Deep Learning for the Detection of Acquired and Non-Acquired Skills in Students' Algorithmic Assessments

Autor/inn/en	Carvalho, Floran; Henriet, Julien; Greffier, Francoise; Betbeder, Marie-Laure; Leon-Henri, Dana
Titel	Deep Learning for the Detection of Acquired and Non-Acquired Skills in Students' Algorithmic Assessments
Quelle	In: Journal of Education and e-Learning Research, 10 (2023) 2, S.111-118 (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Carvalho, Floran) ORCID (Henriet, Julien) ORCID (Greffier, Francoise) ORCID (Betbeder, Marie-Laure) ORCID (Leon-Henri, Dana)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2518-0169
Schlagwörter	Learning Processes; Algorithms; Artificial Intelligence; Programming Languages; Classification; Natural Language Processing; Intelligent Tutoring Systems + Suchen Sie Ihr Suchwort? Learning process; Lernprozess; Algorithm; Algorithmus; Künstliche Intelligenz; Classification system; Klassifikation; Klassifikationssystem; Natürliche Sprache; Intelligentes Tutorsystem
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01