Using fair AI to predict students’ math learning outcomes in an online platform

References

• Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133–148. https://doi.org/10.1080/01587919.2018.1553562
   Web of Science ®Google Scholar
• Baker, R. S., & Hawn, A. (2021). Algorithmic Bias in Education. International Journal of Artificial Intelligence in Education, 1–41. https://doi.org/10.1007/s40593-021-00285-9.
   Web of Science ®Google Scholar
• Barron-Estrada, M. L., Zatarain-Cabada, R., & Oramas-Bustillos, R. (2019). Emotion recognition for education using sentiment analysis. Research in Computing Science, 148(5), 71–80. https://doi.org/10.13053/rcs-148-5-8
   Google Scholar
• Beer, D. (2017). The social power of algorithms. Information, Communication & Society, 20(1), 1–13. https://doi.org/10.1080/1369118X.2016.1216147
   Web of Science ®Google Scholar
• Beutel, A., Chen, J., Zhao, Z., & Chi, E. H.. (2017). Data decisions and theoretical implications when adversarially learning fair representations. In Proceedings of the 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning (pp. 1–5). https://arxiv.org/pdf/1707.00075.pdf.
   Google Scholar
• Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In S. A. Friedler, & C. Wilson (Eds.), Proceedings of machine learning research: Vol. 81. Proceedings of the 1st conference on fairness, accountability and transparency (pp. 149–159). PMLR.
   Google Scholar
• Bolukbasi, T., Chang, K.-W., Zou, J. Y., Saligrama, V., & Kalai, A. T. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. Advances in Neural Information Processing Systems, 29, 4349–4357. https://proceedings.neurips.cc/paper/2016/file/a486cd07e4ac3d270571622f4f316ec5-Paper.pdf
   Google Scholar
• Bustos López, M., Alor-Hernández, G., Sánchez-Cervantes, J. L., Paredes-Valverde, M. A., & Salas-Zárate, M. D. P. (2020). Edurecomsys: An educational resource recommender system based on collaborative filtering and emotion detection. Interacting with Computers, 32(4), 407–432. https://doi.org/10.1093/iwc/iwab001
   Web of Science ®Google Scholar
• CarterJrR. A., Rice, M., Yang, S., & Jackson, H. A. (2020). Self-regulated learning in online learning environments: Strategies for remote learning. Information and Learning Sciences, 121(5/6), 321–329. https://doi.org/10.1108/ILS-04-2020-0114
   Web of Science ®Google Scholar
• Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953
   Web of Science ®Google Scholar
• Choi, S. P., Lam, S. S., Li, K. C., & Wong, B. T. (2018). Learning analytics at low cost: At-risk student prediction with clicker data and systematic proactive interventions. Journal of Educational Technology & Society, 21(2), 273–290. https://www.jstor.org/stable/26388407
   Web of Science ®Google Scholar
• Christodoulou, E., Ma, J., Collins, G. S., Steyerberg, E. W., Verbakel, J. Y., & Van Calster, B. (2019). A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. Journal of Clinical Epidemiology, 110, 12–22. https://doi.org/10.1016/j.jclinepi.2019.02.004
   PubMed Web of Science ®Google Scholar
• Chui, K. T., Fung, D. C. L., Lytras, M. D., & Lam, T. M. (2020). Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Computers in Human Behavior, 107, 105584. https://doi.org/10.1016/j.chb.2018.06.032
   Web of Science ®Google Scholar
• Cobos, R., & Olmos, L. (2018). A learning analytics tool for predictive modeling of dropout and certificate acquisition on MOOCs for professional learning. In R. Jiao & M. Xie (Eds.), 2018 IEEE international conference on industrial engineering and engineering management (IEEM) (pp. 1533–1537). IEEE. https://doi.org/10.1109/ieem.2018.8607541
   Google Scholar
• Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Computers in Human Behavior, 73, 247–256. https://doi.org/10.1016/j.chb.2017.01.047
   Web of Science ®Google Scholar
• Crossley, S., Paquette, L., Dascalu, M., McNamara, D. S., & Baker, R. S. (2016). Combining click-stream data with NLP tools to better understand MOOC completion. In LAK '16: Proceedings of the sixth international conference on learning analytics & knowledge (pp. 6–14). ACM. https://doi.org/10.1145/2883851.2883931
   Google Scholar
• Dawson, S., Jovanovic, J., Gašević, D., & Pardo, A. (2017). From prediction to impact: Evaluation of a learning analytics retention program. In LAK ‘17: Proceedings of the seventh international learning analytics & knowledge conference (pp. 474–478). ACM. https://doi.org/10.1145/3027385.3027405
   Google Scholar
• Ding, M., Wang, Y., Hemberg, E., & O'Reilly, U. M. (2019). Transfer learning using representation learning in massive open online courses. In LAK ‘19: Proceedings of the seventh international learning analytics & knowledge conference (pp. 145–154). ACM. https://doi.org/10.1145/3303772.3303794
   Google Scholar
• Doleck, T., Lemay, D. J., Basnet, R. B., & Bazelais, P. (2020). Predictive analytics in education: A comparison of deep learning frameworks. Education and Information Technologies, 25(3), 1951–1963. https://doi.org/10.1007/s10639-019-10068-4
   Web of Science ®Google Scholar
• Dreiseitl, S., & Ohno-Machado, L. (2002). Logistic regression and artificial neural network classification models: A methodology review. Journal of Biomedical Informatics, 35(5–6), 352–359. https://doi.org/10.1016/S1532-0464(03)00034-0
   PubMed Web of Science ®Google Scholar
• Drijvers, P., Doorman, M., Boon, P., Reed, H., & Gravemeijer, K. (2010). The teacher and the tool: Instrumental orchestrations in the technology-rich mathematics classroom. Educational Studies in Mathematics, 75(2), 213–234. https://doi.org/10.1007/s10649-010-9254-5
   Web of Science ®Google Scholar
• Du, X., Zhang, M., Shelton, B. E., & Hung, J.-L. (2019). Learning anytime, anywhere: A spatio-temporal analysis for online learning. Interactive Learning Environments, 34–48. https://doi.org/10.1080/10494820.2019.1633546
   Web of Science ®Google Scholar
• Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. In S. Goldwasser (Ed.), Proceedings of the 3rd innovations in theoretical computer science conference (pp. 214–226). ACM. https://doi.org/10.1145/2090236.2090255
   Google Scholar
• Efron, B. (1987). Better bootstrap confidence intervals. Journal of the American Statistical Association, 82(397), 171–185. https://doi.org/10.1080/01621459.1987.10478410
   Web of Science ®Google Scholar
• Elshami, W., Taha, M. H., Abuzaid, M., Saravanan, C., Al Kawas, S., & Abdalla, M. E. (2021). Satisfaction with online learning in the new normal: Perspective of students and faculty at medical and health sciences colleges. Medical Education Online, 26(1), 1920090. https://doi.org/10.1080/10872981.2021.1920090
   PubMed Web of Science ®Google Scholar
• Fei, M., & Yeung, D. Y. (2015). Temporal models for predicting student dropout in massive open online courses. In P. Cui, J. Dy, C. Aggarwal, Z.-H. Zhou, A. Tuzhilin, H. Xiong, & X. Wu (Eds.), 2015 IEEE international conference on data mining workshop (ICDMW) (pp. 256–263). IEEE. https://doi.org/10.1109/icdmw.2015.174
   Google Scholar
• Gardner, J., Brooks, C., & Baker, R. (2019). Evaluating the fairness of predictive student models through slicing analysis. In LAK '19: Proceedings of the 9th international conference on learning analytics & knowledge (pp. 225–234). ACM. https://doi.org/10.1145/3303772.3303791
   Google Scholar
• Giang, V. (2018, May 8th). The potential hidden bias in automated hiring systems. Fast Company. https://www.fastcompany.com/40566971/the-potential-hidden-bias-in-automated-hiring-systems Accessed November 10, 2020
   Google Scholar
• Hao, B., Abbasi Yadkori, Y., Wen, Z., & Cheng, G. (2019). Bootstrapping upper confidence bound. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), Advances in neural information processing systems: Vol. 32 (pp. 12123–12133). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2019/file/412758d043dd247bddea07c7ec558c31-Paper.pdf
   Google Scholar
• Hardman, J., Paucar-Caceres, A., & Fielding, A. (2013). Predicting students’ progression in higher education by using the random forest algorithm. Systems Research and Behavioral Science, 30(2), 194–203. https://doi.org/10.1002/sres.2130
   Web of Science ®Google Scholar
• Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. In D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, & R. Garnett (Eds.), Advances in neural information processing systems: Vol. 29 (pp. 3315–3323). Curran Associates, Inc.
   Google Scholar
• Harkin, B., Webb, T. L., Chang, B. P., Prestwich, A., Conner, M., Kellar, I., Benn, Y., & Sheeran, P. (2016). Does monitoring goal progress promote goal attainment? A meta-analysis of the experimental evidence. Psychological Bulletin, 142(2), 198–229. https://doi.org/10.1037/bul0000025
   PubMed Web of Science ®Google Scholar
• Hoeffding, W. (1963). Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58(301), 13–30. https://doi.org/10.1080/01621459.1963.10500830
   Web of Science ®Google Scholar
• Huan, W., Wu, Y., Zhang, L., & Wu, X. (2020). Fairness through equality of effort. In A. Seghrouchni, G. Sukthankar, T.-Y. Liu, & M. van Steen (Eds.), Companion proceedings of the Web conference 2020 (pp. 743–751). ACM. https://dl.acm.org/doi/10.11453366424.3383558
   Google Scholar
• Hung, J.-L., Rice, K., Kepka, J., & Yang, J. (2020). Improving predictive power through deep learning analysis of K-12 online student behaviors and discussion board content. Information Discovery and Delivery, 48(4), 199–212. https://doi.org/10.1108/IDD-02-2020-0019
   Web of Science ®Google Scholar
• Hutchinson, B., & Mitchell, M. (2019). 50 years of test (un) fairness: Lessons for machine learning. In Proceedings of the conference on fairness, accountability, and transparency (pp. 49–58). ACM. https://doi.org/10.1145/3287560.3287600.
   Google Scholar
• Hutt, S., Gardner, M., Duckworth, A. L., & D'Mello, S. K. (2019). Evaluating fairness and generalizability in models predicting On-time graduation from college applications. In C. Lynch, A. Merceron, M. Desmarais, & R. Nkambou (Eds.), Proceedings of The 12th international conference on educational data mining (EDM 2019) (pp. 79–88). International Educational Data Mining Society.
   Google Scholar
• Jayaprakash, S. M., Moody, E. W., Lauría, E. J., Regan, J. R., & Baron, J. D. (2014). Early alert of academically at-risk students: An open source analytics initiative. Journal of Learning Analytics, 1(1), 6–47. https://doi.org/10.18608/jla.2014.11.3
   Google Scholar
• Kizilcec, R. F., & Lee, H. (2020). Algorithmic fairness in education. ArXiv. https://doi.org/10.48550/arXiv.2007.05443
   Google Scholar
• Koenka, A. C., & Anderman, E. M. (2019). Personalized feedback as a strategy for improving motivation and performance among middle school students. Middle School Journal, 50(5), 15–22. https://doi.org/10.1080/00940771.2019.1674768
   Google Scholar
• Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open university learning analytics dataset. Scientific Data, 4(1), 170171. https://doi.org/10.1038/sdata.2017.171
   PubMedGoogle Scholar
• Le Bras, R., Swayamdipta, S., Bhagavatula, C., Zellers, R., Peters, M., Sabharwal, A., & Choi, Y. (2020). Adversarial filters of dataset biases. In H. Daumé III & A. Singh (Eds.), Proceedings of international conference on machine learning (pp. 1078–1088). PMLR.
   Google Scholar
• Li, C., Xing, W., & Leite, W. L. (2022a). Toward building a fair peer recommender to support help-seeking in online learning. Distance Education, 43(1), 30–55. https://doi.org/10.1080/01587919.2021.2020619
   Web of Science ®Google Scholar
• Li, C., Xing, W., & Leite, W. (2022b). Building socially responsible conversational agents using big data to support online learning: A case with Algebra Nation. British Journal of Educational Technology, 53(4), 776–803. https://doi.org/10.1111/bjet.13227
   Web of Science ®Google Scholar
• Li, C., & Xing, W. (2022). Revealing factors influencing students' perceived fairness: A case with a predictive system for math learning. In R. Kizilcec, K. Davis, & X. Ochoa (Eds.), Proceedings of the Ninth ACM Conference on Learning@ Scale (pp. 409–412). ACM. https://doi.org/10.1145/3491140.3528293
   Google Scholar
• Liang, J., Yang, J., Wu, Y., Li, C., & Zheng, L. (2016). Big data application in education: Dropout prediction in edx MOOCs. In H. Hsiao, J. Liu, R. Mertens, C.-R. Shyu, & C.-H. Yeh (Eds.), 2016 IEEE second international conference on multimedia Big data (BigMM) (pp. 440–443). IEEE. https://doi.org/10.1109/bigmm.2016.70
   Google Scholar
• Lim, L. A., Gasevic, D., Matcha, W., Ahmad Uzir, N. A., & Dawson, S. (2021). Impact of learning analytics feedback on self-regulated learning: Triangulating behavioural logs with students’ recall. In LAK21: 11th international learning analytics and knowledge conference (pp. 364–374). ACM. https://doi.org/10.1145/3448139.3448174
   Google Scholar
• Lodge, J. M., Panadero, E., Broadbent, J., De Barba, P. G., Lodge, J., Horvath, J., & Corrin, L. (2018). Supporting self-regulated learning with learning analytics. In J. Lodge, J. Horvath, & L. Corrin (Eds.), Learning analytics in the classroom: Translating learning analytics research for teachers (pp. 45–55). Routledge. https://doi.org/10.4324/9781351113038-4
   Google Scholar
• Łukasz, K., Sharma, K., Shirvani Boroujeni, M., & Dillenbourg, P. (2016). On generalizability of MOOC models. In T. Barnes, M. Chi, & M. Feng (Eds.), Proceedings of the 9th international conference on educational data mining (pp. 406–411). International Educational Data Mining Society.
   Google Scholar
• Lynch, C., Marras, M., Pechenizkiy, M., Rafferty, A., Ritter, S., Swamy, V., & Yu, R. (2022). FATED 2022: Fairness, accountability, and transparency in educational data. In A. Mitrovic & N. Bosch (Eds.), Proceedings of the 15th international conference on educational data mining (pp. 848–849). International Educational Data Mining Society. https://doi.org/10.5281/zenodo.6853079
   Google Scholar
• Marcinkowski, F., Kieslich, K., Starke, C., & Lünich, M. (2020). Implications of AI (un-) fairness in higher education admissions: The effects of perceived AI (un-) fairness on exit, voice and organizational reputation. Proceedings of the 2020 conference on fairness, accountability, and transparency, 122-130. https://doi.org/10.1145/3351095.3372867
   Google Scholar
• Martinez-Maldonado, R. (2019). A handheld classroom dashboard: Teachers’ perspectives on the use of real-time collaborative learning analytics. International Journal of Computer-Supported Collaborative Learning, 14(3), 383–411. https://doi.org/10.1007/s11412-019-09308-z
   Web of Science ®Google Scholar
• Mayer, R. E. (2019). Thirty years of research on online learning. Applied Cognitive Psychology, 33(2), 152–159. https://doi.org/10.1002/acp.3482
   Web of Science ®Google Scholar
• Metevier, B., Giguere, S., Brockman, S., Kobren, A., Brun, Y., Brunskill, E., & Thomas, P. S. (2019). Offline contextual bandits with high probability fairness guarantees. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), Advances in neural information processing systems: Vol. 32 (pp. 14922–14933). Curran Associates, Inc.
   Google Scholar
• Migut, M., & Worring, M. (2012). Visual exploration of classification models for various data types in risk assessment. Information Visualization, 11(3), 237–251. https://doi.org/10.1177/1473871611433715
   Web of Science ®Google Scholar
• Migut, M. A., Worring, M., & Veenman, C. J. (2015). Visualizing multi-dimensional decision boundaries in 2D. Data Mining and Knowledge Discovery, 29(1), 273–295. https://doi.org/10.1007/s10618-013-0342-x
   Web of Science ®Google Scholar
• Moreno-Marcos, P. M., Munoz-Merino, P. J., Maldonado-Mahauad, J., Perez-Sanagustin, M., Alario-Hoyos, C., & Kloos, C. D. (2020). Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs. Computers & Education, 145, 103728. https://doi.org/10.1016/j.compedu.2019.103728
   Web of Science ®Google Scholar
• Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT press.
   Google Scholar
• Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., & Heffernan, C. (2014). Population validity for educational data mining models: A case study in affect detection. British Journal of Educational Technology, 45(3), 487–501. https://doi.org/10.1111/bjet.12156
   Web of Science ®Google Scholar
• Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. UNESCO. http://repositorio.minedu.gob.pe/bitstream/handle/20.500.12799/6533/Artificial%20intelligence%20in%20education%20challenges%20and%20opportunities%20for%20sustainable%20development.pdf Accessed December 22, 2020
   Google Scholar
• Qiu, L., Liu, Y., Hu, Q., & Liu, Y. (2019). Student dropout prediction in massive open online courses by convolutional neural networks. Soft Computing, 23(20), 10287–10301. https://doi.org/10.1007/s00500-018-3581-3
   Web of Science ®Google Scholar
• Raeder, T., Forman, G., & Chawla, N. V. (2012). Learning from imbalanced data: Evaluation matters. In D. E. Holmes, & L. C. Jain (Eds.), Data mining: Foundations and intelligent paradigms (pp. 315–331). Springer. https://doi.org/10.1007/978-3-642-23166-7_12
   Google Scholar
• Riazy, S., & Simbeck, K. (2019). Predictive algorithms in learning analytics and their fairness. In N. Pinkwart & J. Konert (Eds.), DELFI 2019 (pp. 223–228). Bonn: Gesellschaft für Informatik e.V. https://doi.org/10.18420/delfi2019_305.
   Google Scholar
• Robinson, H., Kilgore, W., & Warren, S. (2017). Care, communication, support: Core for designing meaningful online collaborative learning. Online Learning, 21(4), 29–51. https://doi.org/10.24059/olj.v21i4.1240
   Web of Science ®Google Scholar
• Saqr, M., Fors, U., & Tedre, M. (2017). How learning analytics can early predict under-achieving students in a blended medical education course. Medical Teacher, 39(7), 757–767. https://doi.org/10.1080/0142159X.2017.1309376
   PubMed Web of Science ®Google Scholar
• Schweikert, K. (2019). Bootstrap confidence intervals and hypothesis testing for market information shares. Journal of Financial Econometrics. https://doi.org/10.1093/jjfinec/nbz035
   Web of Science ®Google Scholar
• Sclater, N., Peasgood, A., & Mullan, J. (2016). Learning analytics in higher education. A Review of UK and International Practice. Joint Information Systems Committee. https://www.jisc.ac.uk/sites/default/files/learning-analytics-in-he-v3.pdf Accessed November 12, 2020
   Google Scholar
• Siemens, G. (2013). Learning analytics. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
   Web of Science ®Google Scholar
• Siemens, G., & Baker, R. S. D. (2012). Learning analytics and educational data mining: towards communication and collaboration. In S. Shum, D. Gasevic, & R. Ferguson (Eds.), LAK '12: Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252–254). ACM. https://doi.org/10.1145/2330601.2330661.
   Google Scholar
• Smith, P. S., Nelson, M. M., Trygstad, P. J., & Banilower, E. R. (2013). Unequal distribution of resources for K-12 science instruction: Data from the 2012 national survey of science and mathematics education. Horizon Research. https://files.eric.ed.gov/fulltext/ED548250.pdf Accessed November 14, 2020
   Google Scholar
• Sun, B., & Alkhalifah, T. (2020). ML-descent: An optimization algorithm for full-waveform inversion using machine learning. Geophysics, 85(6), R477–R492. https://doi.org/10.1190/geo2019-0641.1
   Web of Science ®Google Scholar
• Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. Proceedings of Equity and access in algorithms, mechanisms, and optimization: EAAMO '21, 1–9. Article 17. https://doi.org/10.1145/3465416.3483305
   Google Scholar
• Thomas, P. S., da Silva, B. C., Barto, A. G., Giguere, S., Brun, Y., & Brunskill, E. (2019). Preventing undesirable behavior of intelligent machines. Science, 366(6468), 999–1004. https://doi.org/10.1126/science.aag3311
   PubMed Web of Science ®Google Scholar
• Tsai, Y.-S., Perrotta, C., & Gašević, D. (2020). Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment & Evaluation in Higher Education, 45(4), 554–567. https://doi.org/10.1080/02602938.2019.1676396
   Web of Science ®Google Scholar
• U.S. Department of Education Office for Civil rights. (2016). Civil rights Data Collection Data Snapshot: College and Career readiness. Retrieved from: https://ocrdata.ed.gov/estimations/2015-2016 Accessed November 14, 2020
   Google Scholar
• Uskov, V. L., Bakken, J. P., Byerly, A., & Shah, A. (2019). Machine learning-based predictive analytics of student academic performance in STEM education. 2019 IEEE Global Engineering Education Conference (EDUCON), 1370–1376. https://doi.org/10.1109/EDUCON.2019.8725237
   Google Scholar
• Vincent-Lancrin, S., & Van der Vlies, R. (2020). OECD education working papers. OECD Education Working Papers, 218, 1–13. https://doi.org/10.1787/a6c90fa9-en
   Google Scholar
• Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G.-J., & Paas, F. (2019). Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human–Computer Interaction, 35(4–5), 356–373. https://doi.org/10.1080/10447318.2018.1543084
   Web of Science ®Google Scholar
• Xing, W., & Du, D. (2019). Dropout prediction in MOOCs: Using deep learning for personalized intervention. Journal of Educational Computing Research, 57(3), 547–570. https://doi.org/10.1177/0735633118757015.
   Web of Science ®Google Scholar
• Xing, W., Tang, H., & Pei, B. (2019). Beyond positive and negative emotions: Looking into the role of achievement emotions in discussion forums of MOOCs. The Internet and Higher Education, 43. Article 100690. https://doi.org/10.1016/j.iheduc.2019.100690
   Web of Science ®Google Scholar
• Xing, W., Li, C., Chen, G., Huang, X., Chao, J., Massicotte, J., & Xie, C. (2021). Automatic assessment of students’ engineering design performance using a Bayesian network model. Journal of Educational Computing Research, 59(2), 230–256. http://doi.org/10.1177/0735633120960422
   Web of Science ®Google Scholar
• Yang, D., Lavonen, J. M., & Niemi, H. (2018). Online learning engagement: Factors and results-evidence from literature. Themes in ELearning, 11(1), 1–22. http://earthlab.uoi.gr/tel/index.php/themeselearn/article/view/5
   Google Scholar
• Yin, C., & Hwang, G. J. (2018). Roles and strategies of learning analytics in the e-publication era. Knowledge Management & E-Learning: An International Journal, 10(4), 455–468. https://doi.org/10.34105/j.kmel.2018.10.028
   Google Scholar
• Yu, R., Li, Q., Fischer, C., Doroudi, S., & Xu, D. (2020). Towards accurate and fair prediction of college success: Evaluating different sources of student data. In A. Rafferty, J. Whitehill, V. Cavalli-Sforza, & C. Romero (Eds.), Proceedings of the 13th international conference on educational data mining (EDM 2020) (pp. 292–301). International Data Mining Society. https://files.eric.ed.gov/fulltext/ED608066.pdf
   Google Scholar
• Zhao, F., Hwang, G. J., & Yin, C. (2021). A result confirmation-based learning behavior analysis framework for exploring the hidden reasons behind patterns and strategies. Educational Technology & Society, 24(1), 138–151. https://www.jstor.org/stable/26977863
   Web of Science ®Google Scholar
• Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017). Men also like shopping: Reducing gender bias amplification using corpus-level constraints. In M. Palmer, R. Hwa, & S. Riedel (Eds.), Proceedings of the 2017 conference on empirical methods in natural language processing (pp. 2979–2989). ACL. https://doi.org/10.18653/v1/d17-1323
   Google Scholar