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

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Title: Enriching the Learner's Model through the Semantic Analysis of Learning Traces
Language: English
Authors: Ait-Adda, Samia (ORCID 0000-0002-2380-0372), Bousbia, Nabila, Balla, Amar
Source: E-Learning and Digital Media. Jan 2023 20(1):1-24.
Availability: 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
Peer Reviewed: Y
Page Count: 24
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: 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
Geographic Terms: Algeria
DOI: 10.1177/20427530221102993
ISSN: 2042-7530
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.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1361083
Database: ERIC
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  Data: Enriching the Learner's Model through the Semantic Analysis of Learning Traces
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  Data: <searchLink fieldCode="AR" term="%22Ait-Adda%2C+Samia%22">Ait-Adda, Samia</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2380-0372">0000-0002-2380-0372</externalLink>)<br /><searchLink fieldCode="AR" term="%22Bousbia%2C+Nabila%22">Bousbia, Nabila</searchLink><br /><searchLink fieldCode="AR" term="%22Balla%2C+Amar%22">Balla, Amar</searchLink>
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  Data: 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
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  Data: 24
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  Data: Journal Articles<br />Reports - Research
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  Data: 10.1177/20427530221102993
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  Data: 2042-7530
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  Data: 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.
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      – SubjectFull: Semantics
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      – SubjectFull: Learning Processes
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      – TitleFull: Enriching the Learner's Model through the Semantic Analysis of Learning Traces
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