The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

Morrison and Lauren Margulieux, for their insightful and detailed feedback of my thesis. I am honored to have Professor Matti Tedre as my opponent. Thanks for the CNPq and IFMS for their financial support.

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We present the results of a study that explored the emotions experienced by students during interaction with an educational game for math (Heroes of Math Island). Starting from emotion frameworks in affective computing and education, we considered a larger set of emotions than in related research. For emotion labeling, we employed a standard method that relies on trained judges to report emotions over 20-second intervals. However, we asked judges to report all observed emotions in each interval, as opposed to only choosing one, as is standard practice. This variation allows us to discuss the appropriateness of this interval for emotion labeling. We present a detailed analysis of inter-coder reliability, both aggregated and over individual students, that considers not only the matching by judges over emotion type, but also the number of emotions detected.