Dr. Mimar Sinan Fine Arts University. Science and Literacy Faculty, Department of Educational Sciences,
Problem Statement: For this study, a cooperative learning process was designed in which students with different learning styles could help each other in heterogeneous groups to perform teamwork-based activities. One aspect deemed important in this context was whether the instructional environment designed to reach students with different learning styles would allow students to better engage in deep learning.
Purpose of Study: The purpose of the study was to determine the effectiveness of cooperative learning activities in ensuring deep learning according to students’ learning styles.
Methods: For this single-group pretest–posttest study, a purposive sampling method was used to form the sample of 39 students attending the course Special Teaching Methods as part of a pedagogical certification program at a state university in Turkey. During the study, the Grasha– Riechmann Student Learning Style Inventory was used to determine students’ learning styles and the study process questionnaire to determine their learning approaches. Covariance analysis was performed for all research questions.
Findings and Results: Posttest student scores for the deep learning approach demonstrated significant differences depending on learning style. According to these scores, students with cooperative and competitive learning styles fared better with the deep learning approach than students with avoidant, dependent, and participative learning styles. By contrast, the students’ posttest scores for surface learning demonstrated no significant differences regarding learning styles.
Conclusions and Recommendations: The researchers recommend increasing both the duration of study activities and their focus on different techniques of cooperative learning, as well as considering the basic principles of cooperative learning to ensure effective designs for teamwork-based discussion activities, including those used for research.
Keywords: Cooperative learning, learning style, deep learning, surface learning.