Published
2023-02-03
Issue
Section
Articles
How to Cite
Deep learning for the detection of acquired and non-acquired skills in students' algorithmic assessments
Floran Carvalho
FEMTO-ST DISC Institute, Univ. Bourgogne-Franche-Comte, 16 route de Gray, F-25030 Besançon Cedex, France.
https://orcid.org/0000-0001-8671-9830
Julien Henriet
FEMTO-ST DISC Institute, Univ. Bourgogne-Franche-Comte, 16 route de Gray, F-25030 Besançon Cedex, France.
https://orcid.org/0000-0002-7671-4574
Francoise Greffier
ELLIADD (EA 4661), Univ. Bourgogne-Franche-Comte, 30-32 rue Megevand CS 81807, F-25030 Besançon Cedex, France.
https://orcid.org/0000-0003-1285-5842
Marie-Laure Betbeder
FEMTO-ST DISC Institute, Univ. Bourgogne-Franche-Comte, 16 route de Gray, F-25030 Besançon Cedex, France.
https://orcid.org/0000-0002-8103-4098
Dana Leon-Henri
ELLIADD (EA 4661), Univ. Bourgogne-Franche-Comte, 30-32 rue Megevand CS 81807, F-25030 Besançon Cedex, France.
https://orcid.org/0000-0001-6196-6173
DOI: https://doi.org/10.20448/jeelr.v10i2.4449
Keywords: Algorithmic learning, Deep learning, Personalized learning, Student exercise analysis, Text classification, Natural language processing.
Abstract
This research is part of the Artificial Intelligence Virtual Trainer (AI-VT) project which aims to create a system that can identify the user's skills from a text by means of machine learning. AI-VT is a case-based reasoning learning support system can generate customized exercise lists that are specially adapted to user needs. To attain this outcome, the relevance of the first proposed exercise must be optimized to assist the system in creating personalized user profiles. To solve this problem, this project was designed to include a preliminary testing phase. As a generic tool, AI-VT was designed to be adapted to any field of learning. The most recent application of AI-VT was in the field of computer science specifically in the context of the fundamentals of algorithmic learning. AI-VT can and will also be useful in other disciplines. Developed in Python with the Keras API and the Tensorflow framework, this artificial intelligence-based tool encompasses a supervised learning environment, multi-label text classification techniques and deep neural networks. This paper presents and compares the performance levels of the different models tested on two different data sets in the context of computer programming and algorithms.