Table 1 Common methods for mining data

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)