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Day 2 Poster

Enhancing Moodle with Intelligent Quizzes for Semi-Automatic Monitoring of Student Preparation

This proposal aims to integrate a prototype web application with a UI similar to Google Forms into a new Moodle plugin, enabling teachers and educators to implement intelligent quizzes. The quiz questions can be interconnected based on user responses, and the system can automatically determine the degree of preparation of the students.

For calculating the preparation, the system introduces the concept of Topic Knowledge Score (TKS), an intelligent feature that automatically estimates the knowledge levels of the students. This estimation helps identify topics that require further improvement and suggests areas that may need additional study. To calculate the TKS, the system constructs a directed graph representing topic knowledge. Each node in the graph represents a topic, and the edges represent the dependencies or prerequisites between topics. For example, if topic A is a prerequisite for topic B, an edge is created between nodes A and B. Additionally, the system automatically assigns a degree of preparation to each node based on the answers provided in the quizzes.

The system employs Answer Set Programming, a declarative programming paradigm used in non-monotonic reasoning, logic programming, and Symbolic Artificial Intelligence. By utilizing this approach, the system can infer the knowledge level of students based on the constructed graph.

The ultimate objective of this system is to assist educators in conducting quizzes, particularly in educational institutions. It enables teachers to semi-automatically assess the preparedness of the students in relation to the required topics. The reports generated by the system serve as valuable teaching support tools for educators.

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