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Jing Chen; Bei Fang; Hao Zhang; Xia Xue – Interactive Learning Environments, 2024
High dropout rate exists universally in massive open online courses (MOOCs) due to the separation of teachers and learners in space and time. Dropout prediction using the machine learning method is an extremely important prerequisite to identify potential at-risk learners to improve learning. It has attracted much attention and there have emerged…
Descriptors: MOOCs, Potential Dropouts, Prediction, Artificial Intelligence
Zagallo, Patricia; Meddleton, Shanice; Bolger, Molly S. – CBE - Life Sciences Education, 2016
We present our design for a cell biology course to integrate content with scientific practices, specifically data interpretation and model-based reasoning. A 2-year research project within this course allowed us to understand how students interpret authentic biological data in this setting. Through analysis of written work, we measured the extent…
Descriptors: Undergraduate Students, Science Education, Cytology, Instructional Design
Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen – American Journal of Evaluation, 2016
Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…
Descriptors: Intervention, Multivariate Analysis, Mixed Methods Research, Models
Castellano, Katherine E.; Ho, Andrew D. – Council of Chief State School Officers, 2013
This "Practitioner's Guide to Growth Models," commissioned by the Technical Issues in Large-Scale Assessment (TILSA) and Accountability Systems & Reporting (ASR), collaboratives of the "Council of Chief State School Officers," describes different ways to calculate student academic growth and to make judgments about the…
Descriptors: Guides, Models, Academic Achievement, Achievement Gains