Using electronic portfolios (e-portfolios) to assist learning is an important component of future educational models. A portfolio is a purposeful collection of student work that exhibits the student's efforts, progress and achievements in one or more areas. An e-portfolio contains a variety of information about a person's learning outcomes, such as artifacts, assertions from others, self-reflective information and presentation for different purposes. E-portfolios become sources of evidence for claims about prior conceptual knowledge or skills. This thesis investigates using the information contained in e-portfolios to initialize the learner model for an intelligent tutoring system. We examine the information model from the e-portfolio standardized specification and present a method that may assist users in initializing learner models using e-portfolios as evidence for claims about prior conceptual knowledge or skills. We developed the EP-LM system for testing how accurately a learner model can be built and how beneficial this approach can be for reflective and personalized learning. Experimental results are presented aiming at testing whether accurate learner models can be created through this approach and whether learners can gain benefits in reflective and personalized learning. Monitoring this process can also help ITS developers and experts identify how an initial learner model can automatically arise from an e-portfolio. Additionally, a well-structured learner model, generated by an intelligent tutoring system also can be attached to an e-portfolio for further use by the owner and others.

Programming is one of the core skills required by Computer Science undergraduates in tertiary institutions worldwide, whether for study itself, or to be used as a tool to explore other relevant areas. Unfortunately, programming can be incredibly difficult; this is for several reasons, including the youth, depth, and variety of the field, as well as the youth of the technology that frames it. It can be especially problematic for computing neophytes, with some students repeating programming courses not due to academic laziness, but due to an inability to grasp the core concepts. The research outlined by this thesis focuses on our proposed solution to this problem, a constraint-based intelligent tutoring system for teaching the Java programming language, named J-LATTE. J-LATTE (Java Language Acquisition Tile Tutoring Environment) is designed to solve this problem by providing a problem-solving environment for students to work through programming problems. This environment is unique in that it partitions interaction into a concept mode and a coding mode. Concept mode allows the student to form solutions using high-level Java concepts (in the form of tiles), and coding mode allows the student to enter Java code into these tiles to form a complete Java program. The student can, at any time, ask for feedback on a solution or partial solution that they have formed. A pilot study and two full evaluations were carried out to test the effectiveness of the system. The pilot study was run with an assignment given to a postgraduate Computer Science course, and because of the advanced knowledge level of the students, it was not designed to test teaching effectiveness, but instead was useful in determining usability issues and identifying any software errors. The full evaluations of the system were designed to give insight into the teaching effectiveness of J-LATTE, by comparing the results of using the system against a simulated classroom situation. Unfortunately, the participant base was small, for several reasons that are explained in the thesis. However, the results prove interesting otherwise and for the most part are positive towards the effectiveness of J-LATTE. The participants’ knowledge did improve while interacting with the system, and the subjective data collected shows that students like the interaction style and value the feedback obtained.

Intelligent Tutoring Systems (ITS) have been proven to be efficient in providing students assistance and assessing their performance when they do their homework. Many research projects have been done to analyze how students' knowledge grows and to predict their performance from within intelligent tutoring system. Most of them focus on using correctness of the previous question or the number of hints and attempts students need to predict their future performance, but ignore how they ask for hints and make attempts. In this research work, we build a Sequence of Actions (SOA) model taking advantage of the sequence of hints and attempts a student needed for previous question to predict students' performance. A two step modeling methodology is put forward in the work, which is a combination of Tabling method and the Logistic Regression. We used an ASSISTments dataset of 66 students answering a total of 34,973 problems generated from 5010 questions over the course of two years. The experimental results showed that the Sequence of Action model has reliable predictive accuracy than Knowledge Tracing and Assistance Model and its performance of prediction is improved after combining with Knowledge Tracing.

Predicting student performance is an important part of the student modeling task in Intelligent Tutoring System (ITS). The state-of-art model for predicting student performance - Bayesian Knowledge Tracing (KT) has many critical limitations. One specific limitation is that KT has no underlying mechanism for memory decay represented in the model, which means that no forgetting is happening in the learning process. In addition we notice that numerous modification to the KT model have been proposed and evaluated, however many of these are often based on a combination of intuition and experience in the domain, leading to models without performance improvement. Moreover, KT is computationally expensive, model fitting procedures can take hours or days to run on large datasets. The goal of this research work is to improve the accuracy of student performance prediction by incorporating the memory decay factor which the standard Bayesian Knowledge Tracing had ignored. We also propose a completely data driven and inexpensive approach to model improvement. This alternative allows for researchers to evaluate which aspects of a model are most likely to result in model performance improvements based purely on the dataset features that are computed from ITS system logs.

Student gaming behavior in intelligent tutoring systems (ITS) has been correlated with lower learning rates. The goal of this work is to identify such behavior, produce interventions to discourage this behavior, and by doing so hopefully improve the learning rate of students who would normally display gaming behavior. Detectors have been built to identify gaming behavior. Interventions have been designed to discourage the behavior and their evaluation is discussed.

Thesis (Ph. D.)--Open University. BLDSC no. DX174754.

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: intelligent tutoring systems; its; authoring tool; artificial intelligence. Includes bibliographical references (p. 81-82).

Intelligent Tutoring Systems (ITSs) have been shown to significantly improve students' learning in a variety of domains, including physics, mathematics, and thermodynamics. Tutorial dialogues is one of the strategies used by ITSs and has been empirically shown to significantly improve learning. This project investigates how different students interact with the tutorial dialogues in EER-Tutor, using both eye-gaze data and student-system interaction logs. EER-Tutor is a constraint-based ITS that teaches conceptual database design. In order to have a more comprehensive and accurate picture of a user's interactions with a learning environment, we need to know which interface features s/he visually inspected, what strategies s/he used and what cognitive efforts s/he made to complete tasks. Such knowledge allows intelligent systems to be proactive, rather than reactive, to users' actions. Eye-movement tracking is therefore a potential source of real-time adaptation in a learning environment. Our findings indicate that advanced students are selective of the interface areas they visually focus on whereas novices waste time by paying attention to interface areas that are inappropriate for the task at hand. Novices are also unaware that they need help with understanding the domain concepts discussed in the tutorial dialogues. We were able to accurately classify students, for example as novice or advanced students, using only eye-gaze or EER-Tutor log data as well as a combination of EER-Tutor and eye-gaze features. The cost of eye-tracking is justified as classifiers using only eye-gaze features sometimes perform as well as those utilising both EER-Tutor and eye-gaze data and outperform classifiers using only EER-Tutor data. The ability to classify students will therefore allow an ITS to intervene when needed and better guide students' learning if it detects sub-optimal behaviour.

As one of the central problems in the area of Intelligent Tutoring Systems (ITSs), student modelling has been widely used to assist in systems’ decision making and students’ learning. On the one hand, by reasoning about students’ knowledge in the instructional domain, a system is able to adapt its pedagogical actions in order to provide a customized learning environment. These actions may include individualized problem-selection, tailored instructions and feedback, as well as updating the presentation of student models. On the other hand, students can reflect on their own learning progress by viewing individual Open Student Models (OSMs) and enhance their meta-cognitive skills by learning from the system’s estimation of their knowledge levels. It is believed that making the information in the student model available to students can raise students’ awareness of their strengths and weaknesses in the corresponding domain and hence allow them to develop a more effective and efficient way of learning. An OSM has been developed in EER-Tutor. EER-Tutor is a web-enhanced ITS that supports university students in learning conceptual database modelling. Students design Enhanced Entity-Relationship (EER) diagrams and receive different level of feedback in a problem-solving environment. The pedagogical decisions on feedback generation and problem selection are made according to student models. Previously, student models in EER-Tutor are presented to students on request as skill meters. Skill meters have been proved useful in helping students to improve their meta-cognitive skills. However, as the simplest presentation of a student model, skill meters contain very limited information. Some studies show that an OSM with multiple views is more effective since it supports individual preferences and different educational purposes. The focus of our work is to develop a variety of presentations for the OSM in EER-Tutor. For this purposes, we have modified the system to include not only skill meters but also other presentation styles. An evaluation study has been performed after the development. Both subjective and objective results have been collected. This thesis presents the extended EER-Tutor, followed by the analysis of the evaluation study.

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