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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
CannistrĂ , Marta; Masci, Chiara; Ieva, Francesca; Agasisti, Tommaso; Paganoni, Anna Maria – Studies in Higher Education, 2022
This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…
Descriptors: Dropouts, Potential Dropouts, Dropout Prevention, Dropout Characteristics
Jaylin Lowe; Charlotte Z. Mann; Jiaying Wang; Adam Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2024
Recent methods have sought to improve precision in randomized controlled trials (RCTs) by utilizing data from large observational datasets for covariate adjustment. For example, consider an RCT aimed at evaluating a new algebra curriculum, in which a few dozen schools are randomly assigned to treatment (new curriculum) or control (standard…
Descriptors: Randomized Controlled Trials, Middle School Mathematics, Middle School Students, Middle Schools
Williamson, Ben – Journal of Education Policy, 2016
Educational institutions and governing practices are increasingly augmented with digital database technologies that function as new kinds of policy instruments. This article surveys and maps the landscape of digital policy instrumentation in education and provides two detailed case studies of new digital data systems. The Learning Curve is a…
Descriptors: Visualization, Synchronous Communication, Governance, Data Collection
Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol – Grantee Submission, 2015
Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…
Descriptors: Educational Environment, Predictive Measurement, Predictor Variables, Cooperative Learning
Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol – International Educational Data Mining Society, 2015
Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…
Descriptors: Educational Environment, Predictive Measurement, Predictor Variables, Cooperative Learning
Fusilier, Marcelline; Durlabhji, Subhash; Cucchi, Alain – Journal of Educational Computing Research, 2008
National background of users may influence the process of technology acceptance. The present study explored this issue with the new, integrated technology use model proposed by Sun and Zhang (2006). Data were collected from samples of college students in India, Mauritius, Reunion Island, and United States. Questionnaire methodology and…
Descriptors: Foreign Countries, Data Analysis, Internet, Technology Integration
Peer reviewedTucker, Ledyard R. – Educational and Psychological Measurement, 1973
The inter-battery factor analysis model for predictive systems appears to offer a useful formalization of these systems and provides some guides for the development of the system. (Author)
Descriptors: Analysis of Covariance, Data Collection, Factor Analysis, Mathematical Models
Peer reviewedDonnelly, Michael; And Others – Journal of Medical Education, 1986
Multiple regression analyses were employed to determine the relationships between achievement variables as predictors of the performance on the National Board of Medical Examiners examinations of medical students. Score averages were added to a composite Medical College Admission Test to investigate the increases in prediction accuracy.…
Descriptors: College Entrance Examinations, Comparative Analysis, Data Analysis, Data Collection
Lieshoff, Sylvia – 1993
This paper examines the use of environmental scanning for institutions of higher education to achieve the following objectives: (1) provide early warning of changes that will have an impact on education; (2) define potential threats and opportunities to the institution or department; (3) promote a future orientation in faculty; and (4) alert…
Descriptors: College Planning, Data Analysis, Data Collection, Environmental Scanning
Dees, James W.; Dufilho, L. Paul – 1975
This report summarizes the techniques used in gathering and maintaining a data file on most of the Army aviator trainees who have been through the Officer/Warrant Officer Rotary Wing Aviator Course and the Warrant Officer Candidate Course during the period 1 July 1968-31 December 1969. Specific regression analyses dealing with the prediction of…
Descriptors: Academic Achievement, Data Collection, Demography, Failure
Peer reviewedBrowning, Christine A.; Channell, Dwayne E. – Arithmetic Teacher, 1992
Describes an activity that introduces students to the use of database software to collect and interpret data. Asks middle school students to collect personal information regarding their dominant hand, eye, and thumb. Students use FIND and AND commands to create tables and respond to questions related to the data. (MDH)
Descriptors: Computer Uses in Education, Courseware, Data Analysis, Data Collection

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