NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 4 results Save | Export
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Hutt, Stephen; Ocumpaugh, Jaclyn; Ma, Juliana; Andres, Alexandra L.; Bosch, Nigel; Paquette, Luc; Biswas, Gautam; Baker, Ryan S. – International Educational Data Mining Society, 2021
Self-regulated learning (SRL) is a critical 21st -century skill. In this paper, we examine SRL through the lens of the searching, monitoring, assessing, rehearsing, and translating (SMART) schema for learning operations. We use microanalysis to measure SRL behaviors as students interact with a computer-based learning environment, Betty's Brain. We…
Descriptors: Models, Self Control, Learning Strategies, Student Behavior
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Fancsali, Stephen E.; Li, Hao; Sandbothe, Michael; Ritter, Steven – International Educational Data Mining Society, 2021
Recent work describes methods for systematic, data-driven improvement to instructional content and calls for diverse teams of learning engineers to implement and evaluate such improvements. Focusing on an approach called "design-loop adaptivity," we consider the problem of how developers might use data to target or prioritize particular…
Descriptors: Instructional Development, Instructional Improvement, Data Use, Educational Technology
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Çevik, Mustafa; Tabaru-Örnek, Gizem – International Online Journal of Education and Teaching, 2020
In this study, it was aimed to compare the predictions of the academic achievement of the artificial neural networks (ANN) run in MATLAB and SPSS software and to determine the factors related to their academic achievement. Sample consisted of 465 students who were studying at Grade 4 in primary schools in the Central Anatolian Region of Turkey in…
Descriptors: Comparative Analysis, Academic Achievement, Computer Software, Prediction
Peer reviewed Peer reviewed
Direct linkDirect link
Anika Alam; A. Brooks Bowden – Society for Research on Educational Effectiveness, 2024
Background: The importance of high school completion for jobs and postsecondary opportunities is well- documented. Combined with federal laws where high school graduation rate is a core performance indicator, school systems and states face pressure to actively monitor and assess high school completion. This proposal employs machine learning…
Descriptors: Dropout Characteristics, Prediction, Artificial Intelligence, At Risk Students