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Levin, 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
Makhlouf, Jihed; Mine, Tsunenori – Journal of Educational Data Mining, 2020
In recent years, we have seen the continuous and rapid increase of job openings in Science, Technology, Engineering and Math (STEM)-related fields. Unfortunately, these positions are not met with an equal number of workers ready to fill them. Efforts are being made to find durable solutions for this phenomena, and they start by encouraging young…
Descriptors: Learning Analytics, STEM Education, Science Careers, Career Choice
Almeda, Ma. Victoria; Baker, Ryan S. – Journal of Educational Data Mining, 2020
Given the increasing need for skilled workers in science, technology, engineering, and mathematics (STEM), there is a burgeoning interest to encourage young students to pursue a career in STEM fields. Middle school is an opportune time to guide students' interests towards STEM disciplines, as they begin to think about and plan for their career…
Descriptors: Student Participation, Predictor Variables, STEM Education, Science Careers
Guo, Hongwen; Zhang, Mo; Deane, Paul; Bennett, Randy E. – Journal of Educational Data Mining, 2020
This study investigates the effects of a scenario-based assessment design on students' writing processes. An experimental data set consisting of four design conditions was used in which the number of scenarios (one or two) and the placement of the essay task with respect to the lead-in tasks (first vs. last) were varied. Students' writing…
Descriptors: Instructional Effectiveness, Vignettes, Writing Processes, Learning Analytics