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Jeremiah T. Stark – ProQuest LLC, 2024
This study highlights the role and importance of advanced, machine learning-driven predictive models in enhancing the accuracy and timeliness of identifying students at-risk of negative academic outcomes in data-driven Early Warning Systems (EWS). K-12 school districts have, at best, 13 years to prepare students for adulthood and success. They…
Descriptors: High School Students, Graduation Rate, Predictor Variables, Predictive Validity
Duncan Culbreth; Rebekah Davis; Cigdem Meral; Florence Martin; Weichao Wang; Sejal Foxx – TechTrends: Linking Research and Practice to Improve Learning, 2025
Monitoring applications (MAs) use digital and online tools to collect and track data on student behavior, and they have become increasingly popular among schools. Empirical research on these complex surveillance platforms is scant, and little is known about the efficacy or impact that they have on students. This study used a multi-method…
Descriptors: High School Students, COVID-19, Pandemics, Progress Monitoring
Mohan, Kaushik; Bergner, Yoav; Halpin, Peter – Technology, Knowledge and Learning, 2020
Technology-based assessments that involve collaboration among students offer many sources of process data, although it remains unclear which aspects of these data are most meaningful for making inferences about students' collaborative skills. Recent research has focused mainly on theory-based rubrics for qualitative coding of process data (e.g.,…
Descriptors: Computer Assisted Testing, Student Evaluation, Cooperation, Grade 12
Jiang, Yang; Gong, Tao; Saldivia, Luis E.; Cayton-Hodges, Gabrielle; Agard, Christopher – Large-scale Assessments in Education, 2021
In 2017, the mathematics assessments that are part of the National Assessment of Educational Progress (NAEP) program underwent a transformation shifting the administration from paper-and-pencil formats to digitally-based assessments (DBA). This shift introduced new interactive item types that bring rich process data and tremendous opportunities to…
Descriptors: Data Use, Learning Analytics, Test Items, Measurement
Admiraal, Wilfried; Vermeulen, Jordi; Bulterman-Bos, Jacquelien – Technology, Pedagogy and Education, 2020
Computer-based assessments can provide students with feedback to guide their learning as well as inform teachers who extract information to prepare their teaching. Five secondary school teachers were monitored during one school year to answer the following research questions: (1) What kind of student data do teachers use for their teaching…
Descriptors: Learning Analytics, Computer Assisted Testing, Data Use, Formative Evaluation
Filderman, Marissa J.; Austin, Christy R.; Toste, Jessica R. – Intervention in School and Clinic, 2019
The process of implementing intensive reading interventions using data-based decision-making (DBDM) becomes increasingly challenging as students move into the secondary grades and reading tasks correspondingly become more complex. This article provides teachers with guidelines to support effective implementation of DBDM for students with or at…
Descriptors: Data Use, Reading Difficulties, At Risk Students, Secondary School Teachers
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use