Teachers emotional intelligence to predictive work performance using linear and non-linear models
Keywords:
EI, work performance, teachers, TVE, Artificial Intelligence (AI)
Abstract
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 (R2 = 0.8814502 and 0.8375132) 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 and the Nigerian higher education system.
References
Abba, M. L., Patil, N., Leupold, J. H., Moniuszko, M., Utikal, J., Niklinski, J., & Allgayer, H. (2017). Micrornas as novel targets and tools in cancer therapy. Cancer Letters, 387(88), 84-94. doi:https://doi.org/10.1016/j.canlet.2016.03.043
Amirian, S. M. R., & Behshad, A. (2016). Emotional intelligence and self-efficacy of Iranian teachers: A research study on university degree and teaching experience. Journal of Language Teaching and Research, 7(3), 548-560. doi:https://doi.org/10.1016/j.sbspro.2010.12.205
Ayonmike, C. S., Okwelle, P. C., & Okeke, B. C. (2015). Towards quality Technical Vocational Education and Training (Tvet) Programmes in Nigeria: challenges and improvement strategies. Journal of Education and Learning, 4(1), 25-34. doi:https://doi.org/10.5539/jel.v4n1p25
Blašková, M., Blaško, R., & Kucharcíková, A. (2014). Competences and competence model of university teachers. ˇ Procedia-Social and Behavioral Sciences, 159, 457-467. doi:https://doi.org/10.1016/j.sbspro.2014.12.407
Bose, S., & Guha, A. (2018). Emotional intelligence and professional adjustment of secondary school teachers. International Journal of Research in Social Sciences, 8(4), 579-590.
Botey, M., Vaquero-Diego, M., & Sastre, F. (2020). Perceived emotional intelligence of university professors based on the nature of the subject taught. Technological Forecasting and Social Change, 161, 120-292. doi:https://doi.org/10.1016/j.techfore.2020.120292
Boyatzis, R. E., Goleman, D., & Rhee, K. (2000). Clustering competence in emotional intelligence: Insights from the Emotional Competence Inventory (ECI). Handbook of Emotional Intelligence, 99(6), 343-362.
Boyatzis, R. E., & Saatcioglu, A. (2008). A 20-year view of trying to develop emotional, social and cognitive intelligence competencies in graduate management education. Journal of Management Development, 27(1), 92-108. doi:https://doi.org/10.1108/02621710810840785
Castillo, M. Á. S., & Del Valle, I. D. (2017). Is emotional intelligence the panacea for a better job performance? A study on low-skilled back office jobs. Employee Relations, 39(5), 683-698. doi:https://doi.org/10.1108/ER-11-2016-0216
Cordova, A., Dolci, J., & Gianfrate, G. (2015). The determinants of crowdfunding success: Evidence from technology projects. Procedia-Social and Behavioral Sciences, 181, 115-124. doi:https://doi.org/10.1016/j.sbspro.2015.04.872
Debes, G. (2021). The predictive power of emotional intelligence on self efficacy: A case of school principals. International Online Journal of Education and Teaching, 8(1), 148-167.
Dolev, N., & Leshem, S. (2017). Developing emotional intelligence competence among teachers. Teacher Development, 21(1), 21-39. doi:https://doi.org/10.1080/13664530.2016.1207093
Elkiran, G., Nourani, V., Abba, S., & Abdullahi, J. (2018). Artificial intelligence-based approaches for multi-station modelling of dissolve oxygen in river. Global Journal of Environmental Science and Management, 4(4), 439-450. doi:https://doi.org/10.22034/GJESM.2018.04.005
Ezeanokwasa, F., Nwachukwu, O. P., & Yaba, L. (2014). Effective entrepreneurship skills in reducing unemployment through small business innovation in Nigeria. Journal of Information and Knowledge Management, 4(11), 97-102.
Ghanizadeh, A., & Moafian, F. (2011). Critical thinking and emotional intelligence: Investigating the relationship among EFL learners and the contribution of age and gender. Iranian Journal of Applied Linguistics, 14(1), 23-48.
Gleason, K. M., McDaniel, M. R., Feinglass, J., Baker, D. W., Lindquist, L., Liss, D., & Noskin, G. A. (2010). Results of the Medications at Transitions and Clinical Handoffs (MATCH) study: An analysis of medication reconciliation errors and risk factors at hospital admission. Journal of General Internal Medicine, 25(5),
441-447. doi:https://doi.org/10.1007/s11606-010-1256-6
Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M., & Watts, D. J. (2010). Predicting consumer behavior with web search. Proceedings of the National Academy of Sciences, 107(41), 17486-17490. doi:https://doi.org/10.1073/pnas.1005962107
Graham, L. J., White, S. L., Cologon, K., & Pianta, R. C. (2020). Do teachers’ years of experience make a difference in the quality of teaching? Teaching and Teacher Education, 96, 10-20. doi:https://doi.org/10.1016/j.tate.2020.103190
Ismail, K., Nopiah, Z. M., & Rasul, M. S. (2020). Emotional intelligence and work performance among vocational teachers. Journal of Technical Education and Training, 12(3), 106-117. doi:https://doi.org/10.30880/jtet.2020.12.03.011
Katyal, S., & Awasthi, E. (2005). Gender differences in emotional intelligence among adolescents of Chandigarh. Journal of Human Ecology, 17(2), 153-155. doi:https://doi.org/10.1080/09709274.2005.11905771
Khademi, F., Jamal, S. M., Deshpande, N., & Londhe, S. (2016). Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression. International Journal of Sustainable Built Environment, 5(2), 355-369. doi:https://doi.org/10.1016/j.ijsbe
.2016.09.003
Mahasneh, A. M. (2016). Emotional intelligence as a predictor of teacher sense of self-efficacy among student teachers in Jordan. North American Journal of Psychology, 18(1), 1-10.
Mohamad, M., & Jais, J. (2016). Emotional intelligence and job performance: A study among Malaysian teachers. Procedia Economics and Finance, 35, 674-682. doi:https://doi.org/10.1016/S2212-5671(16)00083-6
Nedal, O. A., & Alcoriza, M. G. O. (2018). Challenges in education: The untold story of students in Lanao Kapanglao, Glan, Sarangani province. Journal of Advances in Humanities and Social Sciences, 4(3), 118-126. doi:https://doi.org/10.20474/jahss-4.3.1
Nwachukwu, J. C., Srinivasan, S., Bruno, N. E., Parent, A. A., Hughes, T. S., Pollock, J. A., . . . Garcia-Ordonez, R. D. (2014). Resveratrol modulates the inflammatory response via an estrogen receptor-signal integration network. Elife, 3(6), 1-10. doi:https://doi.org/10.7554/eLife.02057.001
Okpala, O. R., Omojuwa, Y., Elenwo, C. G., & Opoko, P. A. (2017). Sustainable urbanization: Investigating problems encountered in uncontrolled urban growth in Nyanya-A suburb of Abuja, Nigeria. International Journal of Humanities, Arts and Social Sciences, 3(1), 13–19. doi:https://doi.org/10.20469/ijhss.3.20003-1
Oz, H. (2015). Emotional intelligence as a predictor of l2 communication. Procedia-Social and Behavioral Sciences, 186, 424-430. doi:https://doi.org/10.1016/j.sbspro.2015.04.117
Rastegar, M., & Memarpour, S. (2009). The relationship between emotional intelligence and self-efficacy among Iranian EFL teachers. System, 37(4), 700-707. doi:https://doi.org/10.1016/j.system.2009.09.013
Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition and Personality, 9(3), 185-211. doi:https://doi.org/10.2190/DUGG-P24E-52WK-6CDG
Thorndike, A., & Colony, R. (1982). Sea ice motion in response to geostrophic winds. Journal of Geophysical Research: Oceans, 87(C8), 5845-5852. doi:https://doi.org/10.1029/JC087iC08p05845
Usman, M., Zarebanadkouki, M., Waseem, M., Katsoyiannis, I. A., & Ernst, M. (2020). Mathematical modeling of arsenic (v) adsorption onto iron oxyhydroxides in an adsorption-submerged membrane hybrid system. Journal of Hazardous Materials, 400(5), 123-221. doi:https://doi.org/10.1016/j.jhazmat.2020.123221
Zhou, T., Brown, M., Snavely, N., & Lowe, D. G. (2017). Unsupervised learning of depth and ego-motion from video. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, California, CA.
Amirian, S. M. R., & Behshad, A. (2016). Emotional intelligence and self-efficacy of Iranian teachers: A research study on university degree and teaching experience. Journal of Language Teaching and Research, 7(3), 548-560. doi:https://doi.org/10.1016/j.sbspro.2010.12.205
Ayonmike, C. S., Okwelle, P. C., & Okeke, B. C. (2015). Towards quality Technical Vocational Education and Training (Tvet) Programmes in Nigeria: challenges and improvement strategies. Journal of Education and Learning, 4(1), 25-34. doi:https://doi.org/10.5539/jel.v4n1p25
Blašková, M., Blaško, R., & Kucharcíková, A. (2014). Competences and competence model of university teachers. ˇ Procedia-Social and Behavioral Sciences, 159, 457-467. doi:https://doi.org/10.1016/j.sbspro.2014.12.407
Bose, S., & Guha, A. (2018). Emotional intelligence and professional adjustment of secondary school teachers. International Journal of Research in Social Sciences, 8(4), 579-590.
Botey, M., Vaquero-Diego, M., & Sastre, F. (2020). Perceived emotional intelligence of university professors based on the nature of the subject taught. Technological Forecasting and Social Change, 161, 120-292. doi:https://doi.org/10.1016/j.techfore.2020.120292
Boyatzis, R. E., Goleman, D., & Rhee, K. (2000). Clustering competence in emotional intelligence: Insights from the Emotional Competence Inventory (ECI). Handbook of Emotional Intelligence, 99(6), 343-362.
Boyatzis, R. E., & Saatcioglu, A. (2008). A 20-year view of trying to develop emotional, social and cognitive intelligence competencies in graduate management education. Journal of Management Development, 27(1), 92-108. doi:https://doi.org/10.1108/02621710810840785
Castillo, M. Á. S., & Del Valle, I. D. (2017). Is emotional intelligence the panacea for a better job performance? A study on low-skilled back office jobs. Employee Relations, 39(5), 683-698. doi:https://doi.org/10.1108/ER-11-2016-0216
Cordova, A., Dolci, J., & Gianfrate, G. (2015). The determinants of crowdfunding success: Evidence from technology projects. Procedia-Social and Behavioral Sciences, 181, 115-124. doi:https://doi.org/10.1016/j.sbspro.2015.04.872
Debes, G. (2021). The predictive power of emotional intelligence on self efficacy: A case of school principals. International Online Journal of Education and Teaching, 8(1), 148-167.
Dolev, N., & Leshem, S. (2017). Developing emotional intelligence competence among teachers. Teacher Development, 21(1), 21-39. doi:https://doi.org/10.1080/13664530.2016.1207093
Elkiran, G., Nourani, V., Abba, S., & Abdullahi, J. (2018). Artificial intelligence-based approaches for multi-station modelling of dissolve oxygen in river. Global Journal of Environmental Science and Management, 4(4), 439-450. doi:https://doi.org/10.22034/GJESM.2018.04.005
Ezeanokwasa, F., Nwachukwu, O. P., & Yaba, L. (2014). Effective entrepreneurship skills in reducing unemployment through small business innovation in Nigeria. Journal of Information and Knowledge Management, 4(11), 97-102.
Ghanizadeh, A., & Moafian, F. (2011). Critical thinking and emotional intelligence: Investigating the relationship among EFL learners and the contribution of age and gender. Iranian Journal of Applied Linguistics, 14(1), 23-48.
Gleason, K. M., McDaniel, M. R., Feinglass, J., Baker, D. W., Lindquist, L., Liss, D., & Noskin, G. A. (2010). Results of the Medications at Transitions and Clinical Handoffs (MATCH) study: An analysis of medication reconciliation errors and risk factors at hospital admission. Journal of General Internal Medicine, 25(5),
441-447. doi:https://doi.org/10.1007/s11606-010-1256-6
Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M., & Watts, D. J. (2010). Predicting consumer behavior with web search. Proceedings of the National Academy of Sciences, 107(41), 17486-17490. doi:https://doi.org/10.1073/pnas.1005962107
Graham, L. J., White, S. L., Cologon, K., & Pianta, R. C. (2020). Do teachers’ years of experience make a difference in the quality of teaching? Teaching and Teacher Education, 96, 10-20. doi:https://doi.org/10.1016/j.tate.2020.103190
Ismail, K., Nopiah, Z. M., & Rasul, M. S. (2020). Emotional intelligence and work performance among vocational teachers. Journal of Technical Education and Training, 12(3), 106-117. doi:https://doi.org/10.30880/jtet.2020.12.03.011
Katyal, S., & Awasthi, E. (2005). Gender differences in emotional intelligence among adolescents of Chandigarh. Journal of Human Ecology, 17(2), 153-155. doi:https://doi.org/10.1080/09709274.2005.11905771
Khademi, F., Jamal, S. M., Deshpande, N., & Londhe, S. (2016). Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression. International Journal of Sustainable Built Environment, 5(2), 355-369. doi:https://doi.org/10.1016/j.ijsbe
.2016.09.003
Mahasneh, A. M. (2016). Emotional intelligence as a predictor of teacher sense of self-efficacy among student teachers in Jordan. North American Journal of Psychology, 18(1), 1-10.
Mohamad, M., & Jais, J. (2016). Emotional intelligence and job performance: A study among Malaysian teachers. Procedia Economics and Finance, 35, 674-682. doi:https://doi.org/10.1016/S2212-5671(16)00083-6
Nedal, O. A., & Alcoriza, M. G. O. (2018). Challenges in education: The untold story of students in Lanao Kapanglao, Glan, Sarangani province. Journal of Advances in Humanities and Social Sciences, 4(3), 118-126. doi:https://doi.org/10.20474/jahss-4.3.1
Nwachukwu, J. C., Srinivasan, S., Bruno, N. E., Parent, A. A., Hughes, T. S., Pollock, J. A., . . . Garcia-Ordonez, R. D. (2014). Resveratrol modulates the inflammatory response via an estrogen receptor-signal integration network. Elife, 3(6), 1-10. doi:https://doi.org/10.7554/eLife.02057.001
Okpala, O. R., Omojuwa, Y., Elenwo, C. G., & Opoko, P. A. (2017). Sustainable urbanization: Investigating problems encountered in uncontrolled urban growth in Nyanya-A suburb of Abuja, Nigeria. International Journal of Humanities, Arts and Social Sciences, 3(1), 13–19. doi:https://doi.org/10.20469/ijhss.3.20003-1
Oz, H. (2015). Emotional intelligence as a predictor of l2 communication. Procedia-Social and Behavioral Sciences, 186, 424-430. doi:https://doi.org/10.1016/j.sbspro.2015.04.117
Rastegar, M., & Memarpour, S. (2009). The relationship between emotional intelligence and self-efficacy among Iranian EFL teachers. System, 37(4), 700-707. doi:https://doi.org/10.1016/j.system.2009.09.013
Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition and Personality, 9(3), 185-211. doi:https://doi.org/10.2190/DUGG-P24E-52WK-6CDG
Thorndike, A., & Colony, R. (1982). Sea ice motion in response to geostrophic winds. Journal of Geophysical Research: Oceans, 87(C8), 5845-5852. doi:https://doi.org/10.1029/JC087iC08p05845
Usman, M., Zarebanadkouki, M., Waseem, M., Katsoyiannis, I. A., & Ernst, M. (2020). Mathematical modeling of arsenic (v) adsorption onto iron oxyhydroxides in an adsorption-submerged membrane hybrid system. Journal of Hazardous Materials, 400(5), 123-221. doi:https://doi.org/10.1016/j.jhazmat.2020.123221
Zhou, T., Brown, M., Snavely, N., & Lowe, D. G. (2017). Unsupervised learning of depth and ego-motion from video. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, California, CA.
Published
2021-06-15
Section
Articles
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