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Download full textLevin, Nathan A. – Journal of Educational Data Mining, 2021
The Big Data for Education Spoke of the NSF Northeast Big Data Innovation Hub and ETS co-sponsored an educational data mining competition in which contestants were asked to predict efficient time use on the NAEP 8th grade mathematics computer-based assessment, based on the log file of a student's actions on a prior portion of the assessment. In…
Descriptors: Learning Analytics, Data Collection, Competition, Prediction
Malkiewich, Laura; Baker, Ryan S.; Shute, Valerie; Kai, Shimin; Paquette, Luc – International Educational Data Mining Society, 2016
Educational games have become hugely popular, and educational data mining has been used to predict student performance in the context of these games. However, models built on student behavior in educational games rarely differentiate between the types of problem solving that students employ and fail to address how efficacious student problem…
Descriptors: Classification, Problem Solving, Educational Games, Models
Direct linkBurke, Mack D.; Davis, John L.; Hagan-Burke, Shanna; Lee, Yuan-Hsuan; Fogarty, Melissa Shea – Journal of Positive Behavior Interventions, 2014
School-wide positive behavior support (SWPBS) focuses on promoting social competence through the establishment of behavior expectations that are explicitly taught and reinforced by all teachers across all settings. This study investigated the validity of using adherence to SWPBS behavior expectations as a screening tool for predicting behavior…
Descriptors: Risk, Interpersonal Competence, Prediction, Behavior Problems
Sabourin, Jennifer L.; Rowe, Jonathan P.; Mott, Bradford W.; Lester, James C. – Journal of Educational Data Mining, 2013
Over the past decade, there has been growing interest in real-time assessment of student engagement and motivation during interactions with educational software. Detecting symptoms of disengagement, such as off-task behavior, has shown considerable promise for understanding students' motivational characteristics during learning. In this paper, we…
Descriptors: Student Behavior, Classification, Learner Engagement, Data Analysis
Anderson, Daniel; Alonzo, Julie; Tindal, Gerald – Behavioral Research and Teaching, 2011
In this technical report, we document the results of a cross-validation study designed to identify optimal cut-scores for the use of the easyCBM[R] mathematics test in the state of Washington. A large sample, randomly split into two groups of roughly equal size, was used for this study. Students' performance classification on the Washington state…
Descriptors: Testing Programs, Mathematics Tests, Prediction, Measurement Techniques
Park, Bitnara Jasmine; Anderson, Daniel; Irvin, P. Shawn; Alonzo, Julie; Tindal, Gerald – Behavioral Research and Teaching, 2011
Within a response to intervention (RTI) framework, students are typically identified as "academically at-risk" if they score below a specified cut-point on a benchmark screener. Students identified as at-risk are provided with an intervention intended to increase achievement. In the following technical report, we describe a process for…
Descriptors: Intervention, Diagnostic Tests, Response to Intervention, At Risk Students
Irvin, P. Shawn; Park, Bitnara Jasmine; Anderson, Daniel; Alonzo, Julie; Tindal, Gerald – Behavioral Research and Teaching, 2011
This technical report presents results from a cross-validation study designed to identify optimal cut scores when using easyCBM[R] reading tests in Washington state. The cross-validation study analyzes data from the 2009-2010 academic year for easyCBM[R] reading measures. A sample of approximately 900 students per grade, randomly split into two…
Descriptors: Intervention, Diagnostic Tests, Response to Intervention, At Risk Students
Park, Bitnara Jasmine; Irvin, P. Shawn; Anderson, Daniel; Alonzo, Julie; Tindal, Gerald – Behavioral Research and Teaching, 2011
This technical report presents results from a cross-validation study designed to identify optimal cut scores when using easyCBM[R] reading tests in Oregon. The cross-validation study analyzes data from the 2009-2010 academic year for easyCBM[R] reading measures. A sample of approximately 2,000 students per grade, randomly split into two groups of…
Descriptors: Testing Programs, Reading Tests, Prediction, Measurement Techniques
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries
