Using Student Mood And Task Performance To Train Classifier Algorithms To Select Effective Coaching Strategies Within Intelligent Tutoring Systems (its)

The ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System\u27s (ITS) coaching strategy based on the student\u27s mood. As a step toward this goal, this study evaluated the relationships between each student\u27s mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student\u27s performance. Outcomes included methods to increase the perception of the intelligent tutor to allow it to adapt coaching strategies (methods of instruction) to the student\u27s affective needs to mitigate barriers to performance (e.g. negative affect) during the one-to-one tutoring process. The study evaluated whether the affective state (specifically mood) of the student moderated the student\u27s interaction with the tutor and influenced performance. This research examined the relationships, interactions and influences of student mood in the selection of ITS coaching strategies to determine which strategies were more effective in terms of student performance given the student\u27s mood, state (recent sleep time, previous knowledge and training, and interest level) and actions (e.g. mouse movement rate). Two coaching strategies were used in this study: Student-Requested Feedback (SRF) and Tutor-Initiated Feedback (TIF). The SRF coaching strategy provided feedback in the form of hints, questions, direction and support only when the student requested help. The TIF coaching strategy provided feedback (hints, questions, direction or support) at key junctures in the learning process when the student either made progress or failed to make progress in a timely fashion. The relationships between the coaching strategies, mood, performance and other variables of interest were considered in light of five hypotheses. At alpha = .05 and beta at least as great as .80, significant effects were limited in predicting performance. Highlighted findings include no significant differences in the mean performance due to coaching strategies, and only small effect sizes in predicting performance making the regression models developed not of practical significance. However, several variables including performance, energy level and mouse movement rates were significant, unobtrusive predictors of mood. Regression algorithms were developed using Arbuckle\u27s (2008) Analysis of MOment Structures (AMOS) tool to compare the predicted performance for each strategy and then to choose the optimal strategy. A set of production rules were also developed to train a machine learning classifier using Witten & Frank\u27s (2005) Waikato Environment for Knowledge Analysis (WEKA) toolset. The classifier was tested to determine its ability to recognize critical relationships and adjust coaching strategies to improve performance. This study found that the ability of the intelligent tutor to recognize key affective relationships contributes to improved performance. Study assumptions include a normal distribution of student mood variables, student state variables and student action variables and the equal mean performance of the two coaching strategy groups (student-requested feedback and tutor-initiated feedback ). These assumptions were substantiated in the study. Potential applications of this research are broad since its approach is application independent and could be used within ill-defined or very complex domains where judgment might be influenced by affect (e.g. study of the law, decisions involving risk of injury or death, negotiations or investment decisions). Recommendations for future research include evaluation of the temporal, as well as numerical, relationships of student mood, performance, actions and state variables

A Multimodal Database as a Background for Emotional Synthesis, Recognition and Training in E-Learning Systems

Abstract. This paper presents a multimodal database developed within the EUfunded project MYSELF. The project aims at developing an e-learning platform endowed with affective computing capabilities for the training of relational skills through interactive simulations. The database includes data coming from 34 participants and concerning physiological parameters, vocal nonverbal features, facial mimics and posture. Ten different emotions were considered (anger, joy, sadness, fear, contempt, shame, guilt, pride, frustration and boredom), ranging from primary to self-conscious emotions of particular relevance in learning process and interpersonal relationships. Preliminary results and analyses are presented, together with directions for future work.

The role of emotions in e-learning in psychotherapy: a mixed methods study

Research questions 1. What is the role of emotions in e-learning (in particular as it relates to a case example of online psychotherapy education)? 2. What methods can be used to detect and measure emotions in e-learning? Methodology These research questions are addressed by a systematic literature review and analysis of the following student data from the University of Sheffield’s online MSc in Psychotherapy Studies: i. a set of mental health/well-being outcome measures ii. a linguistic analysis of forum postings using Linguistic Inquiry and Word Count (LIWC) iii. qualitative interviews with ex-students Findings i. the mental health/well-being measure scores do not reliably detect changes in emotional processing ii. the LIWC picks up individuals’ emotional change but does not correlate strongly with the outcome measures. iii. those learners who can trust others online are more satisfied, and more likely to engage in transformative learning. Of the methods employed, the student interviews gave the best insight into students’ emotional experience. Conclusions Emotions are central topics for learning in a psychotherapy course; they are pivotal in terms of how students engage with an e-learning course, and with learners and tutors. Student engagement and satisfaction in e-learning are engendered by a collaborative learning approach, which encourages sharing of emotions through self-disclosure. Being online may provoke anxiety and make it harder for some students to develop the kind of trusting relationships needed to self-disclose; for other students, the anxiety is less problematic, and being online is a liberating and positive factor in their learning. The research suggests that learning could be personalized according to students’ emotional preferences by adopting a learning analytics approach – providers of e-learning need to be aware of the emotional experience of e-learners, and equipped to respond appropriately to maximise opportunities for engagement and transformative learning