Jay Holland

Authoring constraint-based tutors in ASPIRE

This paper presents a project the goal of which is to develop ASPIRE, a complete authoring and deployment environment for constraint-based intelligent tutoring systems (ITSs). ASPIRE is based on our previous work on constraint-based tutors and WETAS, the tutoring shell. ASPIRE consists of the authoring server (ASPIRE-Author), which enables domain experts to easily develop new constraint-base tutors, and a tutoring server (ASPIRE-Tutor), which deploys the developed systems. Preliminary evaluation shows that ASPIRE is successful in producing domain models, but more thorough evaluation is planned.

Do your eyes give it away? using eye tracking data to understand students' attitudes towards open student model representations

There is sufficient evidence to show that allowing students to see their own student model is an effective learning and metacognitive strategy. Different tutors have different representations of these open student models, all varying in complexity and detail. EER-Tutor has a number of open student model representations available to the student at any particular time. These include skill meters, kiviat graphs, tag clouds, concept hierarchies, concept lists, and treemaps. Finding out which representation best helps the student at their level of expertise is a difficult task. Do they really understand the representation they are looking at? This paper looks at a novel way of using eye gaze tracking data to see if such data provides us with any clues as to how students use these representations and if they understand them.

Thermo-Tutor: An Intelligent Tutoring System for thermodynamics

We present the design and an evaluation of Thermo-Tutor, an Intelligent Tutoring System (ITS) that teaches thermodynamic cycles in closed systems. Thermo-Tutor provides opportunities for students to practice their skills by solving problems. When a student submits a solution, Thermo-Tutor analyzes it and provides appropriate feedback. We discuss the support for problem solving, and the student model the ITS maintains. An initial evaluation of Thermo-Tutor was performed at the University of Canterbury. The findings show that the ITS supports student learning effectively.

The effects of domain and collaboration feedback on learning in a collaborative intelligent tutoring system

We present initial results from a study comparing the effects of domain and collaboration feedback on learning within COLLECT-UML, a collaborative problem-solving ITS. Using COLLECT-UML, two students in separate physical locations (a collaborative pair) construct UML class diagrams to solve problems together. In the default version, COLLECT-UML provides both domain and collaboration feedback. In this study however, collaborative pairs were randomly assigned to one of four modes (treatment conditions) which varied the feedback presented by the system: no feedback (NF), domain feedback only (DF), collaborative feedback only (CF), and both domain and collaborative feedback (DCF). All conditions improved significantly between pre- and post-test, showing that practicing within COLLECT-UML helps learning. At a surface level, collaborative pairs in all modes had similar amounts of collaboration. The DCF mode had significantly higher learning gains than the other modes, indicating the value of receiving both domain and collaborative feedback. Surprisingly, the CF mode had the lowest learning gains (lower than NF), suggesting that, in this case, good collaboration without domain feedback could have simply reinforced erroneous domain knowledge.

A Virtual Reality Environment for Rehabilitation of Prospective Memory in Stroke Patients

Prospective Memory (PM), or remembering to perform actions in the future, is of crucial importance for everyday life. This kind of memory is often impaired in stroke survivors and can interfere with independent living. We have developed a computer-based treatment which uses visual imagery to teach participants how to remember time- and event-based prospective memory tasks better. After the treatment, participants practiced their PM skills using videos first, and later in a Virtual Reality (VR) environment. The VR environment uses Constraint-Based Modeling (CBM) to track the user actions and provide individual feedback. We report on a study with 15 stroke survivors, which shows that our treatment is highly effective.

The Effect of Interaction Granularity on Learning with a Data Normalization Tutor

Intelligent Tutoring Systems (ITSs) have proven their effectiveness in many instructional domains, ranging in the complexity of domain theories and tasks students are to perform. The typical effect sizes achieved by ITSs are around 1SD, which are still low in comparison to the effectiveness of expert human tutors. Recently there have been several analyses done in order to identify the factors that contribute to success of human tutors, and to replicate it in ITSs. VanLehn [6] proposes that the crucial factor is the granularity of interaction: the lower the level of discussions between the (human or artificial) tutor and the student, the higher the effectiveness. We investigated the effect of interaction granularity in the context of NORMIT, a constraint-based tutor that teaches data normalization. Our study compared the standard version of NORMIT, which provided hints in response to errors, to a version which used adaptive tutorial dialogues instead. The results show that the interaction granularity hypothesis holds in our experimental situation, and that the effect size achieved is consistent with other reported studies of a similar nature.

Exploring two strategies for teaching procedures

Due to high cost and complexity of Intelligent Tutoring Systems (ITS), current systems typically implement a single teaching strategy, and comparative evaluations of alternative strategies are rare. We explore two competing strategies for teaching database normalization. Each data normalization problem consists of a number of tasks, some of which are optional. The first strategy enforces the procedural nature of the data normalization by providing an interface that requires the student to complete the current task (i.e. a part of the problem) before attempting the next one. The alternative strategy provides more freedom to the student, allowing him/her to select the task to work on. We performed an evaluation study which showed that the former, more restrictive strategy results in better problem-solving skills.