{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:23:10Z","timestamp":1775913790028,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,12,12]],"date-time":"2019-12-12T00:00:00Z","timestamp":1576108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Program of the National Natural Science Foundation of China","award":["71834001"],"award-info":[{"award-number":["71834001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Massive open online courses (MOOCs), which have been deemed a revolutionary teaching mode, are increasingly being used in higher education. However, there remain deficiencies in understanding the relationship between online behavior of students and their performance, and in verifying how well a student comprehends learning material. Therefore, we propose a method for predicting student performance and mastery of knowledge points in MOOCs based on assignment-related online behavior; this allows for those providing academic support to intervene and improve learning outcomes of students facing difficulties. The proposed method was developed while using data from 1528 participants in a C Programming course, from which we extracted assignment-related features. We first applied a multi-task multi-layer long short-term memory-based student performance predicting method with cross-entropy as the loss function to predict students\u2019 overall performance and mastery of each knowledge point. Our method incorporates the attention mechanism, which might better reflect students\u2019 learning behavior and performance. Our method achieves an accuracy of 92.52% for predicting students\u2019 performance and a recall rate of 94.68%. Students\u2019 actions, such as submission times and plagiarism, were related to their performance in the MOOC, and the results demonstrate that our method predicts the overall performance and knowledge points that students cannot master well.<\/jats:p>","DOI":"10.3390\/e21121216","type":"journal-article","created":{"date-parts":[[2019,12,12]],"date-time":"2019-12-12T11:06:41Z","timestamp":1576148801000},"page":"1216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Predicting Student Performance and Deficiency in Mastering Knowledge Points in MOOCs Using Multi-Task Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4237-1961","authenticated-orcid":false,"given":"Shaojie","family":"Qu","sequence":"first","affiliation":[{"name":"Network Information Technology Center, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Wu","sequence":"additional","affiliation":[{"name":"Network Information Technology Center, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuri","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaihao","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1126\/science.1168018","article-title":"Opening education","volume":"323","author":"Smith","year":"2009","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1126\/science.11826962","article-title":"MIT OpenCourseWare: Unlocking knowledge, empowering minds","volume":"329","author":"Carson","year":"2010","journal-title":"Science"},{"key":"ref_3","first-page":"36","article-title":"MOOCs and the future of higher education","volume":"34","author":"Peter","year":"2013","journal-title":"J. Higher Ed. Theory Pract."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Christensen, G., Steinmetz, A., Alcorn, B., Bennett, A., Woods, D., and Emanuel, E. (2014). The MOOC phenomenon: Who takes massive open online courses and why?. SSRN Electron. J.","DOI":"10.2139\/ssrn.2350964"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ho, A., Ho, A., Reich, J., Nesterko, S., Seaton, D., Mullaney, T., Waldo, J., and Chuang, I. (2014). HarvardX and MITx: The first year of open online courses, fall 2012-summer 2013. SSRN Electron. J.","DOI":"10.2139\/ssrn.2381263"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1145\/2240236.2240246","article-title":"Will massive open online courses change how we teach?","volume":"55","author":"Martin","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.compedu.2007.05.016","article-title":"Data mining in course management systems: Moodle case study and tutorial","volume":"51","author":"Romero","year":"2008","journal-title":"Comput. Ed."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.compedu.2016.02.006","article-title":"Students\u2019 LMS interaction patterns and their relationship with achievement: A case study in higher education","volume":"96","author":"Cerezo","year":"2016","journal-title":"Comput. Ed."},{"key":"ref_9","first-page":"230","article-title":"Implementing technology to prevent online cheating: A case study at a small southern regional university (SSRU)","volume":"2","author":"Wayne","year":"2009","journal-title":"MERLOT J. Online Learn. Teach."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pang, Y., Song, M., Jin, Y., and Zhang, Y. (2015, January 20\u201323). Survey of MOOC related research. Proceedings of the International Conference on Database Systems for Advanced Applications, Hanoi, Vietnam.","DOI":"10.1007\/978-3-319-22324-7_15"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e1187","DOI":"10.1002\/widm.1187","article-title":"Educational data science in massive open online courses","volume":"7","author":"Romero","year":"2016","journal-title":"Wiley Interdiscip. Rev Data Min. Knowl. Discov."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"60264","DOI":"10.1109\/ACCESS.2018.2875742","article-title":"Predicting Achievement of Students in Smart Campus","volume":"6","author":"Qu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1111\/jcal.12270","article-title":"Predicting student performance in a blended MOOC","volume":"34","author":"Conijn","year":"2018","journal-title":"J. Comput. Assist. Learn."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Meier, Y., Xu, J., Atan, O., and Schaar, M. (2015, January 14\u201317). Personalized grade prediction: A data mining approach. Proceedings of the 2015 IEEE International Conference on Data Mining, Atlantic City, NJ, USA.","DOI":"10.1109\/ICDM.2015.54"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kahan, T., Soffer, T., and Nachmias, R. (2017). Types of participant behavior in a massive open online course. Int. Rev. Res. Open Distribut. Learn., 18.","DOI":"10.19173\/irrodl.v18i6.3087"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4129","DOI":"10.1109\/TLA.2016.7785943","article-title":"Discovery engagement patterns MOOCs through cluster analysis","volume":"14","author":"Rodrigues","year":"2016","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3677","DOI":"10.1109\/TSP.2016.2546228","article-title":"Mining MOOC clickstreams: Video-watching behavior vs. in-video quiz performance","volume":"64","author":"Brinton","year":"2016","journal-title":"IEEE Trans. Signal Proc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.compedu.2016.04.008","article-title":"Detecting and preventing \u201cmultiple-account\u201d cheating in massive open online courses","volume":"100","author":"Northcutt","year":"2016","journal-title":"Comput. Ed."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.compedu.2017.01.015","article-title":"Copying@Scale: Using harvesting accounts for collecting correct answers in a MOOC","volume":"108","author":"Alexandron","year":"2017","journal-title":"Comput. Ed."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1093\/nsr\/nwx105","article-title":"An overview of multi-task learning","volume":"5","author":"Zhang","year":"2017","journal-title":"Natl. Sci. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1023\/A:1007327622663","article-title":"A Bayesian\/information theoretic model of learning to learn via multiple task sampling","volume":"28","author":"Baxter","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Duong, L., Cohn, T., Bird, S., and Cook, P. (2015, January 26\u201331). Low Resource Dependency Parsing: Cross-lingual Parameter Sharing in a Neural Network Parser. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Beijing, China.","DOI":"10.3115\/v1\/P15-2139"},{"key":"ref_23","unstructured":"Yang, Y., and Hospedales, T. (2016). Trace Norm Regularised Deep Multi-Task Learning. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hashimoto, K., Xiong, C., Tsuruoka, Y., and Socher, R. (2017, January 9\u201311). A joint many-task model: Growing a neural network for multiple NLP tasks. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.","DOI":"10.18653\/v1\/D17-1206"},{"key":"ref_25","unstructured":"Kendall, A., Gal, Y., and Cipolla, R. (2018, January 18\u201323). Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. Proceedings of the 2018 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Luo, P., Loy, C., and Tang, X. (2014, January 6\u201312). Facial landmark detection by deep multi-task learning. Proceedings of the Computer Vision\u2014ECCV 2014, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10599-4_7"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","article-title":"Multitask learning","volume":"28","author":"Caruana","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to forget: Continual prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3459","DOI":"10.1109\/TIP.2018.2818328","article-title":"Spatio-temporal attention-based LSTM networks for 3D action recognition and detection","volume":"99","author":"Song","year":"2018","journal-title":"IEEE Trarns. Image Proc."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Trigeorgis, G., Ringeval, F., Brueckner, R., Marchi, E., Nicolaou, M.A., Schuller, B., and Zafeiriou, S. (2016, January 20\u201325). Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7472669"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., and Savarese, S. (July, January 26). Social lstm: Human trajectory prediction in crowded spaces. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, Z., Xia, J., Li, F., Li, Z., and Li, Q. (2019). A Peak Traffic Congestion Prediction Method Based on Bus Driving Time. Entropy, 21.","DOI":"10.3390\/e21070709"},{"key":"ref_34","unstructured":"Duch, W., Wieczorek, T., Biesiada, J., and Blachnik, M. (2004, January 25\u201329). Comparison of Feature Ranking Methods Based on Information Entropy. Proceedings of the 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), Budapest, Hungary."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1007\/s11432-010-3117-7","article-title":"Information entropy for ordinal classification","volume":"53","author":"Hu","year":"2010","journal-title":"Sci. China Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"493","DOI":"10.3390\/e10040493","article-title":"Entropy and uncertainty","volume":"10","author":"Robinson","year":"2008","journal-title":"Entropy"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1023\/A:1010091220143","article-title":"The Cross-Entropy Method for Combinatorial and Continuous Optimization","volume":"1","author":"Rubinstein","year":"1999","journal-title":"Methodol. Comput. Appl. Probab."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Almgren, K., Krishna, M., Aljanobi, F., and Lee, J. (2018). AD or Non-AD: A Deep Learning Approach to Detect Advertisements from Magazines. Entropy, 20.","DOI":"10.3390\/e20120982"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1145\/3115432","article-title":"Image captioning with deep bidirectional LSTMs and multi-task learning","volume":"14","author":"Wang","year":"2018","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Dong, X., Chowdhury, S., Qian, L., Li, X., Guan, Y., Yang, J., and Yu, Q. (2019). Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0216046"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/12\/1216\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:41:44Z","timestamp":1760190104000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/12\/1216"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,12]]},"references-count":40,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["e21121216"],"URL":"https:\/\/doi.org\/10.3390\/e21121216","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,12]]}}}