What Can Bayesian Network Contribute to Educational Research?

  • Xin Gu ,
  • Mengqi Mao ,
  • Shufeng Ma ,
  • Senyu Chen
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  • 1. Department of Educational Psychology, Faculty of Education, East China Normal University, Shanghai 200062, China
    2. School of Health Sciences, University of Manchester, Manchester, M13 9PL, UK

Online published: 2022-10-27

Abstract

This paper proposes to use the Bayesian network method to analyze educational research data in view of the complexity, uncertainty and dynamic characteristics of current educational research problems. In terms of research paradigm, Bayesian network integrates theoretical and data-driven research procedures, determines a prior model based on educational research theories and expert experience, and updates data evidence that can support or oppose the theoretical model by collecting new data. In terms of data analysis, Bayesian network brings the uncertainty of variables or variables’ relations into the model and gives accurate inference and prediction by means of probability. In terms of application, Bayesian network can evaluate students’ knowledge mastery, ability training and competence development in real teaching and learning situations. This offers methods and technical support for dynamic evaluation of teaching and learning process.

Cite this article

Xin Gu , Mengqi Mao , Shufeng Ma , Senyu Chen . What Can Bayesian Network Contribute to Educational Research?[J]. Journal of East China Normal University(Educational Sciences), 2022 , 40(11) : 110 -122 . DOI: 10.16382/j.cnki.1000-5560.2022.11.009

References

null 陈森宇, 毛梦琪, 吴双, 米热努尔, 黄汇, & 马淑风 (2021). 青少年在合作学习中的同伴互助行为研究. 第二十三届全国心理学学术会议摘要集, 516?517.
null 侯杰泰, 温忠麟, &成子娟. (2004). 结构方程模型及其应用. 北京: 教育科学出版社.
null 柳炳祥, 田原, 彭永康, & 邱娟 基于贝叶斯网络的人才培养模式绩效评价 计算机教育 2018 2 18 20 柳炳祥, 田原, 彭永康, & 邱娟. (2018). 基于贝叶斯网络的人才培养模式绩效评价. 计算机教育,(2),18—20.
null 马晓强, 彭文蓉, & 萨丽·托马斯 学校效能的增值评价——对河北省保定市普通高中学校的实证研究 教育研究 2006 10 77 84 马晓强, 彭文蓉, & 萨丽·托马斯. (2006). 学校效能的增值评价—对河北省保定市普通高中学校的实证研究. 教育研究,(10),77—84.
null 孟志远, 卢潇, & 胡凡刚 大数据驱动教育变革的理论路径与应用思考——首届中国教育大数据发展论坛探析 远程教育杂志 2017 35 2 9 18 孟志远, 卢潇, & 胡凡刚. (2017). 大数据驱动教育变革的理论路径与应用思考—首届中国教育大数据发展论坛探析. 远程教育杂志,35(2),9—18.
null 宋丽红 基于贝叶斯网的认知诊断模型构建 心理科学 2016 39 4 783 789 宋丽红. (2016). 基于贝叶斯网的认知诊断模型构建. 心理科学,39(4),783—789.
null 温忠麟, 侯杰泰, & 张雷 调节效应与中介效应的比较和应用 心理学报 2005 37 2 268 274 温忠麟, 侯杰泰, & 张雷. (2005). 调节效应与中介效应的比较和应用. 心理学报,37(2),268—274.
null 闫志勇, 李明, 倪劲峰, & 周学海 贝叶斯网络在自适应教育超媒体中的应用 计算机工程与应用 2002 38 8 217 219 闫志勇, 李明, 倪劲峰, & 周学海. (2002). 贝叶斯网络在自适应教育超媒体中的应用. 计算机工程与应用,38(8),217—219.
null 杨向东. (2014). 理论驱动的心理与教育测量学. 上海: 华东师范大学出版社.
null 杨现民, 郭利明, 王东丽, & 邢蓓蓓 数据驱动教育治理现代化: 实践框架, 现实挑战与实施路径 现代远程教育研究 2020 32 2 73 84 杨现民, 郭利明, 王东丽, & 邢蓓蓓. (2020). 数据驱动教育治理现代化: 实践框架, 现实挑战与实施路径. 现代远程教育研究,32(2),73—84.
null 张晓勇, 彭军 & 文孟飞 基于贝叶斯网络的网络交互教学成效评价系统 现代远程教育研究 2012 4 85 90 张晓勇, 彭军 & 文孟飞. (2012). 基于贝叶斯网络的网络交互教学成效评价系统. 现代远程教育研究,(4),85—90.
null Anderson, R. C., Chinn, C., Waggoner, M., & Nguyen, K. (1998). Intellectually stimulating story discussions. In J. Osborn & F. Lehr (Eds. ), Literacy for all: Issues in teaching and learning. (pp. 170–186). New York: Guilford.
null Almond, R. Mislevy, R. Steinberg, L., Yan, D. & Williamson, D. (2015). Bayesian networks in educational assessment. New York: Springer.
null Belland, B. R., Walker, A. E., & Kim, N. J A Bayesian network meta-analysis to synthesize the influence of contexts of scaffolding use on cognitive outcomes in STEM education Review of Educational Research 2017 87 6 1042 1081 Belland, B. R., Walker, A. E., & Kim, N. J. (2017). A Bayesian network meta-analysis to synthesize the influence of contexts of scaffolding use on cognitive outcomes in STEM education. Review of Educational Research, 87(6), 1042—1081.
null Carmona, C., Castillo, G., & Millán, E Designing a dynamic Bayesian network for modeling students' learning styles IEEE International Conference on Advanced Learning Technologies 2008 346 350 Carmona, C., Castillo, G., & Millán, E. (2008). Designing a dynamic Bayesian network for modeling students' learning styles. IEEE International Conference on Advanced Learning Technologies, 346—350.
null De Campos, L. M., & Huete, J. F A new approach for learning belief networks using independence criteria International Journal of Approximate Reasoning 2000 24 1 11 37 De Campos, L. M., & Huete, J. F. (2000). A new approach for learning belief networks using independence criteria. International Journal of Approximate Reasoning, 24(1), 11—37.
null De Klerk, S, Veldkamp, B., & Eggen, T. Psychometric analysis of the performance data of simulation-based assessment: A systematic review and a Bayesian network example Computers & Education 2015 85 23 34 De Klerk, S, Veldkamp, B., & Eggen, T. (2015). Psychometric analysis of the performance data of simulation-based assessment: A systematic review and a Bayesian network example. Computers & Education, 85, 23—34.
null García, P., Amandi, A., Schiaffino, S., & Campo, M. Evaluating Bayesian networks’ precision for detecting students’ learning styles Computers & Education 2007 49 3 794 808 García, P., Amandi, A., Schiaffino, S., & Campo, M. (2007). Evaluating Bayesian networks’ precision for detecting students’ learning styles. Computers & Education, 49(3), 794—808.
null Geiger, D., Verma, T., & Pearl, J Identifying independence in Bayesian networks Networks 1990 20 5 507 534 Geiger, D., Verma, T., & Pearl, J. (1990). Identifying independence in Bayesian networks. Networks, 20(5), 507—534.
null Grimm, K. J., Helm, J., Rodgers, D., & O'Rourke, H Analyzing cross-lag effects: A comparison of different cross-lag modeling approaches New directions for child and adolescent development 2021 2021 175 11 33 Grimm, K. J., Helm, J., Rodgers, D., & O'Rourke, H. (2021). Analyzing cross-lag effects: A comparison of different cross-lag modeling approaches. New directions for child and adolescent development, 2021(175), 11—33.
null Gupta, S. & Kim, H. Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities European Journal of Operational Research 2008 190 3 818 833 Gupta, S. & Kim, H. (2008). Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities. European Journal of Operational Research, 190(3), 818—833.
null Huang, B. & Hew, K. F Implementing a theory-driven gamification model in higher education flipped courses: Effects on out-of-class activity completion and quality of artifacts Computers & Education 2018 125 254 272 Huang, B. & Hew, K. F. (2018). Implementing a theory-driven gamification model in higher education flipped courses: Effects on out-of-class activity completion and quality of artifacts. Computers & Education, 125, 254—272.
null Kurilovas, E On data-driven decision-making for quality education Computers in Human Behavior 2020 107 105774 Kurilovas, E. (2020). On data-driven decision-making for quality education. Computers in Human Behavior, 107, 105774.
null Mandinach, E. B A perfect time for data use: Using data-driven decision making to inform practice Educational Psychologist 2012 47 2 71 85 Mandinach, E. B. (2012). A perfect time for data use: Using data-driven decision making to inform practice. Educational Psychologist, 47(2), 71—85.
null Mouri, K., Okubo, F., Shimada, A., & Ogata, H. (2016). Bayesian network for predicting students’ final grade using e-book logs in university education. In 2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT) (pp. 85−89).
null Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo: Morgan Kaufmann.
null Pearl, J Causal inference in statistics: An overview Statistics surveys 2009 3 96 146 Pearl, J. (2009). Causal inference in statistics: An overview. Statistics surveys, 3, 96—146.
null Pietro, L., Mugion, R., Musella, F., Renzi, M., & Vicard, P. Reconciling internal and external performance in a holistic approach: A Bayesian network model in higher education Expert Systems with Applications 2015 42 5 2691 2702 Pietro, L., Mugion, R., Musella, F., Renzi, M., & Vicard, P. (2015). Reconciling internal and external performance in a holistic approach: A Bayesian network model in higher education. Expert Systems with Applications, 42(5), 2691—2702.
null Pinto, P. C., Nagele, A., Dejori, M., Runkler, T. A., & Sousa, J. M Using a local discovery ant algorithm for Bayesian network structure learning IEEE transactions on evolutionary computation 2009 13 4 767 779 Pinto, P. C., Nagele, A., Dejori, M., Runkler, T. A., & Sousa, J. M. (2009). Using a local discovery ant algorithm for Bayesian network structure learning. IEEE transactions on evolutionary computation, 13(4), 767—779.
null Reichenberg, R Dynamic Bayesian networks in educational measurement: Reviewing and advancing the state of the field Applied Measurement in Education 2018 31 4 335 350 Reichenberg, R. (2018). Dynamic Bayesian networks in educational measurement: Reviewing and advancing the state of the field. Applied Measurement in Education, 31(4), 335—350.
null Sabourin, J., Mott, B., & Lester, J. (2013). Utilizing dynamic Bayes nets to improve early prediction models of self-regulated learning. In S. Carberry, S. Weibelzahl, A. Micarelli, G. Semeraro (Eds. ), User modeling, adaptation, and personalization (pp. 228−241). New York: Springer.
null Scanagatta, M., Salmerón, A., & Stella, F A survey on Bayesian network structure learning from data Progress in Artificial Intelligence 2019 8 4 425 439 Scanagatta, M., Salmerón, A., & Stella, F. (2019). A survey on Bayesian network structure learning from data. Progress in Artificial Intelligence, 8(4), 425—439.
null Scutari, M. (2009). Learning Bayesian networks with the bnlearn R package. arXiv preprint arXiv: 0908.3817.
null Sinharay, S Model diagnostics for Bayesian networks Journal of Educational and Behavioral Statistics 2006 31 1 1 33 Sinharay, S. (2006). Model diagnostics for Bayesian networks. Journal of Educational and Behavioral Statistics, 31(1), 1—33.
null Van de Schoot, R., Winter, S. D., Ryan, O., Zondervan-Zwijnenburg, M., & Depaoli, S A systematic review of Bayesian articles in psychology: The last 25 years Psychology Methods 2017 22 2 217 239 Van de Schoot, R., Winter, S. D., Ryan, O., Zondervan-Zwijnenburg, M., & Depaoli, S. (2017). A systematic review of Bayesian articles in psychology: The last 25 years. Psychology Methods, 22(2), 217—239.
null Xenos, M Prediction and assessment of student behavior in open and distance education in computers using Bayesian networks Computer & Education 2004 43 345 359 Xenos, M. (2004). Prediction and assessment of student behavior in open and distance education in computers using Bayesian networks. Computer & Education, 43, 345—359.
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