Intelligent Tutoring Systems (ITSs) are problem-solving environments that provide individualised instruction and are able to adapt to the abilities and needs of each individual student in order to maximise effective learning. They provide feedback on students' actions, but a problem arises when students do not always understand the feedback they receive. Therefore, it would be beneficial for students to be able to ask for additional clarifications at any time, and to receive feedback customised to their individual differences. This research focuses on providing an additional help channel in ITSs where students are able to ask free-form questions, as well as accounting for the students' psychometric measure of spatial ability. We describe ERM-Tutor, the test-bed ITS chosen for implementing our research framework. ERM-Tutor is a constraint-based tutoring system for teaching logical database design. Students practise this procedural task in ERM-Tutor by solving each step and receiving feedback on their solutions. We also present our approach to addressing the meta-cognitive skill of question-asking in ERM-Tutor. We added a question-asking module that enables students to ask free-form questions and receive the most appropriate answers stored in the system. In addition, we investigated the potential of tailoring the feedback messages towards the learners' psychometric measure of spatial ability. We modified ERM-Tutor to provide not only textual feedback messages, but also multimedia messages, containing a combination of text and pictures. We performed a series of evaluation studies in order to evaluate the effectiveness of our proposed solutions. All our studies were conducted with tertiary students enrolled in an introductory database course. The students had attended lectures on logical database design and were asked to use ERM-Tutor to develop and practise their mapping skills. The results show an overall improvement in performance and learning gain for all students using ERM-Tutor. Interactions with the question-asking module show that most questions asked by students were task-focused, directly requesting help on specific errors. The results confirm the need for addressing students' questions inside an ITS environment. Furthermore, there were no conclusive results to support a difference in effectiveness of the textual versus multimedia feedback presentation modes with respect to the students' spatial ability. However, we observed a number of trends indicating that matching the instruction presentation mode towards the students spatial ability influences their perception of the system and motivation to use it, more than their learning gain. Our results show promising indications for further explorations. We present our approaches, full analyses of the collected data from our evaluation studies, as well as our research contributions to the ITSs field. We also portray a number of future directions that will contribute towards maximising the effectiveness of learning in ITSs.

Empirical research provides evidence of strong interaction between cognitive and affective processes in the human mind. Education research proposes a model of constructive learning that relates cognitive and affective processes in an evolving cycle of affective states. Intelligent Tutoring Systems (ITSs) are capable of providing comprehensive cognitive support. Affective support in ITSs, however, is lagging behind; the in-depth exploration of cognitive and affective processes in ITSs is yet to be seen. Our research focuses on the integration of affective support in an ITS enhanced with an affective pedagogical agent. In our work we adopt the dimensional (versus categorical) view of emotions for modelling affective states of the agent and the ITSs users. In two stages we develop and evaluate an affective pedagogical agent. The affective response of the first agent version is based on the appraisal of the interaction state; this agent's affective response is displayed as affective facial expressions. The pilot study at the end of the first stage of the project confirms the viability of our approach which combines the dimensional view of emotions with the appraisal of interaction state. In the second stage of the project we develop a facial feature tracking application for real-time emotion recognition in a video-stream. Affective awareness of the second version of the agent is based on the output from the facial feature tracking application and the appraisal of the interaction state. This agent's response takes the form of affectoriented messages designed to interrupt the state of negative flow. The evaluation of the affect-aware agent against an unemotional affect-unaware agent provides positive results, thus confirming the superiority of the affect-aware agent. Although the uptake of the agent was not unanimous, the agent established and maintained good rapport with the users in a role of a caring tutor. The results of the pilot study and the final evaluation validate our choices in the design of affective interaction. In both experiments, the participants appreciated the addition of audible feedback messages, describing it as an enhancement which helped them save time and maintain their focus. Finally, we offer directions for future research on affective support which can be conducted within the framework developed in the course of this project.

Intelligent tutoring systems and computer-supported collaborative environments have been designed to enhance human learning in various domains. While a number of solid techniques have been developed in the Artificial Intelligence in Education (AIED) field to foster human learning in fundamental science domains, there is still a lack of evidence about how to support learning in so-called ill-defined domains that are characterized by the absence of formal domain theories, uncertainty about best solution strategies and teaching practices, and learners' answers represented through text and argumentation. This dissertation investigates how to support students' learning in the ill-defined domain of professional ethics through a computer-based learning system. More specifically, it examines how to support students in the analysis of case studies, which is a common pedagogical practice in the ethics domain. This dissertation describes our design considerations and a resulting system called Umka. In Umka learners analyze case studies individually and collaboratively that pose some ethical or professional dilemmas. Umka provides various types of support to learners in the analysis task. In the individual analysis it provides various kinds of feedback to arguments of learners based on predefined system knowledge. In the collaborative analysis Umka fosters learners' interactions and self-reflection through system suggestions and a specifically designed visualization. The system suggestions offer learners the chance to consider certain helpful arguments of their peers, or to interact with certain helpful peers. The visualization highlights similarities and differences between the learners' positions, and illustrates the learners' level of acceptance of each other's positions. This dissertation reports on a series of experiments in which we evaluated the effectiveness of Umka's support features, and suggests several research contributions. Through this work, it is shown that despite the ill-definedness of the ethics domain, and the consequent complications of text processing and domain modelling, it is possible to build effective tutoring systems for supporting students' learning in this domain. Moreover, the techniques developed through this research for the ethics domain can be readily expanded to other ill-defined domains, where argument, qualitative analysis, metacognition and interaction over case studies are key pedagogical practices.

Emotions play a significant role in many human mental activities, including decision-making, motivation, and cognition. Various intelligent and expert systems can be empowered with emotionally intelligent capabilities, especially systems that interact with humans and mimic human behaviour. However, most current methods in affect recognition studies use intrusive, lab-based, and expensive tools which are unsuitable for real-world situations. Inspired by studies on keystrokes dynamics, this thesis investigates the effectiveness of diagnosing users’ affect through their typing behaviour in an educational context. To collect users’ typing patterns, a field study was conducted in which subjects used a dialogue-based tutoring system built by the researcher. Eighteen dialogue features associated with subjective and objective ratings for users’ emotions were collected. Several classification techniques were assessed in diagnosing users’ affect, including discrimination analysis, Bayesian analysis, decision trees, and neural networks. An artificial neural network approach was ultimately chosen as it yielded the highest accuracy compared with the other methods. To lower the error rate, a hierarchical classification was implemented to first classify user emotions based on their valence (positive or negative) and then perform a finer classification step to determining which emotions the user experienced (delighted, neutral, confused, bored, and frustrated). The hierarchical classifier was successfully able to diagnose users' emotional valence, while it was moderately able to classify users’ emotional states. The overall accuracy obtained from the hierarchical classifier significantly outperformed previous dialogue-based approaches and in line with some affective computing methods.

Thesis (Ph. D.)--University of Canterbury, 2007. / Typescript (photocopy). Includes bibliographical references. Also available via the World Wide Web.

Thesis (M.S.)--State University of New York at Binghamton, Department of Computer Science, Thomas J. Watson School of Engineering and Applied Science, 2007. / Includes bibliographical references.

Thesis (M. Sc.)--University of Canterbury, 2008. / Typescript (photocopy). "January 2008." Includes bibliographical references (p. [121]-128). Also available via the World Wide Web.

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: Intelligent Tutoring Systems; Model Tracing; Machine Learning; Artificial Intelligence; Programming by Demonstration. Includes bibliographical references.

Thesis (M. Sc.)--University of Canterbury, 2009. / Typescript (photocopy). "September 2009." Includes bibliographical references (leaves 89-96). Also available via the World Wide Web.

Thesis (Ph. D.)--University of Canterbury, 2006. / Typescript (photocopy). Includes bibliographical references (p. 246-255). Also available via the World Wide Web.