A New Adaptive Online Learning using Computational Intelligence
This study aimed to develop an online learning system that was adaptive to students who wishedto learn electrical machine modules based on their abilities. Adaptive use of online learning functioned to determine the category of students' ability to access modules in online learning. Online lear...
| Main Authors: | , , , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Published: |
2020
|
| Subjects: |
| _version_ | 1796865429329674240 |
|---|---|
| author | Wahyono, Irawan Dwi Asfani, Khoirudin Mohamad, Mohd. Murtadha Saryono, Djoko Ashar, M. Sunarti, S. |
| author_facet | Wahyono, Irawan Dwi Asfani, Khoirudin Mohamad, Mohd. Murtadha Saryono, Djoko Ashar, M. Sunarti, S. |
| author_sort | Wahyono, Irawan Dwi |
| collection | ePrints |
| description | This study aimed to develop an online learning system that was adaptive to students who wishedto learn electrical machine modules based on their abilities. Adaptive use of online learning functioned to determine the category of students' ability to access modules in online learning. Online learning was also able to provide the determination of modules which can then be done by students, so students can learn independently. Adaptive capabilities in online learning were implemented by utilizing computational intelligence algorithms, namely Naive Bayes and Bayes Network. Naive Bayes was tasked with processing students 'pre-test data in adaptive online learning for the classificationof students' abilities so that after the results of the pretest appeared, students will be given modules that matched their abilities. Whereas Bayes Network used to process student post-test data after students worked on the modules that have been given, adaptive online learning provided the nextmodule to work according to the abilities and desires of students. The testing results of the use of Naive Bayes and Bayes Network on Adaptive Online Learning obtained an average accuracy of 85%. |
| first_indexed | 2024-03-05T20:56:52Z |
| format | Conference or Workshop Item |
| id | utm.eprints-92461 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:56:52Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | utm.eprints-924612021-09-30T15:11:44Z http://eprints.utm.my/92461/ A New Adaptive Online Learning using Computational Intelligence Wahyono, Irawan Dwi Asfani, Khoirudin Mohamad, Mohd. Murtadha Saryono, Djoko Ashar, M. Sunarti, S. QA75 Electronic computers. Computer science This study aimed to develop an online learning system that was adaptive to students who wishedto learn electrical machine modules based on their abilities. Adaptive use of online learning functioned to determine the category of students' ability to access modules in online learning. Online learning was also able to provide the determination of modules which can then be done by students, so students can learn independently. Adaptive capabilities in online learning were implemented by utilizing computational intelligence algorithms, namely Naive Bayes and Bayes Network. Naive Bayes was tasked with processing students 'pre-test data in adaptive online learning for the classificationof students' abilities so that after the results of the pretest appeared, students will be given modules that matched their abilities. Whereas Bayes Network used to process student post-test data after students worked on the modules that have been given, adaptive online learning provided the nextmodule to work according to the abilities and desires of students. The testing results of the use of Naive Bayes and Bayes Network on Adaptive Online Learning obtained an average accuracy of 85%. 2020 Conference or Workshop Item PeerReviewed Wahyono, Irawan Dwi and Asfani, Khoirudin and Mohamad, Mohd. Murtadha and Saryono, Djoko and Ashar, M. and Sunarti, S. (2020) A New Adaptive Online Learning using Computational Intelligence. In: 3rd International Conference on Vocational Education and Electrical Engineering, ICVEE 2020, 3 - 4 October 2020, Virtual, Surabaya. http://dx.doi.org/10.1109/ICVEE50212.2020.9243193 |
| spellingShingle | QA75 Electronic computers. Computer science Wahyono, Irawan Dwi Asfani, Khoirudin Mohamad, Mohd. Murtadha Saryono, Djoko Ashar, M. Sunarti, S. A New Adaptive Online Learning using Computational Intelligence |
| title | A New Adaptive Online Learning using Computational Intelligence |
| title_full | A New Adaptive Online Learning using Computational Intelligence |
| title_fullStr | A New Adaptive Online Learning using Computational Intelligence |
| title_full_unstemmed | A New Adaptive Online Learning using Computational Intelligence |
| title_short | A New Adaptive Online Learning using Computational Intelligence |
| title_sort | new adaptive online learning using computational intelligence |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT wahyonoirawandwi anewadaptiveonlinelearningusingcomputationalintelligence AT asfanikhoirudin anewadaptiveonlinelearningusingcomputationalintelligence AT mohamadmohdmurtadha anewadaptiveonlinelearningusingcomputationalintelligence AT saryonodjoko anewadaptiveonlinelearningusingcomputationalintelligence AT asharm anewadaptiveonlinelearningusingcomputationalintelligence AT sunartis anewadaptiveonlinelearningusingcomputationalintelligence AT wahyonoirawandwi newadaptiveonlinelearningusingcomputationalintelligence AT asfanikhoirudin newadaptiveonlinelearningusingcomputationalintelligence AT mohamadmohdmurtadha newadaptiveonlinelearningusingcomputationalintelligence AT saryonodjoko newadaptiveonlinelearningusingcomputationalintelligence AT asharm newadaptiveonlinelearningusingcomputationalintelligence AT sunartis newadaptiveonlinelearningusingcomputationalintelligence |