{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:03:53Z","timestamp":1790787833709,"version":"4.1.0"},"reference-count":51,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University","award":["IMSIU-DDRSP2504"],"award-info":[{"award-number":["IMSIU-DDRSP2504"]}]},{"name":"Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic Universit","award":["IMSIU-DDRSP2504"],"award-info":[{"award-number":["IMSIU-DDRSP2504"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>The advancement in computing technology, online learning platforms, and pedagogical tools enable educators and learners to connect without temporal and geographical boundaries. The existing deep learning models to predict student performance are either simple recurrent neural networks or artificial neural networks employing demographic and hand-crafted features. This manuscript proposes a model, MultIFAR, that infuses multi-dimensional information representing different aspects of student behavior with an attention-driven deep learning model integrating bidirectional long short-term memory and convolutional networks to learn student representation efficiently. MultIFAR employs student demographic, assessment, and VLE-interaction to understand different aspects of student behavior from multifaceted data. MultIFAR includes bidirectional long short-term memory to process and capture patterns from demographic, assessment, and interaction information. The model applies a convolutional operation on the daily interaction information with the VLE. We also implement the attention mechanism to assign weight to significant features. The empirical evaluation over the Open University Learning Analytics (OULA) dataset establishes the efficacy of MultIFAR against the state-of-the-art approaches and baseline methods. Considering accuracy, MultIFAR reports results from 80.31% to 97.12% over the four different problems of student performance prediction. The ablation analysis reveals that diurnal interaction shows the highest, whereas demographic attributes show the least impact on MultIFAR accuracy. We also extend MultIFAR to predict at-risk and high-performing students early. We also evaluate the model over the balanced dataset and multiclass scenario.<\/jats:p>","DOI":"10.1371\/journal.pone.0333099","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T17:50:47Z","timestamp":1761328247000},"page":"e0333099","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["MultiFAR: Multidimensional information fusion with attention-driven representation learning for student performance prediction"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8936-848X","authenticated-orcid":true,"given":"Mohd","family":"Fazil","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4397-9867","authenticated-orcid":true,"given":"Bader M.","family":"Albahlal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,10,24]]},"reference":[{"issue":"2","key":"pone.0333099.ref001","first-page":"1","article-title":"Predicting at-risk university students in a virtual learning environment via a machine learning algorithm","volume":"107","author":"KT Chui","year":"2020","journal-title":"Computers in Human Behavior."},{"key":"pone.0333099.ref002","doi-asserted-by":"crossref","first-page":"106189","DOI":"10.1016\/j.chb.2019.106189","article-title":"Predicting academic performance of students from VLE big data using deep learning models","volume":"104","author":"H Waheed","year":"2020","journal-title":"Computers in Human Behavior."},{"key":"pone.0333099.ref003","doi-asserted-by":"crossref","unstructured":"Santos JL, Klerkx J, Duval E, Gago D, Rodr\u00edguez L. Success, activity and drop-outs in MOOCs an exploratory study on the UNED COMA courses. In: Proceedings of the Fourth International Conference on Learning Analytics and Knowledge. 2014. p. 98\u2013102. https:\/\/doi.org\/10.1145\/2567574.2567627","DOI":"10.1145\/2567574.2567627"},{"issue":"1","key":"pone.0333099.ref004","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s10639-020-10260-x","article-title":"A two-phase machine learning approach for predicting student outcomes","volume":"26","author":"O Iatrellis","year":"2020","journal-title":"Educ Inf Technol."},{"issue":"6","key":"pone.0333099.ref005","first-page":"1347","article-title":"Modeling student knowledge retention using deep learning and random forests","volume":"13","author":"N Sharada","year":"2018","journal-title":"Journal of Engineering and Applied Sciences."},{"key":"pone.0333099.ref006","doi-asserted-by":"crossref","unstructured":"Wang W, Yu H, Miao C. Deep model for dropout prediction in MOOCs. In: Proceedings of the 2nd International Conference on Crowd Science and Engineering. 2017. p. 26\u201332. https:\/\/doi.org\/10.1145\/3126973.3126990","DOI":"10.1145\/3126973.3126990"},{"issue":"3","key":"pone.0333099.ref007","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1177\/0735633118757015","article-title":"Dropout prediction in MOOCs: using deep learning for personalized intervention","volume":"57","author":"W Xing","year":"2018","journal-title":"Journal of Educational Computing Research."},{"issue":"13","key":"pone.0333099.ref008","doi-asserted-by":"crossref","DOI":"10.1002\/cpe.6244","article-title":"Minkowski sommon feature map-based densely connected deep convolution network with LSTM for academic performance prediction","volume":"33","author":"K Ramanathan","year":"2021","journal-title":"Concurrency and Computation."},{"issue":"24","key":"pone.0333099.ref009","doi-asserted-by":"crossref","first-page":"64369","DOI":"10.1007\/s11042-024-18135-w","article-title":"Optimized deep network based students performance analysis for college admissions","volume":"83","author":"V Parvati","year":"2024","journal-title":"Multimed Tools Appl."},{"issue":"15","key":"pone.0333099.ref010","doi-asserted-by":"crossref","first-page":"23427","DOI":"10.1007\/s11042-022-14083-5","article-title":"Deep auto encoder based on a transient search capsule network for student performance prediction","volume":"82","author":"Rahul","year":"2022","journal-title":"Multimed Tools Appl."},{"key":"pone.0333099.ref011","doi-asserted-by":"crossref","first-page":"109309","DOI":"10.1016\/j.patcog.2023.109309","article-title":"ProbSAP: a comprehensive and high-performance system for student academic performance prediction","volume":"137","author":"X Wang","year":"2023","journal-title":"Pattern Recognition."},{"issue":"1","key":"pone.0333099.ref012","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJICTE.352512","article-title":"Integration of collaborative filtering into naive bayes method to enhance student performance prediction","volume":"20","author":"V Nakhipova","year":"2024","journal-title":"International Journal of Information and Communication Technology Education."},{"key":"pone.0333099.ref013","unstructured":"Yin W, Kann K, Yu M, Schutze H. Comparative study of CNN and RNN for natural language processing. arXiv preprint. 2017. p. 1\u20137. arXiv:1702.01923v1"},{"issue":"1","key":"pone.0333099.ref014","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1111\/exsy.12135","article-title":"Early dropout prediction using data mining: a case study with high school students","volume":"33","author":"C M\u00e1rquez-Vera","year":"2015","journal-title":"Expert Systems."},{"issue":"2","key":"pone.0333099.ref015","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.eswa.2013.07.046","article-title":"A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition","volume":"41","author":"D Thammasiri","year":"2014","journal-title":"Expert Systems with Applications."},{"key":"pone.0333099.ref016","unstructured":"Karimi H, Derr T, Huang J, Tang J. Online academic course performance prediction using relational graph convolutional neural network. In: Proceedings of the 13th International Conference on Educational Data Mining. 2020. p. 444\u201350."},{"key":"pone.0333099.ref017","doi-asserted-by":"crossref","unstructured":"Kaura P, Singh M, Josan GS. Classification and prediction based data mining algorithms to predict slow learners in education sector. In: Proceedings of the 3rd International Conference on Recent Trends in Computing. 2015. p. 500\u20138.","DOI":"10.1016\/j.procs.2015.07.372"},{"key":"pone.0333099.ref018","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.tele.2019.01.007","article-title":"Educational data mining and learning analytics for 21st century higher education: a review and synthesis","volume":"37","author":"H Aldowah","year":"2019","journal-title":"Telematics and Informatics."},{"key":"pone.0333099.ref019","doi-asserted-by":"crossref","first-page":"116392","DOI":"10.1109\/ACCESS.2020.2994561","article-title":"Big educational data & analytics: survey, architecture and challenges","volume":"8","author":"KL-M Ang","year":"2020","journal-title":"IEEE Access."},{"issue":"1","key":"pone.0333099.ref020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/1306039","article-title":"A systematic review of deep learning approaches to educational data mining","volume":"2019","author":"A Hern\u00e1ndez-Blanco","year":"2019","journal-title":"Complexity."},{"issue":"4","key":"pone.0333099.ref021","first-page":"1008","article-title":"Prediction of student academic performance in an engineering dynamics course: development and validation of multivariate regression models","volume":"26","author":"S Huang","year":"2010","journal-title":"International Journal of Engineering Education."},{"key":"pone.0333099.ref022","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.compedu.2012.08.015","article-title":"Predicting student academic performance in an engineering dynamics course: a comparison of four types of predictive mathematical models","volume":"61","author":"S Huang","year":"2013","journal-title":"Computers & Education."},{"key":"pone.0333099.ref023","doi-asserted-by":"crossref","unstructured":"Christian TM, Ayub M. Exploration of classification using NBTree for predicting students performance. In: Proceedings of the 2014 International Conference on Data and Software Engineering. Bandung, Indonesia: IEEE Computer Society; 2014. p. 1\u20136.","DOI":"10.1109\/ICODSE.2014.7062654"},{"issue":"1","key":"pone.0333099.ref024","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1002\/cae.20456","article-title":"Web usage mining for predicting final marks of students that use Moodle courses","volume":"21","author":"C Romero","year":"2013","journal-title":"Comp Applic In Engineering."},{"key":"pone.0333099.ref025","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.dss.2018.09.001","article-title":"Early segmentation of students according to their academic performance: a predictive modelling approach","volume":"115","author":"VL Migu\u00e9is","year":"2018","journal-title":"Decision Support Systems."},{"key":"pone.0333099.ref026","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.compedu.2019.04.001","article-title":"The role of demographics in online learning; a decision tree based approach","volume":"137","author":"S Rizvi","year":"2019","journal-title":"Computers & Education."},{"issue":"1","key":"pone.0333099.ref027","first-page":"1","article-title":"Understanding student learning behavior and predicting their performance","volume":"1","author":"M Wasif","year":"2019","journal-title":"Cognitive Computing in Technology-Enhanced Learning."},{"issue":"3","key":"pone.0333099.ref028","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/MCI.2018.2840738","article-title":"Recent trends in deep learning based natural language processing [review article]","volume":"13","author":"T Young","year":"2018","journal-title":"IEEE Comput Intell Mag."},{"key":"pone.0333099.ref029","doi-asserted-by":"crossref","unstructured":"Zhang L, Qi GJ, Wang L, Luo J. AET vs. AED: unsupervised representation learning by auto-encoding transformations rather than data. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition. California, USA: IEEE\/CVF; 2019. p. 2542\u20132550.","DOI":"10.1109\/CVPR.2019.00265"},{"key":"pone.0333099.ref030","doi-asserted-by":"crossref","first-page":"4211","DOI":"10.1109\/TIFS.2021.3102498","article-title":"DeepSBD: a deep neural network model with attention mechanism for SocialBot detection","volume":"16","author":"M Fazil","year":"2021","journal-title":"IEEE TransInformForensic Secur."},{"issue":"8","key":"pone.0333099.ref031","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1002\/int.22129","article-title":"Virtual learning environment to predict withdrawal by leveraging deep learning","volume":"34","author":"S Hassan","year":"2019","journal-title":"Int J Intell Syst."},{"issue":"2","key":"pone.0333099.ref032","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1109\/TLT.2019.2912162","article-title":"Developing early detectors of student attrition and wheel spinning using deep learning","volume":"12","author":"AF Botelho","year":"2019","journal-title":"IEEE Trans Learning Technol."},{"key":"pone.0333099.ref033","doi-asserted-by":"crossref","unstructured":"Alam MM, Mohiuddin K, Das AK, Islam MdK, Kaonain MdS, Ali MdH. A Reduced feature based neural network approach to classify the category of students. In: Proceedings of the 2nd International Conference on Innovation in Artificial Intelligence. 2018. p. 28\u201332. https:\/\/doi.org\/10.1145\/3194206.3194218","DOI":"10.1145\/3194206.3194218"},{"key":"pone.0333099.ref034","doi-asserted-by":"crossref","unstructured":"Raga RC, Raga JD. Early prediction of student performance in blended learning courses using deep neural networks. In: 2019 International Symposium on Educational Technology (ISET). 2019. p. 39\u201343. https:\/\/doi.org\/10.1109\/iset.2019.00018","DOI":"10.1109\/ISET.2019.00018"},{"key":"pone.0333099.ref035","doi-asserted-by":"crossref","unstructured":"Hu Q, Rangwala H. Reliable deep grade prediction with uncertainty estimation. In: Proceedings of 9th International Conference on Learning Analytics and Knowledge. Arizona, USA: ACM; 2019. p. 76\u201385.","DOI":"10.1145\/3303772.3303802"},{"issue":"2","key":"pone.0333099.ref036","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s00521-020-05045-9","article-title":"Predicting academic performance of students in Chinese-foreign cooperation in running schools with graph convolutional network","volume":"33","author":"P Hai-tao","year":"2020","journal-title":"Neural Comput & Applic."},{"key":"pone.0333099.ref037","doi-asserted-by":"crossref","unstructured":"Junejo NUR, Huang Q, Dong X, Wang C, Humayoo M, Zheng G. SLPNet: student learning performance prediction during the COVID-19 pandemic via a deep neural network. In: Proceedings of CCF National Conference of Computer Applications, Harbin, China. 2024. p. 65\u201375.","DOI":"10.1007\/978-981-97-9674-8_5"},{"issue":"1","key":"pone.0333099.ref038","doi-asserted-by":"crossref","first-page":"24605","DOI":"10.1038\/s41598-024-75242-2","article-title":"SAPPNet: students\u2019 academic performance prediction during COVID-19 using neural network","volume":"14","author":"NUR Junejo","year":"2024","journal-title":"Sci Rep."},{"issue":"1","key":"pone.0333099.ref039","doi-asserted-by":"crossref","first-page":"16251","DOI":"10.1038\/s41598-025-00256-3","article-title":"Accurate multi-category student performance forecasting at early stages of online education using neural networks","volume":"15","author":"NUR Junejo","year":"2025","journal-title":"Sci Rep."},{"issue":"4","key":"pone.0333099.ref040","first-page":"1","article-title":"ANN-LSTM: A deep learning model for early student performance prediction in MOOC","volume":"9","author":"FA Al-azazi","year":"2023","journal-title":"Heliyon."},{"issue":"8","key":"pone.0333099.ref041","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"S Hochreiter","year":"1997","journal-title":"Neural Computation."},{"key":"pone.0333099.ref042","unstructured":"Pascanu R, Gulcehre C, Cho K, Bengio Y. How to construct deep recurrent neural networks. In: Proceedings of the International Conference on Learning Representations. Banff, Canada: Arxiv; 2014. p. 1\u201313."},{"issue":"7553","key":"pone.0333099.ref043","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Y LeCun","year":"2015","journal-title":"Nature."},{"key":"pone.0333099.ref044","doi-asserted-by":"crossref","first-page":"204951","DOI":"10.1109\/ACCESS.2020.3037073","article-title":"A framework for hate speech detection using deep convolutional neural network","volume":"8","author":"PK Roy","year":"2020","journal-title":"IEEE Access."},{"key":"pone.0333099.ref045","doi-asserted-by":"crossref","unstructured":"Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E. Hierarchical attention networks for document classification. In: Proceedings of International Conference on 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. California, USA: ACL; 2016. p. 1480\u20131489.","DOI":"10.18653\/v1\/N16-1174"},{"issue":"1","key":"pone.0333099.ref046","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2017.171","article-title":"Open university learning analytics dataset","volume":"4","author":"J Kuzilek","year":"2017","journal-title":"Scientific Data."},{"issue":"1","key":"pone.0333099.ref047","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MIS.2020.3006961","article-title":"SEPN: a sequential engagement based academic performance prediction model","volume":"36","author":"X Song","year":"2021","journal-title":"IEEE Intell Syst."},{"key":"pone.0333099.ref048","doi-asserted-by":"crossref","first-page":"118868","DOI":"10.1016\/j.eswa.2022.118868","article-title":"Early prediction of learners at risk in self-paced education: a neural network approach","volume":"213","author":"H Waheed","year":"2023","journal-title":"Expert Systems with Applications."},{"key":"pone.0333099.ref049","unstructured":"Huang X, Khetan A, Cvitkovic M, Karnin Z. TabTransformer: tabular data modeling using contextual embeddings. arXiv prerpint 2020. p. 1\u201317. https:\/\/arxiv.org\/abs\/2012.06678"},{"key":"pone.0333099.ref050","unstructured":"Cholakov R, Kolev T. The GatedTabTransformer. An enhanced deep learning architecture for tabular modeling."},{"issue":"7","key":"pone.0333099.ref051","first-page":"1","article-title":"Re-imagining learning analytics a case for starting again?","volume":"46","author":"N Selwyn","year":"2020","journal-title":"Internet and Higher Education."}],"updated-by":[{"DOI":"10.1371\/journal.pone.0340140","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:00:00Z","timestamp":1767312000000}}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0333099","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T17:50:56Z","timestamp":1761328256000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0333099"}},"subtitle":[],"editor":[{"given":"Jinran","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,10,24]]},"references-count":51,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10,24]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0333099","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,24]]}}}