Exploring the Impact of Symbol Spacing and Problem Sequencing on Arithmetic Performance: An Educational Data Mining Approach

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Published Jun 27, 2024
Avery Harrison Closser Anthony F. Botelho Jenny Yun-Chen Chan

Abstract

Experimental research on perception and cognition has shown that inherent and manipulated visual features
of mathematics problems impact individuals’ problem-solving behavior and performance. In a recent study,
we manipulated the spacing between symbols in arithmetic expressions to examine its effect on 174
undergraduate students’ arithmetic performance but found results that were contradictory to most of the
literature (Closser et al., 2023). Here, we applied educational data mining (EDM) methods to that dataset at
the problem level to investigate whether inherent features of the 32 experimental problems (i.e., problem
composition, problem order) may have caused unintended effects on students’ performance. We found that
students were consistently faster to correctly simplify expressions with the higher-order operator on the left,
rather than right, side of the expression. Furthermore, average response times varied based on the symbol
spacing of the current and preceding problem, suggesting that problem sequencing matters. However,
including or excluding problem identifiers in analyses changed the interpretation of results, suggesting that
the effect of sequencing may be impacted by other, undefined problem-level factors. These results advance
cognitive theories on perceptual learning and provide implications for educational researchers: online
experiments designed to investigate students’ performance on mathematics problems should include a
variety of problems, systematically examine the effects of problem order, and consider applying different
data analysis approaches to detect effects of inherent problem features. Moreover, EDM methods can be a
tool to identify nuanced effects on behavior and performance in the context of data from online platforms.

How to Cite

Closser, A. H., Botelho, A., & Chan, J. (2024). Exploring the Impact of Symbol Spacing and Problem Sequencing on Arithmetic Performance: An Educational Data Mining Approach. Journal of Educational Data Mining, 16(1), 84–111. https://doi.org/10.5281/zenodo.11403249
Abstract 71 | HTML Downloads 21 PDF Downloads 26

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Keywords

experimental design, causal inference, bootstrapping, arithmetic, perceptual learning

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EDM 2024 Journal Track