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Finding the optimal teaching strategy for an individual student is difficult even for an experienced teacher. Identifying and incorporating multiple optimal teaching strategies for different students in a class is even harder. This paper presents an Adaptive tutor for online Learning, AtoL, for Computer Science laboratories that identifies and applies the appropriate teaching strategies for students on an individual basis. The optimal strategy for a student is identified in two steps. First, a basic strategy for a student is identified using rules learned from a supervised learning system. Then the basic strategy is refined to better fit the student using models learned using an unsupervised learning system that takes into account the temporal nature of the problem solving process. The learning algorithms as well as the initial experimental results are presented.

PROLOGuses a depih-first search of an AND/OR graph to satisfy queries against its database. It searches sequentially through the clauses of a predicate whose head matches the query, trying to satisfy the goals in the clause body in a sequential left-to-right order. The ordering of clauses and goals is a major factor in the eficiency of a PROLOG program. We have developed a profiler for C-PROLOG that collects statistics including the failure rate of clauses and goals in a C-PROLOG program. These statistics are used by any of several reordering predicates capable of local or global reordering. The intent is to construct a reordered PROLOG program that outputs an equivalent set of answers, and is more efficient. Test results are promising.

The Stock Tracker is an adaptive recommendation system for trading stocks that automatically acquires content-based models of user preferences to tailor its buy and sell advice. The system incorporates an efficient algorithm that exploits the fixed structure of user models and relies on unobtrusive data-gathering techniques. In this paper, we describe our approach to personalized recommendation and its implementation in this domain. We also discuss experiments that evaluate the system's behavior on both human subjects and synthetic users. The results suggest that the Stock Tracker can rapidly adapt its advice to different types of users.