Prediction and assessment of student learning outcomes in calculus
A decision support system based on data mining (DM) and Bayesian belief networks (BBN) is proposed to predict the student learning outcomes and takes the calculus course as an example to help students overcome their learning difficulties. Total of 427 freshmen in Ming Chi University of Technology (Taiwan) did questionnaires to assist this study. The methodologies involves four steps: fuzzy theory to identify the factors on learning outcomes; data mining to construct influence diagram; machine learning to establish the probability tables in BBN; and the model to predict the exam scores at the beginning of course and thereby to help students enhance their scores according to their weakness.
learning outcome data mining Bayesian belief networks
Kevin Fong-Rey Liu Jia-Shen Chen
Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology Taipei 24301, Taiwan, ROC
国际会议
上海
英文
299-303
2011-03-11（万方平台首次上网日期，不代表论文的发表时间）