Randomized A/B tests within online learning platforms represent an exciting direction in learning sciences. With minimal assumptions, they allow causal effect estimation without confounding bias and exact statistical inference even in small samples. However, often experimental samples and/or treatment effects are small, A/B tests are underpowered,…

Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as…

We review the history and current trends in the field of Educational Data Mining (EDM). We consider the methodological profile of research in the early years of EDM, compared to in 2008 and 2009, and discuss trends and shifts in the research conducted by this community. In particular, we discuss the increased emphasis on prediction, the emergence…