PRESERVICE TEACHERS’ ACHIEVEMENT AND RETENTION OF PROBLEM SOLVING SKILLS IN PROGRAMMING : A CHATGPT-BASED FLIPPED CLASSROOM ANALYSIS

This pre-experimental study, utilizing a one-group pretest and posttest design, aims to investigate the effects of a ChatGPT-based flipped classroom approach on preservice teachers’ achievement and retention of problem-solving skills in programming. The study sample comprised 33 preservice teachers (one intact class) from an Institute of Teacher Education Malaysia in Sabah, Malaysia, selected through cluster sampling. A Computer Programming Achievement Test (CPAT) were used as the research instrument, which were administered during pretest, posttest and delayed posttest. Data analysis was conducted using one-way repeated measures ANOVA. The results showed an increased in problem-solving skills from the pretest (M=16.84, SD=8.35) to the posttest (M=31.25, SD=13.25), indicating significant improvement in preservice’ teachers achievement. Additionally, retention of problem-solving skills was evident, with the delayed posttest scores (M=24.43, SD=6.68) significantly higher than the pretest scores, though slightly lower than the posttest scores, suggesting some decay over time but overall retention of the skills learned. This research suggests that integrating artificial intelligence (AI) tools, such as ChatGPT, in educational setting can lead to substantial improvements in learning outcomes, which provides valuable resource for educators aiming to foster advanced problem-solving abilities in their students.