Key factors predicting problem-based learning in online environments: Evidence from multimodal learning analytics
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The adoption of techniques such as machine learning, network analysis, and multimodal analytics has enriched the analytical capabilities within the field.
“The adoption of techniques such as machine learning, network analysis, and multimodal analytics has enriched the analytical capabilities within the field (e.g., Huang et al., 2021;Wang et al., 2023).”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Wang et al., 2023
Some papers embraced both traditional and innovative approaches, including experiments, surveys, ethnography, design-oriented research, and emerging computational methods.
“At times, papers embraced both traditional and innovative approaches, including experiments, surveys, ethnography, and design-oriented research, alongside emerging computational methods, for example, combining survey, LMS data, and multimodal data (e.g., Wang et al., 2023) or longitudinal studies combining a design intervention, a survey, test results and LMS data (e.g., Zhou et al., 2023).”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Wang et al., 2023
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Distributed Representations of Sentences and DocumentsPreprint
Le, Q. V., & Mikolov, T. (2014) · International Conference on Machine Learning · doi:10.48550/arxiv.1405.4053 · Cited by 10,307 · see citing works
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
Powers, D. M. W. (2020) · arXiv · doi:10.48550/arxiv.2010.16061 · Cited by 5,440 · see citing works
Overview of Problem-based Learning: Definitions and Distinctions
Savery, J. R. (2006) · Interdisciplinary Journal of Problem-based Learning · doi:10.7771/1541-5015.1002 · Cited by 2,686 · cited 2× · see citing works
“Researchers summarize four key elements of PBL: the design of ill-structured learning problems, the role of the instructor as a facilitator, students’ self-regulation in the learning process, and peer learning to interact with others (Savery, 2006; Kilinska and Ryberg, 2019).”
BackgroundHandbook of Self-Regulation of Learning and Performance
Schunk, D. H. (2011) · Routledge eBooks · doi:10.4324/9780203839010 · Cited by 2,191 · see citing works
Learning analytics: drivers, developments and challenges
Ferguson, R. (2012) · International Journal of Technology Enhanced Learning · doi:10.1504/ijtel.2012.051816 · Cited by 1,357 · see citing works
Learning Analytics: The Emergence of a Discipline
Siemens, G. (2013) · American Behavioral Scientist · doi:10.1177/0002764213498851 · Cited by 1,284 · see citing works
Arbaugh, J. B., Cleveland-Innes, M., Diaz, S. R., Garrison, D. R., Ice, P., Richardson, J. C., & Swan, K. P. (2008) · The Internet and Higher Education · doi:10.1016/j.iheduc.2008.06.003 · Cited by 1,065 · see citing works
Blikstein, P., & Worsley, M. B. (2016) · Journal of Learning Analytics · doi:10.18608/jla.2016.32.11 · Cited by 471 · see citing works
Support vector machines and Word2vec for text classification with semantic features
Lilleberg, J., Zhu, Y. E., & Zhang, Y. (2015) · IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) · doi:10.1109/icci-cc.2015.7259377 · Cited by 464 · see citing works
Ge, X., & Land, S. M. (2004) · Educational Technology Research and Development · doi:10.1007/bf02504836 · Cited by 429 · see citing works
Problem-Based Learning and Medical Education Forty Years On
Neville, A. J. (2008) · Medical Principles and Practice · doi:10.1159/000163038 · Cited by 408 · see citing works
Dong, A., Jong, M. S., & King, R. B. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.591203 · Cited by 269 · see citing works
The Potential for Collaborative Problem Solving in Design and Technology
Hennessy, S., & Murphy, P. (1999) · International Journal of Technology and Design Education · doi:10.1023/a:1008855526312 · Cited by 258 · see citing works
From signals to knowledge: A conceptual model for multimodal learning analytics
Mitri, D. D., Schneider, J., Specht, M. M., & Drachsler, H. (2018) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12288 · Cited by 250 · see citing works
Students' self-regulation for interaction with others in online learning environments
Cho, M.-H., & Kim, B. J. (2012) · The Internet and Higher Education · doi:10.1016/j.iheduc.2012.11.001 · Cited by 206 · see citing works
Learning analytics in European higher education—Trends and barriers
Tsai, Y.-S., Rates, D., Moreno-Marcos, P. M., Muñoz-Merino, P. J., Jivet, I., Scheffel, M., Drachsler, H., Delgado Kloos, C., & Gašević, D. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103933 · Cited by 192 · see citing works
Spikol, D., Ruffaldi, E., Dabisias, G., & Cukurova, M. (2018) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12263 · Cited by 180 · see citing works
Multimodal learning analytics for game‐based learning
Emerson, A., Cloude, E. B., Azevedo, R., & Lester, J. C. (2020) · British Journal of Educational Technology · doi:10.1111/bjet.12992 · Cited by 174 · see citing works
Zheng, C., Liang, J., Li, M., & Tsai, C. (2018) · System · doi:10.1016/j.system.2018.05.003 · Cited by 173 · see citing works
Dado, M., & Bodemer, D. (2017) · Educational Research Review · doi:10.1016/j.edurev.2017.08.005 · Cited by 156 · see citing works
Editorial: Augmenting Learning Analytics with Multimodal Sensory Data
Ochôa, X., & Worsley, M. B. (2016) · Journal of Learning Analytics · doi:10.18608/jla.2016.32.10 · Cited by 151 · see citing works
Using a pedagogical agent to deliver conversational style instruction: What benefits can you obtain?
Lin, L., Ginns, P., Wang, T., & Zhang, P. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103658 · Cited by 150 · see citing works
Miao, J., & Ma, L. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.815220 · Cited by 146 · see citing works
Development and Initial Validation of the Online Learning Value and Self-Efficacy Scale
Artino, A. R., & McCoach, D. B. (2008) · Journal of Educational Computing Research · doi:10.2190/ec.38.3.c · Cited by 145 · see citing works
The role of social network analysis as a learning analytics tool in online problem based learning
Saqr, M., & Alamro, A. (2019) · BMC Medical Education · doi:10.1186/s12909-019-1599-6 · Cited by 134 · see citing works
Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach
Sharma, K., Papamitsiou, Z. K., & Giannakos, M. N. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12854 · Cited by 131 · see citing works
The effectiveness of learning analytics for identifying at-risk students in higher education
Foster, E., & Siddle, R. (2019) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2019.1682118 · Cited by 92 · see citing works
Saqr, M., Nouri, J., Vartiainen, H., & Malmberg, J. (2020) · BMC Medical Education · doi:10.1186/s12909-020-01997-7 · Cited by 87 · see citing works
Permatasari, B. D., Gunarhadi, G., & Riyadi, R. (2019) · International Journal of Evaluation and Research in Education (IJERE) · doi:10.11591/ijere.v8i1.15594 · Cited by 86 · see citing works
Direction of collaborative problem solving-based STEM learning by learning analytics approach
Chen, L., Yoshimatsu, N., Goda, Y., Okubo, F., Taniguchi, Y., Oi, M., Konomi, S., Shimada, A., Ogata, H., & Yamada, M. (2019) · Research and Practice in Technology Enhanced Learning · doi:10.1186/s41039-019-0119-y · Cited by 72 · see citing works
Joshi, A., Desai, P., & Tewari, P. G. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.05.138 · Cited by 63 · see citing works
Guiding students’ online complex learning-task behavior through representational scripting
Slof, B., Erkens, G., Kirschner, P. A., Jaspers, J., & Janssen, J. J. H. M. (2010) · Computers in Human Behavior · doi:10.1016/j.chb.2010.02.007 · Cited by 51 · see citing works
The influence of prior knowledge on the effectiveness of guided experiment design
van Riesen, S. A. N., Gijlers, H., Anjewierden, A. A., & de Jong, T. (2019) · Interactive Learning Environments · doi:10.1080/10494820.2019.1631193 · Cited by 51 · see citing works
Horak, A. K., & Galluzzo, G. R. (2017) · Journal of Advanced Academics · doi:10.1177/1932202x16683424 · Cited by 43 · see citing works
Data-driven problem based learning: enhancing problem based learning with learning analytics
Ζώτου, Μ., Tambouris, E., & Tarabanis, K. A. (2020) · Educational Technology Research and Development · doi:10.1007/s11423-020-09828-8 · Cited by 43 · see citing works
What is Learning Analytics about? A Survey of Different Methods Used in 2013-2015Preprint
Khalil, M., & Ebner, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.02878 · Cited by 23 · see citing works
Nkhoma, C., Dang-Pham, D., Hoang, A.-P., Nkhoma, M. Z., Hoai, T. L., & Thomas, S. (2019) · Behaviour and Information Technology · doi:10.1080/0144929x.2019.1617349 · Cited by 18 · see citing works
Tempelaar, D. T., Rienties, B., & Giesbers, B. (2014) · Communications in computer and information science · doi:10.1007/978-3-319-08657-6_7 · Cited by 16 · see citing works
Learning Analytics Dashboard for Problem-based Learning
Pan, Z., Li, C., & Liu, M. (2020) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3386527.3406751 · Cited by 15 · see citing works
Data Dashboards to Support Facilitating Online Problem-Based Learning
Hogaboam, P., Chen, Y., Hmelo‐Silver, C. E., Lajoie, S. P., Bodnar, S., Kazemitabar, M., Wiseman, J., & Chan, L. K. (2016) · Quarterly review of distance education · doi:10.1108/qrde-12-2016-0006 · Cited by 13 · see citing works
Web 2.0 Technologies Supporting Problem Based Learning: A Systematic Literature Review
Ünal, E. (2019) · Journal of Problem Based Learning in Higher Education · doi:10.5278/ojs.jpblhe.v7i1.2845 · Cited by 12 · see citing works
Ludwig, S., & Rausch, A. (2022) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12770 · Cited by 8 · see citing works
Connecting Learning Analytics and Problem-Based Learning – Potentials and Challenges
Kilińska, D., & Ryberg, T. (2018) · Journal of Problem Based Learning in Higher Education · doi:10.5278/ojs.jpblhe.v0i0.2545 · Cited by 7 · see citing works
Project-Based Learning (PBL) in a Higher Education Project
Sohmen, V. S. (2020) · Advances in higher education and professional development book series · doi:10.4018/978-1-7998-1306-4.ch005 · Cited by 7 · see citing works
Triantafyllou, E., Xylakis, E., Nilsson, N. C., & Timčenko, O. (2018) · VBN Forskningsportal (Aalborg Universitet) · Cited by 2 · see citing works
Flagged first, then by citations
2 of the 47 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Binkley M. Erstad O. Herman J. Raizen S. Ripley M. Miller-Ricci M.. (2012). Defining Twenty-first Century Skills. Assessment and Teaching of 21st Century Skills. Dordrecht: Springer.
- Guo A. Tao Y. (2016). Research improvement of feature words weight based on TFIDF algorithm. IEEE Information Technology, Networking, Electronic and Automation Control Conference, 2016 Chongqing, China. 415– 419.
Cited by
Sorted by citations
Unpacking student engagement in higher education learning analytics: a systematic review
Bergdahl, N., Bond, M., Sjöberg, J., Dougherty, M., & Oxley, E. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00493-y · Cited by 138 · see citing works
Orhan, A. (2024) · Journal of Computer Assisted Learning · doi:10.1111/jcal.13033 · Cited by 29 · see citing works
Elmoazen, R., Saqr, M., Hirsto, L., & Tedre, M. (2024) · International Journal of Computer-Supported Collaborative Learning · doi:10.1007/s11412-024-09431-6 · Cited by 17 · see citing works
Optimizing Formative Assessment with Learning Analytics
Banihashem, S. K., Gašević, D., Noroozi, O., Jarodzka, H., Brinke, D. J.-T., & Drachsler, H. (2025) · Review of Educational Research · doi:10.3102/00346543251370753 · Cited by 35 · see citing works
Zhang, F., Yang, H., & Li, S. (2024) · Sustainability · doi:10.3390/su16041389 · Cited by 3 · see citing works
Wambua, A., Maurice, P. M., & Munyao, M. (2025) · Discover Education · doi:10.1007/s44217-025-00607-2 · Cited by 2 · see citing works
Yang, S., Zhu, S., Qin, W., Mai, Y., Guo, Q., & Li, H. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13710-6 · Cited by 7 · see citing works
Jiang, Q. (2024) · African Journal of Humanities and Social Sciences · doi:10.51483/afjhss.4.1.2024.1-13 · Cited by 1 · see citing works
Li, H., Xing, W., & Adhami, N. (2025) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale66047.2025.11346736
Multimodal Machine Learning Model to Predict Student Performance: A Review
Arti, Prashar, D., & Prashar, K. (2025) · International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) · doi:10.1109/ietacs68750.2025.11385692
A Cognitive Multimodal Framework for Automated Summary Assessment and Learning Performance Analysis
Rastogi, A., Goswami, S., Vig, M., & Agrawal, A. (2026) · Cureus Journal of Computer Science. · doi:10.7759/s44389-026-00074-2
Meta-Analysis in Multimodal Literacy Studies
Gupta, B. P., Bobir, N., Irisbaeva, K., Daniyarovna, E. D., Qayumov, B., Abdullaeva, S., Hikmat, K., & Qalandarova, B. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6986-0.ch008
Jia, M. (2026) · International Journal of Web-Based Learning and Teaching Technologies · doi:10.4018/ijwltt.422105
- Authors
- Wang, X., Sun, D., Cheng, G., & Luo, H.
- Affiliations
- Beijing Normal University (CN)
- Dalian University of Technology (CN)
- Open University of China (CN)
- Central China Normal University (CN)
- Corresponding author
- Sun, Di
- Luo, Heng
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2023-02-06
- Volume
- 14
- Pages
- 1080294-1080294
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC9939689
- OpenAlex
- W4319321752
- Cited by
- 13
- References
- 47
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 5
- FWCI
- 2.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Social Science Foundation of Liaoning Province (L21BSH002)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Problem and Project Based Learning
- Social Sciences › Education › Online and Blended Learning
- Keywords
- problem-based learning, online learning, multimodal learning analytics, peer learning, student self-regulation, online forums, learning outcome prediction, student engagement, social work education, learning analytics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
