Exploring the Impact of Symbol Spacing and Problem Sequencing on Arithmetic Performance: An Educational Data Mining Approach (Journal of Educational Data Mining, 2024)
Experimental research on perception and cognition has shown that inherent and manipulated visual featuresof 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 174undergraduate students’ arithmetic performance but found results that were contradictory to most of theliterature (Closser et al., 2023). Here, we applied educational data mining (EDM) methods to that dataset atthe problem level to investigate whether inherent features of the 32 experimental problems (i.e., problemcomposition, problem order) may have caused unintended effects on students’ performance. We found thatstudents 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 symbolspacing 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 thatthe effect of sequencing may be impacted by other, undefined problem-level factors. These results advancecognitive theories on perceptual learning and provide implications for educational researchers: onlineexperiments designed to investigate students’ performance on mathematics problems should include avariety of problems, systematically examine the effects of problem order, and consider applying differentdata analysis approaches to detect effects of inherent problem features. Moreover, EDM methods can be atool to identify nuanced effects on behavior and performance in the context of data from online platforms.