The education sector has been quick to recognize the power of predictive analytics to enhance student success rates. However, there are challenges to widespread adoption, including the lack of accessibility and the potential perpetuation of inequalities. These challenges present in different stages of modeling, including data preparation, model…

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…

Liberal Arts programs are often characterized by their open curriculum. Yet, the abundance of courses available and the highly personalized curriculum are often overwhelming for students who must select courses relevant to their academic interests and suitable to their academic background. This paper presents the course recommender system that we…

Predicting school dropout rates is an important issue for the smooth execution of an educational system. This problem is solved by classifying students into two classes using educational activities related statistical datasets. One of the classes must identify the students who have the tendency to persist. The other class must identify the…

This feature focuses on the five areas an institution needs to know before implementing holistic measures. These include: what does a holistic review entail, how to be legally complaint, Sedlacek's noncognitive variables, applying student success measures, and the vital importance of training.

These are the appendices to the report, "Using High School Data to Predict College Success in Palau" (ED610714). Prior research, particularly for the United States, has shown that earning a community college credential increases an individual's likelihood of gaining stable employment, earning a living wage, and working in a higher-paying…

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…

This supplemental report provides technical documentation for the main report (published separately). A significantly higher percentage of AISD graduates enrolled in postsecondary institutions in 2014 (66%) than enrolled in 2013 (63%). Eighty-one percent of Class of 2013 graduates enrolled and persisted in a postsecondary institution 2 consecutive…

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…

The study used earliest available student data from a flagship university in the southeast United States to build data mining models like logistic regression with different variable selection methods, decision trees, and neural networks to explore important student characteristics associated with retention leading to graduation. The decision tree…

This article reports on a case study of one New Zealand university faculty involved in the second phase of a three-phase study investigating the experiences of talented undergraduate students. Talented undergraduate students are a largely forgotten group in research. The current study sought to investigate who the talented students were, and then…

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…

Educators nationwide confront a troubling phenomenon: Increasingly, students leave high school unready for college, as evidenced by high rates of placement into remedial courses and low rates of college completion. Many students also lack either the attitudes or skills essential to succeed in a postsecondary setting, or knowledge of how to apply…

The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…

The basic objective of this research program was to develop and to test an accountability model of the educational process. In cooperation with the Pittsburgh School District, a major effort was devoted toward the development of a data base, the construction of appropriate models and the conduct of analyses of the data. Some analysis was…