The Effect of Flipped Learning Used in Text-Based Coding Education on Students’ Computational Thinking Skills and Attitudes: A Mixed Methods Study

Abstract

This study aims to examine the effectiveness of the flipped learning (FL) model, an innovative teaching approach in coding education. The research investigates whether implementing text-based coding education using the FL model creates a significant difference in middle school students’ self-efficacy perceptions regarding computational thinking skills and their attitudes toward coding compared to traditional teaching. Designed using an explanatory sequential mixed-methods approach, the study employed a quasi-experimental design. The study group consisted of a total of 60 students enrolled in the 6th grade of a private middle school, divided into an experimental (n=30) and a control (n=30) group. During the eight-week process, the experimental group learned the Small Basic programming language according to the flipped learning (FL) model, while the control group received traditional instruction. Quantitative data were collected using the CTSPSS and CEAS scales, while qualitative data were collected through semi-structured interviews with selected students from the experimental group. The findings showed that the experimental group obtained significantly higher scores than the control group on both scales. Qualitative findings also revealed that students came to class prepared thanks to flipped learning (FL), were able to learn at their own pace, and developed positive attitudes towards coding. In conclusion, it was determined that the flipped learning (FL) model is an effective strategy in text-based coding instruction.