- Title
- An Innovative framework to improve course and student outcomes
- Creator
- Alalawi, Khalid; Athauda, Rukshan; Chiong, Raymond
- Relation
- 2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA). Proceedings of the 2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA) (Sydney, Australia 24-26 November, 2021) p. 1-6
- Publisher Link
- http://dx.doi.org/10.1109/CITISIA53721.2021.9719985
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2021
- Description
- 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.
- Subject
- educational technology framework; student performance prediction; machine learning; constructive alignment; effective feedback; learning analytics; SDG 4; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1437083
- Identifier
- uon:40232
- Identifier
- ISBN:9781665417846
- Language
- eng
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