Volume 14 · Number 3 · Pages 285–287
Machine Learning and the Perils of Prolific Pattern Finding

Bruce Sherin

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Abstract

Open peer commentary on the article “Studying Conceptual Change in Classrooms: Using Association Rule Mining to Detect Changes in Students’ Explanations of the Effects of Urban Planning and Social Policy” by Arthur Hjorth & Uri Wilensky. Abstract: Horth and Wilensky have taken an important first step in introducing a new method for capturing changes in student thinking, one that draws from the field of machine learning. However, I argue that there is much work to be done by educational researchers, as we seek to understand how best to apply methods from machine learning, and to appropriately interpret the results they produce.

Citation

Sherin B. (2019) Machine learning and the perils of prolific pattern finding. Constructivist Foundations 14(3): 285–287. https://constructivist.info/14/3/285

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References

Bayardo R. J. & Agrawal R. (1999) Mining the most interesting rules. In: Fayyad U., Chaudhuri S. & Madigan D. (eds.) Proceedings of the Fifth ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD ’99) ACM, New York: 145–154. ▸︎ Google︎ Scholar
Lallich S., Teytaud O. & Prudhomme E. (2007) Association rule interestingness: Measure and statistical validation. In: Guillet F. J. & Hamilton H. J. (eds.) Quality measures in data mining. Springer, Berlin: 251–275. ▸︎ Google︎ Scholar
Lavrač N., Flach P. & Zupan B. (1999) Rule evaluation measures: A unifying view. In: Džeroski S. & Flach P. (eds.) Inductive logic programming. Springer, Berlin: 174–185. ▸︎ Google︎ Scholar
Omiecinski E. R. (2003) Alternative interest measures for mining associations in databases. IEEE Transactions on Knowledge and Data Engineering 15(1): 57–69. ▸︎ Google︎ Scholar
Sherin B. L. (2013) A computational study of commonsense science: An exploration in the automated analysis of clinical interview data. Journal of the Learning Sciences 22(4): 600–635. ▸︎ Google︎ Scholar
Sherin B. L., Kersting N. B. & Berland M. (2018) Learning analytics in support of qualitative analysis. In: Kay J. & Luckin R. (eds.) Rethinking learning in the digital age: Making the learning sciences count. Proceedings of the 13th International Conference of the Learning Sciences (ICLS 2018) Volume 3. International Society of the Learning Sciences, London: 464–471. ▸︎ Google︎ Scholar

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