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Knowl. Discov. Data"],"published-print":{"date-parts":[[2022,2,28]]},"abstract":"<jats:p>\n            Prediction tasks about students have practical significance for both student and college. Making multiple predictions about students is an important part of a smart campus. For instance, predicting whether a student will fail to graduate can alert the student affairs office to take predictive measures to help the student improve his\/her academic performance. With the development of information technology in colleges, we can collect digital footprints that encode heterogeneous behaviors continuously. In this article, we focus on modeling heterogeneous behaviors and making multiple predictions together, since some prediction tasks are related and learning the model for a specific task may have the data sparsity problem. To this end, we propose a variant of\n            <jats:bold>Long-Short Term Memory (LSTM)<\/jats:bold>\n            and a soft-attention mechanism. The proposed LSTM is able to learn the student profile-aware representation from heterogeneous behavior sequences. The proposed soft-attention mechanism can dynamically learn different importance degrees of different days for every student. In this way, heterogeneous behaviors can be well modeled. In order to model interactions among multiple prediction tasks, we propose a co-attention mechanism based unit. With the help of the stacked units, we can explicitly control the knowledge transfer among multiple tasks. We design three motivating behavior prediction tasks based on a real-world dataset collected from a college. Qualitative and quantitative experiments on the three prediction tasks have demonstrated the effectiveness of our model.\n          <\/jats:p>","DOI":"10.1145\/3458023","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T21:06:18Z","timestamp":1626815178000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Jointly Modeling Heterogeneous Student Behaviors and Interactions among Multiple Prediction Tasks"],"prefix":"10.1145","volume":"16","author":[{"given":"Haobing","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University Shanghai, China"}]},{"given":"Yanmin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University Shanghai, China"}]},{"given":"Tianzi","family":"Zang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University Shanghai, China"}]},{"given":"Yanan","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University Shanghai, China"}]},{"given":"Jiadi","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University Shanghai, China"}]},{"given":"Feilong","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2021,7,20]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 3rd International Conference on Learning Representations.","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2015 . Neural machine translation by jointly learning to align and translate . In Proceedings of the 3rd International Conference on Learning Representations. Retrieved from http:\/\/arxiv.org\/abs\/1409.0473. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the 3rd International Conference on Learning Representations. Retrieved from http:\/\/arxiv.org\/abs\/1409.0473."},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 2015 IEEE Conference on Computer Communications. IEEE, 2299\u20132307","author":"Christopher","year":"2015","unstructured":"Christopher G. Brinton and Mung Chiang. 2015. MOOC performance prediction via clickstream data and social learning networks . In Proceedings of the 2015 IEEE Conference on Computer Communications. IEEE, 2299\u20132307 . DOI:https:\/\/doi.org\/10.1109\/INFOCOM. 2015 .7218617 10.1109\/INFOCOM.2015.7218617 Christopher G. Brinton and Mung Chiang. 2015. MOOC performance prediction via clickstream data and social learning networks. In Proceedings of the 2015 IEEE Conference on Computer Communications. IEEE, 2299\u20132307. DOI:https:\/\/doi.org\/10.1109\/INFOCOM.2015.7218617"},{"key":"e_1_2_1_3_1","first-page":"586","article-title":"Predicting students\u2019 marks from Moodle logs using neural network models","volume":"1","author":"Calvo-Flores M. Delgado","year":"2006","unstructured":"M. Delgado Calvo-Flores , E. Gibaja Galindo , M. C. Pegalajar Jim\u00e9nez , and O. P\u00e9rez Pineiro . 2006 . Predicting students\u2019 marks from Moodle logs using neural network models . Current Developments in Technology-Assisted Education 1 , 2 (2006), 586 \u2013 590 . M. Delgado Calvo-Flores, E. Gibaja Galindo, M. C. Pegalajar Jim\u00e9nez, and O. P\u00e9rez Pineiro. 2006. Predicting students\u2019 marks from Moodle logs using neural network models. 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