Machine Learning techniques for Knowledge ...
Type de document :
Partie d'ouvrage
Titre :
Machine Learning techniques for Knowledge Tracing: A systematic literature review
Auteur(s) :
Ramirez Luelmo, Sergio Ivan [Auteur]
Trigone-CIREL
El Mawas, Nour [Auteur]
Trigone-CIREL
Heutte, Jean [Auteur]
Trigone-CIREL
Trigone-CIREL
El Mawas, Nour [Auteur]
Trigone-CIREL
Heutte, Jean [Auteur]
Trigone-CIREL
Éditeur(s) ou directeur(s) scientifique(s) :
in J. Uhomoibhi
Titre de l’ouvrage :
Proceedings of the 13th International Conference on Computer Supported Education (CSEDU)
Éditeur :
Science and Technology Publications, Lda
Date de publication :
2021
Mot(s)-clé(s) en anglais :
Machine Learning
Knowledge Tracing
Learner Model
Literature Review
Technology Enhanced Learning
Knowledge Tracing
Learner Model
Literature Review
Technology Enhanced Learning
Discipline(s) HAL :
Sciences de l'Homme et Société/Psychologie
Sciences de l'Homme et Société/Education
Informatique [cs]/Environnements Informatiques pour l'Apprentissage Humain
Sciences de l'Homme et Société/Education
Informatique [cs]/Environnements Informatiques pour l'Apprentissage Humain
Résumé en anglais : [en]
Machine Learning (ML) techniques are being intensively applied in educational settings. They are employed to predict competences and skills, grade exams, recognize behavioural academic patterns, evaluate open answers, ...
Lire la suite >Machine Learning (ML) techniques are being intensively applied in educational settings. They are employed to predict competences and skills, grade exams, recognize behavioural academic patterns, evaluate open answers, suggest appropriate educational resources, and group or associate students with similar learning characteristics or academic interests. Knowledge Tracing (KT) allows modelling the learner's mastery of skill and to meaningfully predict student’s performance, as it tracks within the Learner Model (LM) the knowledge state of students based on observed outcomes from their previous educational practices, such as answers, grades and/or behaviours. In this study, we survey commonly used ML techniques for KT figuring in 51 papers on the topic, out of an original search pool of 628 articles from 5 renowned academic sources, encompassing the latest research, based on the PRISMA method. We identify and review relevant aspects of ML for KT in LM that help paint a more accurate panorama on the topic and hence, contribute to alleviate the difficulty of choosing an appropriate ML technique for KT in LM. This work is dedicated to MOOC designers/providers, pedagogical engineers and researchers who need an overview of existing ML techniques for KT in LM.Lire moins >
Lire la suite >Machine Learning (ML) techniques are being intensively applied in educational settings. They are employed to predict competences and skills, grade exams, recognize behavioural academic patterns, evaluate open answers, suggest appropriate educational resources, and group or associate students with similar learning characteristics or academic interests. Knowledge Tracing (KT) allows modelling the learner's mastery of skill and to meaningfully predict student’s performance, as it tracks within the Learner Model (LM) the knowledge state of students based on observed outcomes from their previous educational practices, such as answers, grades and/or behaviours. In this study, we survey commonly used ML techniques for KT figuring in 51 papers on the topic, out of an original search pool of 628 articles from 5 renowned academic sources, encompassing the latest research, based on the PRISMA method. We identify and review relevant aspects of ML for KT in LM that help paint a more accurate panorama on the topic and hence, contribute to alleviate the difficulty of choosing an appropriate ML technique for KT in LM. This work is dedicated to MOOC designers/providers, pedagogical engineers and researchers who need an overview of existing ML techniques for KT in LM.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Projet ANR :
Source :