research-article

Jointly Modeling Heterogeneous Student Behaviors and Interactions among Multiple Prediction Tasks

Authors:

Feilong TangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 16, Issue 1

Article No.: 16, Pages 1 - 24

https://doi.org/10.1145/3458023

Published: 20 July 2021 Publication History

Abstract

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 Long-Short Term Memory (LSTM) 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.

References

[1]

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.

[2]

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, 2299–2307.

[3]

M. Delgado Calvo-Flores, E. Gibaja Galindo, M. C. Pegalajar Jiménez, and O. Pérez Pineiro. 2006. Predicting students’ marks from Moodle logs using neural network models. Current Developments in Technology-Assisted Education 1, 2 (2006), 586–590.

[4]

Luis Cano, Erick Hein, Mauricio Rada-Orellana, and Claudio Ortega. 2018. A case study of library data management: A new method to analyze borrowing behavior. In Proceedings of the 5th International Conference on Information Management and Big Data.,Communications in Computer and Information Science, Vol. 898, Springer, 112–120.

[5]

Rich Caruana. 1997. Multi-task learning. Machine Learning 28, 1 (1997), 41–75.

Digital Library

[6]

Weiyu Chen, Christopher G. Brinton, Da Cao, Amanda Mason-Singh, Charlton Lu, and Mung Chiang. 2019. Early detection prediction of learning outcomes in online short-courses via learning behaviors. IEEE Transactions on Learning Technologies 12, 1 (2019), 44–58.

[7]

Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555. Retrieved from https://arxiv.org/abs/1412.3555.

[8]

Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multi-task learning. In Proceedings of the 25th International Conference on Machine Learning, Vol. 307, ACM, 160–167.

Digital Library

[9]

Jifeng Dai, Kaiming He, and Jian Sun. 2016. Instance-aware semantic segmentation via multi-task network cascades. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 3150–3158.

[10]

Mi Fei and Dit-Yan Yeung. 2015. Temporal models for predicting student dropout in massive open online courses. In Proceedings of the IEEE International Conference on Data Mining Workshop. IEEE, 256–263.

Digital Library

[11]

Mingyu Feng, Neil Heffernan, and Kenneth Koedinger. 2009. Addressing the assessment challenge with an online system that tutors as it assesses. User Modeling and User-Adapted Interaction 19, 3 (2009), 243–266.

Digital Library

[12]

Alex Graves and Jürgen Schmidhuber. 2005. Framewise phoneme classification with bidirectional LSTM networks. In Proceedings of the 2005 IEEE International Joint Conference on Neural Networks. 2047–2052.

[13]

Chu Guan, Xinjiang Lu, Xiaolin Li, Enhong Chen, Wenjun Zhou, and Hui Xiong. 2015. Discovery of college students in financial hardship. In Proceedings of the 2015 IEEE International Conference on Data Mining. IEEE, 141–150.

Digital Library

[14]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the 2015 IEEE International Conference on Computer Vision. IEEE, 1026–1034.

Digital Library

[15]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In Proceedings of the 4th International Conference on Learning Representations. Retrieved from http://arxiv.org/abs/1511.06939.

[16]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735–1780.

Digital Library

[17]

Anupam Khan and Soumya K. Ghosh. 2018. Data mining based analysis to explore the effect of teaching on student performance. Education and Information Technologies 23, 4 (2018), 1677–1697.

Digital Library

[18]

Anupam Khan and Soumya K. Ghosh. 2021. Student performance analysis and prediction in classroom learning: A review of educational data mining studies. Education and Information Technologies 26, 1 (2021), 205–240.

Digital Library

[19]

Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations. Retrieved from http://arxiv.org/abs/1412.6980.

[20]

K. Kumar and M. A. M. Raj. 2016. Improving efficacy of library services: ARIMA modelling for predicting book borrowing for optimizing resource utilization. Library Philosophy and Practice (e-journal), Paper 1395 (2016).

[21]

Haobing Liu, Yanmin Zhu, and Yanan Xu. 2020. Learning from heterogeneous student behaviors for multiple prediction tasks. In Proceedings of the 25th International Conference on Database Systems for Advanced Applications, Part II.Lecture Notes in Computer Science, Vol. 12113, Springer, 297–313.

[22]

Manuel Ignacio Lopez, J. M. Luna, C. Romero, and S. Ventura. 2012. Classification via clustering for predicting final marks based on student participation in forums. In Proceedings of the 5th International Conference on Educational Data Mining. EDM.

[23]

Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh. 2016. Hierarchical question-image co-attention for visual question answering. In Proceedings of the 30th International Conference on Neural Information Processing System. 289–297. Retrieved from https://proceedings.neurips.cc/paper/2016/hash/9dcb88e0137649590b755372b040afad-Abstract.html.

Digital Library

[24]

Xinjiang Lu, Zhiwen Yu, Chuanren Liu, Yanchi Liu, Hui Xiong, and Bin Guo. 2020. Inferring lifetime status of point-of-interest: A multi-task multi-class approach. ACM Transactions on Knowledge Discovery from Data 14, 1 (2020), 1–27.

Digital Library

[25]

Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, Nov (2008), 2579–2605.

[26]

Behrouz Minaei-Bidgoli, Deborah A. Kashy, Gerd Kortemeyer, and William F. Punch. 2003. Predicting student performance: An application of data mining methods with an educational web-based system. In Proceedings of the 33rd Annual Frontiers in Education. Vol. 1, IEEE, T1A–13–T1A–18.

[27]

Nguyen Thai Nghe, Paul Janecek, and Peter Haddawy. 2007. A comparative analysis of techniques for predicting academic performance. In Proceedings of the 2007 37th Annual Frontiers in Education Conference-Global Engineering: Knowledge Without Borders, Opportunities Without Passports. IEEE, T2G–7–T2G–12.

[28]

Edin Osmanbegovic and Mirza Suljic. 2012. Data mining approach for predicting student performance. Economic Review: Journal of Economics and Business 10, 1 (2012), 3–12. Retrieved from https://EconPapers.repec.org/RePEc:tuz:journl:v:10:y:2012:i:1:p:3-12.

[29]

W. Nicholson Price and I. Glenn Cohen. 2019. Privacy in the age of medical big data. Nature Medicine 25, 1 (2019), 37–43.

[30]

Rajeev Ranjan, Vishal M. Patel, and Rama Chellappa. 2017. Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 41, 1 (2017), 121–135.

Digital Library

[31]

Cristóbal Romero, Manuel-Ignacio López, Jose-María Luna, and Sebastián Ventura. 2013. Predicting students’ final performance from participation in on-line discussion forums. Computers & Education 68, C (2013), 458–472.

[32]

Amirah Mohamed Shahiri, Wahidah Husain, and Nur’aini Abdul Rashid. 2015. A review on predicting student’s performance using data mining techniques. Procedia Computer Science 72 (2015), 414–422.

[33]

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15, 1 (2014), 1929–1958. Retrieved from http://dl.acm.org/citation.cfm?id=2670313.

Digital Library

[34]

Otgontsetseg Sukhbaatar, Tsuyoshi Usagawa, and Lodoiravsal Choimaa. 2019. An artificial neural network based early prediction of failure-prone students in blended learning course. International Journal of Emerging Technologies in Learning 14, 19 (2019), 77–92. Retrieved from https://www.online-journals.org/index.php/i-jet/article/view/10366.

[35]

Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Proceedings of the 27th International Conference on Neural Information Processing Systems. Vol. 2, 3104–3112. Retrieved from https://proceedings.neurips.cc/paper/2014/hash/a14ac55a4f27472c5d894ec1c3c743d2-Abstract.html.

Digital Library

[36]

Mack Sweeney, Jaime Lester, and Huzefa Rangwala. 2015. Next-term student grade prediction. In Proceedings of the 2015 IEEE International Conference on Big Data. IEEE, 970–975.

Digital Library

[37]

Tuomas Tanner and Hannu Toivonen. 2010. Predicting and preventing student failure-using the k-nearest neighbour method to predict student performance in an online course environment. International Journal of Learning Technology 5, 4 (2010), 356–377.

Digital Library

[38]

Mei Tian. 2011. Application of chaotic time series prediction in forecasting of library borrowing flow. In Proceedings of the 2011 International Conference on Internet Computing and Information Services. IEEE, 557–559.

Digital Library

[39]

Rui Wang, Gabriella Harari, Peilin Hao, Xia Zhou, and Andrew T. Campbell. 2015. SmartGPA: How smartphones can assess and predict academic performance of college students. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 295–306.

Digital Library

[40]

Runhua Wang, Yi Tang, and Lei Li. 2012. Application of BP neural network to prediction of library circulation. In Proceedings of the 11th IEEE International Conference on Cognitive Informatics and Cognitive Computing. IEEE, 420–423.

[41]

Ya-huei Wang and Hung-Chang Liao. 2011. Data mining for adaptive learning in a TESL-based e-learning system. Expert Systems with Applications 38, 6 (2011), 6480–6485.

Digital Library

[42]

Jie Xu, Yuli Han, Daniel Marcu, and Mihaela Van Der Schaar. 2017. Progressive prediction of student performance in college programs. In Proceedings of the 31st AAAI Conference on Artificial Intelligence. AAAI, 1604–1610. Retrieved from http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14234.

Digital Library

[43]

Huaxiu Yao, Min Nie, Han Su, Hu Xia, and Defu Lian. 2017. Predicting academic performance via semi-supervised learning with constructed campus social network. In Proceedings of the 22nd International Conference on Database Systems for Advanced Applications, Part II.Lecture Notes in Computer Science, Vol. 10178, Springer, 597–609.

[44]

Hsiang-Fu Yu, Hung-Yi Lo, Hsun-Ping Hsieh, Jing-Kai Lou, Todd G. McKenzie, Jung-Wei Chou, Po-Han Chung, Chia-Hua Ho, Chun-Fu Chang, Yin-Hsuan Wei, Jui-Yu Weng, En-Syu Yan, Che-Wei Chang, Tsung-Ting Kuo, Yi-Chen Lo, Po Tzu Chang, Chieh Po, Chien-Yuan Wang, Yi-Hung Huang, Chen-Wei Hung, Yu-Xun Ruan, Yu-Shi Lin, Shou-De Lin, Hsuan-Tien Lin, and Chih-Jen Lin. 2011. Feature engineering and classifier ensemble for KDD cup 2010. In Proceedings of the JMLR Workshop and Conference Proceedings.

[45]

Daqing Zhang, Bin Guo, Bin Li, and Zhiwen Yu. 2010. Extracting social and community intelligence from digital footprints: An emerging research area. In Proceedings of the 7th International Conference on Ubiquitous Intelligence and Computing .Lecture Notes in Computer Science, Vol. 6406, Springer, 4–18.

Digital Library

[46]

Xi Zhang, Guangzhong Sun, Yigong Pan, Hao Sun, Yu He, and Jiali Tan. 2018. Students performance modeling based on behavior pattern. Journal of Ambient Intelligence and Humanized Computing 9, 5 (2018), 1659–1670.

[47]

Yu Zhu, Hao Li, Yikang Liao, Beidou Wang, Ziyu Guan, Haifeng Liu, and Deng Cai. 2017. What to do next: Modeling user behaviors by Time-LSTM. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 3602–3608.

Digital Library

Cited By

Zhu G(2024)Analysis and Prediction Model of Learning Behavior in the Digital Transformation of Tertiary Education2024 International Conference on Language Technology and Digital Humanities (LTDH)10.1109/LTDH64262.2024.00044(183-190)Online publication date: 5-Jul-2024
https://doi.org/10.1109/LTDH64262.2024.00044
Eich LFrancisco RBarbosa J(2024)Identifying Student Behavior in Smart Classrooms: A Systematic Literature Mapping and TaxonomiesInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2383812(1-22)Online publication date: 7-Aug-2024
https://doi.org/10.1080/10447318.2024.2383812
Liu WGu YGe Y(2024)Multi-factor stock trading strategy based on DQN with multi-BiGRU and multi-head ProbSparse self-attentionApplied Intelligence10.1007/s10489-024-05463-554:7(5417-5440)Online publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1007/s10489-024-05463-5
Show More Cited By

Index Terms

Jointly Modeling Heterogeneous Student Behaviors and Interactions among Multiple Prediction Tasks
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

Recommendations

Jointly Modeling Individual Student Behaviors and Social Influence for Prediction Tasks
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Prediction tasks about students such as predicting students' academic performances have practical real-world significance at both the student level and the college level. With the rapid construction of smart campuses, colleges not only offer residence ...
Learning from Heterogeneous Student Behaviors for Multiple Prediction Tasks
Database Systems for Advanced Applications
Abstract
Prediction tasks about students have practical real-world significance at both student level and university level. For example, predicting if a student will fail to graduate can alert the university student affairs office to take predictive ...
Multi-task Multi-view Learning for Heterogeneous Tasks
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Multi-task multi-view learning deals with the learning scenarios where multiple tasks are associated with each other through multiple shared feature views. All previous works for this problem assume that the tasks use the same set of class labels. ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 16, Issue 1

February 2022

475 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3472794

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

Issue’s Table of Contents

Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2021

Accepted: 01 March 2021

Revised: 01 March 2021

Received: 01 June 2020

Published in TKDD Volume 16, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Funding Sources

National Key AI Program of China
National Science Foundation of China
Shanghai Municipal Science and Technology Commission
Program for Changjiang Young Scholars in University of China, the Program for China Top Young Talents, the Program for Shanghai Top Young Talents, SJTU Global Strategic
Oceanic Interdisciplinary Program of Shanghai Jiao Tong University
Scientific Research Fund of Second Institute of Oceanography

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
301
Total Downloads

Downloads (Last 12 months)34
Downloads (Last 6 weeks)4

Reflects downloads up to 02 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhu G(2024)Analysis and Prediction Model of Learning Behavior in the Digital Transformation of Tertiary Education2024 International Conference on Language Technology and Digital Humanities (LTDH)10.1109/LTDH64262.2024.00044(183-190)Online publication date: 5-Jul-2024
https://doi.org/10.1109/LTDH64262.2024.00044
Eich LFrancisco RBarbosa J(2024)Identifying Student Behavior in Smart Classrooms: A Systematic Literature Mapping and TaxonomiesInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2383812(1-22)Online publication date: 7-Aug-2024
https://doi.org/10.1080/10447318.2024.2383812
Liu WGu YGe Y(2024)Multi-factor stock trading strategy based on DQN with multi-BiGRU and multi-head ProbSparse self-attentionApplied Intelligence10.1007/s10489-024-05463-554:7(5417-5440)Online publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1007/s10489-024-05463-5
Liu HZhu YWang CDing JYu JTang F(2023)Incorporating Heterogeneous User Behaviors and Social Influences for Predictive AnalysisIEEE Transactions on Big Data10.1109/TBDATA.2022.31930289:2(716-732)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TBDATA.2022.3193028
Liu HDing JZhu YTang FYu JJiang RGuo Z(2023)Modeling multi-aspect preferences and intents for multi-behavioral sequential recommendationKnowledge-Based Systems10.1016/j.knosys.2023.111013280:COnline publication date: 25-Nov-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.111013
Jashma Suresh PDinesh Acharya UReddy N(2023)Mining frequent Itemsets from transaction databases using hybrid switching frameworkMultimedia Tools and Applications10.1007/s11042-023-14484-082:18(27571-27591)Online publication date: 16-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-023-14484-0
Niu KLu GPeng XZhou YZeng JZhang K(2023)CNN autoencoders and LSTM-based reduced order model for student dropout predictionNeural Computing and Applications10.1007/s00521-023-08894-235:30(22341-22357)Online publication date: 8-Aug-2023
https://dl.acm.org/doi/10.1007/s00521-023-08894-2

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Issue’s Table of Contents