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Session Type: Coordinated Paper Session
The validity of inferences drawn from educational assessments is contingent not only on the quality of the test items but also on the level of engagement exhibited by the test takers. Disengagement can distort scores, potentially underestimating a student's abilities. This session delves into the multifaceted nature of test (dis)engagement, the methodologies to detect it, and address its effects. Presentation 1 examines the impact of disengagement-adjusted scoring, recommending score-adjustment procedures consider both rapid guessing and performance change information. Presentation 2 uses Monte Carlo simulations to quantify how disengagement affects the recovery of latent growth curve model parameter estimates and exemplifies how to mitigate related issues. Presentation 3 uses Monte Carlo simulations to examine the parameter recovery of the multidimensional Nested Logit Model, which measures ability and test-taking effort jointly to gain a deeper comprehension of test-takers’ response behaviors. Presentation 4 employs AI algorithms to analyze students' navigation patterns in the NAEP assessments, offering insights into test engagement. Lastly, Presentation 5 introduces and validates the Cognitive Modeling Method, a theory-driven approach to detect not-fully-effortful responses to further improve inferences drawn from assessments. Together, these presentations highlight the importance of (dis)engagement in assessments and provide novel approaches to ensure valid score interpretation.
The Impact of Disengagement-Adjusted Scoring on Test Score Validity - Steven Wise; Megan Kuhfeld, NWEA; Jazmin Isaacs, NWEA
Quantifying the Effect of Test Disengagement on Student Growth Estimates - James Soland, University of Virginia
Joint Modeling of Ability and Test-Taking Effort with Multidimensional Nested Logit Model - Okan Bulut, University of Alberta; Kylie Gorney, Michigan State University; Seyma N. Yildirim-Erbasli, Concordia University of Edmonton
Investigate Students’ Engagement with the NAEP Assessment Through Item Navigation - Hongwen Guo, Hongwen Guo/ETS; Matthew S Johnson, ETS; Luis Saldivia, ETS; Michelle Worthington, ETS
Introducing and Evaluating a Theory-Driven Method to Detect Not-Fully-Effortful Responses - Burcu Arslan, Educational Testing Service Global B.V.; Blair Lehman, Brighter Research; Marlit Annalena Lindner, IWM - Leibniz Institut für Wissensmedien, University of Tübingen