Abstract
Pedagogic systems in higher education have often relied on the traditional approach using face-to-face tutorial sessions with an on-line presence to deliver information relating to the specific course of study. There is however a paradigm shift in pedagogic systems characterised by the availability of teaching materials ‘anytime’ and ‘anywhere’ using on-line e-learning applications. Moreover, there is a realisation that there is a need to introduce increasing levels of personalisation to address the diverse student population with differing learning styles while encouraging and maintaining student engagement. This paper considers the approach to personalisation and proposes an approach designed to accommodate the dynamic pedagogic requirements of the diverse student cohort. In this paper we consider the provision of higher education and vocational education and propose an approach to educational provision predicated on personalisation to enable monitoring of students’ progress through testing and monitoring and, based on a student’s progress, set new tasks until all available tasks are completed. We then consider extensions to the current proposed approach to introduce autonomous task grading and task assignment based on intelligent context-aware informatics implemented using fuzzy rule-based approach extended using hedge algebras with Kansai engineering to increase the granularity of fuzzy variables. We posit that our proposed system offers benefits for both the traditional student cohort and distance learning students.
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Moore, P., Zhao, Z., Van Pham, H. (2020). Towards Cloud-Based Personalised Student-Centric Context-Aware e-Learning Pedagogic Systems. In: Barolli, L., Hussain, F., Ikeda, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2019. Advances in Intelligent Systems and Computing, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-22354-0_30
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