Teachers emotional intelligence to predictive work performance using linear and non-linear models - IDRIS A; SEZER K; TAHIRU ZANNA; HASSAN SALEH; ADAMU GAMBO (Journal of Advanced Research in Social Sciences and Humanities, 2021)
Aim: Changes and reforms in educational systems worldwide have affected teachers’ effectiveness in the classroom. However, despite these developments, evaluating and understanding how to predict a teacher’s success is still difficult. In this study, which addresses a gap in the literature, the importance of emotional intelligence to teachers’ professional success is explored. This study investigates the link between Emotional Intelligence (EI) and professional success along its four facets: emotional regulation, emotional awareness, emotional motivation, and social ability (relationship management). Methodology: A total of 160 teachers from six different technical universities in Northeastern Nigeria were surveyed. Questionnaires were used to collect the data, then analyzed with an AI model (FFNN, LSSVM, NF, and MLR). Findings: The models were assessed using the determination coefficient (R2), root means square error (RMSE), and correlation coefficient (R). The result obtained from the simple models showed that Neuro-Fuzzy Sub Clustering Hybrid (NF-SCH) shows both training and testing, the correctness of models has been improved, which increases the accuracy of the single models up to 17%, 18%, and 20% FFNN, MLR and LSSVM for calibration and up to 40%, 73% and 70% FFNN, MLR and LSSVM for verification respectively the results show a strong connection between emotional intelligence and work satisfaction. Implications/Novel Contribution: Ultimately, this research contributes to the literature on emotional intelligence and has real-world implications for management in education administration in the Nigerian higher education system.