Key factors predicting problem-based learning in online environments: Evidence from multimodal learning analytics
Problem-based learning (PBL) has been used in different domains, and there is overwhelming evidence of its value. As an emerging field with excellent prospects, learning analytics (LA)—especially multimodal learning analytics (MMLA)—has increasingly attracted the attention of researchers in PBL. How...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-02-01
|
| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1080294/full |
| _version_ | 1811170868339933184 |
|---|---|
| author | Xiang Wang Di Sun Gang Cheng Gang Cheng Heng Luo |
| author_facet | Xiang Wang Di Sun Gang Cheng Gang Cheng Heng Luo |
| author_sort | Xiang Wang |
| collection | DOAJ |
| description | Problem-based learning (PBL) has been used in different domains, and there is overwhelming evidence of its value. As an emerging field with excellent prospects, learning analytics (LA)—especially multimodal learning analytics (MMLA)—has increasingly attracted the attention of researchers in PBL. However, current research on the integration of LA with PBL has not related LA results with specific PBL steps or paid enough attention to the interaction in peer learning, especially for text data generated from peer interaction. This study employed MMLA based on machine learning (ML) to quantify the process engagement of peer learning, identify log behaviors, self-regulation, and other factors, and then predict online PBL performance. Participants were 104 fourth-year students in an online course on social work and problem-solving. The MMLA model contained multimodal data from online discussions, log files, reports, and questionnaires. ML classification models were built to classify text data in online discussions. The results showed that self-regulation, messages post, message words, and peer learning engagement in representation, solution, and evaluation were predictive of online PBL performance. Hierarchical linear regression analyses indicated stronger predictive validity of the process indicators on online PBL performance than other indicators. This study addressed the scarcity of students’ process data and the inefficiency of analyzing text data, as well as providing information on targeted learning strategies to scaffold students in online PBL. |
| first_indexed | 2024-04-10T17:04:59Z |
| format | Article |
| id | doaj.art-3ebd6b7aab28435990dc58a16ba814d3 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-10T17:04:59Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-3ebd6b7aab28435990dc58a16ba814d32023-02-06T06:14:35ZengFrontiers Media S.A.Frontiers in Psychology1664-10782023-02-011410.3389/fpsyg.2023.10802941080294Key factors predicting problem-based learning in online environments: Evidence from multimodal learning analyticsXiang Wang0Di Sun1Gang Cheng2Gang Cheng3Heng Luo4Faculty of Education, Beijing Normal University, Beijing, ChinaFaculty of Humanities and Social Sciences, Dalian University of Technology, Dalian, Liaoning, ChinaDepartment of Information Technology, The Open University of China, Beijing, ChinaEngineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education, Beijing, ChinaSchool of Educational Information Technology, Central China Normal University, Wuhan, Hubei, ChinaProblem-based learning (PBL) has been used in different domains, and there is overwhelming evidence of its value. As an emerging field with excellent prospects, learning analytics (LA)—especially multimodal learning analytics (MMLA)—has increasingly attracted the attention of researchers in PBL. However, current research on the integration of LA with PBL has not related LA results with specific PBL steps or paid enough attention to the interaction in peer learning, especially for text data generated from peer interaction. This study employed MMLA based on machine learning (ML) to quantify the process engagement of peer learning, identify log behaviors, self-regulation, and other factors, and then predict online PBL performance. Participants were 104 fourth-year students in an online course on social work and problem-solving. The MMLA model contained multimodal data from online discussions, log files, reports, and questionnaires. ML classification models were built to classify text data in online discussions. The results showed that self-regulation, messages post, message words, and peer learning engagement in representation, solution, and evaluation were predictive of online PBL performance. Hierarchical linear regression analyses indicated stronger predictive validity of the process indicators on online PBL performance than other indicators. This study addressed the scarcity of students’ process data and the inefficiency of analyzing text data, as well as providing information on targeted learning strategies to scaffold students in online PBL.https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1080294/fullproblem-based learningpeer engagementlearning processmultimodal learning analyticsonline learning |
| spellingShingle | Xiang Wang Di Sun Gang Cheng Gang Cheng Heng Luo Key factors predicting problem-based learning in online environments: Evidence from multimodal learning analytics Frontiers in Psychology problem-based learning peer engagement learning process multimodal learning analytics online learning |
| title | Key factors predicting problem-based learning in online environments: Evidence from multimodal learning analytics |
| title_full | Key factors predicting problem-based learning in online environments: Evidence from multimodal learning analytics |
| title_fullStr | Key factors predicting problem-based learning in online environments: Evidence from multimodal learning analytics |
| title_full_unstemmed | Key factors predicting problem-based learning in online environments: Evidence from multimodal learning analytics |
| title_short | Key factors predicting problem-based learning in online environments: Evidence from multimodal learning analytics |
| title_sort | key factors predicting problem based learning in online environments evidence from multimodal learning analytics |
| topic | problem-based learning peer engagement learning process multimodal learning analytics online learning |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1080294/full |
| work_keys_str_mv | AT xiangwang keyfactorspredictingproblembasedlearninginonlineenvironmentsevidencefrommultimodallearninganalytics AT disun keyfactorspredictingproblembasedlearninginonlineenvironmentsevidencefrommultimodallearninganalytics AT gangcheng keyfactorspredictingproblembasedlearninginonlineenvironmentsevidencefrommultimodallearninganalytics AT gangcheng keyfactorspredictingproblembasedlearninginonlineenvironmentsevidencefrommultimodallearninganalytics AT hengluo keyfactorspredictingproblembasedlearninginonlineenvironmentsevidencefrommultimodallearninganalytics |