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Zehner, Fabian; Harrison, Scott; Eichmann, Beate; Deribo, Tobias; Bengs, Daniel; Andersen, Nico; Hahnel, Carolin – International Educational Data Mining Society, 2020
The "2nd Annual WPI-UMASS-UPENN EDM Data Mining Challenge" required contestants to predict efficient testtaking based on log data. In this paper, we describe our theory-driven and psychometric modeling approach. For feature engineering, we employed the Log-Normal Response Time Model for estimating latent person speed, and the Generalized…
Descriptors: Data Analysis, Competition, Classification, Prediction
Zehner, Fabian; Eichmann, Beate; Deribo, Tobias; Harrison, Scott; Bengs, Daniel; Andersen, Nico; Hahnel, Carolin – Journal of Educational Data Mining, 2021
The NAEP EDM Competition required participants to predict efficient test-taking behavior based on log data. This paper describes our top-down approach for engineering features by means of psychometric modeling, aiming at machine learning for the predictive classification task. For feature engineering, we employed, among others, the Log-Normal…
Descriptors: National Competency Tests, Engineering Education, Data Collection, Data Analysis
Skaggs, Gary; Bourque, Mary Lyn – 1998
Political and legislative pressures have posed a number of measurement issues and challenges to the development of sound, valid voluntary national tests (VNTs). This paper focuses on what appear to be the most difficult technical issues related to the VNT proposed by President Clinton in 1997. Technical issues refer to psychometric issues, as…
Descriptors: Academic Achievement, Achievement Tests, Classification, Difficulty Level