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. ..

Tutoring Bishop-Pawn Endgames: An Experiment in Using Knowledge-Based Chess as a Domain for Intelligent Tutoring

Most research in computer chess has focussed on creating an excellent chess player, with relatively little concern given to modelling how humans play chess. The research reported in this paper is aimed at investigating knowledgebased chess in the context of building a prototype chess tutor, UMRAO, which helps students learn how to play bishop-pawn endgames. In tutoring it is essential to take a knowledge-based approach, since students must learn how to manipulate strategic concepts, not how to carry out large-scale lookahead searches. UMRAO uses an extension of Michie's [1977] advice language to represent expert and novice chess plans. For any given endgame the system is able to compile the plans into a strategy graph, which elaborates strategies (both well-formed and ill-formed) that students might use as they solve the endgame problem. A strategy graph can be compiled "off-line", where real time performance is not important. Later, during tutoring, the strategy graph can be accessed ..

UMRAO: A Chess Endgame Tutor

Most research in computer chess has focussed on creating an excellent chess player, with relatively little concern given to modelling how humans play chess. The research reported in this paper is aimed at investigating knowledge-based chess in the context of building a prototype chess tutor, UMRAO, which helps students learn how to play bishop-pawn endgames. In tutoring it is essential to take a knowledge-based approach, since students must learn how to manipulate strategic concepts, not how to carry out minimax search. UMRAO uses an extension of Michic's advice language to represent expert and novice chess plans. For any given endgame the system is able to compile the plans into a strategy graph, which elaborates strategies (both well-formed and ill-formed) that students might use as they solve the endgame problem. Strategy graphs can be compiled "off-line " so that they can be used in real time tutoring. We show that the normally rigid "model tracing " tutoring paradigm can be used in a flexible way in this domain.

Supporting the Learning of Recursive Problem Solving

: This research is about the problem solving activities of novice programmers as they learn to create recursive LISP programs. Their problem solving not only includes the issue of mental models, but also how to use these mental models in conjunction with other problem solving techniques. In fact, at various stages of their learning, learners seem to use different packages of problem solving methods. Each of these packages we call a mental method. In this paper, we discuss the PETAL learning environment which assists learners in the use of three of these mental methods: the syntactic method, the analytic method and the analysis/synthesis method. PETAL externalizes each mental method through its own customized interface, called a programming environment tool (PET). Such externalization helps learners internalize concepts, and organize relevant knowledge and generally leads to improved learning. The PETAL System itself is presented. Next we discuss a study where one group of students used..

The Influence of Local DNA Sequence and DNA Repair Background on the Mutational Specificity of 1-Nitroso-8-Nitropyrene in Escherichia Coli: Inferences for Mutagenic Mechanisms

We have examined the mutational specificity of 1-nitroso-8-nitropyrene (1,8-NONP), an activated metabolite of the carcinogen 1,8-dinitropyrene, in the lacI gene of Escherichia coli strains which differ with respect to nucleotide excision repair (+/-ΔuvrB) and MucA/B-mediated error-prone translesion synthesis (+/-pKM101). Several different classes of mutation were recovered, of which frameshifts, base substitutions, and deletions were clearly induced by 1,8-NONP treatment. The high proportion of point mutations (>92%) which occurred at G·C sites correlates with the percentage of 1,8-NONP-DNA adducts which occur at the C(8) position of guanine. The most prominent frameshift mutations were -(G·C) events, which were induced by 1,8-NONP treatment in all strains, occurred preferentially in runs of guanine residues, and whose frequency increased markedly with the length of the reiterated sequence. Of the base substitution mutations G·C -> T·A transversions were induced to the greatest extent by 1,8-NONP. The distribution of the G·C -> T·A transversions was not influenced by the nature of flanking bases, nor was there a strand preference for these events. The presence of plasmid pKM101 specifically increased the frequency of G·C -> T·A transversions by a factor of 30-60. In contrast, the -(G·C) frameshift mutation frequency was increased only 2-4-fold in strains harboring pKM101 as compared to strains lacking this plasmid. There was, however, a marked influence of pKM101 on the strand specificity of frameshift mutation; a preference was observed for -G events on the transcribed strand. The ability of the bacteria to carry out nucleotide excision repair had a strong effect on the frequency of all classes of mutation but did not significantly influence either the overall distribution of mutational classes or the strand specificity of G·C -> T·A transversions and -(G·C) frameshifts. Deletion mutations were induced in the Δuvr, pKM101 strain. The endpoints of the majority of the deletion mutations were G·C rich and contained regions of considerable homology. The specificity of 1,8-NONP-induced mutation suggests that DNA containing 1,8-NONP adducts can be processed through different mutational pathways depending on the DNA sequence context of the adduct and the DNA repair background of the cell