{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T06:04:18Z","timestamp":1789365858856,"version":"build-2803163510"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Learning behavior and student engagement are important educational constructs that are associated with academic performance. Learning analytics provides opportunities to examine these relationships using large educational datasets. However, clear construct separation, appropriate statistical validation, and comparison with alternative predictive models remain necessary.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed a publicly available educational analytics dataset containing 14,003 records. After removing 1,534 exact duplicate records, we retained 12,469 unique records. Learning behavior was represented by study hours, online course participation, and assignment completion. Student engagement was represented by attendance, discussion participation, and extracurricular involvement. Academic performance was represented by examination score, while final grade was excluded because its calculation procedure was not documented. We used ordinary least squares regression to examine the statistical associations. We also included an observed variable path model and machine learning models as alternative specifications. We used an 80% training and 20% held-out test split for predictive validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Learning behavior showed a small and nonsignificant association with student engagement (\u03b2\u2081\u202f=\u202f0.0017,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.8112). Student engagement showed a negative association with academic performance (\u03b3\u2081\u202f=\u202f\u22121.2485,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.0014), whereas learning behavior showed a positive association (\u03b3\u2082\u202f=\u202f1.0075,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.0009). The baseline model showed limited explanatory power (R\n                      <jats:sup>2<\/jats:sup>\n                      \u202f=\u202f0.004). On the held-out test set, the random forest model provided the strongest predictive performance (R\n                      <jats:sup>2<\/jats:sup>\n                      \u202f=\u202f0.564, RMSE\u202f=\u202f11.719, MAE\u202f=\u202f8.896, and MAPE\u202f=\u202f14.013%). The robustness analysis showed similar coefficient magnitudes across the alternative static specifications.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The findings indicate statistical associations among learning behavior, student engagement, and academic performance within the analytical dataset. They do not establish causal relationships or temporal relationships because the dataset is cross-sectional and contains no documented temporal ordering or repeated observations. The results support transparent construct definition, comparative model evaluation, and out-of-sample validation in learning analytics research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1908197","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T05:39:43Z","timestamp":1789364383000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning behavior, student engagement, and academic performance in learning analytics"],"prefix":"10.3389","volume":"17","author":[{"given":"Wenwei","family":"Huang","sequence":"first","affiliation":[{"name":"School of Marxism, HeNan Geology Mineral College","place":["Zhengzhou, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cui","family":"Yue","sequence":"additional","affiliation":[{"name":"School of Marxism, Xuchang University","place":["Xuchang, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"12135","DOI":"10.1038\/s41598-026-41794-8","article-title":"Spherical bipolar fuzzy decision model for green infrastructure selection","volume":"16","author":"Aarthi","year":"2026","journal-title":"Sci. Rep."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJOPCD.331751","article-title":"Learning analytics for data-driven decision making: enhancing instructional personalization and student engagement in online higher education","volume":"13","author":"Al-Zahrani","year":"2023","journal-title":"Int. J. Online Pedagog. Course Des."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1186\/s41239-024-00493-y","article-title":"Unpacking student engagement in higher education learning analytics: a systematic review","volume":"21","author":"Bergdahl","year":"2024","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1177\/1046496420915243","article-title":"Effects of leader networking behaviors and vertical faultlines on support for innovation","volume":"51","author":"Chung","year":"2020","journal-title":"Small Group Res."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"3533","DOI":"10.1007\/s11218-024-09941-z","article-title":"Determinants of academic achievement from the middle to secondary school education: a systematic review","volume":"27","author":"Costa","year":"2024","journal-title":"Soc. Psychol. Educ."},{"key":"ref6","doi-asserted-by":"crossref","DOI":"10.4324\/9780203879962","volume-title":"An Introduction to Latent variable Growth Curve Modeling: Concepts, Issues, and Application","author":"Duncan","year":"2013"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"59","DOI":"10.3102\/00346543074001059","article-title":"School engagement: potential of the concept, state of the evidence","volume":"74","author":"Fredricks","year":"2004","journal-title":"Rev. Educ. Res."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1108\/LODJ-05-2023-0227","article-title":"Why and when is frequent supervisory negative feedback undesirable? The role of trust in supervisor and attribution of supervisor motives","volume":"45","author":"Guo","year":"2024","journal-title":"Leadersh. Organ. Dev. J."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"7849","DOI":"10.3390\/su15107849","article-title":"Learning analytics on student engagement to enhance students\u2019 learning performance: a systematic review","volume":"15","author":"Johar","year":"2023","journal-title":"Sustainability"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"105246","DOI":"10.1016\/j.compedu.2025.105246","article-title":"A topical review of research in computer-supported collaborative learning: questions and possibilities","volume":"228","author":"Kaliisa","year":"2025","journal-title":"Comput. Educ."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"177255","DOI":"10.1016\/j.ejphar.2025.177255","article-title":"Unraveling the effectiveness of new media teaching strategies in pharmacology education under different educational backgrounds: insights from 6447 students","volume":"989","author":"Li","year":"2025","journal-title":"Eur. J. Pharmacol."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"772","DOI":"10.3390\/app15020772","article-title":"Artificial intelligence in educational data mining and human-in-the-loop machine learning and machine teaching: analysis of scientific knowledge","volume":"15","author":"L\u00f3pez-Meneses","year":"2025","journal-title":"Appl. Sci."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"100293","DOI":"10.1016\/j.jik.2022.100293","article-title":"The innovation effect of administrative hierarchy on intercity connection: the machine learning of twin cities","volume":"8","author":"Luo","year":"2023","journal-title":"J. Innov. Knowl."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"5","DOI":"10.3102\/0013189X018002005","article-title":"Meaning and values in test validation: the science and ethics of assessment","volume":"18","author":"Messick","year":"1989","journal-title":"Educ. Res."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1007\/s10648-025-10046-y","article-title":"A systematic review on student engagement in undergraduate mathematics: conceptualization, measurement, and learning outcomes","volume":"37","author":"Papageorgiou","year":"2025","journal-title":"Educ. Psychol. Rev."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-026-61471-0","article-title":"The role of PE teacher exchanges rotation (TER) in teaching quality: moderated mediation of job satisfaction and cross cultural competence","volume":"1","author":"Peng","year":"2026","journal-title":"Sci. Rep."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"2583","DOI":"10.1109\/TDSC.2025.3627604","article-title":"Invisible trails? An identity alignment scheme based on online tracking","volume":"23","author":"Shi","year":"2025","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"1380","DOI":"10.1177\/0002764213498851","article-title":"Learning analytics: the emergence of a discipline","volume":"57","author":"Siemens","year":"2013","journal-title":"Am. Behav. Sci."},{"key":"ref20","first-page":"252","author":"Siemens","year":"2012"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40359-026-05227-5","article-title":"The combined efiects of teachers\u2019 caring behavior and academic self-efficacy on the academic achievement of Chinese undergraduates: a fsQCA approach","volume":"1","author":"Tang","year":"2026","journal-title":"BMC Psychol."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"101077","DOI":"10.1016\/j.iheduc.2026.101077","article-title":"Mapping the structure of student burnout in online learning: an integrated Gaussian model and directed acyclic graph approach","volume":"70","author":"Tu","year":"2026","journal-title":"Internet High. Educ."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s41239-025-00515-3","article-title":"Engagement with analytics feedback and its relationship to self-regulated learning competence and course performance","volume":"22","author":"Uzun","year":"2025","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1177\/0018726720984838","article-title":"Employee proactive goal regulation and job performance: the role modeling and interacting effects of leader proactive goal regulation","volume":"75","author":"Wang","year":"2022","journal-title":"Hum. Relations"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"78","DOI":"10.3390\/bs15010078","article-title":"Exploring students online learning behavioral engagement in university: factors, academic performance and their relationship","volume":"15","author":"Wang","year":"2025","journal-title":"Behav. Sci."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"107539","DOI":"10.1016\/j.scs.2026.107539","article-title":"Learning from residuals: a knowledge-data-driven hybrid model for explaining the social-ecological influences on urban agglomeration resilience","volume":"147","author":"Wu","year":"2026","journal-title":"Sustain. Cities Soc."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"1360","DOI":"10.3390\/bs15101360","article-title":"Leveraging learning analytics to model student engagement in graduate statistics: a problem-based learning approach in agricultural education","volume":"15","author":"Xu","year":"2025","journal-title":"Behav. Sci."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"11801","DOI":"10.1109\/TITS.2021.3107310","article-title":"Short-term lateral behavior reasoning for target vehicles considering driver preview characteristic","volume":"23","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3779436","article-title":"Knowpath: an LLM-supported knowledge graph construction and path finding framework to explainable MOOC recommendations","volume":"44","author":"Zhu","year":"2026","journal-title":"ACM Trans. Inf. 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