Machine learning models were constructed to predict student performance in an introductory mechanics class at a large land-grant university in the United States using data from 2061 students. Students were classified as either being at risk of failing the course (earning a D or F) or not at risk (earning an A, B, or C). The models focused on…

Prediction of student performance is one of the most important subjects of educational data mining. Artificial neural networks are seen to be an effective tool in predicting student performance in e-learning environments. In the studies carried out with artificial neural networks, performance predictions based on student scores are generally made,…

The use of machine learning and data mining techniques across many disciplines has exploded in recent years with the field of educational data mining growing significantly in the past 15 years. In this study, random forest and logistic regression models were used to construct early warning models of student success in introductory calculus-based…

In higher education, many studies have tried to establish which student activities predict achievement in blended courses, with the aim of optimizing course design. In this paper, we examine whether taking into account temporal patterns of student activity and instructional conditions of a course help to explain course performance. A course with a…

The Predict-Observe-Explain (POE) learning cycle improves understanding of the connection between empirical results and theoretical concepts when students engage in hands-on experimentation. This study explored whether training students to use a POE strategy when learning from social science texts that describe theories and experimental results…

In prior studies, academic procrastination has been discussed as an influencing factor of academic misconduct. However, empirical studies were conducted solely cross-sectionally and investigated only a few forms of academic misconduct. This large scale web-based study examined the responses of between 1359 and 2207 participants from different…

Physiology instructors often are faced with the challenge of providing informative and educationally stimulating laboratories while trying to design them in such a way that encourages students to be actively involved in their own learning. With many laboratory experiments designed with simplicity and efficiency as the primary focus, it is…

The authors developed a methodology for evaluating student answers on homework assigned in 3 sections (total N = 167) of an undergraduate educational psychology course. The potential of homework to predict exam scores was compared with that of two established predictors (critical thinking and participation in class discussion). The findings…

The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…

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 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…