Pramuditha Suraweera

Constraint-Based Tutors: A Success Story

Student modeling (SM) is recognized as one of the central problems in the area of Intelligent Tutoring Systems. Numerous SM approaches have been proposed and used with more or less success. Constraint-based modeling is a new approach, which has been used successfully in three tutors developed in our group. The approach is extremely efficient, and it overcomes many problems that other student modelling approaches suffer from. We present the advantages of CBM over other similar approaches, describe three constraint-based tutors and present our future research plans.

Intelligent Tutors for All: The Constraint-Based Approach

This paper presents a new type of intelligent tutoring systems, called constraint-based tutors. The system have been thoroughly evaluated and proven to achieve significant learning gains.

KERMIT: A Constraint-Based Tutor for Database Modeling

KERMIT is an intelligent tutoring system that teaches conceptual database design using the Entity-Relationship data model. Database design is an open-ended task: although there is an outcome defined in abstract terms, there is no procedure to use to find that outcome. So far, constraint based modelling has been used in a tutor that teaches a database language (SQL-Tutor) and a system that teaches punctuation and capitalisation rules (CAPIT). Both systems have proved to be extremely effective in evaluations performed in real classrooms. In this paper, we present experiences in using CBM in an open-ended domain. We describe systems architecture and functionality. KERMIT has also been evaluated in the context of genuine teaching activities. We present the results of an evaluation study with students taking a database course, which show that KERMIT is an effective system. The students enjoyed the systems adaptability and found it a valuable asset to their learning.

Evaluating an Animated Pedagogical Agent

The paper presents SmartEgg, an animated pedagogical agent developed for SQLT-Web, an intelligent SQL tutor on the Web. It has been shown in previous studies that pedagogical agents have a significant motivational impact on students. Our hypothesis was that even a very simple and constrained agent, like SmartEgg, would enhance learning. We report on an evaluation study that confirmed our hypothesis.

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.

The Role of Domain Ontology in Knowledge Acquisition for ITSs

There have been several attempts to automate knowledge acquisition for ITSs that teach procedural tasks. The goal of our project is to automate the acquisition of domain models for constraint-based tutors for both procedural and non-procedural tasks. We propose a three-phase approach: building a domain ontology, acquiring syntactic constraints directly from the ontology, and engaging the author in a dialog, in order to induce semantic constraints using machine learning techniques. An ontology is arguably easier to create than the domain model. Our hypothesis is that the domain ontology is also useful for reflecting on the domain, so would be of great importance for building constraints manually. This paper reports on an experiment performed in order to test this hypothesis. The results show that constraints sets built using a domain ontology are superior, and the authors who developed the ontology before constraints acknowledge the usefulness of an ontology in the knowledge acquisition process.

Widening the Knowledge Acquisition Bottleneck for Constraint-based Tutors

Intelligent Tutoring Systems (ITS) are effective tools for education. However, developing them is a labour-intensive and time-consuming process. A major share of the effort is devoted to acquiring the domain knowledge that underlies the systems intelligence. The goal of this research is to reduce this knowledge acquisition bottleneck and better enable domain experts with no programming and knowledge engineering expertise to build the domain models required for ITS. In pursuit of this goal we developed an authoring system capable of producing a domain model with the assistance of a domain expert. Unlike previous authoring systems, the Constraint Authoring System (CAS) has the ability to acquire knowledge for both procedural and non-procedural tasks. CAS was developed to generate the knowledge required for constraint-based tutoring systems, reducing both effort and the amount of knowledge engineering and programming expertise required: the domain expert only has to model a domain ontology and provide example problems (with solutions). We developed novel machine learning algorithms to reason about this information and thus generate a domain model. A series of evaluation studies have produced promising results. The initial evaluation revealed that the task of composing the domain ontology aids the manual composition of a domain model. The second study showed that CAS is effective in generating constraints for non procedural database modelling and the procedural data normalisation. The final study demonstrated that CAS is also effective in generating constraints when assisted by only novice ITS authors; under these conditions it still produced constraint sets that were over 90% complete.

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.

Teaching Database Design with Constraint-Based Tutors.

Design tasks are difficult to teach, due to large, unstructured solution spaces, underspecified problems, non-existent problem solving algorithms and stopping criteria. In this paper, we comment on our approach to develop KERMIT, a constraint-based tutor that taught database design. In later work, we re-implemented KERMIT as EER-Tutor, and extended its instructional domain. Several evaluation studies performed with KERMIT and EER-Tutor show that they are effective Intelligent Tutoring Systems (ITSs). We also comment on various extensions made to EER-Tutor over the years. There are several contributions of our research, such as developing effective problem-solving support for conceptual database design in terms of interface design. Our database design tutors deal with large solution spaces efficiently by specifying constraints that capture equivalent solution states, and using ideal solutions to capture the semantics of the problem. Instead of requiring a problem solver, the ITS checks whether the student’s database schema is correct by matching it to constraints and the ideal solution. Another contribution of our work is in guidelines for developing effective feedback to the student.