Cloud-supported machine learning system for context-aware adaptive M-learning

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dc.contributor.authorAdnan, Muhammad
dc.contributor.authorHabib, Asad
dc.contributor.authorAshraf, Jawad
dc.contributor.authorMussadiq, Shafaq
dc.date.accessioned2024-10-29T15:16:56Z
dc.date.available2024-10-29T15:16:56Z
dc.date.issued2019-07-26
dc.description.abstractIt is a knotty task to amicably identify the sporadically changing real-world context information of a learner during M-learning processes. Contextual information varies greatly during the learning process. Contextual information that affects the learner during a learning process includes background knowledge, learning time, learning location, and environmental situation. The computer programming skills of learners improve rapidly if they are encouraged to solve real-world programming problems. It is important to guide learners based on their contextual information in order to maximize their learning performance. In this paper, we proposed a cloud-supported machine learning system (CSMLS), which assists learners in learning practical and applied computer programming based on their contextual information. Learners? contextual information is extracted from their mobile devices and is processed by an unsupervised machine learning algorithm called density-based spatial clustering of applications with noise (DBSCAN) with a rule-based inference engine running on a back-end cloud. CSMLS is able to provide real-time, adaptive, and active learning support to students based on their contextual information characteristics. A total of 150 students evaluated the performance and acceptance of CSMLS for a complete academic semester, i.e. 6 months. Experimental results revealed the threefold success of CSMLS: extraction of students? context information, supporting them in appropriate decision-making, and subsequently increasing their computer programming skills.
dc.funderNo external funder
dc.identifier.citationAdnan, M. et al. (2019) Cloud-supported machine learning system for context-aware adaptive M-learning. Turkish Journal of Electrical Engineering and Computer Sciences: 27 (4), 31
dc.identifier.doihttps://doi.org/10.3906/elk-1811-196
dc.identifier.issn1303-6203
dc.identifier.urihttps://hdl.handle.net/2086/24417
dc.publisherThe Scientific and Technological Research Council of Turkey (TUBITAK-ULAKBIM) - DIGITAL COMMONS JOURNALS
dc.relation.ispartofTURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
dc.titleCloud-supported machine learning system for context-aware adaptive M-learning
dc.typeArticle
oaire.citation.issue4
oaire.citation.volume27

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