@article{Closser_Botelho_Chan_2024, title={Exploring the Impact of Symbol Spacing and Problem Sequencing on Arithmetic Performance: An Educational Data Mining Approach}, volume={16}, url={https://jedm.educationaldatamining.org/index.php/JEDM/article/view/767}, DOI={10.5281/zenodo.11403249}, abstractNote={<p>Experimental research on perception and cognition has shown that inherent and manipulated visual features<br />of mathematics problems impact individuals’ problem-solving behavior and performance. In a recent study,<br />we manipulated the spacing between symbols in arithmetic expressions to examine its effect on 174<br />undergraduate students’ arithmetic performance but found results that were contradictory to most of the<br />literature (Closser et al., 2023). Here, we applied educational data mining (EDM) methods to that dataset at<br />the problem level to investigate whether inherent features of the 32 experimental problems (i.e., problem<br />composition, problem order) may have caused unintended effects on students’ performance. We found that<br />students were consistently faster to correctly simplify expressions with the higher-order operator on the left,<br />rather than right, side of the expression. Furthermore, average response times varied based on the symbol<br />spacing of the current and preceding problem, suggesting that problem sequencing matters. However,<br />including or excluding problem identifiers in analyses changed the interpretation of results, suggesting that<br />the effect of sequencing may be impacted by other, undefined problem-level factors. These results advance<br />cognitive theories on perceptual learning and provide implications for educational researchers: online<br />experiments designed to investigate students’ performance on mathematics problems should include a<br />variety of problems, systematically examine the effects of problem order, and consider applying different<br />data analysis approaches to detect effects of inherent problem features. Moreover, EDM methods can be a<br />tool to identify nuanced effects on behavior and performance in the context of data from online platforms.</p>}, number={1}, journal={Journal of Educational Data Mining}, author={Closser, Avery Harrison and Botelho, Anthony and Chan, Jenny}, year={2024}, month={Jun.}, pages={84–111} }