Smart Learning Objects Retrieval for E-Learning with Contextual Recommendation Based on Collaborative Filtering

Autor/inn/en	Tahir, Sidra; Hafeez, Yaser; Abbas, Muhammad Azeem; Nawaz, Asif; Hamid, Bushra
Titel	Smart Learning Objects Retrieval for E-Learning with Contextual Recommendation Based on Collaborative Filtering
Quelle	In: Education and Information Technologies, 27 (2022) 6, S.8631-8668 (38 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Tahir, Sidra)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-022-10966-0
Schlagwörter	Man Machine Systems; Artificial Intelligence; Technology Uses in Education; Online Courses; Curriculum Design; Resource Units; Higher Education; Electronic Learning + Suchen Sie Ihr Suchwort? Mensch-Maschine-System; Künstliche Intelligenz; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Online course; Online-Kurs; Lehrplangestaltung; Hochschulbildung; Hochschulsystem; Hochschulwesen
Abstract	With the increase in Technology Enhanced Learning (TEL), the effective retrieval and availability of Learning Objects (LOs) for course designers is a significant concern. Text-based LOs can be accessed from structured LO repositories (LORs) and unstructured sources. Different LOR structures and semantically diversified LOs hinder the process of course designing because of various repository formats and standards. The proposed research provides an innovative Machine Learning and filter based context-aware LO recommender System for the designing of course. The proposed model helps to easily search and access diversified and semantically related LOs. The proposed solution will also help the academia to design customized courses gratifying higher education curriculum needs while reusing existing LOs. Through automatic feature extraction and classification, the proposed system -- Dynamic Recommendation of Filtered LOs (DRFLO) -- retrieves a set of semantically relevant LOs from heterogeneous LORs. Using the additional recommendation and ranking models, LOs are contextually ranked. Finally, a ranked list of LOs is recommended on a simple query from the course designer according to learning preferences while designing a course. The performance of the proposed model is quite remarkable in terms of precision, recall and F-measure. Furthermore, the experimental results have also been compared with a common search engine and an accuracy of 93% was achieved. The proposed research further produced, pre-test and post-test experiments to ensure the validity of the DRFLO approach. The proposed DRFLO System is a machine learning based research effort which provides an efficient mean for retrieving LOs while designing a course. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01