What Can Bayesian Network Contribute to Educational Research？

doi:10.16382/j.cnki.1000-5560.2022.11.009

Abstract

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

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.

Figures/Tables 12

References 0

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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