (a subset of which are shown in FIGS. 2A and 2B), the interface may detect that the class as a whole is lagging behind in three of the compliance topics, and convey this information to the instructor. As a result, the instructor may turn to the Behavior Tracking module to gain insights into whether the users may be confused by or skipping past certain lectures. This module can also give the instructor useful information about the learning process as a whole, such as which of the videos users tend to focus on or skip over the most, either for the video overall or within a given interval of the videos, as well as actionable recommendations (and/or algorithmic adjustments) based on this analysis. By reviewing the user results, the instructor can determine which chunks of material should be addressed specifically with the users and how the author of the course can improve the course to increase user engagement. That is, instructors have the ability to assess the course, as configured by the author. Also, the instructor could look for specific learning styles among the users (e.g., visual, verbal, auditory learner, or some combination) by having the interface analyze the amount of each mode available to users on the respective learning path that the user focused on.

FIGS. 3A through 3D illustrate four types of visuals that an instructor may select to view user behavior, including, for example, boxplots of the overall fractions of videos completed by each user who watched the respective videos (FIG. 3A) where the trainer has chosen which videos to show in the plot, and which users to highlight; a histogram of the average number of times users paused within particular intervals of a video (FIG. 3B), where the video, granularity (i.e., distance of each interval) and overall interval of interest have been set by the trainer; a histogram of the average number of times users skipped past particular intervals of the video (FIG. 3C) where the same parameters are set; and clusters of users by their auditory and visual learning dimensions (FIG. 3D) where the trainer has chosen the number of clusters to extract from the data for the plot.

From these graphs, the instructor could conclude that video 3 has a particularly low completion rate relative to some of the other videos (FIG. 3A), with users skipping over the portion between 2:30 and 4:00 quite frequently (FIG. 3C), with an average of almost one skip per user. Detections such as this would also be displayed automatically by the interface, using an algorithm to determine the outliers of the distributions of time spent across the intervals as described previously. If the material in video 3 corresponds to one of the concepts depicted in FIG. 2, then the instructor could determine that either this material must be improved, or that users must be directed to spend more time with this video, and could communicate this to them accordingly. On the other hand, the trainer can see that video 4 has a particularly high completion rate (top-left), with users pausing well over once on average between 2:00 and 2:30 (top-right); however, the instructor can see that Bob was one of the users who focused on this video much less compared with the others (top-left). If this material corresponds to concept C in FIG. 2, then the trainer could instruct Bob to pay attention to that material more carefully before revisiting the corresponding assessments. Additionally, the instructor could prepare supplementary material to explain what was instructed between 2:00 and 2:30 more thoroughly, if in fact the reason for pausing seemed to be confusion with that content.

In an embodiment, the instructor may also be interested in the amount of interactions that are occurring between the users on the discussion forums. Rather than having to peruse the multitude of question, answer, and comment discussions created by the users on the forums themselves, the instructor may desire a more convenient visualization, as well as recommendations, through the Social Learning Network tracker module. An example of such a display is shown in FIGS. 4A-B, where the instructor has the ability to vary the number of interactions required for there to be a link present between two users. Here, the links are assumed to be undirected. While the visual on the left appears to be noisy, which only requires a single interaction for there to be a link, the one on the right requires at least three interactions and paints a much clearer picture. The trainer would be able to make a number of conclusions from this; for instance, since the users lying at the center of a group of nodes are those that interact with the largest number of others, they likely either asking (i.e., information seekers) or answering (i.e., information givers) many of the questions posed by other users. Through further investigation, the instructor could either task these users with assisting those in need and rewarding them accordingly (e.g., with a statement of distinction for the course), or reach out to them to offer assistance on an individual basis, depending on which category they fall in. The connected groups shown in FIG. 4B also may correspond to efficient study groups; recommendations on how to form these groups, on a concept-by-concept basis, would also be provided through the interface using the methods described previously to compare user proficiency with their information providing and seeking scores.

Finally, the instructor may be interested in which learning paths individual users are following, as well as the structure of the course in general, in which case he/she would turn to the Learning Path Tracker module.

FIGS. 5A and 5B show embodiments of visuals that can be generated from this module, from which the trainer can see that the course consists of 12 units, and that the adaptation is based entirely on navigation adaptation (as opposed to presentation adaptation, described in the corresponding description of this module). In the visual at the top (FIG. 5A), the trainer has chosen to see the top 3 learning paths traversed, from which he/she can see that these encompass 75% of the users. Depending on the number of users in the course, this may be too dense in the sense that a single learning path encapsulates a large number of users, and the trainer could conclude that the paths in the course should be further separated to better individualize for each user, perhaps through presentation-based adaptation. The instructor could further differentiate learning to accomplish this through human intelligence, or work with the author to define more paths via automated adaptation. At the bottom (FIG. 5B), the instructor is able to visually compare the learning paths taken by individual users. Here, he/she has chosen to visualize those taken by Alice and Bob. Clearly, these two users are not on the standard paths taken by the majority of others, and the instructor may be able to determine that the material these users were struggling with (concepts C and A, respectively) are not as well represented on these paths.

This module could assist the instructor in two other ways as well. First, he/she would be able to send an email to only those users who are on a specific learning path to advise them to peruse some supplementary material that is likely to benefit this group in particular. Second, if the instructor is able to add comments to specific portions of the material for individuals to see, he/she could choose to only have certain comments on a particular video appear to users who follow a certain learning path, when those comments are meant for this group in particular. Third, an instructor could decide to focus attention on providing additional explanation for the paths that have the most users traversing them.

These four tracking modules have been described largely as operating independent of one another. However, the combination of information provided by them could be useful to an instructor as well, and in an embodiment of the present invention, such visuals and/or descriptive statistics would also be available. For example, the invention can provide a visual of how concept proficiency or learning behavior varies depending on the learning path chosen, or on how different social learning network clusters of users tend to indicate varying levels of concept proficiency. Also, these modules, and the Learning Behavior tracker in particular, emphasize the importance of the proposed method of real time communication between the end user devices, the web server, and the instructor workstation over the respective network interfaces, so that the data displays can be updated and re-rendered in a fine-granular fashion. Finally, it should be emphasized that the six modules shown in FIG. 1 are only examples of what will typically be included in an embodiment of the interface. In an embodiment, the interface can have other modules as well as or in place of those shown in the embodiment in FIG. 1 for additional management, tracking, and recommendation functionalities.

Although the description above and accompanying drawings contains much specificity, the details provided should not be construed as limiting the scope of the embodiments, but merely as describing some of the features of the embodiments. The description and figures should not to be taken as restrictive and are understood as broad and general teachings in accordance with the present invention. While the embodiments have been described using specific terms, such description is for illustrative purposes only, and it is to be understood that modifications and variations to such embodiments, including, but not limited to, the substitutions of equivalent features and terminology may be readily apparent to those of skill in the art based upon this disclosure without departing from the spirit and scope of the invention.


1. A method for adjusting an in-process module delivery sequence of an online course directed to a learner based on student usage of modules in the sequence, said online course formed as a collection of modules, said delivery using a web server in communication with a content repository and including interfaces to student-controlled processing workstations comprising the steps of:

capturing location and time stamp data regarding a learner's clicks and mouse movements;
analyzing said data for determining duration of use of each module of said course and assessing learner performance based on performance metrics embedded in said course;
fitting a motif to said data;
providing a visual display of said durations and performance;
presenting a graphical display of recommended adjustments of delivery of subsequent modules to said learner based on said fit motif and differences from said fit motif; and
adjusting the module delivery directed to said learner.

2. The method of claim 1, wherein said visual display includes at least one histogram and at least one set of recommendations showing potential improvement for adjustments in course delivery to at least one learner.

3. The method of claim 1, wherein said visual display includes at least one scatter plot and a set of recommendations showing potential improvement for adjustments in course delivery to at least one learner.

4. The method of claim 1, wherein said analysis includes learner assessment.

5. The method of claim 1, wherein said motif is determined at least in part based on data collected regarding other learners in the same course.

6. The method of claim 1, wherein said motif is determined at least in part based on data collected regarding the same learner in other courses.

7. A method for an instructor to individualize content directed to a learner in an ongoing online course, said online course formed as a collection of modules, to a learner using a web server and including interfaces to learner-controlled processing workstations, comprising the steps of:

capturing location and time stamp data regarding a learner's clicks and mouse movements;
determining the duration of use of modules of said course and assessing learner performance based on performance metrics embedded in said course;
providing a visual display of said durations and performance;
fitting a motif to said data;
recommending to an instructor adjustments to delivery of subsequent modules to said learner based on said motif and differences from said motif; and
recommending to an instructor introduction of or modification to existing course modules.

8. The method of claim 7, wherein said visual display includes at least one histogram and a set of recommendations showing potential improvement for adjustments in course delivery to at least one user.

9. The method of claim 7, wherein said visual display includes at least one scatter plot and a set of recommendations showing potential improvement for adjustments in course delivery to at least one user.

10. The method of claim 7, wherein said data includes results of user assessment.

11. The method of claim 7, wherein said motif is determined at least in part based on data collected regarding other learners in the same course.

12. The method of claim 7, wherein said motif is determined at least in part based on data collected regarding the same learner in other courses.

13. A method for improving module-based course delivery using a web server including interfaces to learner-controlled processing workstations comprising the steps of:

capturing time stamp and location data of a learner's clicks and mouse movements;
analyzing said data for determining duration of use of modules of said course;
assessing student performance based on performance metrics embedded in said course;
providing a visual display of said durations and performance;
fitting a motif to said data;
recommending adjustments to delivery of subsequent modules to said learner based on said motif and differences from said motif; and
recommending creation of or modification to existing course content.

14. The method of claim 13, wherein said visual display includes at least one histogram and a set of recommendations showing potential improvement for adjustments in course delivery to at least one user.

15. The method of claim 13, wherein said visual display includes at least one scatter plot and a set of recommendations showing potential improvement for adjustments in course delivery to at least one user.

16. The method of claim 13, wherein said data includes results of user assessment.

17. The method of claim 13, wherein said motif is determined at least in part based on data collected regarding other learners in the same course.

18. The method of claim 13, wherein said motif is determined at least in part based on data collected regarding the same learner in other courses.

Patent History
Publication number: 20160063881
Type: Application
Filed: May 14, 2015
Publication Date: Mar 3, 2016
Inventors: Christopher Greg Brinton (Berkeley Heights, NJ), Mung Chiang (Princeton, NJ), Sangtae Ha (Superior, CO), William D. Ju (Mendham, NJ), Stefan Rudiger Rill (Augsburg), James Craig Walker (Chester Springs, PA)
Application Number: 14/712,108
International Classification: G09B 7/00 (20060101); G09B 5/02 (20060101);