Educational Data Mining (EDM) is the application of Data Mining (DM) techniques to educational data. EDM has emerged as a research area in recent years for researchers all over the world in many different areas (e.g. computer science, education, psychology, psychometrics, statistics, intelligent tutoring systems, e-learning, adaptive hypermedia, etc.) to analyze large data sets in order to resolve educational questions.

Multiple-instance learning (MIL) is a variation on supervised learning, where the task is to learn a concept from data consisting of a sequence of instances, each labeled as positive or negative, and each described as a set of vectors. The instance is positive if at least one of the vectors in its set lies within the intended concept, and negative if none of the vectors lies within the concept; the task is to learn an accurate description of the concept from this information.

Multi-label classification is a classification paradigm that allows solving problems where patterns can be associated to more than one label. These are problems of increasing actuality, like document classification, and can be tackled with a more efficient approach than classical classification, which only allows one label per pattern.

Pattern mining is a Data Mining problem that involves the finding of relationships among the items in a database. Frequent patterns are itemsets, subsequences, or substructures that appear in a data set with frequency no less than a user-specified threshold. The problem was originally proposed in the context of market basket data in order to find frequent groups of items that are bought together.

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