This study presents a systematic review of the literature on the predicting student retention in higher education through machine learning algorithms based on measures such as dropout risk, attrition risk, and completion risk. A systematic review methodology was employed comprised of review protocol, requirements for study selection, and analysis…

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…

This paper aims to provide the reader with a comprehensive background for understanding current knowledge on Learning Analytics (LA) and Educational Data Mining (EDM) and its impact on adaptive learning. It constitutes an overview of empirical evidence behind key objectives of the potential adoption of LA/EDM in generic educational strategic…

This literature review examines current practice in reporting school dropout rates and the impact that school dropout may have on adult education programs and policies. First, I investigate the five dropout estimates commonly reported by the U.S. Department of Education (USDOE), examining how these measures vary in their estimation of school…

Members of the educational community are increasingly concerned that the dropout rate is too high. However, concerned educators and researchers do not agree who should be included in the data to determine the dropout rate. National definitions are needed so that accurate comparisons can be made. There is also a need to assess the effectiveness of…