A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors
Scholars have indicated the importance of providing guidance and support for individual learners. In the past decades, most studies have developed adaptive learning systems to address this issue mainly based on students’ cognitive status, such as their learning achievements. However, several educato...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-01-01
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| Series: | Computers and Education: Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X20300035 |
| _version_ | 1818556419260022784 |
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| author | Gwo-Jen Hwang Han-Yu Sung Shao-Chen Chang Xing-Ci Huang |
| author_facet | Gwo-Jen Hwang Han-Yu Sung Shao-Chen Chang Xing-Ci Huang |
| author_sort | Gwo-Jen Hwang |
| collection | DOAJ |
| description | Scholars have indicated the importance of providing guidance and support for individual learners. In the past decades, most studies have developed adaptive learning systems to address this issue mainly based on students’ cognitive status, such as their learning achievements. However, several educators have pointed out the need to take learners’ affective status into account. Therefore, this study proposed an expert system approach by taking into account both the affective and cognitive status of individual learners. An adaptive learning system was implemented based on the proposed approach. In addition, an experiment was conducted in a fifth-grade Mathematics course to compare the learning performances and perceptions of the students who learned with the adaptive learning system with affective and cognitive status analysis, a cognitive-status-based adaptive learning system, and a conventional learning system. The ANCOVA results revealed that the adaptive learning model with the affective and cognitive performance analysis mechanism outperformed the other two approaches in terms of improving the students’ learning achievement (F = 3.12, p < 0.05) and reducing their mathematical anxiety (F = 5.59, p < 0.01). In addition, it was found that the proposed approach helped the low achievers successfully complete the learning tasks, while those learning with the conventional cognitive factor-based approach were more likely to give up some learning tasks, and were more reliant on the detailed version of the instructional materials. |
| first_indexed | 2024-12-13T23:47:06Z |
| format | Article |
| id | doaj.art-847822783a534d5dae94ee23abf97e12 |
| institution | Directory Open Access Journal |
| issn | 2666-920X |
| language | English |
| last_indexed | 2024-12-13T23:47:06Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers and Education: Artificial Intelligence |
| spelling | doaj.art-847822783a534d5dae94ee23abf97e122022-12-21T23:26:55ZengElsevierComputers and Education: Artificial Intelligence2666-920X2020-01-011100003A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factorsGwo-Jen Hwang0Han-Yu Sung1Shao-Chen Chang2Xing-Ci Huang3Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, 43, Sec.4, Keelung Rd., Taipei, 106, Taiwan; Corresponding author.Department of Allied Health Education and Digital Learning, National Taipei University of Nursing and Health Sciences, No.365, Ming-te Road, Peitou District, Taipei City, TaiwanDepartment of International Bachelor Program in Informatics and the Department of Information Communication, Yuan Ze University, No. 135, Yuandong Rd., Zhongli Dist., Taoyuan City, 320, TaiwanGraduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, 43, Sec.4, Keelung Rd., Taipei, 106, TaiwanScholars have indicated the importance of providing guidance and support for individual learners. In the past decades, most studies have developed adaptive learning systems to address this issue mainly based on students’ cognitive status, such as their learning achievements. However, several educators have pointed out the need to take learners’ affective status into account. Therefore, this study proposed an expert system approach by taking into account both the affective and cognitive status of individual learners. An adaptive learning system was implemented based on the proposed approach. In addition, an experiment was conducted in a fifth-grade Mathematics course to compare the learning performances and perceptions of the students who learned with the adaptive learning system with affective and cognitive status analysis, a cognitive-status-based adaptive learning system, and a conventional learning system. The ANCOVA results revealed that the adaptive learning model with the affective and cognitive performance analysis mechanism outperformed the other two approaches in terms of improving the students’ learning achievement (F = 3.12, p < 0.05) and reducing their mathematical anxiety (F = 5.59, p < 0.01). In addition, it was found that the proposed approach helped the low achievers successfully complete the learning tasks, while those learning with the conventional cognitive factor-based approach were more likely to give up some learning tasks, and were more reliant on the detailed version of the instructional materials.http://www.sciencedirect.com/science/article/pii/S2666920X20300035Artificial intelligenceAdaptive learningFussy inferenceExpert systemPersonalization |
| spellingShingle | Gwo-Jen Hwang Han-Yu Sung Shao-Chen Chang Xing-Ci Huang A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors Computers and Education: Artificial Intelligence Artificial intelligence Adaptive learning Fussy inference Expert system Personalization |
| title | A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors |
| title_full | A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors |
| title_fullStr | A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors |
| title_full_unstemmed | A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors |
| title_short | A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors |
| title_sort | fuzzy expert system based adaptive learning approach to improving students learning performances by considering affective and cognitive factors |
| topic | Artificial intelligence Adaptive learning Fussy inference Expert system Personalization |
| url | http://www.sciencedirect.com/science/article/pii/S2666920X20300035 |
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