1. bookVolumen 2022 (2022): Edición 2 (April 2022)
Detalles de la revista
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Formato
Revista
eISSN
2299-0984
Primera edición
16 Apr 2015
Calendario de la edición
4 veces al año
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Inglés
access type Acceso abierto

Understanding Utility and Privacy of Demographic Data in Education Technology by Causal Analysis and Adversarial-Censoring

Publicado en línea: 03 Mar 2022
Volumen & Edición: Volumen 2022 (2022) - Edición 2 (April 2022)
Páginas: 245 - 262
Recibido: 31 Aug 2021
Aceptado: 16 Dec 2021
Detalles de la revista
License
Formato
Revista
eISSN
2299-0984
Primera edición
16 Apr 2015
Calendario de la edición
4 veces al año
Idiomas
Inglés
Abstract

Education technologies (EdTech) are becoming pervasive due to their cost-effectiveness, accessibility, and scalability. They also experienced accelerated market growth during the recent pandemic. EdTech collects massive amounts of students’ behavioral and (sensitive) demographic data, often justified by the potential to help students by personalizing education. Researchers voiced concerns regarding privacy and data abuses (e.g., targeted advertising) in the absence of clearly defined data collection and sharing policies. However, technical contributions to alleviating students’ privacy risks have been scarce. In this paper, we argue against collecting demographic data by showing that gender—a widely used demographic feature—does not causally affect students’ course performance: arguably the most popular target of predictive models. Then, we show that gender can be inferred from behavioral data; thus, simply leaving them out does not protect students’ privacy. Combining a feature selection mechanism with an adversarial censoring technique, we propose a novel approach to create a ‘private’ version of a dataset comprising of fewer features that predict the target without revealing the gender, and are interpretive. We conduct comprehensive experiments on a public dataset to demonstrate the robustness and generalizability of our mechanism.

Keywords

[1] Adam B. The 101 Hottest EdTech Tools According to Education Experts (Updated For 2020), 6. Search in Google Scholar

[2] June Ahn, Fabio Campos, Ha Nguyen, Maria Hays, and Jan Morrison. Co-Designing for Privacy, Transparency, and Trust in K-12 Learning Analytics. In LAK21: 11th International Learning Analytics and Knowledge Conference, pages 55–65. Association for Computing Machinery, New York, NY, USA, 2021.10.1145/3448139.3448145 Search in Google Scholar

[3] Nil-Jana Akpinar, Aaditya Ramdas, and Umut Acar. Analyzing student strategies in blended courses using clickstream data. arXiv preprint arXiv:2006.00421, 2020. Search in Google Scholar

[4] Reham Mohammad Almohtadi and Intisar Turki Aldarabah. University Students’ Attitudes toward the Formal Integration of Facebook in Their Education: Investigation Guided by Rogers’ Attributes of Innovation. World Journal of Education, 11(1):20–28, 2021. Search in Google Scholar

[5] Kimberly E Arnold and Niall Sclater. Student Perceptions of Their Privacy in Leaning Analytics Applications. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference, LAK ’17, pages 66–69, New York, NY, USA, 2017. Association for Computing Machinery.10.1145/3027385.3027392 Search in Google Scholar

[6] Yousef Atoum, Liping Chen, Alex X Liu, Stephen D H Hsu, and Xiaoming Liu. Automated Online Exam Proctoring. IEEE Transactions on Multimedia, 19(7):1609–1624, 2017.10.1109/TMM.2017.2656064 Search in Google Scholar

[7] Peter C Austin. Comparing paired vs non-paired statistical methods of analyses when making inferences about absolute risk reductions in propensity-score matched samples. Statistics in medicine, 30(11):1292–1301, 2011.10.1002/sim.4200311030721337595 Search in Google Scholar

[8] Seyyed Kazem Banihashem, Khadijeh Aliabadi, Saeid Pourroostaei Ardakani, Ali Delaver, and Mohammadreza Nili Ahmadabadi. Learning Analytics: A Systematic Literature Review. Interdisciplinary Journal of Virtual Learning in Medical Sciences, 9(2):–, 2018. Search in Google Scholar

[9] Peter Bautista and Paul Salvador Inventado. Protecting Student Privacy with Synthetic Data from Generative Adversarial Networks. In International Conference on Artificial Intelligence in Education, pages 66–70, 2021.10.1007/978-3-030-78270-2_11 Search in Google Scholar

[10] Nigel Bosch, R Wes Crues, Najmuddin Shaik, and Luc Paquette. “Hello,[REDACTED]”: Protecting Student Privacy in Analyses of Online Discussion Forums. In EDM, 2020. Search in Google Scholar

[11] Macy A Burchfield, Joshua Rosenberg, Conrad Borchers, Tayla Thomas, Ben Gibbons, and Christian Fischer. Are Violations of Student Privacy “Quick and Easy”? Investigating the Privacy of Students’ Images and Names in the Context of K-12 Educational Institution’s Posts on Facebook. 2021.10.31219/osf.io/5tpb9 Search in Google Scholar

[12] Kursat; Celik Berkan Cagiltay Nergiz Ercil ; Cagiltay. An Analysis of Course Characteristics, Learner Characteristics, and Certification Rates in MITx MOOCs. International Review of Research in Open and Distributed Learning, 21(3):121–139, 2020.10.19173/irrodl.v21i3.4698 Search in Google Scholar

[13] Cassandra Willer. 27 Tech Tools Teachers Can Use to Inspire Classroom Creativity. Search in Google Scholar

[14] Changsheng Chen, Jingyun Long, Junxiao Liu, Zongjun Wang, Minglei Shan, and Yuming Dou. Behavioral Patterns of Completers in Massive Open Online Courses (MOOCs): The Use of Learning Analytics to Reveal Student Categories. In 2020 International Conference on Advanced Education, Management and Social Science (AEMSS2020), pages 56–63, 2020.10.2991/assehr.k.200723.102 Search in Google Scholar

[15] Guanliang Chen, Dan Davis, Jun Lin, Claudia Hauff, and Geert-Jan Houben. Beyond the MOOC Platform: Gaining Insights about Learners from the Social Web. In Proceedings of the 8th ACM Conference on Web Science, WebSci ’16, pages 15–24, New York, NY, USA, 2016. Association for Computing Machinery. Search in Google Scholar

[16] Christopher Pappas. The Best Learning Management Systems (2020 Update), 2020. Search in Google Scholar

[17] Chia Yuan Chuang, Scotty D Craig, and John Femiani. Detecting probable cheating during online assessments based on time delay and head pose. Higher Education Research & Development, 36(6):1123–1137, 2017.10.1080/07294360.2017.1303456 Search in Google Scholar

[18] Matthieu Courbariaux and Yoshua Bengio. BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. CoRR, abs/1602.02830, 2016. Search in Google Scholar

[19] Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David. Binaryconnect: Training deep neural networks with binary weights during propagations. In Advances in neural information processing systems, pages 3123–3131, 2015. Search in Google Scholar

[20] R Wes Crues, Genevieve M Henricks, Michelle Perry, Suma Bhat, Carolyn J Anderson, Najmuddin Shaik, and Lawrence Angrave. How Do Gender, Learning Goals, and Forum Participation Predict Persistence in a Computer Science MOOC? ACM Trans. Comput. Educ., 18(4), 9 2018.10.1145/3152892 Search in Google Scholar

[21] Ying Cui, Fu Chen, Ali Shiri, and Yaqin Fan. Predictive analytic models of student success in higher education: A review of methodology. Information and Learning Sciences, 2019.10.1108/ILS-10-2018-0104 Search in Google Scholar

[22] Paula G de Barba, Donia Malekian, Eduardo A Oliveira, James Bailey, Tracii Ryan, and Gregor Kennedy. The importance and meaning of session behaviour in a MOOC. Computers & Education, 146:103772, 2020.10.1016/j.compedu.2019.103772 Search in Google Scholar

[23] Hendrik Drachsler and Wolfgang Greller. Privacy and Analytics: It’s a DELICATE Issue a Checklist for Trusted Learning Analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, LAK ’16, pages 89–98, New York, NY, USA, 2016. Association for Computing Machinery.10.1145/2883851.2883893 Search in Google Scholar

[24] Harrison Edwards and Amos J Storkey. Censoring Representations with an Adversary. In Yoshua Bengio and Yann LeCun, editors, 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, 2016. Search in Google Scholar

[25] Yanai Elazar and Yoav Goldberg. Adversarial removal of demographic attributes from text data. arXiv preprint arXiv:1808.06640, 2018. Search in Google Scholar

[26] Facebook. Facebook for Education. 2021. Search in Google Scholar

[27] Facebook. https://education.facebook.com/, 2 2021. Search in Google Scholar

[28] Wenzheng Feng, Jie Tang, and Tracy Xiao Liu. Understanding Dropouts in MOOCs. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01):517–524, 7 2019.10.1609/aaai.v33i01.3301517 Search in Google Scholar

[29] Tobias Fiebig, Seda Gürses, Carlos H Gañán, Erna Kotkamp, Fernando Kuipers, Martina Lindorfer, Menghua Prisse, and Taritha Sari. Heads in the Clouds: Measuring the Implications of Universities Migrating to Public Clouds. arXiv preprint arXiv:2104.09462, 2021. Search in Google Scholar

[30] Andy Field, Jeremy Miles, and Zoë Field. Discovering statistics using R. Sage publications, 2012. Search in Google Scholar

[31] Alvaro Figueira. Predicting Grades by Principal Component Analysis: A Data Mining Approach to Learning Analyics. In 2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT), pages 465–467, 2016.10.1109/ICALT.2016.103 Search in Google Scholar

[32] Sreya Francis, Irene Tenison, and Irina Rish. Towards Causal Federated Learning For Enhanced Robustness and Privacy. CoRR, abs/2104.06557, 2021. Search in Google Scholar

[33] Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backpropagation. In International conference on machine learning, pages 1180–1189, 2015. Search in Google Scholar

[34] Gerrit De Vynck and Mark Bergen. Google Classroom Users Doubled as Quarantines Spread, 2020. Search in Google Scholar

[35] Google. Google Classroom, 2021. Search in Google Scholar

[36] Brenda Edith Guajardo Leal, Valenzuela GonzĆ, and others. Student Engagement as a Predictor of xMOOC Completion: An Analysis from Five Courses on Energy Sustainability. Online Learning, 23(2):105–123, 2019. Search in Google Scholar

[37] Kalervo Gulson, Carlo Perrotta, Ben Williamson, and Kevin Witzenberger. Should We be Worried about Google Classroom? The Pedagogy of Platforms in Education. Search in Google Scholar

[38] Song Guo, Deze Zeng, and Shifu Dong. Pedagogical Data Analysis Via Federated Learning Toward Education 4.0. American Journal of Education and Information Technology, 4(2):56, 2020.10.1145/3404709.3404751 Search in Google Scholar

[39] Mehmet Emre Gursoy, Ali Inan, Mehmet Ercan Nergiz, and Yucel Saygin. Privacy-Preserving Learning Analytics: Challenges and Techniques. IEEE Transactions on Learning Technologies, 10(1):68–81, 2017. Search in Google Scholar

[40] Jihun Hamm. Minimax filter: Learning to preserve privacy from inference attacks. The Journal of Machine Learning Research, 18(1):4704–4734, 2017. Search in Google Scholar

[41] Rakibul Hasan, Cristobal Cheyre Forestier, Yong-Yeol Ahn, Roberto Hoyle, and Apu Kapadia. The Impact of Viral Posts on Visibility and Behavior of Professionals: A Longitudinal Study of Scientists on Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 6 2022. Search in Google Scholar

[42] Arto Hellas, Petri Ihantola, Andrew Petersen, Vangel V Ajanovski, Mirela Gutica, Timo Hynninen, Antti Knutas, Juho Leinonen, Chris Messom, and Soohyun Nam Liao. Predicting Academic Performance: A Systematic Literature Review. In Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2018 Companion, pages 175–199, New York, NY, USA, 2018. Association for Computing Machinery.10.1145/3293881.3295783 Search in Google Scholar

[43] Alex Hern. Google faces lawsuit over email scanning and student data, 2014. Search in Google Scholar

[44] Daniel E Ho, Kosuke Imai, Gary King, and Elizabeth A Stuart. Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis, 15(3):199–236, 2007.10.1093/pan/mpl013 Search in Google Scholar

[45] Daniel E Ho, Kosuke Imai, Gary King, and Elizabeth A Stuart. MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. Journal of Statistical Software, 42(8):1–28, 2011. Search in Google Scholar

[46] Tore Hoel, Dai Griffiths, and Weiqin Chen. The Influence of Data Protection and Privacy Frameworks on the Design of Learning Analytics Systems. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference, LAK ’17, pages 243–252, New York, NY, USA, 2017. Association for Computing Machinery.10.1145/3027385.3027414 Search in Google Scholar

[47] HolonIQ. 10 charts to explain the Global Education Technology Market, 1 2021. Search in Google Scholar

[48] Dirk Ifenthaler. Are higher education institutions prepared for learning analytics? TechTrends, 61(4):366–371, 2017.10.1007/s11528-016-0154-0 Search in Google Scholar

[49] Yusuke Iwasawa, Kotaro Nakayama, Ikuko Yairi, and Yutaka Matsuo. Privacy Issues Regarding the Application of DNNs to Activity-Recognition using Wearables and Its Counter-measures by Use of Adversarial Training. In IJCAI, pages 1930–1936, 2017.10.24963/ijcai.2017/268 Search in Google Scholar

[50] Nikhil Indrashekhar Jha, Ioana Ghergulescu, and Arghir-Nicolae Moldovan. OULAD MOOC Dropout and Result Prediction using Ensemble, Deep Learning and Regression Techniques. In CSEDU (2), pages 154–164, 2019. Search in Google Scholar

[51] Srećko Joksimović, Oleksandra Poquet, Vitomir Kovanović, Nia Dowell, Caitlin Mills, Dragan Gašević, Shane Dawson, Arthur C Graesser, and Christopher Brooks. How do we model learning at scale? A systematic review of research on MOOCs. Review of Educational Research, 88(1):43–86, 2018.10.3102/0034654317740335 Search in Google Scholar

[52] Kyle M L Jones, Alan Rubel, and Ellen LeClere. A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics. Journal of the Association for Information Science and Technology, 71(10):1227–1241, 2020. Search in Google Scholar

[53] Katherine Mangan. The Surveilled Student, 2 2021. Search in Google Scholar

[54] Puninder Kaur, Amandeep Kaur, and Rajwinder Kaur. A Systematic Review About Prediction of Academic Behavior Through Data Mining Techniques. Journal of Computational and Theoretical Nanoscience, 17(11):5162–5166, 2020.10.1166/jctn.2020.9358 Search in Google Scholar

[55] Mohammad Khalila and Martin Ebner. De-identification in learning analytics. Journal of Learning Analytics, 3(1):129–138, 2016.10.18608/jla.2016.31.8 Search in Google Scholar

[56] Gary King and Richard Nielsen. Why Propensity Scores Should Not Be Used for Matching. Political Analysis, 27(4):435–454, 2019.10.1017/pan.2019.11 Search in Google Scholar

[57] Mark Klose, Vasvi Desai, Yang Song, and Edward Gehringer. EDM and Privacy: Ethics and Legalities of Data Collection, Usage, and Storage. International Educational Data Mining Society, 2020. Search in Google Scholar

[58] Daniel G Krutka, Ryan M Smits, and Troy A Willhelm. Don’t Be Evil: Should We Use Google in Schools? TechTrends, 2021.10.1007/s11528-021-00599-4797232833758830 Search in Google Scholar

[59] Jakub Kuzilek, Martin Hlosta, and Zdenek Zdrahal. Open university learning analytics dataset. Scientific data, 4(1):1–8, 2017.10.1038/sdata.2017.171570467629182599 Search in Google Scholar

[60] Charles Lang, Charlotte Woo, and Jeanne Sinclair. Quantifying Data Sensitivity: Precise Demonstration of Care When Building Student Prediction Models. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, LAK ’20, pages 655–664, New York, NY, USA, 2020. Association for Computing Machinery.10.1145/3375462.3375506 Search in Google Scholar

[61] Eitel J M Lauría, Joshua D Baron, Mallika Devireddy, Venniraiselvi Sundararaju, and Sandeep M Jayaprakash. Mining Academic Data to Improve College Student Retention: An Open Source Perspective. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, LAK ’12, pages 139–142, New York, NY, USA, 2012. Association for Computing Machinery.10.1145/2330601.2330637 Search in Google Scholar

[62] James J Lohse, Christine A McManus, and David A Joyner. Surveying the MOOC Data Set Universe. In 2019 IEEE Learning With MOOCS (LWMOOCS), pages 159–164, 2019.10.1109/LWMOOCS47620.2019.8939594 Search in Google Scholar

[63] Asim Majeed, Said Baadel, and Anwar Ul Haq. Global triumph or exploitation of security and privacy concerns in e-learning systems. In International Conference on Global Security, Safety, and Sustainability, pages 351–363, 2017.10.1007/978-3-319-51064-4_28 Search in Google Scholar

[64] Roxana Marachi and Lawrence Quill. The case of Canvas: Longitudinal datafication through learning management systems. Teaching in Higher Education, 25(4):418–434, 2020. Search in Google Scholar

[65] Neema Mduma, Khamisi Kalegele, and Dina Machuve. A survey of machine learning approaches and techniques for student dropout prediction. 2019.10.5334/dsj-2019-014 Search in Google Scholar

[66] Pedro Manuel Moreno-Marcos, Pedro J Muñoz-Merino, Carlos Alario-Hoyos, and Carlos Delgado Kloos. Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning. Applied Sciences, 10(5), 2020.10.3390/app10051722 Search in Google Scholar

[67] Pedro Manuel Moreno-Marcos, Ting-Chuen Pong, Pedro J Muñoz-Merino, and Carlos Delgado Kloos. Analysis of the Factors Influencing Learners’ Performance Prediction With Learning Analytics. IEEE Access, 8:5264–5282, 2020.10.1109/ACCESS.2019.2963503 Search in Google Scholar

[68] Benjamin A Motz, Paulo F Carvalho, Joshua R de Leeuw, and Robert L Goldstone. Embedding Experiments: Staking Causal Inference in Authentic Educational Contexts. Journal of Learning Analytics, 5(2):47–59, 8 2018.10.18608/jla.2018.52.4 Search in Google Scholar

[69] Kew Si Na and Zaidatun Tasir. A systematic review of learning analytics intervention contributing to student success in online learning. In 2017 International conference on learning and teaching in computing and engineering (LaTICE), pages 62–68, 2017. Search in Google Scholar

[70] Vinod Nair and Geoffrey E Hinton. Rectified Linear Units Improve Restricted Boltzmann Machines. In ICML, pages 807–814, 2010. Search in Google Scholar

[71] Abdallah Namoun and Abdullah Alshanqiti. Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review. Applied Sciences, 11(1), 2021.10.3390/app11010237 Search in Google Scholar

[72] Natasha Singer. Learning Apps Have Boomed in the Pandemic. Now Comes the Real Test., 3 2021. Search in Google Scholar

[73] David Nicholas, Hamid R Jamali, Eti Herman, Anthony Watkinson, Abdullah Abrizah, Blanca Rodríguez-Bravo, Cherifa Boukacem-Zeghmouri, Jie Xu, Marzena Świgoń, and Tatiana Polezhaeva. A global questionnaire survey of the scholarly communication attitudes and behaviours of early career researchers. Learned Publishing, n/a(n/a). Search in Google Scholar

[74] Luc Paquette, Jaclyn Ocumpaugh, Ziyue Li, Alexandra Andres, and Ryan Baker. Who’s Learning? Using Demographics in EDM Research. Journal of Educational Data Mining, 12(3):1–30, 2020. Search in Google Scholar

[75] F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, M Blondel, P Prettenhofer, R Weiss, V Dubourg, J Vanderplas, A Passos, D Cournapeau, M Brucher, M Perrot, and E Duchesnay. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011. Search in Google Scholar

[76] Bardh Prenkaj, Paola Velardi, Giovanni Stilo, Damiano Distante, and Stefano Faralli. A Survey of Machine Learning Approaches for Student Dropout Prediction in Online Courses. ACM Comput. Surv., 53(3), 5 2020.10.1145/3388792 Search in Google Scholar

[77] Banoor Yousra Rajabalee, Mohammad Issack Santally, and Frank Rennie. A study of the relationship between students’ engagement and their academic performances in an eLearning environment. E-Learning and Digital Media, 17(1):1–20, 2020.10.1177/2042753019882567 Search in Google Scholar

[78] S Ranjeeth, T P Latchoumi, and P Victer Paul. A Survey on Predictive Models of Learning Analytics. Procedia Computer Science, 167:37–46, 2020.10.1016/j.procs.2020.03.180 Search in Google Scholar

[79] Joel R Reidenberg and Florian Schaub. Achieving big data privacy in education. Theory and Research in Education, 16(3):263–279, 2018.10.1177/1477878518805308 Search in Google Scholar

[80] Paul R Rosenbaum. Sensitivity Analysis in Observational Studies. In Wiley StatsRef: Statistics Reference Online. American Cancer Society, 2014.10.1002/9781118445112.stat06358 Search in Google Scholar

[81] Donald B Rubin. Causal Inference Using Potential Outcomes. Journal of the American Statistical Association, 100(469):322–331, 2005.10.1198/016214504000001880 Search in Google Scholar

[82] Jennifer Sabourin, Lucy Kosturko, Clare FitzGerald, and Scott McQuiggan. Student Privacy and Educational Data Mining: Perspectives from Industry. International Educational Data Mining Society, 2015. Search in Google Scholar

[83] Daniel J Solove. A taxonomy of privacy. U. Pa. L. Rev., 154:477, 2005.10.2307/40041279 Search in Google Scholar

[84] Congzheng Song and Vitaly Shmatikov. Overlearning reveals sensitive attributes. arXiv preprint arXiv:1905.11742, 2019. Search in Google Scholar

[85] Elizabeth A Stuart. Matching Methods for Causal Inference: A Review and a Look Forward. Statist. Sci., 25(1):1–21, 2010. Search in Google Scholar

[86] Latanya Sweeney. Simple demographics often identify people uniquely. Health (San Francisco), 671(2000):1–34, 2000. Search in Google Scholar

[87] Mariela Mizota Tamada, José Francisco de Magalhães Netto, and Dhanielly Paulina R de Lima. Predicting and Reducing Dropout in Virtual Learning using Machine Learning Techniques: A Systematic Review. In 2019 IEEE Frontiers in Education Conference (FIE), pages 1–9, 2019. Search in Google Scholar

[88] Leslie Ching Ow Tiong and HeeJeong Jasmine Lee. E-cheating Prevention Measures: Detection of Cheating at Online Examinations Using Deep Learning Approach–A Case Study. arXiv preprint arXiv:2101.09841, 2021. Search in Google Scholar

[89] Shruti Tople, Amit Sharma, and Aditya Nori. Alleviating Privacy Attacks via Causal Learning. In Hal Daumé III and Aarti Singh, editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 9537–9547. PMLR, 8 2020. Search in Google Scholar

[90] Allen Tough. The Adult’s Learning Projects. A Fresh Approach to Theory and Practice in Adult Learning. 1979. Search in Google Scholar

[91] Hajra Waheed, Saeed-Ul Hassan, Naif Radi Aljohani, Julie Hardman, Salem Alelyani, and Raheel Nawaz. Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104:106189, 2020.10.1016/j.chb.2019.106189 Search in Google Scholar

[92] Jacob Whitehill, Kiran Mohan, Daniel Seaton, Yigal Rosen, and Dustin Tingley. Delving deeper into MOOC student dropout prediction. arXiv preprint arXiv:1702.06404, 2017. Search in Google Scholar

[93] Wikipedia. Pearson’s chi-squared test. Search in Google Scholar

[94] Ben Williamson, Sian Bayne, and Suellen Shay. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education, 25(4):351–365, 2020. Search in Google Scholar

[95] Elad Yacobson, Orly Fuhrman, Sara Hershkovitz, and Giora Alexandron. De-identification is not enough to guarantee student privacy: De-anonymizing personal information from basic logs. In Companion Proceedings 10th International Conference on Learning Analytics & Knowledge (LAK20), 2019. Search in Google Scholar

[96] Kui Yu, Xianjie Guo, Lin Liu, Jiuyong Li, Hao Wang, Zhaolong Ling, and Xindong Wu. Causality-Based Feature Selection: Methods and Evaluations. ACM Comput. Surv., 53(5), 9 2020.10.1145/3409382 Search in Google Scholar

[97] Xin Zhang, Cheng-Wei Wu, Philippe Fournier-Viger, Lan-Da Van, and Yu-Chee Tseng. Analyzing students’ attention in class using wearable devices. In 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pages 1–9, 2017.10.1109/WoWMoM.2017.7974306 Search in Google Scholar

[98] Zhongheng Zhang, Hwa Jung Kim, Guillaume Lonjon, and Yibing Zhu. Balance diagnostics after propensity score matching., 1 2019.10.21037/atm.2018.12.10635135930788363 Search in Google Scholar

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