Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-learning Content Personalization

Othmar Othmar Mwambe,Eiji Kamioka,Phan Xuan Tan

doi:10.3390/educsci10020042

Othmar Othmar Mwambe, Eiji Kamioka + Show 1 more

Open Access

https://doi.org/10.3390/educsci10020042

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Journal: Education sciences	Publication Date: Feb 17, 2020
Citations: 5	License type: CC BY 4.0

Affiliation: Shibaura Institute of Technology

Abstract

Adaptive Educational Hypermedia Systems (AEHS) play a crucial role in supporting adaptive learning and immensely outperform learner-control based systems. AEHS’ page indexing and hyperspace rely mostly on navigation supports which provide the learners with a user-friendly interactive learning environment. Such AEHS features provide the systems with a unique ability to adapt learners’ preferences. However, obtaining timely and accurate information for their adaptive decision-making process is still a challenge due to the dynamic understanding of individual learner. This causes a spontaneous changing of learners’ learning styles that makes hard for system developers to integrate learning objects with learning styles on real-time basis. Thus, in previous research studies, multiple levels navigation supports have been applied to solve this problem. However, this approach destroys their learning motivation because of imposing time and work overload on learners. To address such a challenge, this study proposes a bioinformatics-based adaptive navigation support that was initiated by the alternation of learners’ motivation states on a real-time basis. EyeTracking sensor and adaptive time-locked Learning Objects (LOs) were used. Hence, learners’ pupil size dilation and reading and reaction time were used for the adaption process and evaluation. The results show that the proposed approach improved the AEHS adaptive process and increased learners’ performance up to 78%.

Highlights

Since the 1980s, researchers in the fields of educational technology and psychology envisaged the significance of adaptation support for e-learning self-managed systems to meet individual needs [1].They spotted out key elements for adaptation process such as prior knowledge [1] and learners’traits including instructional interventions [2] as well as metacognitive skills [3]
Learning Objects (LOs)—As stated in Section 2.2, this study focuses on a real-time dynamic content delivery for Adaptive Educational Hypermedia Systems (AEHS) adaptation process on e-learning environment
Unlike other approaches discussed before, this study focuses on AEHS adaptive-decision process to support timing for adaptive navigation support that is initiated by learners’ motivation states alteration on a real-time basis

Summary

Introduction

Since the 1980s, researchers in the fields of educational technology and psychology envisaged the significance of adaptation support for e-learning self-managed systems to meet individual needs [1].They spotted out key elements for adaptation process such as prior knowledge [1] and learners’traits including instructional interventions [2] as well as metacognitive skills [3]. Since the 1980s, researchers in the fields of educational technology and psychology envisaged the significance of adaptation support for e-learning self-managed systems to meet individual needs [1]. They spotted out key elements for adaptation process such as prior knowledge [1] and learners’. In the 1980s [1,2] researchers found the significance of self-managed instructional adaptation which raised concerns due to learners’ individual differences. This evolved the concept of modern adaptive learning. No sooner had researchers found the involvement of learners’ learning styles [3] and Learning

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