Predicting student performance has been a critical focus of educational research. With an effective predictive model, schools can identify potentially at-risk students and implement timely interventions to support student success. Recent developments in educational data mining (EDM) have introduced several machine learning techniques that can…

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…

The state of Wisconsin has one of the highest four year graduation rates in the nation, but deep disparities among student subgroups remain. To address this the state has created the Wisconsin Dropout Early Warning System (DEWS), a predictive model of student dropout risk for students in grades six through nine. The Wisconsin DEWS is in use…

Many high school physics courses have deemphasized mathematics, which has changed the nature and role of the inquiry experiments in them. This article lays out an approach built upon an introductory unit, "Patterns in Nature," which aims to engage students in constructing their understanding of physics through contextualizing and…

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…

Solving America's dropout crisis requires immediate, drastic action. Intractable as the dropout problem may seem, recognition of its magnitude has created an environment ripe for action. Most notably, federal regulations adopted in 2008 require states to use more accurate ways of counting dropouts and holding districts and schools more accountable…

The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…

This design research describes the development of a student inquiry project in which upper-secondary school chemistry students learn domain-specific concepts by doing inquiry in a simulated inquiry community. The design of the inquiry project is based on the extended procedural and conceptual knowledge in science (PACKS) model; the student…

The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…

The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…