Theatrical performance constitutes a complicated way for students to express and to communicate with each other, since it targets both various artistic and educational goals. Even though it constitutes a top moment of students’ expression, several students do not feel comfortable when participating in such cultural activities, as performance anxiety, a negative emotional experience stemming from the public audience exposure, affects them. The aim of this research is to apply and evaluate a student segmentation technique with the help of bio-inspired computational intelligence, for identifying high levels of performance anxiety at schoolchildren. A Mayfly-based clustering optimization algorithm is applied on a dataset with 774 instances of students to classify them according to their levels of emotions and performance anxiety that are developed during the event. A comparison with a genetic algorithm as well as particle swarm optimization shows that the proposed method is distinguished by superior categorization capabilities. The findings demonstrate the effective dissimilar student groups formation, with the members of each being distinguished by similar characteristics in terms of emotions and performance anxiety, highlighting the ones with unmanageable emotional experiences. Therefore, the drama educator is able to effortlessly detect, manage students and develop coping practices in those at risk, by acknowledging each group’s characteristics.

Unveiling the intersection of technology and art, this research aims to explore the vital role of Computational Intelligence (CI) techniques in shaping digital theater performances within virtual worlds by managing student populations engaged in these productions. The realm of digital theater offers opportunities for creative expression and collaboration. This necessitates the integration of Computational Intelligence (CI) to establish cohesive teams in digital theater. By doing so, effective collaboration is promoted, and creativity is dynamically enhanced in virtual cultural environments and digital worlds. Central to this study is the use of a CI algorithm called Flying Fox Optimizer, designed to form homogeneous student groups, and optimize theater dynamics. The algorithm utilizes fuzzy logic, eliminating the requirement for fine-tuning by the user. The results reveal that through this mechanism, theater educators of the future can effortlessly create student groups that foster seamless collaboration in virtual theater education.

The self-efficacy of special education teachers is a key aspect of the educational process for inclusive learning and co-teaching. However, research on self-efficacy perceptions in the area of special education and particularly in the field of parallel support is very limited. The aim of this research is to adapt a scale measuring the beliefs of special education teachers’ self-efficacy in supporting students in need of parallel support in secondary education and therefore to investigate those beliefs as well as the factors that affect them. The study involved 147 special educators for supporting students in Greek junior high schools or high schools. To study and investigate the research questions, the “Teachers' Sense of Efficacy Scale” (TSES) of Tschannen-Moran & Woolfolk-Hoy (2001) was adapted. The results demonstrate that special education teachers report high levels of teaching self-efficacy with male special education teachers being distinguished by significantly higher self-efficacy levels, compared to females. Moreover, a positive correlation between the experience of participants and their levels of self-efficacy, a small negative correlation between the number of supported students and their levels of self-efficacy regarding teaching strategies as well as a medium negative correlation between the number of supported students and their levels of self-efficacy regarding student management, were revealed.