Impact of an adaptive environment based on learning analytics on pre-service science teacher behavior and self-regulation
Abstract Learning analytics provides valuable data to inform the best decisions for each learner. This study, based on adaptive environment (AE) learning analytics dashboards, examines how instructor interventions affect student self-regulation abilities and academic performance. It identifies the s...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Smart Learning Environments |
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| Online Access: | https://doi.org/10.1186/s40561-024-00340-7 |
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| author | Yousri Attia Mohamed Abouelenein Shaimaa Abdul Salam Selim Tahani Ibrahim Aldosemani |
| author_facet | Yousri Attia Mohamed Abouelenein Shaimaa Abdul Salam Selim Tahani Ibrahim Aldosemani |
| author_sort | Yousri Attia Mohamed Abouelenein |
| collection | DOAJ |
| description | Abstract Learning analytics provides valuable data to inform the best decisions for each learner. This study, based on adaptive environment (AE) learning analytics dashboards, examines how instructor interventions affect student self-regulation abilities and academic performance. It identifies the self-regulation categories requiring the most support to correct learning paths. Little is known about how interventions in an AE can influence learners' self-regulation based on performance indicators, particularly in science education. The study included 95 Faculty of Education and the Department of Science students. Using a longitudinal clustering approach, researchers identified three unique self-regulated learning (SRL) profiles: oriented, adaptive, and minimally self-regulated learners. While the results showed that the learning analyses were useful in guiding the process of appropriate interventions through an adaptive environment for each student by providing indicators and raising the level of self-regulation for each group separately, the results also showed that there was no change in the classification of self-regulation into groups and that no students moved between groups. These findings highlight the complexity of SRL, suggesting that while interventions can impact engagement and behavior, they may not be sufficient to change the learner's underlying profile. In academic performance, statistically significant differences were found, with the oriented self-regulation group outperforming the adaptive and minimally self-regulated groups. The findings underscore the importance of learning analytics and their indicators for timely interventions in adaptive environments. Additionally, the AE was highly effective, offering students opportunities to review material, which improved their study techniques, test-taking strategies, and overall learning experience. |
| format | Article |
| id | doaj-art-6ef277419fea47baa766283a2e95e2d4 |
| institution | Kabale University |
| issn | 2196-7091 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Smart Learning Environments |
| spelling | doaj-art-6ef277419fea47baa766283a2e95e2d42025-01-26T12:57:28ZengSpringerOpenSmart Learning Environments2196-70912025-01-0112113610.1186/s40561-024-00340-7Impact of an adaptive environment based on learning analytics on pre-service science teacher behavior and self-regulationYousri Attia Mohamed Abouelenein0Shaimaa Abdul Salam Selim1Tahani Ibrahim Aldosemani2Department of Educational Technology, Faculty of Education, Damietta UniversityCurriculum and Instruction of Science, Faculty of Education, Damietta UniversityDepartment of Educational Technology and Instructional Design at Prince Sattam bin, Abdulaziz UniversityAbstract Learning analytics provides valuable data to inform the best decisions for each learner. This study, based on adaptive environment (AE) learning analytics dashboards, examines how instructor interventions affect student self-regulation abilities and academic performance. It identifies the self-regulation categories requiring the most support to correct learning paths. Little is known about how interventions in an AE can influence learners' self-regulation based on performance indicators, particularly in science education. The study included 95 Faculty of Education and the Department of Science students. Using a longitudinal clustering approach, researchers identified three unique self-regulated learning (SRL) profiles: oriented, adaptive, and minimally self-regulated learners. While the results showed that the learning analyses were useful in guiding the process of appropriate interventions through an adaptive environment for each student by providing indicators and raising the level of self-regulation for each group separately, the results also showed that there was no change in the classification of self-regulation into groups and that no students moved between groups. These findings highlight the complexity of SRL, suggesting that while interventions can impact engagement and behavior, they may not be sufficient to change the learner's underlying profile. In academic performance, statistically significant differences were found, with the oriented self-regulation group outperforming the adaptive and minimally self-regulated groups. The findings underscore the importance of learning analytics and their indicators for timely interventions in adaptive environments. Additionally, the AE was highly effective, offering students opportunities to review material, which improved their study techniques, test-taking strategies, and overall learning experience.https://doi.org/10.1186/s40561-024-00340-7Data science applications in educationAdaptive environmentStudent performanceSelf-regulation of learning |
| spellingShingle | Yousri Attia Mohamed Abouelenein Shaimaa Abdul Salam Selim Tahani Ibrahim Aldosemani Impact of an adaptive environment based on learning analytics on pre-service science teacher behavior and self-regulation Smart Learning Environments Data science applications in education Adaptive environment Student performance Self-regulation of learning |
| title | Impact of an adaptive environment based on learning analytics on pre-service science teacher behavior and self-regulation |
| title_full | Impact of an adaptive environment based on learning analytics on pre-service science teacher behavior and self-regulation |
| title_fullStr | Impact of an adaptive environment based on learning analytics on pre-service science teacher behavior and self-regulation |
| title_full_unstemmed | Impact of an adaptive environment based on learning analytics on pre-service science teacher behavior and self-regulation |
| title_short | Impact of an adaptive environment based on learning analytics on pre-service science teacher behavior and self-regulation |
| title_sort | impact of an adaptive environment based on learning analytics on pre service science teacher behavior and self regulation |
| topic | Data science applications in education Adaptive environment Student performance Self-regulation of learning |
| url | https://doi.org/10.1186/s40561-024-00340-7 |
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