Enriching the Learner's Model through the Semantic Analysis of Learning Traces

Autor/inn/en	Ait-Adda, Samia; Bousbia, Nabila; Balla, Amar
Titel	Enriching the Learner's Model through the Semantic Analysis of Learning Traces
Quelle	In: E-Learning and Digital Media, 20 (2023) 1, S.1-24 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Ait-Adda, Samia)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1177/20427530221102993
Schlagwörter	Semantics; Learning Processes; Learning Analytics; Models; Concept Formation; Difficulty Level; Information Retrieval; Course Evaluation; Instructional Design; Search Strategies; Online Courses; Web Based Instruction; Web Sites; Ambiguity (Semantics); Undergraduate Students; Student Attitudes; Computer Science Education; Foreign Countries; Algeria + Suchen Sie Ihr Suchwort? Semantik; Learning process; Lernprozess; Analogiemodell; Concept learning; Begriffsbildung; Schwierigkeitsgrad; Lesson concept; Lessonplan; Unterrichtsentwurf; Suchstrategie; Online course; Online-Kurs; Web Based Training; Web-Design; Schülerverhalten; Computer science lessons; Informatikunterricht; Ausland; Algerien
Abstract	Our aim in this paper is to improve the efficiency of a learning process by using learners' traces to detect particular needs. The analysis of the semantic path of a learner or group of learners during the learning process can allow detecting those students who are in needs of help as well as identify the insufficiently mastered concepts. We examine the possibility of using a student's browsing path during a learning session, based on his navigation traces, to update the learner model. We assume that the domain concepts examined outside the learning platform but that are related to the course concepts are problematic to the learner. Knowing about these concepts may allow the course's author to adapt the course to the learner's needs regarding these concepts, as well as allow the tutor to help and assist the learner on these problematic concepts. We rely on Web data mining methods to filter, organize, and analyze the student's browsing path. More precisely, we use a domain ontology of the course and the similarities that exist between external documents (visited pages) and the domain concepts (the course keywords). This analysis process makes it possible to detect students' learning difficulties and to adapt the course based on the learner's model. (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01