Science and Research Journal |
UIKTEN |
SAR Journal |
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| ISSN 2619-9955 | eISSN 2619-9963 | Frequency:4/year | Peer Reviewed: Yes | UIKTEN Publisher |
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Analysis & Implementation of Personalisation Parameters in the Development of Computer-Based Adaptive Learning Environment
Humam K. Majeed Al-Chalabi, Aqeel M. Ali. Hussein
© 2020 Humam K.Majeed Al‐Chalabi, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: SAR Journal. Volume 3, Issue 1, Pages 3-9, ISSN 2619-9955, https://doi.org/10.18421//SAR31-01, March 2020.
Received: 27 December 2019.
Revised: 20 February 2020.
Accepted: 27 February 2020.
Published: 30 March 2020.
Abstract:
The paper was aimed to identify the ways and to develop the model for the implementation personalised parameter for adaptive learning in the current E-learning environment. The study is qualitative in nature and relied on previous literature. The results highlighted that the personalised parameters which should be considered before the beginning of the course learning process include the level of learner’s knowledge, goals of the leaners, preferences of the language, style of learning, information seeking task, bandwidth, location, and previous level of knowledge. The study also pointed out learning needs, motivation levels, working memory capacity, intelligence, cognitive style, satisfaction, delight, and self-efficacy, the need for help, selfregulated learning, and ongoing interactions, waiting for feedback, ongoing progress, and navigation preference as the parameters for considering in ongoing sessions. The study formulated an implementation design comprising a combination of CBR and RBR strategies. The design is further categorised into standalone and coupling strategies, while the coupling strategy further includes a combination of embedded, co-processing and sequential processing strategies.
Keywords – Software agents, E-learning systems, Adaptive learning, E-learning environment,
Personalisation parameters, Learner model.
