{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T18:14:25Z","timestamp":1788200065544,"version":"build-2803163510"},"reference-count":47,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T00:00:00Z","timestamp":1586390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>As students\u2019 behaviors are important factors that can reflect their learning styles and living habits on campus, extracting useful features of them plays a helpful role in understanding the students\u2019 learning process, which is an important step towards personalized education. Recently, the task of predicting students\u2019 performance from their campus behaviors has aroused the researchers\u2019 attention. However, existing studies mainly focus on extracting statistical features manually from the pre-stored data, resulting in hysteresis in predicting students\u2019 achievement and finding out their problems. Furthermore, due to the limited representation capability of these manually extracted features, they can only understand the students\u2019 behaviors shallowly. To make the prediction process timely and automatically, we treat the performance prediction task as a short-term sequence prediction problem, and propose a two-stage classification framework, i.e., Sequence-based Performance Classifier (SPC), which consists of a sequence encoder and a classic data mining classifier. More specifically, to deeply discover the sequential features from students\u2019 campus behaviors, we first introduce an attention-based Hybrid Recurrent Neural Network (HRNN) to encode their recent behaviors by giving a higher weight to the ones that are related to the students\u2019 last action. Then, to conduct student performance prediction, we further involve these learned features to the classic Support Vector Machine (SVM) algorithm and finally achieve our SPC model. We conduct extensive experiments in the real-world student card dataset. The experimental results demonstrate the superiority of our proposed method in terms of Accuracy and Recall.<\/jats:p>","DOI":"10.3390\/info11040201","type":"journal-article","created":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T14:42:03Z","timestamp":1586443323000},"page":"201","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Student Performance Prediction with Short-Term Sequential Campus Behaviors"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9505-0603","authenticated-orcid":false,"given":"Xinhua","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7153-1229","authenticated-orcid":false,"given":"Xuemeng","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Business, Shandong Normal University, Jinan 250358, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangai","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liancheng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20180210","DOI":"10.1098\/rsif.2018.0210","article-title":"Orderliness predicts academic performance: Behavioural analysis on campus lifestyle","volume":"15","author":"Cao","year":"2018","journal-title":"J. R. Soc. Interface"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.compedu.2019.02.011","article-title":"Predictive power of regularity of pre-class activities in a flipped classroom","volume":"134","author":"Jovanovic","year":"2019","journal-title":"Comput. Educ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Carter, A.S., Hundhausen, C.D., and Adesope, o. (2015, January 9\u201313). The Normalized Programming State Model: Predicting Student Performance in Computing Courses Based on Programming Behavior. Proceedings of the Eleventh Annual International Conference on International Computing Education Research, Omaha, NE, USA.","DOI":"10.1145\/2787622.2787710"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.jrp.2004.10.003","article-title":"Aptitude is not enough: How personality and behavior predict academic performance","volume":"40","author":"Conard","year":"2006","journal-title":"J. Res. Pers."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1002\/widm.1075","article-title":"Data mining in education","volume":"3","author":"Romero","year":"2013","journal-title":"Wiley Interdiscip. Rev.-Data Min. Knowl. Discov."},{"key":"ref_6","first-page":"18","article-title":"Combining unsupervised and supervised classification to build user models for exploratory","volume":"1","author":"Amershi","year":"2009","journal-title":"JEDM"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hou, Y.F., Zhou, P., Xu, J., and Wu, D.O. (2018, January 15\u201319). Course recommendation of MOOC with big data support: A contextual online learning approach. Proceedings of the 2018 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Honolulu, HI, USA.","DOI":"10.1109\/INFCOMW.2018.8406936"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fei, M., and Yeung, D.Y. (2015). Temporal models for predicting student dropout in massive open online courses. ICDMW, 256\u2013263.","DOI":"10.1109\/ICDMW.2015.174"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.compedu.2013.06.009","article-title":"Predicting students\u2019 final performance from participation in online discussion forums","volume":"68","author":"Romero","year":"2013","journal-title":"Comput. Educ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, J., Ren, P.J., Chen, Z.M., Ren, Z.C., Lian, T., and Ma, J. (2017, January 6\u201310). Neural attentive session-based recommendation. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore.","DOI":"10.1145\/3132847.3132926"},{"key":"ref_11","unstructured":"Kumar, A., Irsoy, O., Ondruska, P., Iyyer, M., Bradbury, J., Gulrajani, I., Zhong, V., Paulus, R., and Socher, R. (2016, January 24). Ask me anything: Dynamic memory networks for natural language processing. Proceedings of the 33rd International Conference on Machine Learning(ICML), New York, NY, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"366","DOI":"10.17993\/3ctecno.2019.specialissue2.366-383","article-title":"Predicting student academic performance using data generated in higher educational institutes","volume":"8","author":"Meghji","year":"2019","journal-title":"3C Tecnolog\u00eda"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.iheduc.2015.11.003","article-title":"Identifying significant indicators using LMS data to predict course achievement in online learning","volume":"29","author":"You","year":"2016","journal-title":"Internet High. Educ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/TLT.2016.2616312","article-title":"Predicting student performance from LMS data: A comparison of 17 blended courses using Moodle LMS","volume":"10","author":"Conijn","year":"2016","journal-title":"IEEE Trans. Learn. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Di Mitri, D., Scheffel, M., Drachsler, H., B\u00f6rner, D., Ternier, S., and Specht, M. (2017, January 13\u201317). Learning pulse: A machine learning approach for predicting performance in self-regulated learning using multimodal data. Proceedings of the Seventh International Learning Analytics & Knowledge Conference, New York, NY, USA.","DOI":"10.1145\/3027385.3027447"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"29101","DOI":"10.1007\/s11432-017-9371-y","article-title":"Pre-course student performance prediction with multi-instance multi-label learning","volume":"62","author":"Ma","year":"2019","journal-title":"Sci. China Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhou, M.Y., Ma, M.H., Zhang, Y.K., SuiA, K.X., Pei, D., and Moscibroda, T. (2016, January 12\u201316). EDUM: Classroom education measurements via large-scaleWiFi networks. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany.","DOI":"10.1145\/2971648.2971657"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sweeney, M., Rangwala, H., Lester, J., and Johri, A. (2016). Next-term student performance prediction: A recommender systems approach. arXiv.","DOI":"10.1109\/BigData.2015.7363847"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1109\/TSP.2015.2496278","article-title":"Predicting grades","volume":"64","author":"Meier","year":"2015","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1162\/tacl_a_00067","article-title":"Fully character-level neural machine translation without explicit segmentation","volume":"5","author":"Lee","year":"2017","journal-title":"TACL"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1162\/tacl_a_00065","article-title":"Google\u2019s multilingual neural machine translation system: Enabling zero-shot translation","volume":"5","author":"Johnson","year":"2017","journal-title":"TACL"},{"key":"ref_22","unstructured":"Ren, P.J., Chen, Z.M., Li, J., Ren, Z.C., Ma, J., and De Rijke, M. (February, January 27). RepeatNet: A repeat aware neural recommendation machine for session-based recommendation. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Donkers, T., Loepp, B., and Ziegler, J. (2017, January 27\u201331). Sequential user-based recurrent neural network recommendations. Proceedings of the Eleventh ACM Conference on Recommender Systems, Como, Italy.","DOI":"10.1145\/3109859.3109877"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, B., and Lane, I. (2016). Attention-based recurrent neural network models for joint intent detection and slot filling. arXiv.","DOI":"10.21437\/Interspeech.2016-1352"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chung, Y.A., Wu, C.C., Shen, C.H., Lee, H.Y., and Lee, L. (2017, January 20\u201324). Unsupervised learning of audio segment representations using sequence-to-sequence recurrent neural networks. Proceedings of the Interspeech 2016, Stockholm, Sweden.","DOI":"10.21437\/Interspeech.2016-82"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Li, H., Liao, Y.K., Wang, B.D., Guan, Z.Y., Liu, H.F., and Cai, D. (2017, January 19\u201325). What to Do Next: Modeling User Behaviors by Time-LSTM. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/504"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1002\/asi.20970","article-title":"Early and dynamic student achievement prediction in e-learning courses using neural networks","volume":"60","author":"Lykourentzou","year":"2009","journal-title":"J. Am. Soc. Inf. Sci. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1007\/s10586-017-1624-7","article-title":"Cluster-based distributed architecture for prediction of student\u2019s performance in higher education","volume":"22","author":"Ramanathan","year":"2019","journal-title":"Clust. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Raga, R.C., and Raga, J.D. (2019, January 2\u20134). Early Prediction of Student Performance in Blended Learning Courses Using Deep Neural Networks. Proceedings of the 2019 International Symposium on Educational Technology (ISET), Hradec Kralove, Czech Republic.","DOI":"10.1109\/ISET.2019.00018"},{"key":"ref_30","unstructured":"Askinadze, A., Liebeck, M., and Conrad, S. (2018, January 26\u201330). Predicting Student Test Performance based on Time Series Data of eBook Reader Behavior Using the Cluster-Distance Space Transformation. Proceedings of the 26th International Conference on Computers in Education(ICCE), Manila, Philppines."},{"key":"ref_31","unstructured":"Okubo, F., Yamashita, T., Shimada, A., Taniguchi, Y., and Shin\u2019ichi, K. (2018). On the Prediction of Students\u2019 Quiz Score by Recurrent Neural Network. CEUR Workshop Proc., 2163."},{"key":"ref_32","first-page":"716","article-title":"Behavior-based grade prediction for MOOCs via time series neural networks","volume":"11","author":"Yang","year":"2017","journal-title":"IEEE J. Selected Topics Signal Process."},{"key":"ref_33","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s13042-017-0697-1","article-title":"Multi-kernel SVM based depression recognition using social media data","volume":"10","author":"Peng","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhou, Z.H., Liu, X.H., Jiang, Y.R., Mai, J.G., and Wang, Q.N. (2019, January 25\u201327). Real-time onboard SVM-based human locomotion recognition for a bionic knee exoskeleton on different terrains. Proceedings of the 2019 Wearable Robotics Association Conference (WearRAcon), Scottsdale, AZ, USA.","DOI":"10.1109\/WEARRACON.2019.8719399"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"173","DOI":"10.18280\/isi.240207","article-title":"Image Segmentation Technique Using SVM Classifier for Detection of Medical Disorders","volume":"24","author":"Reddy","year":"2019","journal-title":"ISI"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s10462-018-9620-8","article-title":"Using machine learning to predict student difficulties from learning session data","volume":"52","author":"Hussain","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.1007\/s10639-019-09873-8","article-title":"Predicting students\u2019 final degree classification using an extended profile","volume":"24","author":"Palaniappan","year":"2019","journal-title":"Educ. Inf. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","author":"Vapnik","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_40","first-page":"588","article-title":"Student academic performance prediction using support vector machine","volume":"6","author":"Oloruntoba","year":"2017","journal-title":"Int. J. Eng. Sci. Res. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2202","DOI":"10.4236\/ce.2016.715217","article-title":"Logistic Regression Model for the Academic Performance of First-Year Medical Students in the Biomedical Area","volume":"7","author":"Beck","year":"2016","journal-title":"Creative Educ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Arsad, P.M., Buniyamin, N., and Ab Manan, J.L. (2013, January 25\u201327). A neural network students\u2019 performance prediction model (NNSPPM). Proceedings of the 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICSIMA.2013.6717966"},{"key":"ref_43","first-page":"335","article-title":"Efficiency of decision trees in predicting student\u2019s academic performance","volume":"2","author":"Kumar","year":"2011","journal-title":"Comput. Sci. Inf. Technol."},{"key":"ref_44","first-page":"18","article-title":"Classification and regression by random Forest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_45","unstructured":"Kingma, D.P., and Ba, J. (2014). A method for stochastic optimization. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tan, Y.K., Xu, X.X., and Liu, Y. (2016, January 15). Improved recurrent neural networks for session-based recommendations. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, Boston, MA, USA.","DOI":"10.1145\/2988450.2988452"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yi, M.X. (2018, January 7\u20138). Analysis and sequencing of the influence factors of the value of review on the low carbon Library Line Based on SPSS data analysis. Proceedings of the 2018 International Conference on Advances in Social Sciences and Sustainable Development (ASSSD 2018), Fuzhou, China.","DOI":"10.2991\/asssd-18.2018.61"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/4\/201\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:17:05Z","timestamp":1760174225000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/4\/201"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,9]]},"references-count":47,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["info11040201"],"URL":"https:\/\/doi.org\/10.3390\/info11040201","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,9]]}}}