Association rule mining using genetic programming to provide feedback to instructors from multiple‐choice quiz data

TLDR

The study proposes using association rule mining to enhance multiple‑choice quizzes and course design. The authors preprocess quiz data into matrices, apply a grammar‑guided genetic programming algorithm to mine association rules, evaluate them with objective and subjective measures, and test the approach on real student data from a Moodle AI course, comparing student performance across control and experimental groups. The mined rules provided actionable feedback that led to quiz and course updates, resulting in improved student performance compared to the control group.

Abstract

Abstract This paper proposes the application of association rule mining to improve quizzes and courses. First, the paper shows how to preprocess quiz data and how to create several data matrices for use in the process of knowledge discovery. Next, the proposed algorithm that uses grammar‐guided genetic programming is described and compared with both classical and recent soft‐computing association rule mining algorithms. Then, different objective and subjective rule evaluation measures are used to select the most interesting and useful rules. Experiments have been carried out by using real data of university students enrolled on an artificial intelligence practice Moodle's course on the CLIPS programming language. Some examples of these rules are shown, together with the feedback that they provide to instructors making decisions about how to improve quizzes and courses. Finally, starting with the information provided by the rules, the CLIPS quiz and course have been updated. These innovations have been evaluated by comparing the performance achieved by students before and after applying the changes using one control group and two different experimental groups.