Abstract. In this paper, we propose a data mining technique that evaluates a curriculum's structure based on academic data collected from Computer Science students from 2005 to 2016. Our approach is based on the Synthetic Control Method (SCM), which builds a linear model describing the relation between courses based on student performance information. The proposed model is compared to a linear regression model with positive coefficients. In addition to providing the relation between courses, it can also be used to predict students' grades in a specific course based on their previous grades. The results are visualized in a user-friendly tool, which allows for contrast and comparison between the official structure and the structure found based on the data.

Abstract. In this paper, we present a student dropout prediction strategy based on the classification with reject option paradigm. In such strategy, our method classifies students into dropout prone or non-dropout prone classes and may also reject classifying students when the algorithm does not provide a reliable prediction. The rejected students are the ones that could be classified into either class, and so are probably the ones with more chances of success when subjected to personalized intervention activities. In the proposed method, the reject zone can be adjusted so that the number of rejected students can meet the available workforce of the educational institution. Our method was tested on a dataset collected from 892 undergraduate students from 2005 to 2016.

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