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Bosch, Nigel – Journal of Educational Data Mining, 2021
Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…
Descriptors: Accuracy, Learning Analytics, Models, National Competency Tests
Jing Lu; Chun Wang; Ningzhong Shi – Grantee Submission, 2023
In high-stakes, large-scale, standardized tests with certain time limits, examinees are likely to engage in either one of the three types of behavior (e.g., van der Linden & Guo, 2008; Wang & Xu, 2015): solution behavior, rapid guessing behavior, and cheating behavior. Oftentimes examinees do not always solve all items due to various…
Descriptors: High Stakes Tests, Standardized Tests, Guessing (Tests), Cheating
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Panayiotou, Anastasia; Kyriakides, Leonidas; Creemers, Bert P. M.; McMahon, Léan; Vanlaar, Gudrun; Pfeifer, Michael; Rekalidou, Galini; Bren, Matevž – Educational Assessment, Evaluation and Accountability, 2014
This study investigates the extent to which the factors included in the dynamic model of educational effectiveness are associated with student achievement gains in six different European countries. At classroom level, the dynamic model refers to eight factors relating to teacher behavior in the classroom: orientation, structuring, questioning,…
Descriptors: Teacher Behavior, Academic Achievement, Achievement Gains, Models