| dc.description.abstract | LCS (Learning Companion System), a type of computer assisted learning systems, simulates one or more artificial human-like agents to provide users with a social learning environment. The non-authoritative or non-expert agents of LCSs, called learning companions, can act as the competitor, tutor, tutee, or collaborator of the user. Two approaches exist for implementing the knowledge and learning ability of a learning companion: machine learning and simulation. The advantages of the simulation approach are that implementing multiple learning companions, and tailoring the learning companion to the requirements of learning theory and individual users is more convenient. Furthermore, quantitative simulation is suitable for supporting refinement of learning skills. However, applying quantitative simulation to modeling a learning companion as the tutee of a user remains little researched. On the other hand, some researchers have applied quantitative simulation to modeling a learning companion as a user’s competitor in their LCSs. However, these systems only provide a learning companion with an overall proficiency. The learning companion does not have special proficiencies in different skills.
This thesis aims to propose a quantitative simulation approach to modeling multiple learning companions in the domain of problem solving, particularly, modeling learning companions as tutees or competitors of users, and with specialized skill proficiencies. Problem solving is a domain, in which learning companions require implementation of skill refinement. The approach, called QSLCM (Quantitative-Simulation based Learning Companion Modeling), involves architecture, steps, problem solving modeling, and simulation of the roles of competitor, tutee, or tutor. Several heuristic rules for the approach are also proposed herein. QSLCM architecture is a generalization of the typical Intelligent Tutoring System (ITS) architecture. This architectural view provides a means of comparing the basic differences between ITSs and LCSs. QSLCM enables modeling learning companions by applying two techniques of user modeling, the overlay model and the bug model. User modeling aims to base the construction of the user’s model on observation of the user’s behavior. QSLCM is a reverse process of user modeling; namely, constructing a learning companion pattern to simulate the behavior of a learning companion. Applying the overlay model, the learning companion’s solutions to problems are a subset of the solutions of an expert. Meanwhile, applying the bug model allows the learning companion to make mistakes. In QSLCM, probabilities represent the uncertain skill proficiency behavior of the learning companion. Meanwhile, QSLCM supports modeling different kinds of learning companions by setting different learning companion patterns. Furthermore, QSLCM makes it convenient to change the behavior of learning companions simply by adjusting the learning companion pattern.
Two applications are developed by applying QSLCM to simulate learning companions. The first application, called DwestAgent, presents that QSLCM can support modeling multiple learning companions with different roles and different skill levels. DwestAgent has three learning companions, with skill levels of an expert, average student, and novice, respectively. Each learning companion can act as competitor, peer tutor, or tutee. The second application, called RTS, supports the learning activity of reciprocal tutoring. RTS provides users with a learning companion, which can act as tutee and peer tutor. | en_US |