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Table 1 Common methods for mining data

From: The use of data science for education: The case of social-emotional learning

Method Goal/description Example tasks Reference
Classification To define a set of classes, which are usually mutually exclusive. To predict which classes an individual belongs to. To automatically detect affective states, like confusion, frustration, and boredom. Ghergulescu and Muntean (2014)
Value estimation To estimate the numerical value of some variables for an individual. To estimate learning outcomes regarding student affect and behavioral engagement. Pardos et al. (2014)
Clustering To measure the similarity of individuals described by data. To group similar individuals together by their similarity, but not driven by any specific purpose To create groups of students according to their personal characteristics. He (2013)
Frequent pattern mining To find associations among variables based on their appearing together in transactions and to encode rules. Identifying relationships in learner behavioral patterns and diagnosing student difficulties. Kinnebrew et al. (2013)
Text mining To extract high-quality information from text. Recognize the emotion of interactive text. Tian et al. (2014)
Structural analysis To predict a link that should exist between individuals, and possibly also estimate the strength of the link. To dynamically recommend the tutorial dialog in a manner that is responsive to the sensed states. D’mello and Graesser (2013)
Behavior profiling To characterize the typical or most noticeable behavior of a subgroup or an entire population. To profile anomalous behaviors. Hoque and Picard (2014)