TY  - CONF
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099126609&doi=10.1109%2fICADEIS49811.2020.9276858&partnerID=40&md5=214742d183ca26d194b00a0956cc2bd5
A1  - Sharef, N.M.
A1  - Azmi Murad, M.A.
A1  - Mansor, E.I.
A1  - Nasharuddin, N.A.
A1  - Omar, M.K.
A1  - Samian, N.
A1  - Arshad, N.I.
A1  - Ismail, W.
A1  - Shahbodin, F.
SN  - 9781728182728
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2020///
KW  - Data Science; E-learning; Information systems; Information use
KW  -  Chatbot; Learning outcome; Personalised learning; Real time monitoring; Risk predictions; Students learning
KW  -  Learning systems
ID  - scholars12603
TI  - Learning-Analytics based Intelligent Simulator for Personalised Learning
N1  - cited By 2; Conference of 2020 International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2020 ; Conference Date: 20 October 2020 Through 21 October 2020; Conference Code:165763
N2  - 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. © 2020 IEEE.
AV  - none
ER  -