WO2019180652A1 - Interactive, adaptive, and motivational learning systems using face tracking and emotion detection with associated methods - Google Patents

Abstract

A system for delivering and managing learning and training programmes comprising optical sensors for capturing subject's facial expression, eye movements, point-of-gaze, and head pose of a student subject during a learning session; a domain knowledge data repository comprising concept data entities, each having knowledge and skill content items, and task data entities, each having lecture content material items; a student module configured to estimate the student subject's affective state and cognitive state using the sensory data collected from the optical sensors; and a trainer module configured to select a task data entity for delivery and presentment to the student subject after each completion of a task data entity based on a probability of the student subject's understanding of the associated concept data entity's knowledge and skill content items; wherein the probability of the student subject's understanding is computed using the student subject's estimated affective state and cognitive state.

Description

INTERACTIVE, ADAPTIVE, AND MOTIVATIONAL LEARNING SYSTEMS USING FACE TRACKING AND EMOTION DETECTION WITH ASSOCIATED METHODS

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains material, which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever. Cross-references to Related Applications:

[0001] The present application claims priority to U.S. Patent Application No.

62/646,365 filed March 21, 2018; U.S. Patent Application No. 62/716,935 filed August 9, 2018; U.S. Patent Application No. 62/741,527 filed 4 October 2018; and U.S. Patent Application No. 62/813,170 filed March 4, 2019; the disclosures of which are incorporated herein by reference in their entity.

Field of the Invention:

[0002] The present invention relates generally to methods and systems for providing and delivery of educational programmes and training, including corporate training, academic tutoring, in-class and out-of-class learnings. Particularly, the present invention relates to the customization and augmentation of learning experience, tests, and assessment of learning progress through the use of human emotion detection and analysis, and data mining in education/pedagogy and psychology. Background of the Invention: [0003] In the current education system, especially in South-East Asia, pressure to excel in schools keeps mounting. In a result-oriented based society, the student needs to achieve high grades to have a decent opportunity to enter prestigious academia and obtain advance degrees. The school system continues to rely mainly on class-based lecturing and exercise programs combined with standardized examinations. In general, students receive large amounts of homework (> 1-2 hours) daily. But the effectiveness remains unchecked. Teachers lack the resources to offer a more personalized approach for each student. As the majority of the schools are government run or subsidized and follow the same curriculum set out by education administration distanced from the actual point of delivery of the teaching. There is limited support for personalized learning. Furthermore, the knowledge of each student is only evaluated during examinations held a few times per year.

[0004] These shortcomings have led to the rise of tutoring centers where there are more personal attentions and dialogues between the teachers/tutors and students. Any deviations from the learning path, or knowledge gaps can then be remedied directly as such. However, in the tutoring industry, good quality teachers/tutors are in scarce supply and teacher/tutor training is often done informally and casually on the job. Pressure on fees has further increased pressure on teachers/tutors to take up more and more related administrative tasks such as course material preparation, class scheduling, and other logistics, reducing effecting teaching time. Further, private tutors are expensive and generally reserved for only a few subject areas like high-stakes testing. Most learners never receive this kind of attention and thus, never achieve the mastery levels they might have otherwise.

[0005] The last decades have seen an increased focus on the diagnosis and understanding of psychological disorders such as autism spectrum and attention deficit hyperactivity disorder (ADHD). Parents have high expectations on the ability of educators to spot these disorders in the students, while diagnosing these disorders is difficult and requires professional judgement by the subject matter experts.

[0006] The current development of adaptive learning system is limited to personalize curriculum or study plans for students while any mental or emotional aspect regarding the learning is usually not detected and resulted in the absence of measures of motivation of the student.

[0007] To address aforementioned issues, it would be desirable to have an intelligent learning and training system that models the affective and cognitive states of the student, to assist the teacher/trainer in providing personalized instruction, monitor the student’s mental health and minimize administrative tasks to let the teacher/trainer focus on teaching/training.

Summary of the Invention:

[0008] With abundant accesses to wireless communication and mobile computing technology inside and outside of the classrooms, the present invention provides the means using wireless technology to address the low self-confidence and demotivated emotional issues experienced by students, and the lack of personalized learning experience for the mass general population. The present invention provides a learning and training platform that focuses on the subject’s motivational, emotional, and cognitive needs in predicting the learning outcome and in turn personalizing the contents in the learning materials and the manners delivering lessons.

[0009] It is the objective of the present invention to provide the subjects with better fitting exercises and group challenges, to learn about their interests and hobbies, and subsequently to provide learning exercises based on the subjects’ performances and interests. The lessons are livelier and the subjects are more engaged, resulting a deeper understanding of the learning materials and better test performance. It is also the objective of the present invention to provide such means for assisting teachers/instructors to provide more effective teaching, minimizing workload and supervision, and providing more insights on the needs of individual learner. The present invention takes a holistic approach in measuring and modelling the subject’s cognitive performance, motivation, and affective states, then uses such measurement and models in driving the learning material content selections, providing feedback to the subject and reports to the teacher/instructor.

[0010] The present invention may be incorporated in a variety of applications, which seek for individuals to learn, practice, and revisit learning materials including, but are not limited to, public, private schooling, and tutoring of primary, secondary, and tertiary education, as well as vocational training and corporate training. Embodiments of the present invention can also be adapted to be used in the fields of mental healthcare, e-commerce and retailing, law enforcement, and compliance and safety.

[0011] In accordance to one aspect of the present invention, the system estimates the affective state and cognitive state of the subject by image and/or video capturing and analyzing the subject’s facial expression, pupillary response, eye movements, point-of-gaze, and head pose; and physiologic detection, such as tactile pressure exerted on a tactile sensing device, subject’s handwriting, and tone of voice during a sampling time window. The image or video capture can be performed by using built-in or peripheral cameras in desktop computers, laptop computers, tablet computers, and/or smartphones used by the subject, and/or other optical sensing devices. The captured images and/or videos are then analyzed using machine vision techniques. For example, stalled eye movements, out-of-focus point-of-gaze, and a tilted head pose are signals indicating lack of interest and attention toward the subject matters being presented in the test questions; while a strong tactile pressure detected is a signal indicating anxiety, lack of confidence, and/or frustration in the subject matters being presented in a learning or training session.

[0012] In accordance to one embodiment, selected performance data and behavioral data from the subject are also collected in determining the subject’s understanding of the learning materials. These selected performance data and behavioral data include, but not limited to, correctness of answers, number of successful and unsuccessful attempts, number of toggling between given answer choices, and response speed to test questions of certain types, subject matters, and/or difficulty levels, and working steps toward a solution. For example, the subject’s excessive toggling between given choices and slow response speed in answering a test question indicate doubts and hesitations on the answer to the test question. The subject’s working steps toward a solution to a test problem are captured for matching with the model solution and in turn provides insight to the subject’s understanding of the materials.

[0013] The affective state and cognitive state estimation and performance data are primarily used in gauging the subject’s understanding of and interests in the materials covered in a learning or training programme. While a single estimation is used in providing a snapshot assessment of the subject’s progress in the learning or training programmes and prediction of the subject’s test results on the materials, multiple estimations are used in providing an assessment history and trends of the subject’s progress in the learning or training programme and traits of the subject. Furthermore, the estimated affective states and cognitive states of the subject are used in the modeling of the learning or training programme in terms of choice of subject matter materials, delivery methods, and administration.

Brief Description of the Drawings:

[0014] Embodiments of the invention are described in more detail hereinafter with reference to the drawings, in which:

[0015] FIG. 1 depicts a schematic diagram of a system for delivering and managing interactive and adaptive learning and training programmes in accordance to one embodiment of the present invention;

[0016] FIG. 2 depicts a logical data flow diagram of the system for delivering and managing interactive and adaptive learning and training programmes;

[0017] FIG. 3 depicts an activity diagram of a method for delivering and managing interactive and adaptive learning and training programmes in accordance to one embodiment of the present invention;

[0018] FIG. 4 depicts a flow diagram of an iterative machine learning workflow used by the system in calculating a probability of understanding of lecture materials by the student subject;

[0019] FIG. 5 illustrates a logical data structure used by the system for delivering and managing interactive and adaptive learning and training programmes in accordance to one embodiment of the present invention;

[0020] FIG. 6A depicts a logical block diagram of interlinking models of execution in accordance to one aspect of the present invention;

[0021] FIG. 6B shows the logical components’ details of the interlinking models of execution; [0022] FIG. 7 depicts a logical block diagram of a system comprising a number of execution module in accordance to one embodiment of the present invention;

[0023] FIG. 8 depicts a process flow diagram of a method for measuring confidence level in the subject in accordance to one embodiment of the present invention;

[0024] FIG. 9 depicts a logical block diagram of the system for delivering and managing interactive and adaptive learning and training programmes incorporating a Motivational Model in accordance to one embodiment of the present invention; and [0025] FIG. 10 depicts a listing illustrating the measured subject’s motivational states and corresponding action strategies executed by the system, and the desired impact on the Domain Knowledge model in accordance to one embodiment of the present invention.

Detailed Description:

[0026] In the following description, methods and systems for delivering and managing learning and training programmes and the likes are set forth as preferred examples. It will be apparent to those skilled in the art that modifications, including additions and/or substitutions may be made without departing from the scope and spirit of the invention. Specific details may be omitted so as not to obscure the invention; however, the disclosure is written to enable one skilled in the art to practice the teachings herein without undue experimentation.

[0027] In accordance to various embodiments of the present invention, the method and system for delivering and managing interactive and adaptive learning and training programmes uses a combination of sensing of the subject’s gestures, emotions, and movements, and quantitative measurements of test results and learning progress in estimating the subject’s affective and cognitive states and in turn forming a feedback loop and driving the learning material’s content selections and methods of delivery.

[0028] In accordance to one aspect of the present invention, the system estimates the affective state and cognitive state of the subject by image and/or video capturing and analyzing the subject’s facial expression, pupillary responses, eye movements, point-of-gaze, and head pose, and haptic feedback, such as tactile pressure exerted on a tactile sensing device during a sampling time window. The image or video capture can be performed by using built-in or peripheral cameras in desktop computers, laptop computers, tablet computers, and/or smartphones used by the subject, and/or other optical sensing devices. The captured images and/or videos are then analyzed using machine vision techniques. For example, stalled eye movements, out-of-focus point-of-gaze, and a tilted head pose are signals indicating lack of interest and attention toward the learning materials being presented in the learning or training session; while a strong tactile pressure detected is a signal indicating anxiety, lack of confidence, and/or frustration in the subject matters being asked in a test question.

[0029] In accordance to one embodiment, selected performance data and behavioral data from the subject are also collected in the affective state and cognitive state estimation. These selected performance data and behavioral data include, but not limited to, correctness of answers, number of successful and unsuccessful attempts, toggling between given answer choices, and response speed to test questions of certain types, subject matters, and/or difficulty levels, working steps toward a solution, and the subject’s handwriting and tone of voice. For example, the subject’s repeated toggling between given choices and slow response speed in answering a test question indicating doubts and hesitations on the answer to the test question. The subject’s working steps toward a solution to a test problem are captured for matching with the model solution and in turn provides insight to the subject’s understanding of the lecture materials.

[0030] In accordance to various embodiments, the system for delivering and managing interactive and adaptive learning and training programmes comprises a sensor handling module implemented by a combination of software and firmware executed in general purposed and specially designed computer processors. The sensor handling module manages the various sensors employed by the system. The sensor handling module is in electrical and/or data communications with various electronic sensing devices including, but not limited to, optical and touch sensing devices; input devices including, but not limited to, keyboard, mouse, pointing device, stylus, and electronic pen; image capturing devices; and cameras.

[0031 ] During the operation of the system, input sensory data are continuously collected at various sampling rates and averages of samples of input sensory data are computed. In order to handle the different sampling rates of different sensing devices, a reference rate is chosen (e.g. 5Hz). A slower sampling rate input sensory data is interpolated with zero order hold and then sampled at the reference rate. A higher sampling rate input sensory data is subsampled at the reference rate. After the sample rate alignment, a trace of the last few seconds is kept in memory after which the average is calculated. Effectively this produces a moving average of an input sensory data and acts as a low-pass filter to remove noise.

[0032] Eye movements, point-of-gaze, and head pose detection

[0033] In one embodiment, a low-cost optical sensor built-in in a computing device (e.g. subject facing camera in a tablet computer) is used. At a rate of minimal 5Hz, images are obtained from the sensor. Each image is then processed by face/eye tracking and analysis systems known in the art. The three-dimensional (3D) head orientation is measured in Euler angles (pitch, yaw, and roll). To measure the point-of- gaze, a 3D vector is assumed from the origin of the optical sensor to the center of the pupil of the user, secondly, a 3D vector is determined from the center of the eye-ball to the pupil. These two vectors are then used to calculate the point of gaze. A calibration step helps to compensate for offsets (subject position behind the screen, camera position relative to the screen). Using this data, the planar coordinate of the gaze on the computer screen can be derived.

[0034] Facial expressions and emotions determination

[0035] In another embodiment, the images and/or videos captured as mentioned above, are processed to identify key landmarks on the face such as eyes, tip of the nose, corners of the mouth. The regions between these landmarks are then analyzed and classified into facial expressions such as: attention, brow furrow, brow raise, cheek raise, chin raise, dimpler (lip corners tightened and pulled inwards), eye closure, eye widen, inner brow raise, jaw drop, lid tighten, lip corner depression, lip press, lip pucker (pushed forward), lip stretch, lip such, mouth open, nose wrinkle, smile, smirk, upper lip raise. These expressions are then mapped, using a lookup table, onto the following emotions: anger, contempt, disgust, engagement (expressiveness), fear, joy, sadness, surprise and valence (both positive as negative nature of the person’s experience). Each emotion is encoded as a percentage and output simultaneously. [0036] Physiologic measurement

[0037] The system may comprise a wearable device to measure physiologic parameters not limiting to: heart rate, electro dermal activity (EDA) and skin temperature. This device is linked wirelessly to the client computing device (e.g. tablet computer or laptop computer). The heart rate is derived from observations of the blood volume pulse. The EDA measures skin conductivity as an indicator for sympathetic nervous system arousal. Based on this, features related to stress, engagement, and excitement can be derived. Another approach is to use vision analysis techniques to directly measure the heart rate based on the captured images. This method is based on small changes in light absorption by the veins in the face, when the amount of blood varies due to the heart rate.

[0038] Handwriting analysis

[0039] In another embodiment, test answers may be written on a dedicated note paper using a digital pen and receive commands such as‘step completed’. The written answer is then digitized on the fly and via an intelligent optical character recognition engine, the system can evaluate the content written by the student subject and provide any necessary feedback to guide the student when needed. Studies show that taking longhand notes encourages students to process and reframe information improving the learning results. Alternatively, embodiments may use OCR after the tasks has been completed. The paper is scanned using a copier and the digitized image is fed to OCR software.

[0040] Pedagogical agent- subject interaction

[0041] As a non-limiting example, a pedagogical agent may be non-human animated character with human traits (e.g. an avatar) implemented by a combination of software and/or firmware running in one or more general purposed computer processors and/or specially configured computer processors. It can display the basic emotions by selecting from a set of animations (e.g. animated GIFs), or by using scripted geometric transformation on a static image displayed to the subject in a user interface. Another method is to use SVG based animations. The animation can be annotated with text messages (e.g. displayed in a balloon next to the animation). The text messages are generated by and received from the Teacher Module of the system. The subject’s responses to the pedagogical agent are received by the system for estimating the subject’s affective state.

[0042] The affective state and cognitive state estimation is primarily used in gauging the subject’s understanding of and interests in the materials covered in a learning or training programme. While a single estimation is used in providing a snapshot assessment of the subject’s progress in the learning or training programme and prediction of the subject’s test results on the materials, multiple estimations are used in providing an assessment history and trends of the subject’s progress in the learning or training programme and traits of the subject. Furthermore, the estimated affective states and cognitive states of the subject are used in the modeling of the learning or training programme in terms of choice of subject matter materials, delivery methods, and administration.

[0043] Domain Knowledge

[0044] Referring to FIG. 5. In accordance to one aspect of the present invention, the method and system for delivering and managing interactive and adaptive learning and training programmes logically structure the lecture materials, and the delivery mechanism in a learning and training programme as Domain Knowledge 500. A Domain Knowledge 500 comprises one or more Concept objects 501 and one or more Task objects 502. Each Concept object 501 comprises one or more Knowledge and Skill items 503. The Knowledge and Skill items 503 are ordered by difficulty levels, and two or more Concept objects 501 can be grouped to form a Curriculum. In the case where the present invention is applied in a school, a Curriculum defined by the present invention is the equivalence of the school curriculum and there is one-to-one relationship between a Knowledge and Skill item and a lesson in the school curriculum. The Concept objects can be linked to form a logical tree data structure (Knowledge Tree) such that Concept objects having Knowledge and Skill items that are fundamental and/or basic in a topic are represented by nodes closer to the root of the logical tree and Concept objects having Knowledge and Skill items that are more advance and branches of some common fundamental and/or basic Knowledge and Skill items are represented by nodes higher up in different branches of the logical tree. [0045] Each Task object 502 has various lecture content material 504, and is associated with one or more Concept objects 501 in a Curriculum. The associations are recorded and can be looked up in a question matrix 505. In accordance to one embodiment, a Task object 502 can be classified as: Basic Task, Interactive Task, or Task with an Underlying Cognitive or Expert Model. Each Basic Task comprises one or more lecture notes, illustrations (e.g. video clips and other multi-media content), test questions and answers designed to assess whether the subject has read all the learning materials, and instructional videos with embedded test questions and answers. Each Interactive Task comprises one or more problem-solving exercises each comprises one or more steps designed to guide the subject in deriving the solutions to problems. Each step provides an answer, common misconceptions, and hints. The steps are in the order designed to follow the delivery flow of a lecture. Each Task with an Underlying Cognitive or Expert Model comprises one or more problem-solving exercises and each comprises one or more heuristic rules and/or constraints for simulating problem-solving exercise steps delivered in synchronous with a student subject’s learning progress. This allows a tailored scaffolding (e.g. providing guidance and/or hints) for each student subject based on a point in a problem set or space presented in the problem-solving exercise.

[0046] In accordance to various embodiments, a Task object gathers a set of lecture materials (e.g. lecture notes, illustrations, test questions and answers, problem sets, and problem-solving exercises) relevant in the achievement of a learning goal. In addition to the aforementioned classification, a Task can be one of the following types:

1.) Reading Task: lecture notes or illustrations to introduce a new topic without grading, required to be completed before proceeding to a Practice Task is allowed;

2.) Practice Task: a set of questions from one topic to practice on questions from a new topic until a threshold is reached (e.g. five consecutive successful attempts without hints, or achieve an understanding level of 60% or more);

3.) Mastery Challenge Task: selected questions from multiple topics to let the student subject achieve mastery (achieve an understanding level of 95% or more) on a topic, and may include pauses to promote retention of knowledge (e.g. review opportunities for the student subjects); or 4.) Group Task: a set of questions, problem sets, and/or problem-solving exercises designed for peer challenges to facilitate more engagement from multiple student subjects, maybe ungraded.

[0047] In accordance to one embodiment, the Domain Knowledge, its constituent Task objects and Concept objects, Knowledge and Skill items and Curriculums contained in each Concept object, lecture notes, illustrations, test questions and answers, problem sets, and problem-solving exercises in each Task object are data entities stored a relational database accessible by the system (a Domain Knowledge repository). One or more of Domain Knowledge repositories may reside in third-party systems accessible by the system for delivering and managing interactive and adaptive learning and training programmes.

[0048] In accordance to another aspect of the present invention, the system for delivering and managing interactive and adaptive learning and training programmes logically builds on top of the Domain Knowledge with two logical execution modules: Student Module and Teacher Module. Each of the Modules comprises at least a computer server with a processor configured to execute machine instructions that implement the methods according to the embodiments of the present invention. The implementation includes at least one or more user interfaces (e.g. web pages) that interact (receive user input and display information to users) with the users of the system, data processing logic, and database access layer for accessing a database. Each of the Modules may further comprise user computing devices, each with a processor configured to execute machine instructions that implement, together with the computer server, the methods. Each of the user computing device’s processor may be configured to provide one or more user interfaces (e.g. apps) that interact with each of the users of the system, and to conduct data communication with the computer server.

[0049] Student Module

[0050] The Student Module executes each of one or more of the Task objects associated with a Curriculum in a Domain Knowledge for a student subject. During the execution of the Task objects, the system measures the student subject’s performance and obtain the student subject’s performance metrics in each Task such as: the numbers of successful and unsuccessful attempts to questions in the Task, number of hints requested, and the time spent in completing the Task. The performance metrics obtained, along with the information of the Task object, such as its difficulty level, are fed into a logistic regression mathematical model of each Concept object associated with the Task object. This is also called the knowledge trace of the student subject, which is the calculation of a probability of understanding of the material in the Concept object by the student subject. In one embodiment, the calculation of a probability of understanding uses a time-based moving average of student subject’s answer grades/scores with lesser weight on older attempts, the number of successful attempts, number of failed attempts, success rate (successful attempts over total attempts), time spent, topic difficulty, and question difficulty.

[0051] In one embodiment, the system calculates the probability of understanding of the materials in the Concept object by the student subject using an iterative machine learning workflow to fit mathematical models on to the collected data (student subject’s performance metrics and information of the Task) including, but not limited to, a time -based moving average of student subject’s answer grades/scores with lesser weight on older attempts, the number of successful attempts, number of failed attempts, success rate (successful attempts over total attempts), time spent, topic difficulty, and question difficulty. FIG. 4 depicts a flow diagram of the aforesaid iterative machine learning workflow. In this exemplary embodiment, data is collected (401), validated and cleansed (402); then the validated and cleansed data is used in attempting to fit a mathematical model (403); the mathematical model is trained iteratively (404) in a loop until the validated and cleansed data fit the mathematical model; then the mathematical model is deployed (405) to obtain the probability of understanding of the materials in the Concept object by the student subject; the fitted mathematical model is also looped back to and used in the step of validating and cleansing of the collected data.

[0052] The knowledge trace of the student subject is used by the system in driving Task lecture material items (e.g. questions and problem sets) selection, driving Task object (topic) selection, and driving lecture material ranking. The advantages of the Student Module include that the execution of the Task objects can adapt to the changing ability of the student subject. For non-limiting example, the Student Module estimates the amount of learning achieved by the student, estimate how much learning gain can be expected for the next Task, and provide a prediction of the student subject’s performance in an upcoming test. These data are then used by the Teacher Module and enable hypothesis testing to make further improvement to the system, evaluate teacher/trainer quality and lecture material quality.

[0053] Teacher Module

[0054] The Teacher Module receives the data collected from the execution of the Task objects by the Student Module for making decisions on the learning or training strategy and providing feedbacks to the student subject or teacher/trainer. The system for delivering and managing interactive and adaptive learning and training programmes comprises a teacher module implemented by a combination of software and firmware executed in general purposed and specially designed computer processors. In one embodiment, the Teacher Module executes the followings:

1.) Define the entry points for the Tasks execution. Initially all indicators for Concept Knowledge and Skill items are set to defaults, which are inferred from data in either an application form filled by the student subject or teacher/trainer or an initial assessment of the student subject by the teacher/trainer. Select the subsequent Task to execute. To select the next Task, the system’s teacher module searches through a logical tree data structure of Concept objects (Knowledge Tree), locate a Concept Knowledge and Skill with the lowest skill level (closest to the root of the Knowledge Tree) and then use a matching matrix to lookup the corresponding Task object for making the selection. Once selected, the Task object data is retrieved from the Domain Knowledge repository, and send to the system’s communication module for delivery presentation.

2.) Provide feedback. While the student subject is working on a Task object being executed, the Teacher Module monitors the time spent on a Task step. When a time limit is exceeded, feedback is provided as a function of the current affective state of the student subject. For example, this can be an encouraging, empathetic, or challenging message selected from a generic list, or it is a dedicated hint from the Domain Knowledge.

3.) Drive the system’s pedagogical agent. The Teacher Module matches the current affective state of the student subject with the available states in the pedagogical agent. Besides providing the affective state information, text messages can be sent to the system’s communication module for rendering along with the pedagogical agent’s action in a user interface displayed to the student subject.

4.) Decide when a Concept is mastered. As described earlier, under the Student Module estimates the student subject’s probability of understanding of the materials in each Concept. Based on a predetermined threshold (e.g. 95%), the teacher/trainer can decide when a Concept is mastered.

5.) Flag student subject’s behavior that is recognized to be related to mental disorders. For example, when the execution by the Student Module shows anomalies in the sensory data compared to a known historical context and exhibits significant lower learning progress, the system under the Training Model raises a warning notice to the teacher/trainer. It also provides more detailed information on common markers of disorders such as Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD).

[0055] In accordance to various embodiments, the system for delivering and managing interactive and adaptive learning and training programmes further comprises a Communication Module implemented by a combination of software and firmware executed in general purposed and specially designed computer processors. In one embodiment, one part of the Communication Module resides and is executed in one or more server computers, and other part of the Communication Module resides and is executed in one or more client computers including, but not limited to, desktop computers, laptop computers, tablet computers, smartphones, and other mobile computing devices, among which some are dedicated for use by the student subjects and others by teachers/trainers.

[0056] The Communication Module comprises one or more user interfaces designed to present relevant data from the Domain Knowledge and materials generated by the Student Module and Teacher Module to the student subjects and the teachers/trainers. The user interfaces are further designed to facilitate user interactions in capturing user input (textual, gesture, image, and video inputs) and displaying feedback including textual hints and the simulated pedagogical agent’s actions. Another important feature of the Communication Module is to provide an on-screen (the screen of the computing device used by a student subject) planar coordinates and size of a visual cue or focal point for the current Task object being executed. For a non-limiting example, when a lecture note from a Task object is being displayed on screen, the Communication Module provides the planar coordinates and size of the lecture note display area and this information is used to match with the collected data from a point- of-gaze tracking sensor in order to determine whether the student subject is actually engaged in the Task (looking at the lecture note).

[0057] FIG. 2 depicts a logical data flow diagram of the system for delivering and managing interactive and adaptive learning and training programmes in accordance to various embodiments of the present invention. The logical data flow diagram illustrates how the major components of the system work together in a feedback loop in the operations of Student Module and Teacher Module. In an exemplary embodiment in reference to FIG. 2, during enrollment, a suitable course is selected by the student (or her guardians) in a learning or training programme. This course corresponds directly to a Curriculum object, which is a set of linked Concept objects in the Domain Knowledge 202, and constitutes the learning goal 201 for this student subject. Upon the student subject logging into the system via a user interface rendered by the Communication Module, the Teacher Module selects and retrieves from the Domain Knowledge 202 a suitable Concept object and the associated first Task object. Executed by the Student Module, the Task object data is retrieved from the Domain Knowledge repository, the system renders the Task object data (e.g. lecture notes, test questions, and problem set) on the user interface for the student subject, and the student subject starts working on the task. Meanwhile, the system monitors the learning process 203 by collecting affective state sensory data including, but not limited to, point-of-gaze, emotion, and physiologic data, and cognition state data via Task questions and answers and the student subject’s behavioral-analyzing interactions with the user interface (204). After analyzing the collected affective state sensory data and cognition state data, the learner state 205 is updated. The updated learner state 205 is compared with the learning goal 201. The determined knowledge/skill gap or the fit of the instruction strategy 206 is provided to the Teacher Module again, completing the loop. If the analysis on the collected affective state sensory data and cognition state data shows a probability of understanding that is higher than a threshold, the learning goal is considered achieved

207.

[0058] FIG. 3 depicts an activity diagram illustrating in more details the execution process of the system for delivering and managing interactive and adaptive learning and training programmes by the Student Module and Teacher Module. In an exemplary embodiment referring to FIG. 3, the execution process is as follows:

301. A student subject logs into the system via her computing device running a user interface rendered by the system’s communication module.

302. The student subject select a Curriculum presented to her in the user interface.

303. Upon receiving the user login, successful authentication, and receiving the Curriculum selection, the system’s teacher module, running in a server computer, selects and requests from the Domain Knowledge repository one or more Task objects associated with the Curriculum selected. When no Task object has yet been defined to associate with any Concept objects in the Curriculum selected, the system evaluates the Knowledge Tree and finds the Concept Knowledge and Skills that have not yet practiced or mastered by the student subject as close to the root (fundamental) of the Knowledge Tree as possible. This process is executed by the system’s recommendation engine, which can be implemented by a combination of software and firmware executed in general purposed and specially designed computer processors. The recommendation engine can recommend Practice Tasks, and at lower rate Mastery Challenge Tasks. System-recommended Tasks have a default priority; teachers/trainers-assigned Tasks have a higher priority in the Task selection. In one embodiment, the system further comprises a recommendation engine for recommending the lecture materials (e.g. topic) to be learned next in a Curriculum. Using the estimated affective state and cognitive state data of the student subject, performance data of the student subject, the Knowledge Tree (with all‘edge’ topics listed), the teacher/trainer’s recommendation information, data from collaborative filters (look at data from peer student subjects), and lecture content data (match student attributes with the learning material’s attributes), the recommendation engine recommends the next Task to be executed by the Teacher Module. For example, the student subject’s negative emotion can be eased by recognizing the disliked topics (from the affective state data estimated during the execution of certain Task) and recommending the next Task of a different / favored topic; and recommending the next Task of a disliked topic when student subject’s emotion state is detected position. In another example, the recommendation engine can select the next Task of higher difficulty when the estimated affective state data shows that the student subject is unchallenged. This allows the matching of Tasks with the highest learning gains. This allows the clustering of Tasks based on similar performance data and/or affective state and cognitive state estimation. This also allows the matching of student peers with common interests.

304. If the requested Task objects are found, their data are retrieved and are sent to the student subject’s computing device for presentation in the system’s Communication Module user interface.

305. The student subject selects a Task object to begin the learning session.

306. The system’s Teacher Module retrieves from the Domain Knowledge repository the next item in the selected Task object for rendering in the system’s Communication Module user interface.

307. The system’s Communication Module user interface renders the item in the selected Task object.

308. A camera for capturing the student subject’s face is activated.

309. During the student subject’s engagement in learning materials in the item in the selected Task object (309a), the student subject’s point-of-gaze and facial expressions are analyzed (309b).

310. Depending on the estimated affective state and cognitive state of the student subject based on sensory data collected and information in the student subject’s profile (overlay, includes all past performance data and learning progress data), virtual assistant may be presented in the form of guidance and/or textual hint displayed in the system’s Communication Module user interface.

311. The student subject submit an answer attempt.

312. The answer attempt is graded and the grade is displayed to the student subject in the system’s communication module user interface.

313. The answer attempt and grade is also stored by the system for further analysis. 314. The answer attempt and grade is used in calculating the probability of the student subject’s understanding of the Concept associated with the selected Task object.

315. If the selected Task is completed, the system’s Teacher Module selects and requests the next Task based on the calculated probability of the student subject’s understanding of the associated Concept and repeat the steps from step 303.

316. If the selected Task is not yet completed, the system’s Teacher Module retrieves the next item in the selected Task and repeat the steps from step 306.

317. After all Tasks are completed, the system generates the result report for student subject.

[0059] In accordance to another aspect of the present invention, the system for delivering and managing interactive and adaptive learning and training programmes further comprises an administration module that takes information from the teachers/trainers, student subjects, and Domain Knowledge in offering assistance with the operation of face-to-face learning process across multiple physical education/training centers as well as online, remote learning. In an exemplary embodiment, the administration module comprises a constraint based scheduling algorithm that determines the optimal scheduling of lessons while observing constraints such a teacher/trainer certification, travelling distance for student and trainer, first- come-first-served, composition of the teaching/training group based on learning progress and training strategy. For example, when the teacher/trainer wants to promote peer teaching/training, the scheduling algorithm can select student subjects with complementary skill sets so that they can help each other.

[0060] An in-class learning session may comprise a typical flow such as: student subjects check in, perform a small quiz to evaluate the cognitive state of the student subjects, and the results are presented on the teacher/trainer’s user interface dashboard directly after completion. The session then continues with class wide explanation of a new concept by the teacher/trainer, here the teacher/trainer receives assistance from the system’s pedagogical agent with pedagogical goals and hints. After the explanation, the student subjects may engage with exercises/tasks in which the system provides as much scaffolding as needed. Based on the learning progress and affective states of the student subjects, the system’s Teacher Module decides how to continue the learning session with a few options: e.g. provide educational games to deal with negative emotions, and allow two or more student subjects engage in a small competition for a small prize, digital badge, and the like. The learning session is concluded by checking out. The attendance data is collected for billing purposes and secondly for safety purposes as the parents can verify (or receive a notification from the system) of arrival and departure times of their children.

[0061] Although the embodiments of the present invention described above are primarily applied in academic settings, the present invention can be adapted without undue experimentation to corporate training, surveying, and job performance assessment. In accordance to one embodiment of the present invention, the method and system for delivering and managing interactive and adaptive training programmes logically structure training materials and the delivery mechanism data in a training programme as a Domain Knowledge, with its constituent Concept objects and Task objects having Knowledge and Skill items, and training materials respectively that are relevant to the concerned industry or trade. The system’s operations by the Student Module and the Teacher Module are then substantially similar to those in academic settings. In the application of surveying, the system’s estimation of the subjects’ affective states and cognitive states can be used in driving the selection and presentment of survey questions. This in turn enables more accurate and speedy survey results procurements from the subjects. In the application of job performance assessment, the system’s estimation of the employee subjects’ affective states and cognitive states on duty continuously allows an employer to gauge the skill levels, engagement levels, and interests of the employees and in turn provides assistance in work and role assignments.

[0062] Referring to FIGs. 6A and 6B. In accordance to one embodiment of the present invention, the method and system for delivering and managing interactive and adaptive learning and training programmes incorporate machine learning techniques that are based on interlinking models of execution comprising: a Domain Model 601, an Assessment Model 602, a Learner Model 603, a Deep Learner Model 604, one or more Motivation Operational Models 605, a Transition Model 606, and a Pedagogical Model 607. The interlinking models of execution is purposed for driving, inducing, or motivating certain desirable actions, behavior, and/or outcome from the subject. These certain desirable actions and/or outcome can be, as non-limiting examples, learning certain subject matters, achieving certain academic goals, achieving certain career goals, completing certain job assignments, making certain purchases, and conducting certain commercial activities. These interlinking models of execution together form a machine learning feedback loop comprising the continuous tracking and assessment of learning progress of the subject under the Assessment Model 602, driving the learning activities under the Learner Model 603, motivating the subject under the Deep Learner Model 604 and Motivation Operational Models 605, and selecting and re-selecting knowledge space items under the Domain Model 601 and Transition Model 606, and delivering the knowledge space items and activities from one knowledge state to the next under the Pedagogical Model 607.

[0063] Motivational Model

[0064] While the aforesaid methods and systems that are based on Domain

Knowledge focus on the student subject’s knowledge in the learning materials, the motivational aspects are being overlooked. Motivation elaborates on the influence of confidence, challenge, control, and curiosity in the learning process. In accordance to one embodiment of the present invention that complements the methods and systems based on the aforesaid Domain Knowledge, a virtual instructor artificial intelligence (AI) sub-system is provided, which is built on motivational models. In one embodiment, the virtual instructor AI sub-system is implemented within the Student Module. In summary of the working principle of the AI sub-system, while the student subjects are working on the learning materials, each student subject’s emotions are continuously being detected and analyzed for all emotion components. The AI sub-system analyzes the emotion components and executes a motivation detection process, which comprises an emotion-motivation matching that aims to direct and affect the manner of delivery and content of the learning materials, exert intervention actions, and in turn influence negative emotions into positive ones with the goal of encouraging the student subject to keep learning. The emotion-motivation matching is at least in part based on the intensity of the emotion detected and analyzed, the AI sub-system then determines the level of motivation (or the intervention actions) needed. [0065] Under the Motivational Model, there are a number of motivation dimensions and matching attitude and emotion indicators. A non-limiting exemplary list of motivation dimensions and matching attitude and emotion indicators is shown in

TABLE 1 below.

Competence Am I Capable? The learner believes he or she has the ability to

Autonomy Can I control or The student feels in control by seeing a direct manage my link between her actions and an outcome. learning?

Value Does it interest The student has some interest in the task or sees me? Is it worth the the value of completing it.

effort?

Relatedness What do others Completing the task brings the student social

think? reward, recognition or sense of belonging.

TABLE 1

[0066] TABLE 2 lists the motivational states of the student subject and the matching intervention actions (or hints) proposed by the AI sub-system under the motivation detection process. Motivational interventions are divided into two sets: emotion (meta-cognitive intervention) and content (cognitive intervention). The AI sub-system causes the virtual instructor AI sub-system to first hints to tackle emotion related issues, then hints to tackle the cognition. The motivation detection process further comprises a feedback loop in which the AI sub-system continuously estimates the motivational states of the student subject through emotion detection and analysis; computes the changes in motivational states as the AI sub-system causes to conducts intervention actions, then fine tune the intervention actions in driving toward a desirable change in the motivational states.

Mastery, low effort Increase problem difficulty Display learning progress Mastery, high effort Maintain problem difficulty Praise effort and persistence

TABLE 2 [0067] In accordance to one embodiment of the Motivation Model, three main measurement indicators are used: effort, confidence, and independence. Effort is measured through performance and it refers to how the results were achieved, such as number of attempts. Confidence relies mostly on the student subjects’ beliefs on their efficacy to perform the task. Confidence correlates to persistence or avoidance of the task. Independence is linked to the‘locus of control’. Successes raise efficacy and failure lowers it, but once a strong sense of efficacy is developed, a failure may not have much impact.

[0068] In accordance to one embodiment, the measurement of confidence, in turn, is the measurements of, the student subject’s engagement (amplitude of emotion signal), amount of hints requested by the student subject during a test, the student subject’s past performance, the student subject’s frequency of avoidance in answering test questions. It is assumed that there is no direct causation between positive or negative emotion and confidence. In general student subjects who distrust their self-efficacy have high levels of engagement (positive or negative emotions), and student subjects who have high self-efficacy know how to cope with stressful situations and would show lower levels of engagement. Higher number of hints requested by the student subject correlates with lower confidence. Successful performance in the past is a strong contributor to confidence; however, the effect diminishes when confidence is already high or low. Avoidance can mean skipping of test questions. Thus, a higher avoidance indicates lower confidence. In accordance to one embodiment, the Student Module uses a discrete event model to dynamically update the confidence level of the student subject; then uses a threshold value to classify confidence as low (below threshold) or acceptable (above threshold).

[0069] TABLE 3 below shows one embodiment of the rules and weights of such a discrete event model:

7 Student subject shows high engagement

-1

TABLE 3

TABLE 4 illustrates an example execution of the Student Module in obtaining the confidence measurement using the aforesaid discrete event model.

TABLE 4

FIG. 8 depicts a logical execution flow of the aforesaid discrete event model used in the measurement of confidence. The measurement of confidence is conducted during a test or exercise session. First, the weighted value of each attempt feature (e.g. according to TABFE 3) in each attempt of a test question by the student subject and a sum of the weighted values of the attempt features is computed for a AConfidence value (801); secondly, an unnormalized confidence state value is determined by summing the AConfidence values of all test question attempts (802); thirdly, the unnormalized confidence state value is normalized by a Squash function (e.g. sigmoid) to a value range between 0 to 1 ; lastly, the normalized confidence state value is compared to a threshold value for determination of low or acceptable confidence level.

[0070] In accordance to another embodiment, the Student Module provides a user interface element (e.g. a ‘slider’) for the student subject to report their own confidence during a test, lecture, or training session so to provide the system labelled data, which can be used for as a training data set for training the AI sub-system.

[0071 ] In accordance to one embodiment, the measurement of effort is based on the student subject’s number of attempts at and/or time spent in a question collected by the Student Module. The value of the measurement of effort may be binary (e.g. with the labels: Fittle and Farge). The measurement of independence is based on the clickstream data collected by the Student Module. The clickstream data includes the number taps on the user interface (e.g. touch screen of the computing device used by the student subject) before submitting an answer to a question, and the number toggling between answer selections of a multiple-choice question. The measurement of independence is also based on the number of hints requested by the student subject and the number of questions skipped by the student subject. The value of the measurement of independence may be binary (e.g. with the labels: Low and Acceptable).

[0072] Referring to FIG. 9. In accordance to one embodiment, during a test, lecture, or training session, the Student Module of the system 908, by detecting the emotions and estimating the affective state 903 and cognitive state 902 of the student subject according to aforementioned methods, diagnoses and obtains the motivational state 901 of the student subject. The student subject’s motivational states can then be processed and visualized via the Communication Module and be displayed in a computing device configured for use by the teacher or test administrator. The Teacher Module of the system 908 uses the motivational state 901, cognitive state 902, and the affective state 903 of the student subject to drive the pedagogical agent in providing avatar visual feedback 904 and/or feedback messages 907, to provide hints 905, and to adjust or adapt the difficulty level (select) of the test, lecture, or training materials 906. The motivational states of an individual student subject are used to provide insights to the teacher or test administrator such that interventions can be made earlier with greater effects. The motivational states of a large group of student subject are used in the feedback and analysis of the test, lecture, or training materials.

[0073] FIG. 10 depicts a listing illustrating the measured student subject’s motivational states and corresponding action strategies executed by the system, and the desired impact on the Domain Knowledge model.

[0074] Knowledge Tracing

[0075] In accordance to another embodiment of the present invention that complements the methods and systems based on the aforesaid Domain Knowledge, the system’s Student Module, Teacher Module, and Communication Module are further configured to incorporate a deep learning Knowledge Tracing model. The goals of the Knowledge Tracing model are to predict learning outcomes of the student subject, determine student subject’s cognitive strengths and weaknesses, select better practice exercise materials for test rehearsals for better knowledge and skill retention, detect latent structure (e.g. dependencies, grouping and clustering) in lecture or training materials.

[0076] Motivational Planning

[0077] In accordance to another embodiment of the present invention that complements the methods and systems based on the aforesaid Domain Knowledge, the system’s Student Module, Teacher Module, and Communication Module are further configured to incorporate a Motivational Planning model. The goals of the Motivational Planning model are to define test sequences such that knowledge and skill retention is maximized, and build a planner (Motivational Plan) with consideration of the motivational and cognitive states of the test taking student subject with the goal to manage, if not avoid, negative emotions. The advantages of the system having incorporated the Motivational Planning model in comparison to that without is that the former is more effective in addressing the student subject’s learning achievement gap; and that the Motivational Plan can be generalized across a large range of age groups among the student subjects.

[0078] The present invention can also be applied in medical assessment for cognitive disorders, such as Alzheimers’ dementia and autism ADHD. In a cognitive test (e.g. administered using a tablet computer to a patient subject), the system estimates the patient subject’s affective state and cognitive state using the collected (e.g. from the tablet computer’s built-in camera) and analyzed sensory data on patient subject’s facial expression, eye movements, point-of-gaze, head pose, voice, speech clarity, reaction time, and/or touch responses. The patient subject’s affective state and cognitive state estimation, along with the patient subject’s cognitive test performance data are used to drive the course of the cognitive test, influence the patient subject’s emotions, and provide a real-time diagnosis that is less prone to human error. The patient subject’s affective state and cognitive state estimation can also be matched and used alongside with MRI data on the patient subject’s brain activity in further study.

[0079] The electronic embodiments disclosed herein may be implemented using computing devices, computer processors, or electronic circuitries including but not limited to application specific integrated circuits (ASIC), field programmable gate arrays (FPGA), and other programmable logic devices configured or programmed according to the teachings of the present disclosure. Computer instructions or software codes running in the general purpose or specialized computing devices, computer processors, or programmable logic devices can readily be prepared by practitioners skilled in the software or electronic art based on the teachings of the present disclosure.

[0080] All or portions of the electronic embodiments may be executed in one or more general purpose or computing devices including server computers, personal computers, laptop computers, mobile computing devices such as smartphones and tablet computers.

[0081] The electronic embodiments include computer storage media having computer instructions or software codes stored therein which can be used to program computers or microprocessors to perform any of the processes of the present invention. The storage media can include, but are not limited to, floppy disks, optical discs, Blu-ray Disc, DVD, CD-ROMs, and magneto-optical disks, ROMs, RAMs, flash memory devices, or any type of media or devices suitable for storing instructions, codes, and/or data.

[0082] Various embodiments of the present invention also may be implemented in distributed computing environments and/or Cloud computing environments, wherein the whole or portions of machine instructions are executed in distributed fashion by one or more processing devices interconnected by a communication network, such as an intranet, Wide Area Network (WAN), Local Area Network (LAN), the Internet, and other forms of data transmission medium.

Claims

Claims:

What is claimed is: 1. A system for delivering and managing learning and training programmes comprising:

one or more optical sensors configured for capturing and generating sensory data on a student subject during a learning session;

one or more electronic databases including one or more domain knowledge data entities, each domain knowledge data entity comprising one or more concept data entities and one or more task data entities, wherein each concept data entity comprises one or more knowledge and skill content items, wherein each task data entity comprises one or more lecture content material items, wherein each task data entity is associated with at least one concept data entity, and wherein a curriculum is formed by grouping a plurality of the concept data entities;

a student module executed by one or more computer processing devices configured to estimate the student subject’s affective state and cognitive state using the sensory data collected from the optical sensors;

a teacher module executed by one or more computer processing devices configured to select a subsequent task data entity and retrieve from the electronic databases the task data entity’s lecture content material items for delivery and presentment to the student subject after each completion of a task data entity in the learning session; and

a recommendation engine executed by one or more computer processing devices configured to create a list of task data entities available for selection of the subsequent task data entity, wherein the task data entities available for selection are the task data entities associated with the one or more concept data entities forming the curriculum selected;

wherein the student module is further configured to determine the student subject’s motivational state by measuring a confidence level, an effort level, and an independence level of the student subject; wherein the confidence level is measured by one or more of:

an amplitude of emotion extracted from student subject’s affective state;

amount of hints requested by the student subject;

the student subject’s past performance; and

the student subject’s frequency of avoidance in attempting the task data entities;

wherein the effort level is measured by the student subject’s number of attempts at and/or time spent in one or more of the task data entities;

wherein the independence level is measured using the student subject’s clickstream data; and

wherein the selection of a task data entity from the list of task data entities available for selection is based on the student subject’s motivational state associated concept data entity’s knowledge and skill content items.

2. The system of claim 1, further comprising:

one or more physiologic measuring devices configured for capturing one or more of the student subject’s tactile pressure exerted on a tactile sensing device, heart rate, electro dermal activity (EDA), skin temperature, and touch response, and generating additional sensory data during a learning session;

wherein the student module is further configured to estimate the student subject’s affective state and cognitive state using the sensory data collected from the optical sensors and the additional sensory data collected from the physiologic measuring devices.

3. The system of claim 1, further comprising:

one or more voice recording devices configured for capturing the student subject’s voice and speech clarity, and generating additional sensory data during a learning session;

wherein the student module is further configured to estimate the student subject’s affective state and cognitive state using the sensory data collected from the optical sensors and the additional sensory data collected from the voice recording devices.

4. The system of claim 1, further comprising:

one or more handwriting capturing devices configured for capturing the student subject’s handwriting, and generating additional sensory data during a learning session;

wherein the student module is further configured to estimate the student subject’s affective state and cognitive state using the sensory data collected from the optical sensors and the additional sensory data collected from the handwriting capturing devices.

5. The system of claim 1, further comprising:

one or more pedagogical agents configured for capturing the student subject’s interaction with the pedagogical agents, and generating additional sensory data during a learning session;

wherein the student module is further configured to estimate the student subject’s affective state and cognitive state using the sensory data collected from the optical sensors and the additional sensory data collected from the pedagogical agents.

6. The system of claim 1 , wherein each of the lecture content material items is a lecture note, an illustration, a test question, a video with an embedded test question, a problem-solving exercise having multiple steps designed to provide guidance in deriving a solution to a problem, or a problem-solving exercise having one or more heuristic rules or constraints for simulating problem-solving exercise steps delivered in synchronous with the student subject’s learning progress.

7. The system of claim 1,

wherein a plurality of the concept data entities are linked to form a logical tree data structure; wherein concept data entities having knowledge and skill content items that are fundamental in a topic are represented by nodes closer to a root of the logical tree data structure and concept data entities having knowledge and skill content items that are advance and branches of a common fundamental knowledge and skill content item are represented by nodes higher up in different branches of the logical tree data structure; wherein the recommendation engine is further configured to create a list of task data entities available for selection of the subsequent task data entity, wherein the task data entities available for selection are the task data entities associated with the one or more concept data entities forming the curriculum selected and the one or more concept data entities having knowledge and skill items not yet mastered by the student subject and as close to the roots of the logical tree data structures that the concept data entities belonging to.

8. The system of claim 1, wherein the sensory data comprises one or more of a student subject’s facial expression, eye movements, point-of-gaze, and head pose.

9. The system of claim 1,

wherein the selection of a task data entity from the list of task data entities available for selection is based on a probability of the student subject’s understanding of the associated concept data entity’ s knowledge and skill content items and the student subject’s estimated affective state;

wherein when the student subject’s estimated affective state indicates a negative emotion, a task data entity that is associated with a concept data entity having knowledge and skill content items that are favored by the student subject is selected over another task data entity that is associated with another concept data entity having knowledge and skill content items that are disliked by the student subject; and

wherein when the student subject’s estimated affective state indicates a positive emotion, a task data entity that is associated with a concept data entity having knowledge and skill content items that are disliked by the student subject is selected over another task data entity that is associated with another concept data entity having knowledge and skill content items that are favored by the student subject.