Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-Learning Content Personalization
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| Title: | Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-Learning Content Personalization |
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| Authors: | Mwambe, Othmar Othmar (ORCID |
| Source: | Education Sciences. 2020 10. |
| Availability: | MDPI AG. Klybeckstrasse 64, 4057 Basel, Switzerland. Tel: e-mail: indexing@mdpi.com; Web site: http://www.mdpi.com |
| Peer Reviewed: | Y |
| Page Count: | 20 |
| Publication Date: | 2020 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Biology, Information Science, Technology Uses in Education, Hypermedia, Multimedia Instruction, Student Needs, Preferences, Cognitive Processes, Resource Units, Electronic Learning, Prior Learning, Synchronous Communication, Program Effectiveness, Foreign Countries, College Students, Computer Science Education |
| Geographic Terms: | Japan, Thailand |
| ISSN: | 2227-7102 |
| 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%. |
| Abstractor: | As Provided |
| Entry Date: | 2020 |
| Accession Number: | EJ1245599 |
| Database: | ERIC |
| 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%. |
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| ISSN: | 2227-7102 |
