Association Rules Mining for Reducing Items from Emotion Regulation Questionnaires

Abstract

Regulating learners’ negative emotions during a learning session is an important factor in educational settings that aims at enhancing learner’s cognitive performance and achievement outcomes. In this context, many researchers in psychology have proposed emotion regulation questionnaires that help to assess the use of emotion regulation strategies by learners in order to regulate their emotions while learning. However, the number of items in each questionnaire is large which may annoy the learner and prevent him/her from completing all of the items; this may lead to inappropriate emotional regulation. Thus, we propose in this paper a machine learning method for mining items in order to reduce the fully associated ones to one item. First of all, the paper presents a critical overview on statistical methods applied for reducing the large number of items in questionnaires. Then, after the introduction of the selected data set about the emotion regulation questionnaires, we detail the association rules mining method and discuss the obtained results about the significant association rules between the items. This can lead to uphold the items’ reduction without loss of reliability.