Supporting Tutorial Feedback to Student Help Requests and Errors in Symbolic Differentiation
The provision of intelligent, user-adaptive, and effective feedback requires human tutors to exploit their expert knowledge about the domain of instruction, and to diagnose students’ actions through a potentially huge space of possible solutions and misconceptions. Designers and developers of intelligent tutoring systems strive to simulate good human tutors, and to replicate their reasoning and diagnosis capabilities as well as their pedagogical expertise. This is a huge undertaking because it requires an adequate acquisition, formalisation, and operationalisation of material that supports reasoning, diagnosis, and natural interaction with the learner. In this paper, we describe SLOPERT, a glass-box reasoner and diagnoser for symbolic differentiation. Its expert task model, which is enriched with buggy rules, has been informed by an analysis of human-human tutorial dialogues. SLOPERT can provide natural step-by-step solutions for any given problem as well as diagnosis support for typical student errors. SLOPERT’s capabilities thus support the generation of natural problem-solving hints and scaffolding help.
KeywordsTask Model Intelligent Tutoring System Solution Graph Pedagogical Expertise Negative Exponent
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