Misconceptions et inginious

Details

Supervisors

Bonaventure, Olivier

Faculty

Ecole polytechnique de Louvain

Degree label

Master [60] en sciences informatiques

Abstract

The study of misconceptions in programming has become increasingly important as programming education continues to expand globally. Despite the growing emphasis on digital skills, many students struggle with programming concepts, often due to ingrained misconceptions. This thesis explores the challenges related to identifying and addressing these misconceptions, particularly within the context of automatic code evaluation systems. The primary objective of this work is to develop a series of detection functions aimed at identifying common misconceptions in students' code submissions, regardless of whether the code successfully compiles. To achieve this, the thesis presents the design and implementation of an API that integrates these detection functions into online learning platforms. The effectiveness of these solutions is demonstrated using the Inginious platform, where specific tags are employed to flag detected misconceptions, providing valuable feedback to both students and educators. Ultimately, this project contributes to the field by offering tools that enhance programming education through early detection and correction of conceptual misunderstandings, thus supporting more effective and personalized learning experiences.