Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze and predict students' failure,…

Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze, and predict students' failure,…

The increased usage of computer-based learning platforms and online tools in classrooms presents new opportunities to not only study the underlying constructs involved in the learning process, but also use this information to identify and aid struggling students. Many learning platforms, particularly those driving or supplementing instruction, are…

Improving student retention is an important and challenging problem for universities. This paper reports on the development of a student attrition model for predicting which first year students are most at-risk of leaving at various points in time during their first semester of study. The objective of developing such a model is to assist…

This year's study is the 35th in a series of annual reports on trends in dropout and attrition rates in Texas public schools. The 2019-20 study builds on a series of studies by the Intercultural Development Research Association (IDRA) that track the number and percent of students in Texas who are lost from public school enrollment prior to…

This study examined the use of an institutionally-specific risk prediction model in the university's College of Education. Set in a large, urban, public university, the risk model predicted incoming students' first-semester GPAs, which, in turn, predicted the students' risk of attrition. Additionally, the study investigated advising practices…

Set in a large, urban, public university, this study explores the use of an institutionally specific risk instrument developed to identify students who had a high risk of attrition and the effectiveness of subsequent interventions deployed through advising. Though implemented throughout the institution, this study identified control and treatment…

The increasing competition for graduate students among business schools has resulted in a greater emphasis on graduate business student retention. In an effort to address this issue, the current article uses survival analysis, decision trees and TreeNet® to identify factors that can be used to identify students who are at risk of dropping out of a…

This article presents a project intended to produce a model for predicting the risk of attrition of individual students enrolled at the University of South Florida. The project is premised upon the principle that college student attrition is as highly individual and personal as any other aspect of the college-going experience. Students make…

The NEAIR (North East Association for Institutional Research) 2011 Conference Proceedings is a compilation of papers presented at the Boston, Massachusetts conference. Papers in this document include: (1) Are Students Dropping Out or Dragging Out the College Experience? The Roles of Socioeconomic Status and Academic Background (Leslie S. Stratton…

A five-scale instrument developed from a theoretical model of college attrition correctly identified the persistence/voluntary withdrawal decisions of 78.5 percent of 773 freshmen in a large, residential university. Findings showed that student relationships with faculty were particularly important. (Author/PHR)

Two prediction models using age and entry-level reading and math scores to differentiate completers and noncompleters were tested with 1,319 community college adult basic education students. Persisters and dropouts were not homogeneous groups; for example, General Educational Development completers differed from other completers, and early and…

A causal model to explain student attrition was tested at a major midwestern land-grant university with a sample of 1,513 full-time, unmarried freshmen who were 21 years old or younger. The causal model was reduced from 23 to 10 variables: an intent variable, three attitudinal variables, and two each of organizational, personal, and environmental…

A model of student attrition was synthesized from psychological, sociological, and educational sources, and contains six sets of variables: background, organizational, personal, environmental, attitudinal, and intent to leave. The model was tested with 1,909 full-time and unmarried university freshmen at a major midwestern university. The sample…

The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…