The nature of teaching and learning has evolved over the years, especially as technology has evolved. Innovative application of educational analytics has gained momentum. Indeed, predictive analytics have become increasingly salient in education. Considering the prevalence of learner-system interaction data and the potential value of such data, it…

Data showing what students earn after attending higher education institutions have become increasingly available, bolstering calls from policymakers and advocates that government financial aid programs should be tied to those outcomes. Often overlooked, however, is that these data and policies usually reflect the earnings of only students who…

This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…

Determining which students are at risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of research and practice in both K-12 and higher education. The detectors produced from this type of predictive modeling research are increasingly used in early warning…

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…

Massive open online courses (MOOCs) are being used across the world to provide millions of learners with access to education. Many who begin these courses complete them successfully, or to their own satisfaction, but the high numbers who do not finish remain a subject of concern. In 2013, a team from Stanford University analyzed engagement…

Purpose: In an era of unprecedented student measurement and emphasis on data-driven educational decision making, the full potential for using data to target resources to students has yet to be realized. This study explores the utility of machine-learning techniques with large-scale administrative data to identify student dropout risk. Research…

Our project is motivated by the early dropout and low completion rate problem in MOOCs. We have extended traditional features for MOOC analysis with richer and higher granularity information to make more accurate predictions of dropout and performance. The results show that finer-grained temporal information increases the predictive power in the…

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…

Learning analytics has been as used a tool to improve the learning process mainly at the micro-level (courses and activities). However, another of the key promises of learning analytics research is to create tools that could help educational institutions at the meso- and macro-level to gain better insight into the inner workings of their programs…

This paper examined attrition in primary schools in Nigeria with specific reference and focus on some policies and programmes of Nigeria's educational system with a view to highlighting its possible effect on the attainment of the Education for All (EFA) goals by 2015. It reviewed equal educational opportunity in relation to school environments…

The National Dropout Prevention Center for Students with Disabilities (NDPC-SD) was assigned the task of compiling, analyzing, and summarizing the data for Indicator 2--Dropout--from the FFY 2011 Annual Performance Reports (APRs) and the revised State Performance Plans (SPPs), which were submitted to the Office of Special Education Programs (OSEP)…

The National Dropout Prevention Center for Students with Disabilities (NDPC-SD) was assigned the task of compiling, analyzing, and summarizing the data for Indicator 1--Graduation--from the FFY 2011 Annual Performance Reports (APRs) and amended State Performance Plans (SPPs), which were submitted by states to the Office of Special Education…

Nationally, more than one million youth drop out of high school each year. One in four young people do not graduate with their age mates. Thus, in recent years, national leaders have directed sustained attention to what they term the "dropout crisis," particularly in high schools that are graduating less than two-thirds of their…

An underlying theme of this paper is that it can be easier and more efficient to conduct valid and effective research studies in online environments than in traditional classrooms. Taking advantage of the "big data" available in an online university, we conducted a study in which a massive online database was used to predict student…