The teaching process involves complex, multifaceted relationships. For this reason, teachers’ engagement in teaching can be shaped by factors such as the teacher’s goals, the conditions of the teaching environment, and the teacher’s cognitive characteristics. Determining the impact of these cognitive, subjective, and environmental conditions on teacher engagement, which reflects teachers’ performance, can provide useful information for introducing interventions that can improve teacher education programs.
Cognitive flexibility, a metacognitive function, is defined as the ability to adapt to the situation and provide new and different solutions when an unexpected situation arises. Studies in the literature show that cognitive flexibility is negatively correlated with expertise, convergent thinking, and stress, while it has positive relationships with critical thinking, creative thinking, spatial perception, and visual memory.
This study aims to examine the effect of prospective teachers’ cognitive flexibility levels on engagement in a teaching simulation, including 40 teacher candidates. The simulation task was created by organizing the infrastructure and scenarios of the teaching simulation called SimInClass. SimInClass is a game-based, three-dimensional computer simulation designed to develop teachers in line with specific goals, leveraging the positive effects of serious games and incorporating authentic events. The focus in this environment is on classroom management training in classes with virtual students using artificial intelligence.
It is recommended to measure cognitive flexibility in unexpected situations that occur in environmental conditions during cognitive performance. Therefore, the study includes cases in which undesirable behaviors are exhibited in a classroom modeled similarly to the authentic classroom teaching environment. For this purpose, engagement data was collected from the facial expressions of 40 teacher candidates in a simulation that included teaching tasks similar to those in the authentic classroom. In this way, teachers’ reactions to unexpected events in the classroom could be observed simultaneously with the application. Inferring affective and cognitive states from facial expressions is a frequently preferred method in recent years. Numerical values were determined for each unit and combinations of associated motor muscles according to the “Facial Expression Coding System (FACS).” With this system, iMotions software, an automatic cognitive-affective state recognition program from facial expressions, was used.
Data on participants’ cognitive flexibility were collected with the Wisconsin Card Sorting Test, using a computer-based version of the test. The cognitive flexibility levels of teacher candidates were determined according to their perseverative errors. Subsequently, teacher candidates were clustered according to cognitive flexibility metrics.
According to the analysis carried out, it was found that the engagement levels of the teacher candidates differ according to their cognitive flexibility and the sections of the course. Based on these results, it is recommended to examine the effects of different cognitive factors on teaching. In this way, objective data based on experience can be produced for teacher training problems, contributing further to the development of intervention systems.
