Data Analysis Application to Investigate Relationships between Metacognition and Learning Styles

  • Anass Hsissi LAVETE – e-learning Research Team, University of science & technology, University Hassan I, SETTAT, Morocco
  • Hakim Allali LAVETE – e-learning Research Team, University of science & technology, University Hassan I, SETTAT, Morocco
  • Abdelmajid Hajami LAVETE – e-learning Research Team, University of science & technology, University Hassan I, SETTAT, Morocco
Keywords: component, learning styles, metacognition, data analysis, PCA, learning environment


The purpose of this study is to examine if learning styles predict effectiveness in learning. Participants of the study consisted of 80 students selected from different classes of the university of science and Technology –Settat, Morocco, two questionnaires on learning styles and metacognition related to reading and comprehension were handed out to members of the sample during academic year 2016/2017. Data analysis techniques were used to understand our dataset; principal component analysis was used to discover significant patterns within data. Results indicate that there are significant relations between the dimensions of learning styles, metacognition and performance in comprehension activity. Results also indicate that learning styles significantly explain and predict all sub-dimensions of metacognition.

As perspective for our research, we tend to offer to each student the appropriate environment and conditions to enhance learning, in the conclusion we will discuss the implications and recommendations for designing an adaptive learning and intelligent environment based on an identification of students’ profiles.




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Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems