Understanding the evolution of mathematics performance in primary education and the implications for STEM learning: A Markovian approach
作者：
Highlights：
• Model students’ performance in mathematics over time as a stochastic process.
• Create longitudinal datasets linking student scores on end-of-grade math exams.
• Analyze longitudinal datasets based on a variety of demographic factors.
• Use Markov chains to probabilistically characterize movement of students’ scores.
摘要
•Model students’ performance in mathematics over time as a stochastic process.•Create longitudinal datasets linking student scores on end-of-grade math exams.•Analyze longitudinal datasets based on a variety of demographic factors.•Use Markov chains to probabilistically characterize movement of students’ scores.
论文关键词：Mathematics education,Longitudinal student data,Markov chain,Educational data mining
论文评审过程：Available online 29 October 2014.
论文官网地址：https://doi.org/10.1016/j.chb.2014.09.037
