Motivational and metacognitive feedback in an ITS: linking past states and experiences to current problems

Feedback is an important element in learning as it can provide learners with both information about progress as well as external motivational stimuli, providing them with an opportunity for reflection. Motivation and metacognition are strongly intertwined, with learners high in self-efficacy more likely to use a variety of self-regulatory learning strategies, as well as to persist longer on challenging tasks. Learning from past experience involves metacognitive processes as an act of reflecting upon one’s own experience and, coupled with existing knowledge, aids the acquisition and construction of further knowledge. The aim of the research was to improve the learner’s focus on the process and experience of problem solving while using an Intelligent Tutoring System (ITS), by addressing the primary question: what are the effects of including motivational and metacognitive feedback based on the learner’s past states and experiences? An existing ITS, SQL-Tutor, was used in a study with participants from first year undergraduate degrees studying a database module. The study used two versions of SQL-Tutor: the Control group used a base version providing domain feedback and the Study group used an extended version that also provided motivational and metacognitive feedback. Three sources of data collection were used: module summative assessments, ITS log files and a post-study questionnaire. The analysis included both pre-post comparisons and how the participants interacted with the system, for example their persistence in problem-solving and the degree to which they referred to past learning. Comparisons between groups showed some differing trends both in learning and behaviour in favour of the Study group, though these trends were not significantly different. The study findings showed promise for the use of motivational and metacognitive feedback based on the learners’ past states and experiences that could be used as a basis for future research work and refinement

Mitigating User Frustration through Adaptive Feedback based on Human-Automation Etiquette Strategies

The objective of this study is to investigate the effects of feedback and user frustration in human-computer interaction (HCI) and examine how to mitigate user frustration through feedback based on human-automation etiquette strategies. User frustration in HCI indicates a negative feeling that occurs when efforts to achieve a goal are impeded. User frustration impacts not only the communication with the computer itself, but also productivity, learning, and cognitive workload. Affect-aware systems have been studied to recognize user emotions and respond in different ways. Affect-aware systems need to be adaptive systems that change their behavior depending on users’ emotions. Adaptive systems have four categories of adaptations. Previous research has focused on primarily function allocation and to a lesser extent information content and task scheduling. However, the fourth approach, changing the interaction styles is the least explored because of the interplay of human factors considerations. Three interlinked studies were conducted to investigate the consequences of user frustration and explore mitigation techniques. Study 1 showed that delayed feedback from the system led to higher user frustration, anger, cognitive workload, and physiological arousal. In addition, delayed feedback decreased task performance and system usability in a human-robot interaction (HRI) context. Study 2 evaluated a possible approach of mitigating user frustration by applying human-human etiquette strategies in a tutoring context. The results of Study 2 showed that changing etiquette strategies led to changes in performance, motivation, confidence, and satisfaction. The most effective etiquette strategies changed when users were frustrated. Based on these results, an adaptive tutoring system prototype was developed and evaluated in Study 3. By utilizing a rule set derived from Study 2, the tutor was able to use different automation etiquette strategies to target and improve motivation, confidence, satisfaction, and performance using different strategies, under different levels of user frustration. This work establishes that changing the interaction style alone of a computer tutor can affect a user’s motivation, confidence, satisfaction, and performance. Furthermore, the beneficial effect of changing etiquette strategies is greater when users are frustrated. This work provides a basis for future work to develop affect-aware adaptive systems to mitigate user frustration

Affective modelling and feedback in programming practice systems

指導教員：角　

E-Learning

Technology development, mainly for telecommunications and computer systems, was a key factor for the interactivity and, thus, for the expansion of e-learning. This book is divided into two parts, presenting some proposals to deal with e-learning challenges, opening up a way of learning about and discussing new methodologies to increase the interaction level of classes and implementing technical tools for helping students to make better use of e-learning resources. In the first part, the reader may find chapters mentioning the required infrastructure for e-learning models and processes, organizational practices, suggestions, implementation of methods for assessing results, and case studies focused on pedagogical aspects that can be applied generically in different environments. The second part is related to tools that can be adopted by users such as graphical tools for engineering, mobile phone networks, and techniques to build robots, among others. Moreover, part two includes some chapters dedicated specifically to e-learning areas like engineering and architecture

Domain independent strategies in an affective tutoring system

There have been various attempts to develop an affective tutoring system (ATS) framework that considers and reacts to a student’s emotions while learning. However, there is a gap between current systems and the theory underlying human appraisal models. The current frameworks rely on a single appraisal and reaction phase. In contrast, the human appraisal process (Lazarus, 1991) involves two phases of appraisal and reaction (i.e. primary and secondary appraisal phases). This thesis proposes an affective tutoring (ATS) framework that introduces two phases of appraisal and reaction (i.e. primary and secondary appraisal and reaction phases). This proposed framework has been implemented and evaluated in a system to teach Data Structures. In addition, the system employs both domain-dependent and domain-independent strategies for coping with students’ affective states. This follows the emotion regulation model (Lazarus, 1991) that underpins the ATS framework which argues that individuals use both kinds of strategies in solving daily life problems. In comparison, current affective (ITS) frameworks concentrate on the use of domain-dependent strategies to cope with students’ affective states. The evaluation of the system provides some support for the idea that the ATS framework is useful both in improving students’ affective states (i.e. during and by the end of a learning session) and also their learning performance

Digital Assistants for Self-Regulated Learning: Towards a State-Of-The-Art Overview

We observe a continuous shift from face-to-face to hybrid or online education. Today, learners are exposed to a high level of autonomy and, at the same time, have less contact with peers and teachers. In this environment, the ability to self-regulate one’s learning is becoming more relevant to achieve positive learning results and academic success. However, the application of self-regulated learning is not trivial. A potential solution for this challenge comes in the form of digital assistants like chatbots or pedagogical agents that provide structure for the learners. Existing research on digital assistants for self-regulated learning is scattered across several fields. In this research-in-progress paper, we present preliminary results of a systematic literature review (SLR) study providing an overview of the state-of- the-art of digital assistants supporting SRL. Our results show that future research in this domain should focus on affect, behavioral, and context regulation and more longitudinal studies are required

Annual Report of Undergraduate Research Fellows from August 2015 to May 2016

Annual Report of Undergraduate Research Fellows from August 2015 to May 2016

Evaluating Human–Automation Etiquette Strategies to Mitigate User Frustration and Improve Learning in Affect-Aware Tutoring

Human–automation etiquette applies human–human etiquette conventions to human–computer interaction (HCI). The research described in this paper investigates how to mitigate user frustration and support student learning through changes in the style in which a computer tutor interacts with a learner. Frustration can significantly impact the quality of learning in tutoring. This study examined an approach to mitigate frustration through the use of different etiquette strategies to change the amount of imposition feedback placed on the learner. An experiment was conducted to explore how varying the interaction style of system feedback impacted aspects of the learning process. System feedback was varied through different etiquette strategies. Participants solved mathematics problems under different frustration conditions with feedback given in different etiquette styles. Changing etiquette strategies from one math problem to the next led to changes in motivation, confidence satisfaction, and performance. The most effective etiquette strategies changed depending on if the user was frustrated or not. This work aims to provide mechanisms to support the promotion of individualized learning in the context of high level math instruction by basing affect-aware adaptive tutoring system design on varying etiquette strategies