Predictive models on students' academic performance can be built by using historical data for modelling students' learning behaviour. Such models can be employed in educational settings to determine how new students will perform and in predicting whether these students should be classed as at-risk of failing a course. Stakeholders can use…

Persistence in learning processes is perceived as a central value; therefore, dropouts from studies are a prime concern for educators. This study focuses on the quantitative analysis of data accumulated on 362 students in three academic course website log files in the disciplines of mathematics and statistics, in order to examine whether student…

Learning analytics and digital badges are emerging research fields in educational science. They both show promise for enhancing student retention in higher education, where withdrawals prior to degree completion remain at about 30% in Organisation for Economic Cooperation and Development member countries. This integrative review provides an…

The purpose of this study was to describe the coaching that occurred at Head Start (HS) grantees as a result of the Early Learning Mentor Coach (ELMC) initiative. This provided a unique opportunity to describe the different dimensions of coaching within HS settings from the perspective of multiple stakeholders--administrators, coaches, and staff.…

This document is one of eight reports prepared to support the development of a new learning system, an effort that is the first step in a major initiative undertaken by the Stupski Foundation. The report was created collaboratively by researchers from McRel with guidance from officers of the Stupski Foundation. Its purpose is to provide members of…