Gordon I. McCalla

Student Modelling: The Key to individualized Knowledge-Based Instruction

1. Background.- 1. The State of Student Modelling.- 2. Artificial Intelligence Techniques for Student Modelling.- 2. Granularity-Based Reasoning and Belief Revision in Student Models.- 3. Student Modelling Through Qualitative Reasoning.- 4. Modeling the Student in Sherlock II.- 5. Using Machine Learning to Advise a Student Model.- 6. Building a Student Model for an Intelligent Tutoring System.- 3. Human Cognition and Student Modelling.- 7. Constraint-Based Student Modeling.- 8. Strengthening the Novice-Expert Shift Using the Self-Explanation Effect.- 9. Diagnosing and Evaluating the Acquisition Process of Problem Solving Schemata in the Domain of Functional Programming.- 4. Formalizing Student Modelling.- 10. Modelling a Students Inconsistent Beliefs and Attention.- 11. A Formal Approach To ILEs.- 12. Formal Approaches to Student Modelling.- 5. Epilogue.- 13. Re-Writing Cartesian Student Models.

Social Capital in Virtual Learning Communities and Distributed Communities of Practice

Abstract. Social capital has recently emerged as an important interdisciplinary research area. It is frequently used as a framework for understanding various social issues in temporal communities, neighbourhoods and groups. In particular, researchers in the social sciences and the humanities have used social capital to understand trust, shared understanding, reciprocal relationships, social network structures, common norms and cooperation, and the roles these entities play in various aspects of temporal communities. Despite proliferation of research in this area, little work has been done to extend this effort to technology-driven learning communities (also known as virtual learning communities). This paper surveys key interdisciplinary research areas in social capital. It also explores how the notions of social capital and trust can be extended to virtual communities, including virtual learning communities and distributed communities of practice. Research issues surrounding social capital and trust as they relate to technology-driven learning communities are identified.

Using planning techniques in intelligent tutoring systems

Abstract This paper proposes an architecture for building better Computer-Assisted Instruction (CAI) programs by applying and extending Artificial Intelligence (AI) techniques which were developed for planning and controlling the actions of robots. A detailed example shows how programs built according to this architecture are able to plan global teaching strategies using local information. Since the students behavior can never be accurately predicted, the pre-planned teaching strategies may be foiled by sudden surprises and obstacles. In such cases, the planning component of the program is dynamically reinvoked to revise the unsuccessful strategy, often by recognizing student misconceptions and planning a means to correct them. This plan-based teaching strategy scheme makes use of global course knowledge in a flexible way that avoids the rigidity of earlier CAI systems. It also allows larger courses to be built than has been possible in most AI-based “intelligent tutoring systems” (ITSs), which seldom address the problem of global teaching strategies.

Multi-Agent Multi-User Modeling in I-Help

This paper describesthe user modeling approach applied in I-Help, a distributed multi-agent based collaborative environment for peer help. There is a multitude of user modeling information in I-Help, developed by the various software agents populating the environment. These ‘user model fragments’ have been created in a variety of specific contexts to help achieve various goals. They are inherently inconsistent with one another and reflect not only characteristics of the users, but also certain social relationships among them. The paper explores some of the implications of multi-agent user modeling in distributed environments.

The Intelligent Helpdesk: Supporting Peer-Help in a University Course

Universities, experiencing growths in student enrollment and reductions in operating budgets, are faced with the problem of providing adequate help resources for students. Help resources are needed at an institution-wide and also at a course-specific level, due to the limited time of instructors to provide help and answer questions. The Intelligent IntraNet Peer Help Desk provides an integration and application of previously developed ARIES Lab tools for peer help to university teaching. One of its components, CPR, provides a subject-oriented discussion forum and FAQ-list providing students with electronic help. Another component, PHelpS, suggests an appropriate peer to provide human help. In both cases it is peer help, since the help originates from students themselves. The selection of the appropriate help resource (electronic or human) is based on modelling student knowledge and on a conceptual model of the subject material.

What is knowledge representation

In this chapter, we overview eight major approaches to knowledge representation: logical representations, semantic networks, procedural representations, logic programming formalisms, frame-based representations, production system architectures, and knowledge representation languages. The fundamentals of each approach are described, and then elaborated upon through illustrative examples chosen from actual systems which employ the approach. Where appropriate, comparisons among the various schemes are drawn. The chapter concludes with a set of general principles which have grown out of the different approaches.

User Modelling in I-Help: What, Why, When and How

This paper describes user modelling in I-Help, a system to facilitate communication amongst learners. There are two I-Help components: Private and Public Discussions. In the Private Discussions learners take part in a one-on-one interaction with a partner (possibly a peer). The Public Discussions are open - everyone in the group has access to all discussion forums relevant to that group. The Public Discussions are most suited to discussion of issues where there might be a variety of valid viewpoints, or different solutions to a problem. It is also useful for straightforward questions and answers that have wide-spread applicability. The Private Discussions are better suited for more intensive interactions involving peer tutoring or in-depth discussions. Because there is only one helper in such situations, I-Help requires a method of selecting an appropriate helper for an individual. We describe the user modelling that takes place in each part of I-Help, in particular to effect this matchmaking for Private Discussions. This modelling takes advantage of a distributed multi-agent architecture, allowing currently relevant user model fragments in various locations to be integrated and computed at the time they are required.

Revising deductive knowledge and stereotypical knowledge in a student model

A user/student model must be revised when new information about the user/student is obtained. But a sophisticated user/student model is a complex structure that contains different types of knowledge. Different techniques may be needed for revising different types of knowledge. This paper presents a student model maintenance system (SMMS) which deals with revision of two important types of knowledge in student models: deductive knowledge and stereotypical knowledge. In the SMMS, deductive knowledge is represented by justified beliefs. Its revision is accomplished by a combination of techniques involving reason maintenance and formal diagnosis. Stereotypical knowledge is represented in the Default Package Network (DPN). The DPN is a knowledge partitioning hierarchy in which each node contains concepts in a sub-domain. Revision of stereotypical knowledge is realized by propagating new information through the DPN to change default packages (stereotypes) of the nodes in the DPN. A revision of deductive knowledge may trigger a revision of stereotypical knowledge, which results in a desirable student model in which the two types of knowledge exist harmoniously.

The Knowledge Frontier

In this chapter, we overview eight major approaches to knowledge representation: logical representations , semantic networks , procedural representations, logic programming formalisms. frame -based representations, production system architectures, and knowledge representation languages. The fundamentals of each approach are described, and then elaborated upon through illustrative examples chosen from actual systems which employ the approach. Where appropriate, comparisons among the various schemes are drawn. The chapter concludes with a set of general principles which have grown out of the dlfferen t approaches. Based on the paper, Approaches to Knowledge Representation, G. McCalla and N. Cercone, appearing in COMPUTER, Volume 16, Number 10, October, 1983. 2 The Knowledge Frontier:

Granularity-Based Reasoning and Belief Revision in Student Models

In this chapter we discuss two important research topics surrounding student modelling: 1) how to represent knowledge about a student at various grain sizes and reason with this knowledge to enhance the capabilities of an intelligent tutoring system, and 2) how to maintain a consistent view of a student’s knowledge as the system-student interaction evolves. The ability to represent and reason about knowledge at various levels of detail is important for robust tutoring. A tutor can benefit from incorporating an explicit notion of granularity into its representation and can take advantage of granularity-based representations in reasoning about student behaviour. As the student’s understanding of concepts evolves and changes, the student model must track these changes. This leads to a difficult student model maintenance problem. Both of these topics are full of interesting subtleties and deep issues requiring years of research to be resolved (if they ever are), but a start has been made. In this chapter we characterize the main requirements for each topic, discuss some of our work that tackles these topics, and, finally, indicate important areas for future research.