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

在领域专业知识、任务模型(问题解决状态)和教学专业知识(预测学生错误)的背景下对学生输入进行诊断是在智能教学系统中提供有效的支架和补救反馈的核心。一个主要的见解是，学生的错误很少是随机的，而是由于正确执行错误的程序造成的。因此，有效的教学依赖于深入的认知分析来诊断并随后修复这些不正确的部分。该项目旨在开发逻辑编程和元级推理框架下的认知诊断方法。它将采用并进一步发展算法调试技术，以识别不正确(基于PROLOG的)程序中的学生错误；它将促进归纳逻辑编程的进步，从学生行为和背景知识的实例中综合PROLOG程序，并将提出自动代码扰动，以系统地注入错误以纠正程序代码。这些技术将被用来构建软件，以支持教师提高他们的诊断技能，并支持学生学习基本的算术和符号微分。基于逻辑的方法不仅将补充现有的认知诊断方法；它还将揭示它们的性质，增加比较分析，从而有助于采用更系统的方法深入分析智能教学系统中的学生输入。

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.