Analysis of Influence Factors for Learning Outcomes with Bayesian Network
Department of Informatics, Graduate School of Informatics and Engineering, The University of Electro-Communications
1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan
This study identifies and analyzes the influence factors for learning outcomes at a university with a Bayesian network. It is based on a fact-finding survey on university student life and learning. Suitable constraints and a score metric for the Bayesian network learning are determined via cross-validation, and the learning outcome variables are categorized into subsets according to six abilities: cooperativeness, expressiveness, foreign language, collecting and organizing information, logical thinking, and sociability. The learned network suggests that two to seven factors influence each ability. In addition, it is confirmed that the probability distributions of all most of the identified factors shift to high agreement/experience levels, as self-knowledge levels for the acquired abilities increase, i.e., positive effects exist for most factors for each identified ability.
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