2015 journal article
Understanding the evolution of mathematics performance in primary education and the implications for STEM learning: A Markovian approach
COMPUTERS IN HUMAN BEHAVIOR, 47, 4–17.
author keywords: Mathematics education; Longitudinal student data; Markov chain; Educational data mining
TL;DR:
This work conducts an extensive examination of tens of thousands of student records and uses Markov chain models to probabilistically characterize the movement of students' scores from one grade level to the next, the first step in developing a framework to forecast individual students' development of mathematical knowledge over time.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: August 6, 2018
National reports have documented deficiencies in the vertical alignment of mathematical learning in K-12 education. Many students fail to master requisite concepts before advancing to more complex ideas, leaving them ill-prepared to succeed in higher level Science, Technology, Engineering, and Mathematics (STEM) coursework. In this paper, we model elementary and middle school students’ performance in mathematics over time as a stochastic process to forecast their proficiency by the eighth grade. We conduct an extensive examination of tens of thousands of student records and extract useful information. We use this data to present a longitudinal analysis of student performance on the North Carolina End-of-Grade mathematics exam and use Markov chain models to probabilistically characterize the movement of students’ scores from one grade level to the next. This work is the first step in developing a framework to forecast individual students’ development of mathematical knowledge over time.