贝叶斯网络方法能为教育研究带来什么？

doi:10.16382/j.cnki.1000-5560.2022.11.009

摘要/Abstract

摘要：

针对当前教育研究问题的复杂性、不确定性与动态性特征，本文提出使用贝叶斯网络方法分析教育实证研究数据。在研究范式上，贝叶斯网络结合了理论驱动与数据驱动的研究方法，根据教育研究理论与专家经验确定先验模型，通过后续采集的数据迭代模型，不断更新能够支持或反对理论模型的数据证据。在数据分析方法上，贝叶斯网络将变量或变量关系的不确定性纳入模型，以概率的方式给出精确的推断结论和预测信息。在模型应用上，贝叶斯网络能够在真实教学情境中实时评估学生的知识掌握、能力培养、素养发展等，为教学与学习过程的动态评估提供方法和技术上的支持。

关键词: 贝叶斯网络, 概率推理, 先验知识, 教育过程数据

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.

Key words: Bayesian network, probabilistic inference, prior knowledge, process data in education

顾昕, 毛梦琪, 马淑风, 陈森宇. 贝叶斯网络方法能为教育研究带来什么？[J]. 华东师范大学学报(教育科学版), 2022, 40(11): 110-122.

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.

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论坛聊天		回复消息			阅读消息			发布消息			不参与
论坛聊天		参与	聆听	缺席	参与	聆听	缺席	参与	聆听	缺席	参与	聆听	缺席
信息加工	活跃	0.85	0.7	0.55	0.75	0.6	0.45	1	0.85	0.7	0.65	0.5	0.35
信息加工	沉思	0.15	0.3	0.45	0.25	0.4	0.55	0	0.15	0.3	0.35	0.5	0.65

	软件平台	类别数据	连续数据	约束算法	评分算法	动态模型	参数估计
Bnlearn	R	√	√	√	√	√	√
Deal	R	√	√	×	√	×	√
pcalg	R	√	√	√	×	×	√
Banjo	Java	√	×	×	√	√	×
Free-BN	Java	√	√	√	√	×	×
BNFinder	Python	√	√	×	√	√	×
BNT	Matlab	√	√	√	√	√	√

互助行为	$ {T}_{1} $	$ {T}_{2} $	$ {T}_{3} $	$ {T}_{4} $	$ {T}_{5} $	$ {T}_{6} $	$ {T}_{7} $
讨论促进	46 (32.9%)	57 (40.7%)	43 (30.7%)	36 (25.7%)	36 (25.7%)	27 (19.3%)	30 (21.4%)
行为支持	24 (17.1%)	31 (22.1%)	39 (27.9%)	32 (22.9%)	36 (25.7%)	35 (25.0%)	25 (17.9%)
认知支持	75 (53.6%)	64 (45.7%)	71 (50.7%)	73 (52.1%)	69 (49.3%)	69 (49.3%)	73 (52.1%)
情感支持	40 (28.6%)	41 (29.3%)	36 (25.7%)	25 (17.9%)	42 (30.0%)	30 (21.4%)	39 (27.9%)
总互助行为	185	193	189	166	183	161	167
同伴关系提名	11						30
同伴喜欢程度（低）	41						50
同伴喜欢程度（高）	45						73

		同伴关系提名=0			同伴关系提名=1
P(同伴关系提名)		0.918			0.082
		同伴喜欢程度=0	同伴喜欢程度=1	同伴喜欢程度=2	同伴喜欢程度=0	同伴喜欢程度=1	同伴喜欢程度=2
P(同伴喜欢程度)		0.418	0.527	0.055	0.015	0.015	0.884
P(讨论促进)	0	0.777	0.617	0.430	0.500	0.928	0.598
P(讨论促进)	1	0.223	0.383	0.570	0.500	0.071	0.402
P(行为支持)	0	0.943	0.779	0.709	0.500	0.928	0.598
P(行为支持)	1	0.057	0.221	0.291	0.500	0.071	0.402
P(认知支持)	0	0.537	0.456	0.291	0.500	0.928	0.205
P(认知支持)	1	0.463	0.544	0.709	0.500	0.071	0.795
P(情感支持)	0	0.832	0.691	0.570	0.500	0.928	0.303
P(情感支持)	1	0.168	0.309	0.430	0.500	0.071	0.696

		同伴关系提名=0			同伴关系提名=1
P(同伴关系提名)		0.784			0.216
		同伴喜欢程度=0	同伴喜欢程度=1	同伴喜欢程度=2	同伴喜欢程度=0	同伴喜欢程度=1	同伴喜欢程度=2
P(同伴喜欢程度)		0.155	0.680	0.165	0.006	0.333	0.661
P(讨论促进)	0	0.820	0.786	0.830	0.500	0.795	0.698
P(讨论促进)	1	0.180	0.214	0.170	0.500	0.205	0.302
P(行为支持)	0	0.937	0.826	0.665	0.500	0.992	0.748
P(行为支持)	1	0.063	0.174	0.335	0.500	0.008	0.252
P(认知支持)	0	0.646	0.414	0.500	0.500	0.697	0.450
P(认知支持)	1	0.354	0.586	0.500	0.500	0.303	0.550
P(情感支持)	0	0.704	0.773	0.775	0.500	0.598	0.550
P(情感支持)	1	0.296	0.227	0.225	0.500	0.402	0.450