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Ryan S. Baker; Stephen Hutt; Nigel Bosch; Jaclyn Ocumpaugh; Gautam Biswas; Luc Paquette; J. M. Alexandra Andres; Nidhi Nasiar; Anabil Munshi – Educational Technology Research and Development, 2024
In this paper, we propose a new method for selecting cases for in situ, immediate interview research: detector-driven classroom interviewing (DDCI). Published work in educational data mining and learning analytics has yielded highly scalable measures that can detect key aspects of student interaction with computer-based learning in close to…
Descriptors: Electronic Learning, Anxiety, Metacognition, Data Collection
Ethan R. Van Norman; Emily R. Forcht – Journal of Education for Students Placed at Risk, 2024
This study evaluated the forecasting accuracy of trend estimation methods applied to time-series data from computer adaptive tests (CATs). Data were collected roughly once a month over the course of a school year. We evaluated the forecasting accuracy of two regression-based growth estimation methods (ordinary least squares and Theil-Sen). The…
Descriptors: Data Collection, Predictive Measurement, Predictive Validity, Predictor Variables
Thompson, Greg – British Journal of Sociology of Education, 2017
This article critically considers the promise of computer adaptive testing (CAT) and digital data to provide better and quicker data that will improve the quality, efficiency and effectiveness of schooling. In particular, it uses the case of the Australian NAPLAN test that will become an online, adaptive test from 2016. The article argues that…
Descriptors: Foreign Countries, Computer Assisted Testing, Adaptive Testing, National Competency Tests
Van Norman, Ethan R.; Ysseldyke, James E. – School Psychology Review, 2020
Within multitiered systems of support, assessment practices that limit the amount of time students miss instruction should be prioritized. At the same time, decisions about student response to intervention need to be based upon technically adequate data. We evaluated the impact of data collection frequency and trend estimation method on the…
Descriptors: Data Collection, Adaptive Testing, Computer Assisted Testing, Computation
O'Keeffe, Cormac – E-Learning and Digital Media, 2017
International Large Scale Assessments have been producing data about educational attainment for over 60 years. More recently however, these assessments as tests have become digitally and computationally complex and increasingly rely on the calculative work performed by algorithms. In this article I first consider the coordination of relations…
Descriptors: Achievement Tests, Foreign Countries, Secondary School Students, International Assessment

Jones, Douglas H.; Jin, Zhiying – Psychometrika, 1994
Replenishing item pools for on-line ability testing requires innovative and efficient data collection. A method is proposed to collect test item calibration data in an on-line testing environment sequentially using locally D-optimum designs, thereby achieving high Fisher information for the item parameters. (SLD)
Descriptors: Ability, Adaptive Testing, Computer Assisted Testing, Data Collection
Walberg, Herbert J. – 1985
The value of statistical research depends on valid comparisons which can usefully influence educational policy. Educational research needs to extend the measures of learning (such as the National Assessment of Educational Progress) through nationally-calibrated absolute measures and through computer-assisted and adaptive testing. Direct sampling…
Descriptors: Academic Achievement, Academic Standards, Adaptive Testing, Computer Assisted Testing
Thomas, Gregory P. – 1986
This paper argues that no single measurement strategy serves all purposes and that applying methods and techniques which allow a variety of data elements to be retrieved and juxtaposed may be an investment in the future. Item response theory, Rasch model, and latent trait theory are all approaches to a single conceptual topic. An abbreviated look…
Descriptors: Achievement Tests, Adaptive Testing, Criterion Referenced Tests, Data Collection