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ERIC Number: ED537229
Record Type: Non-Journal
Publication Date: 2012-Jun
Pages: 4
Abstractor: As Provided
Reference Count: 8
The Real World Significance of Performance Prediction
Pardos, Zachary A.; Wang, Qing Yang; Trivedi, Shubhendu
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, Jun 19-21, 2012)
In recent years, the educational data mining and user modeling communities have been aggressively introducing models for predicting student performance on external measures such as standardized tests as well as within-tutor performance. While these models have brought statistically reliable improvement to performance prediction, the real world significance of the differences in errors has been largely unexplored. In this paper we take a deeper look at what reported errors actually mean in the context of high stakes test score prediction as well as student mastery prediction. We report how differences in common error and accuracy metrics on prediction tasks translate to impact on students and depict how standard validation methods can lead to overestimated accuracies in these prediction tasks. Two years of student tutor use and corresponding student state test scores are used for the analysis of test prediction while a simulation study is conducted to investigate the correspondence between performance prediction error and latent knowledge prediction. (Contains 7 tables and 2 footnotes.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation
Authoring Institution: International Educational Data Mining Society