Learning-Analytics based Intelligent Simulator for Personalised Learning - Sharef, N.M. (Institute of Electrical and Electronics Engineers Inc., 2020)
Personalised learning enables instructions to be tailored specific to students learning needs, while making sure learning outcomes are attained. Instructors require information that could facilitate them in adapting their pedagogy design so the learning delivery could be optimized. However, existing solutions are limited to descriptive analytic and intervention facilitation is confined to students at risk prediction based on their course engagement frequency. Tools to predict final grade is available but very scarce. Besides, realtime monitoring of reaction to learning events are not available. Therefore, this paper proposes a solution that integrates Internet of Things, learning analytic and chatbot to fill the said gaps. The paper also presents the experience of pilot developments towards the current version of solution. Â© 2020 IEEE.