We investigated the effects of a learning analytics-driven prediction modeling platform and a brief digital self-regulated learning skill training program targeted to support undergraduate biology students identified as likely to perform poorly in the course. A prediction model comprising prior knowledge scores and learning management system log…

The article aims to develop a machine-learning algorithm that can predict student's graduation in the Industrial Engineering course at the Federal University of Amazonas based on their performance data. The methodology makes use of an information package of 364 students with an admission period between 2007 and 2019, considering characteristics…

The current project, Exploring Math Education Relations by Analyzing Large Data Sets (EMERALDS) II, is an attempt to identify specific Common Core State Standards procedural, conceptual, and problem-solving competencies in earlier grades that best predict success in algebraic areas in later grades. The data for this study include two cohorts of…