The introduction of math solving photo apps in late 2014 presented students with a tempting new way to solve math problems quickly and accurately. Despite widespread acknowledgement that students increasingly use these apps to complete their coursework, as well as growing concerns about cheating as more students learn online, the prevalence and impact of this technology remains largely unexplored. This study uses a large dataset consisting of 700 unique math exercises and over 82 million student submissions to investigate changes in exercise answering speeds during the last decade. Through a series of exploratory analyses, we identify dramatic shifts in exercise submission speed distributions in recent years, with increasing numbers of rapid responses suggesting growing student reliance on math solving photo technology to answer math problems on homework and exams. Our analyses also reveal that decreases in exercise answering speeds have occurred contemporaneously with the introduction and proliferation of math solving photo apps in education and we further substantiate the role of these tools by verifying that exercise susceptibility to math solving photo apps is associated with decreases in submission speeds. We discuss potential applications of our findings to improve math assessment design and support students in adopting better learning strategies.
CITATION STYLE
Sloan-Lynch, J., Gay, N., & Watkins, R. (2022). Too Fast for Their Own Good: Analyzing a Decade of Student Exercise Responses to Explore the Impact of Math Solving Photo. In ACM International Conference Proceeding Series (pp. 67–76). Association for Computing Machinery. https://doi.org/10.1145/3506860.3506868
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