Deep cognitive diagnosis in intelligent tutoring systems in the framework of logic programming and meta-level reasoning

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The diagnosis of student input in the context of domain expertise, task model (problem solving state), and pedagogical expertise (anticipating student errors) is central for the provision of effective scaffolding and remedial feedback in intelligent tutoring systems. A main insight is that student errors are seldom random, but result from correctly executing an erroneous procedure. Effective teaching, thus, depends on deep cognitive analyses to diagnose and subsequently repair those incorrect parts. This project aims at developing methods for cognitive diagnosis in the framework of logic programming and meta-level reasoning. It will adapt and further develop the technique of algorithmic debugging to identify student error in incorrect (Prolog-based) procedures; it will contribute to advances in inductive logic programming to synthesise Prolog programs from example student behaviour and background knowledge, and it will propose automatic code perturbation to systematically inict bugs to correct program code. The techniques will be used to build software to support teachers to improve their diagnostic skills, and students to learn basic arithmetic and symbolic differentiation. The logic-based approach will not only complement existing methods for cognitive diagnosis; it will also shed new light on their nature, add to a comparative analysis, and thus contribute toward a more systematic approach for the deep analysis of student input in intelligent tutoring systems.