Modeling and Verification of Adaptive eLearning System Modeling and Verification of Adaptive eLearning System

Modeling and Verification of Adaptive eLearning System Modeling and Verification of Adaptive eLearning System | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 5 November 2025 V1 Latest version Share on Modeling and Verification of Adaptive eLearning System Modeling and Verification of Adaptive eLearning System Authors : Ching Hay Chau 0009-0007-6727-2448 [email protected] and Ching Hay Authors Info & Affiliations https://doi.org/10.22541/au.176236434.42836329/v1 168 views 122 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The eLearning system has become a vital component of modern education. To address variations in students' abilities, learning histories, and preferences, adaptive eLearning has been introduced. While numerous models have been developed for adaptive eLearning, their reliability is typically assessed only through student evaluations post-implementation. This dissertation aims to propose a verification tool to assist adaptive eLearning system developers in validating the logic of their designs. Given the lack of standardised models, a higher-level approach to modelling is recommended using Event-Condition-Action (ECA) rules. The dissertation explores the use of the SPIN model checking tool to analyse eLearning models, benefiting both developers and educators. To support this approach, the extension of PROMELAthe programming language used in SPIN-is investigated. A graphical user interface (GUI)-based integrated development environment (IDE) has been developed to manage these extensions and handle Linear Temporal Logic (LTL) formulae for verifying the correctness of eLearning models. Test cases are included to demonstrate the functionality and effectiveness of the PROMELA extensions. Supplementary Material File (adaptive-learning-systems-chau-2025.pdf) Download 1.26 MB Information & Authors Information Version history V1 Version 1 05 November 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords adaptive learning promela smart contracts software verification spin Authors Affiliations Ching Hay Chau 0009-0007-6727-2448 [email protected] View all articles by this author Ching Hay View all articles by this author Metrics & Citations Metrics Article Usage 168 views 122 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ching Hay Chau, Ching Hay. Modeling and Verification of Adaptive eLearning System Modeling and Verification of Adaptive eLearning System. Authorea . 05 November 2025. DOI: https://doi.org/10.22541/au.176236434.42836329/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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