Abstract
The advent of E-learning has allowed students to have access to a massive group of educators and learning resources. However, the concept of online learning still lacks a quality that deems it inferior to classroom education and that is the ability to understand the needs of individual students. With reference to online learning, the complexity of different online resources plays a crucial role in determining the usefulness of that resource for a given user. As a result, students get intimidated by these divergences in explanations, making the effectiveness of e-learning subject to a user's psychology and self-motivation. Thus, there is a need to understand the dynamics of a student's learning behavior before suggesting resources. In order to address this need, in this research paper, we present an Adaptive Educational Hypermedia System (AEHS) called SQLearn which assesses the performance of students with an assessment as they study a topic and consequently assists their learning experience. SQLearn consists of two main components, the Testing Platform, and the Web Browser Extension which works in unison to understand students learning behavior. After analyzing a student's learning behavior, the designed system is capable of suggesting them online resources to help them grasp concepts they is weak at. The system is also capable of making inferences based on the students answering behavior to help them maintain an optimum learning and answering speed. In order to test the efficacy of the designed system, a pilot study was conducted with 11 undergraduate students. This study helped bolster claims regarding the usefulness of the system while also motivating the creation of a more accurate system.
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Appendix - A
Appendix - A
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1.
SQL Recommendation System - Google Chrome Extension: https://chrome.google.com/webstore/detail/sql-recommender-system/pkdlcabmdmmjdjpaflaphjehabkglgpk
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SQL Recommendation System - Testing Platform: https://sqlrecommender.southeastasia.cloudapp.azure.com/
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Study Completion Form: https://forms.gle/bnYj6seGpzbEBCzw6.
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Bhuse, P., Jain, J., Shaju, A., John, V., Joshi, A., Rajendran, R. (2021). SQLearn: A Browser Based Adaptive SQL Learning Environment. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. Design and Evaluation. HCII 2021. Lecture Notes in Computer Science(), vol 12792. Springer, Cham. https://doi.org/10.1007/978-3-030-77857-6_9
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DOI: https://doi.org/10.1007/978-3-030-77857-6_9
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