Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61383
Title: An Innovative Framework to Improve Course and Student Outcomes
Contributor(s): Alalawi, Khalid (author); Athauda, Rukshan (author); Chiong, Raymond  (author)orcid 
Publication Date: 2021
DOI: 10.1109/CITISIA53721.2021.9719985
Handle Link: https://hdl.handle.net/1959.11/61383
Abstract: 

This paper presents a novel framework aimed at improving educational outcomes in tertiary-level courses. The framework integrates concepts from educational data mining, learning analytics and education research domains. The framework considers the entire life cycle of courses and includes processes and supporting technology artefacts. Well-established pedagogy principles such as Constructive Alignment (CA) and effective feedback principles are incorporated to the framework. Mapping of learning outcomes, assessment tasks and teaching/learning activities using CA enables generating revision/study plans and determining the progress and achievement of students, in addition to assisting with course evaluation. Student performance prediction models are used to identify students at risk of failure early on for interventions. Tools are provided for academics to select student groups for intervention and provide personalised feedback. Feedback reports are generated based on effective feedback principles. Learning analytics dashboards provide information on students' progress and course evaluation. An evaluation of the framework based on a case study and quasi-experimental design on real-world courses is outlined. This research and the framework have the potential to significantly contribute to this important field of study.

Publication Type: Conference Publication
Conference Details: CITISIA 2021 - IEEE Conference on Innovative Technologies in Intelligent System and Industrial Application, 24th - 26th November, 2021
Source of Publication: CITISIA 2021 - IEEE Conference on Innovative Technologies in Intelligent System and Industrial Application, Proceedings, p. 1-6
Publisher: IEEE
Place of Publication: United States of America
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Appears in Collections:Conference Publication
School of Science and Technology

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