{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T12:39:58Z","timestamp":1774528798613,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62262074"],"award-info":[{"award-number":["62262074"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62363015"],"award-info":[{"award-number":["62363015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42261068"],"award-info":[{"award-number":["42261068"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Plan Project of Yunnan Province","award":["202405AC350083"],"award-info":[{"award-number":["202405AC350083"]}]},{"DOI":"10.13039\/501100004479","name":"Natural Science Foundation of Jiangxi Province","doi-asserted-by":"publisher","award":["20242BAB25112"],"award-info":[{"award-number":["20242BAB25112"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Massive open online courses (MOOCs) represent an innovative online learning paradigm that has garnered considerable popularity in recent years, attracting a multitude of learners to MOOC platforms due to their accessible and adaptable instructional structure. However, the elevated dropout rate in current MOOCs limits their advancement. Current dropout prediction models predominantly employ fixed-size convolutional kernels for feature extraction, which insufficiently address temporal dependencies and consequently demonstrate specific limitations. We propose a Lie Group-based feature context-local fusion attention model for predicting dropout in MOOCs. This model initially extracts shallow features using Lie Group machine learning techniques and subsequently integrates multiple parallel dilated convolutional modules to acquire high-level semantic representations. We design an attention mechanism that integrates contextual and local features, effectively capturing the temporal dependencies in the study behaviors of learners. We performed multiple experiments on the XuetangX dataset to evaluate the model\u2019s efficacy. The results show that our method attains a precision score of 0.910, exceeding the previous state-of-the-art approach by 3.3%.<\/jats:p>","DOI":"10.3390\/informatics12040127","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T11:04:31Z","timestamp":1763723071000},"page":"127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MOOC Dropout Prediction via a Dilated Convolutional Attention Network with Lie Group Features"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8706-1533","authenticated-orcid":false,"given":"Yinxu","family":"Liu","sequence":"first","affiliation":[{"name":"School of Physics and Information Engineering, Zhaotong University, Zhaotong 657000, China"}]},{"given":"Chengjun","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jiangxi Normal University, Nanchang 330022, China"},{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China"}]},{"given":"Desheng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Physics and Information Engineering, Zhaotong University, Zhaotong 657000, China"}]},{"given":"Yuncheng","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Physics and Information Engineering, Zhaotong University, Zhaotong 657000, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103728","DOI":"10.1016\/j.compedu.2019.103728","article-title":"Temporal Analysis for Dropout Prediction Using Self-Regulated Learning Strategies in Self-Paced MOOCs","volume":"145","year":"2020","journal-title":"Comput. Educ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s41239-016-0024-z","article-title":"From Massive Access to Cooperation: Lessons Learned and Proven Results of a Hybrid xMOOC\/cMOOC Pedagogical Approach to MOOCs","volume":"13","year":"2016","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1111\/bjet.12268","article-title":"Will MOOCs Transform Learning and Teaching in Higher Education? Engagement and Course Retention in Online Learning Provision","volume":"46","author":"Morgan","year":"2015","journal-title":"Br. J. Educ. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"14823","DOI":"10.1007\/s00521-021-06122-3","article-title":"A Systematic Analysis Using Classification Machine Learning Algorithms to Understand Why Learners Drop out of MOOCs","volume":"33","author":"Rawat","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2885","DOI":"10.1007\/s11063-022-10745-5","article-title":"Educational Data Mining: Dropout Prediction in XuetangX MOOCs","volume":"54","author":"Xu","year":"2022","journal-title":"Neural Process. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8971","DOI":"10.1007\/s00500-021-05795-1","article-title":"Dropout Prediction Model in MOOC Based on Clickstream Data and Student Sample Weight","volume":"25","author":"Jin","year":"2021","journal-title":"Soft Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"29","DOI":"10.33093\/jiwe.2023.2.2.3","article-title":"Dropout Prediction Model for College Students in MOOCs Based on Weighted Multi-Feature and SVM","volume":"2","author":"Yujiao","year":"2023","journal-title":"J. Inform. Web Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8404653","DOI":"10.1155\/2019\/8404653","article-title":"MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine","volume":"2019","author":"Chen","year":"2019","journal-title":"Math. Probl. Eng."},{"key":"ref_9","first-page":"743","article-title":"Early Prediction of University Dropouts\u2014A Random Forest Approach","volume":"240","author":"Behr","year":"2020","journal-title":"Jahrb. Natl. Stat."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s41239-023-00400-x","article-title":"Dropout Prediction and Decision Feedback Supported by Multi Temporal Sequences of Learning Behavior in MOOCs","volume":"20","author":"Xia","year":"2023","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"22341","DOI":"10.1007\/s00521-023-08894-2","article-title":"CNN Autoencoders and LSTM-Based Reduced Order Model for Student Dropout Prediction","volume":"35","author":"Niu","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e2572","DOI":"10.7717\/peerj-cs.2572","article-title":"A Hybrid Model Integrating Recurrent Neural Networks and the Semi-Supervised Support Vector Machine for Identification of Early Student Dropout Risk","volume":"10","author":"Sarlan","year":"2024","journal-title":"PeerJ Comput. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, H.-C., Prasetyo, E., Tseng, S.-S., Putra, K.T., Kusumawardani, S.S., and Weng, C.-E. (2022). Week-Wise Student Performance Early Prediction in Virtual Learning Environment Using a Deep Explainable Artificial Intelligence. Appl. Sci., 12.","DOI":"10.3390\/app12041885"},{"key":"ref_14","first-page":"187","article-title":"Ensemble Deep Learning Network Model for Dropout Prediction in MOOCs","volume":"14","author":"Kumar","year":"2023","journal-title":"Int. J. Electr. Comput. Eng. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7102","DOI":"10.1080\/10494820.2023.2300000","article-title":"Early Prediction of MOOC Dropout in Self-Paced Students Using Deep Learning","volume":"32","author":"Wen","year":"2024","journal-title":"Interact. Learn. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lee, Y., Shin, D., Loh, H., Lee, J., Chae, P., Cho, J., Park, S., Lee, J., Baek, J., and Kim, B. (2021). Deep Attentive Study Session Dropout Prediction in Mobile Learning Environment. arXiv.","DOI":"10.5220\/0009347700260035"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1007\/s10115-022-01774-6","article-title":"A Prediction Model of Student Performance Based on Self-Attention Mechanism","volume":"65","author":"Chen","year":"2023","journal-title":"Knowl. Inf. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1080\/10494820.2020.1802300","article-title":"MOOC Student Dropout Prediction Model Based on Learning Behavior Features and Parameter Optimization","volume":"31","author":"Jin","year":"2023","journal-title":"Interact. Learn. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7165","DOI":"10.1007\/s13369-024-09287-w","article-title":"Optimised SMOTE-Based Imbalanced Learning for Student Dropout Prediction","volume":"50","author":"Masood","year":"2025","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rebelo Marcolino, M., Reis Porto, T., Thompsen Primo, T., Targino, R., Ramos, V., Marques Queiroga, E., Munoz, R., and Cechinel, C. (2025). Student Dropout Prediction through Machine Learning Optimization: Insights from Moodle Log Data. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-93918-1"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"113189","DOI":"10.1109\/ACCESS.2023.3323202","article-title":"A Time-Aware Approach for MOOC Dropout Prediction Based on Rule Induction and Sequential Three-Way Decisions","volume":"11","author":"Blundo","year":"2023","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kim, S., Choi, E., Jun, Y.-K., and Lee, S. (2023). Student Dropout Prediction for University with High Precision and Recall. Appl. Sci., 13.","DOI":"10.3390\/app13106275"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Alghamdi, S., Soh, B., and Li, A. (2025). ISELDP: An Enhanced Dropout Prediction Model Using a Stacked Ensemble Approach for In-Session Learning Platforms. Electronics, 14.","DOI":"10.3390\/electronics14132568"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"71474","DOI":"10.1109\/ACCESS.2018.2881275","article-title":"An Integrated Framework With Feature Selection for Dropout Prediction in Massive Open Online Courses","volume":"6","author":"Qiu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kabathova, J., and Drlik, M. (2021). Towards Predicting Student\u2019s Dropout in University Courses Using Different Machine Learning Techniques. Appl. Sci., 11.","DOI":"10.3390\/app11073130"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2213292","DOI":"10.1155\/2022\/2213292","article-title":"MOOC Dropout Prediction Based on Multidimensional Time-Series Data","volume":"2022","author":"Shou","year":"2022","journal-title":"Math. Probl. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1109\/TE.2024.3398771","article-title":"IC-BTCN: A Deep Learning Model for Dropout Prediction of MOOCs Students","volume":"67","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Educ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"96954","DOI":"10.1109\/ACCESS.2023.3312150","article-title":"Deep FM-Based Predictive Model for Student Dropout in Online Classes","volume":"11","author":"Alruwais","year":"2023","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"225324","DOI":"10.1109\/ACCESS.2020.3045157","article-title":"MOOC Dropout Prediction Using FWTS-CNN Model Based on Fused Feature Weighting and Time Series","volume":"8","author":"Zheng","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","first-page":"8255965","article-title":"Dropout Rate Prediction of Massive Open Online Courses Based on Convolutional Neural Networks and Long Short-Term Memory Network","volume":"2022","author":"Tang","year":"2022","journal-title":"Mob. Inf. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"121187","DOI":"10.1016\/j.eswa.2023.121187","article-title":"Ensemble Models Based on CNN and LSTM for Dropout Prediction in MOOC","volume":"235","author":"Talebi","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107271","DOI":"10.1016\/j.compeleceng.2021.107271","article-title":"Deep Analytic Model for Student Dropout Prediction in Massive Open Online Courses","volume":"93","author":"Mubarak","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"108409","DOI":"10.1016\/j.compeleceng.2022.108409","article-title":"MOOC Dropout Prediction Using a Fusion Deep Model Based on Behaviour Features","volume":"104","author":"Zheng","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"107315","DOI":"10.1016\/j.compeleceng.2021.107315","article-title":"CLSA: A Novel Deep Learning Model for MOOC Dropout Prediction","volume":"94","author":"Fu","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"202993","DOI":"10.1109\/ACCESS.2020.3035687","article-title":"Power of Attention in MOOC Dropout Prediction","volume":"8","author":"Yin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3257","DOI":"10.1007\/s10639-023-11960-w","article-title":"Learning Behavior Feature Fused Deep Learning Network Model for MOOC Dropout Prediction","volume":"29","author":"Liu","year":"2024","journal-title":"Educ. Inf. Technol."},{"key":"ref_37","first-page":"405","article-title":"MOOCs Dropout Prediction via Classmates Augmented Time-Flow Hybrid Network","volume":"Volume 1969","author":"Luo","year":"2024","journal-title":"Neural Information Processing"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"9977","DOI":"10.1007\/s12652-020-02747-9","article-title":"Attention-Based Hierarchical Recurrent Neural Networks for MOOC Forum Posts Analysis","volume":"12","author":"Capuano","year":"2021","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1108\/ITSE-10-2021-0188","article-title":"An Explainable Attention-Based Bidirectional GRU Model for Pedagogical Classification of MOOCs","volume":"19","author":"Sebbaq","year":"2022","journal-title":"Interact. Technol. Smart Educ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e15382","DOI":"10.1016\/j.heliyon.2023.e15382","article-title":"ANN-LSTM: A Deep Learning Model for Early Student Performance Prediction in MOOC","volume":"9","author":"Ghurab","year":"2023","journal-title":"Heliyon"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Xu, C., Zhu, G., and Shu, J. (2022). A Combination of Lie Group Machine Learning and Deep Learning for Remote Sensing Scene Classification Using Multi-Layer Heterogeneous Feature Extraction and Fusion. Remote Sens., 14.","DOI":"10.3390\/rs14061445"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3155","DOI":"10.1109\/TAC.2023.3239430","article-title":"Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design","volume":"68","author":"Galimberti","year":"2023","journal-title":"IEEE Trans. Autom. Control."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_44","first-page":"1","article-title":"A Lightweight and Robust Lie Group-Convolutional Neural Networks Joint Representation for Remote Sensing Scene Classification","volume":"60","author":"Xu","year":"2022","journal-title":"Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1109\/LGRS.2020.2986779","article-title":"Robust Joint Representation of Intrinsic Mean and Kernel Function of Lie Group for Remote Sensing Scene Classification","volume":"18","author":"Xu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"12581","DOI":"10.1109\/TPAMI.2023.3282631","article-title":"UniFormer: Unifying Convolution and Self-Attention for Visual Recognition","volume":"45","author":"Li","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Apostolidis, E., Balaouras, G., Mezaris, V., and Patras, I. (December, January 29). Combining Global and Local Attention with Positional Encoding for Video Summarization. Proceedings of the 2021 IEEE International Symposium on Multimedia (ISM), Naple, Italy.","DOI":"10.1109\/ISM52913.2021.00045"},{"key":"ref_48","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, The MIT Press."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"114410","DOI":"10.1016\/j.measurement.2024.114410","article-title":"A Novel Unsupervised Deep Learning Approach for Vibration-Based Damage Diagnosis Using a Multi-Head Self-Attention LSTM Autoencoder","volume":"229","author":"Ghazimoghadam","year":"2024","journal-title":"Measurement"},{"key":"ref_50","unstructured":"Feng, W., Tang, J., and Liu, T.X. (February, January 27). Understanding Dropouts in MOOCs. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/978-3-031-32883-1_45","article-title":"Using Feature Interaction for Mining Learners\u2019 Hidden Information in MOOC Dropout Prediction","volume":"Volume 13891","author":"Frasson","year":"2023","journal-title":"Augmented Intelligence and Intelligent Tutoring Systems"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"11499","DOI":"10.1007\/s10639-022-11068-7","article-title":"Dropout Prediction in Moocs Using Deep Learning and Machine Learning","volume":"27","author":"Basnet","year":"2022","journal-title":"Educ. Inf. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Pan, F., Huang, B., Zhang, C., Zhu, X., Wu, Z., Zhang, M., Ji, Y., Ma, Z., and Li, Z. (2022). A Survival Analysis Based Volatility and Sparsity Modeling Network for Student Dropout Prediction. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0267138"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"108989","DOI":"10.1016\/j.compeleceng.2023.108989","article-title":"PMCT: Parallel Multiscale Convolutional Temporal Model for MOOC Dropout Prediction","volume":"112","author":"Niu","year":"2023","journal-title":"Comput. Electr. Eng."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/4\/127\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T11:27:05Z","timestamp":1763724425000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/12\/4\/127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,21]]},"references-count":54,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["informatics12040127"],"URL":"https:\/\/doi.org\/10.3390\/informatics12040127","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,21]]}}}