Language teacher emotional intelligence: A scoping review

Yicheng Cai; Honggang Liu

doi:10.59400/fes.v2i4.1599

Language teacher emotional intelligence: A scoping review

Yicheng Cai School of Foreign Languages, Soochow University, Suzhou 215006, China
Honggang Liu School of Foreign Languages, Soochow University, Suzhou 215006, China

Ariticle ID: 1599

100 Views, 53 PDF Downloads

DOI: https://doi.org/10.59400/fes.v2i4.1599

Keywords: language teachers; emotional intelligence; scoping review; foreign language; second language

Abstract

The current study reviewed and synthesized empirical research on foreign/second language teacher emotional intelligence (EI) using a scoping review approach. Specifically, 15 articles published between 2018 and 2024 were included and analyzed with a particular emphasis on the theoretical perspectives, research themes, research background, and methodologies. The results indicated that there were a variety of models and theories in language teacher EI research, with the trait EI model and theories of self-efficacy and emotion regulation receiving the most attention. Moreover, most reviewed studies regarded language teacher EI as an independent variable or antecedent, examining its impact on other individual and environmental variables. Underdeveloped regions, other target languages apart from English, multilingual teachers, and primary or secondary school teachers haven’t received much attention in the included articles. Additionally, homogenization in research design among these studies was noticeable, with quantitative studies, cross-sectional design, and self-reported questionnaires being prevalent. Finally, implications were provided based on these findings for the further development of language teacher EI.

References

[1] Salovey P, Mayer JD. Emotional Intelligence. Imagination, Cognition and Personality. 1990; 9(3): 185–211. doi: 10.2190/dugg-p24e-52wk-6cdg

[2] Thorndike E. Intelligence and its uses. Harper's Magazine. 1920; 140: 227–235.

[3] Gardner H. Frames of mind: The theory of multiple intelligences. New York: Basic Books; 1983.

[4] Brackett MA, Rivers SE, Salovey P. Emotional Intelligence: Implications for Personal, Social, Academic, and Workplace Success. Social and Personality Psychology Compass. 2011; 5(1): 88–103. doi: 10.1111/j.1751-9004.2010.00334.x

[5] Wang Y, Wang Y. The Interrelationship Between Emotional Intelligence, Self-Efficacy, and Burnout Among Foreign Language Teachers: A Meta-Analytic Review. Frontiers in Psychology. 2022; 13. doi: 10.3389/fpsyg.2022.913638

[6] Cardoso-Pulido MJ, Guijarro-Ojeda JR, Pérez-Valverde C. A Correlational Predictive Study of Teacher Well-Being and Professional Success in Foreign Language Student Teachers. Mathematics. 2022; 10(10): 1720. doi: 10.3390/math10101720

[7] Arksey H, O’Malley L. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology. 2005; 8(1): 19–32. doi: 10.1080/1364557032000119616

[8] Turner K, Stough C. Pre-service teachers and emotional intelligence: a scoping review. The Australian Educational Researcher. 2019; 47(2): 283–305. doi: 10.1007/s13384-019-00352-0

[9] Mays N, Roberts E, Popay J. Synthesising research evidence. In: Fulop N, Allen P, Clarke A, Black N (editors). Methods for studying the delivery and organisation of health services. London: Routledge; 2001.

[10] O’Brien K, Wilkins A, Zack E, et al. Scoping the Field: Identifying Key Research Priorities in HIV and Rehabilitation. AIDS and Behavior. 2009; 14(2): 448-458. doi: 10.1007/s10461-009-9528-z

[11] O’Flaherty J, Phillips C. The use of flipped classrooms in higher education: A scoping review. The Internet and Higher Education. 2015; 25: 85-95. doi: 10.1016/j.iheduc.2015.02.002

[12] Petrides KV. Psychometric properties of the trait emotional intelligence questionnaire (TEIQue). In: Stough C, Saklofske DH, Parker JD (editors). Assessing Emotional Intelligence: Theory, Research, and Applications. Boston: Springer; 2009. pp. 85–102.

[13] Bar-On R. Emotional and social intelligence: Insights from the Emotional Quotient Inventory (EQ-I). In: Bar-On R, Parker J D (editors). The handbook of emotional intelligence. San Francisco: Jossey-Bass; 2000. pp. 363–388.

[14] Wong C and Law K. The effects of leader and follower emotional intelligence on performance and attitude: An exploratory study. The Leadership Quarterly. 2002; 13: 243–274. doi: 10.1016/S1048-9843(02)00099-1

[15] Goleman D. Emotional intelligence. Bantam Books. 1995.

[16] Mayer J, Salovey P, Caruso D. Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) Item Booklet. Multi-Health Systems; 2002.

[17] Bandura A. Social foundations of thought and action: A social cognitive theory. Upper Saddle River: Prentice Hall; 1986.

[18] Bandura A. Self-efficacy: The exercise of control. New York: Freeman; 1997.

[19] Cochran-Smith M, Ell F, Grudnoff L, et al. When complexity theory meets critical realism: A platform for research on initial teacher education. Teacher Education Quarterly. 2014; 41(1): 105–122.

[20] Davis B, Sumara DJ. Challenging images of knowing: complexity science and educational research. International Journal of Qualitative Studies in Education. 2005; 18(3): 305–321. doi: 10.1080/09518390500082293

[21] Aelterman A, Engels N, Van Petegem K, et al. The well‐being of teachers in Flanders: the importance of a supportive school culture. Educational Studies. 2007; 33(3): 285–297. doi: 10.1080/03055690701423085

[22] McCallum F, Price D, eds. Nurturing Wellbeing Development in Education. Routledge; 2015. doi: 10.4324/9781315760834

[23] Csikszentmihalyi M. Beyond boredom and anxiety: Experiencing flow in work and play. San Francisco: Jossey-Bass; 1975.

[24] Csikszentmihalyi M. Flow: The Psychology of Optimal Experience. New York: Harper and Row; 1990.

[25] McCrae R, Costa P. The five-factor theory of personality. In: John OP, Robins RW, Pervin LA (editors). Handbook of personality: Theory and research, 3rd ed. New York: Guilford; 2008. pp. 159–181.

[26] Fredrickson BL. What Good Are Positive Emotions? Review of General Psychology. 1998; 2(3): 300–319. doi: 10.1037/1089-2680.2.3.300

[27] Bakker AB, Demerouti E. Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology. 2017; 22(3): 273–285. doi: 10.1037/ocp0000056

[28] Marzano RJ, Gaddy BB, Foseid MC, et al. A Handbook for Classroom Management that Works. ASCD; 2005.

[29] Blatchford P, Kutnick P, Baines E, et al. Toward a social pedagogy of classroom group work. International Journal of Educational Research. 2003; 39(1-2): 153–172. doi: 10.1016/S0883-0355(03)00078-8

[30] Gillies RM, Boyle M. Teachers’ reflections on cooperative learning: Issues of implementation. Teaching and Teacher Education. 2010; 26(4): 933–940. doi: 10.1016/j.tate.2009.10.034

[31] Davis FD. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly. 1989; 13(3): 319. doi: 10.2307/249008

[32] Kliueva E, Tsagari D. Emotional literacy in EFL classes: The relationship between teachers’ trait emotional intelligence level and the use of emotional literacy strategies. System. 2018; 78: 38–53. doi: 10.1016/j.system.2018.07.006

[33] Seydi Shahivand E, Moradkhani S. The relationship between EFL teachers’ trait emotional intelligence and reflective practices: a structural equation modeling approach. Innovation in Language Learning and Teaching. 2019; 14(5): 466–480. doi: 10.1080/17501229.2019.1620241

[34] Su H, Zhang J, Xie M, et al. The relationship between teachers’ emotional intelligence and teaching for creativity: The mediating role of working engagement. Frontiers in Psychology. 2022; 13: 929933. doi: 10.3389/fpsyg.2022.929933

[35] Zhi R, Wang Y, Wang Y. The Role of Emotional Intelligence and Self-efficacy in EFL Teachers’ Technology Adoption. The Asia-Pacific Education Researcher. 2023; 33(4): 845–856. doi: 10.1007/s40299-023-00782-6

[36] Kostić-Bobanović M. Perceived emotional intelligence and self-efficacy among novice and experienced foreign language teachers. Economic Research-Ekonomska Istraživanja. 2020; 33(1): 1200–1213. doi: 10.1080/1331677x.2019.1710232

[37] Anwar RH, Zaki S, Memon N, et al. Exploring the Interplay of Trait Emotional Intelligence and ESL Teacher Effectiveness: Is Self-Efficacy the Mechanism Linking Them? Sage Open. 2021; 11(4). doi: 10.1177/21582440211061378

[38] Dewaele J. Online questionnaires. In: Phakiti A, De Costa P, Plonsky L, Starfield S (editors). The Palgrave handbook of applied linguistics research methodology. London: Palgrave Macmillan; 2018. pp. 269–286.

[39] Kang DM. An Elementary School EFL Teacher’s Emotional Intelligence and Emotional Labor. Journal of Language, Identity & Education. 2020; 21(1): 1–14. doi: 10.1080/15348458.2020.1777867

[40] Sobhanmanesh A. English as a Foreign Language Teacher Flow: How Do Personality and Emotional Intelligence Factor in? Frontiers in Psychology. 2022; 13. doi: 10.3389/fpsyg.2022.793955

[41] Wang X. Exploring positive teacher-student relationships: the synergy of teacher mindfulness and emotional intelligence. Frontiers in Psychology. 2023; 14. doi: 10.3389/fpsyg.2023.1301786

[42] Moskowitz S, Dewaele JM. Through the looking glass of student perception: How foreign language students see teacher trait emotional intelligence and why it matters. Studies in Second Language Learning and Teaching. 2020; 10(2): 239–256. doi: 10.14746/ssllt.2020.10.2.2

[43] Yang N. An Investigation Into the Interplay Between Chinese EFL Teachers’ Emotional Intelligence, Ambiguity Tolerance, and Work Engagement. Frontiers in Psychology. 2022; 13. doi: 10.3389/fpsyg.2022.929933

[44] Dewaele JM, Wu A. Predicting the emotional labor strategies of Chinese English Foreign Language teachers. System. 2021; 103: 102660. doi: 10.1016/j.system.2021.102660

[45] Bar-On R. The Bar-On Emotional Quotient Inventory (EQ-i): Rationale, description and summary of psychometric properties. In: Geher G (editor). Measuring emotional intelligence: Common ground and controversy. New York: Nova Science Publishers; 2004. pp. 115–145.

[46] Bar-On R. The Bar-On model of emotional-social intelligence (ESI). Psicothema. 2006; 18: 13–25.

[47] Petrides KV. Ability and Trait Emotional Intelligence. The Wiley‐Blackwell Handbook of Individual Differences. Published online April 2011: 656–678. doi: 10.1002/9781444343120.ch25

[48] Petrides KV, Furnham A. The Role of Trait Emotional Intelligence in a Gender‐Specific Model of Organizational Variables1. Journal of Applied Social Psychology. 2006; 36(2): 552–569. doi: 10.1111/j.0021-9029.2006.00019.x

[49] Schutte N, Malouff J, Hall L, et al. Development and validation of a measure of emotional intelligence. Personality and Individual Differences. 1998; 25(2): 167–177. doi: 10.1016/S0191-8869(98)00001-4

[50] Yuan R, Yang M. Unpacking Language Teacher Educators’ Expertise: A Complexity Theory Perspective. TESOL Quarterly. 2021; 56(2): 656–687. doi: 10.1002/tesq.3088

[51] Petrides KV, Niven L, Mouskounti T. The trait emotional intelligence of ballet dancers and musicians. Psicothema. 2006; 18: 101–107.

[52] Petrides KV, Mavroveli S. Theory and applications of trait emotional intelligence. Psychology. 2018; 23(1): 24–36. doi: 10.12681/psy_hps.23016

[53] Mayer J, Salovey P. What is emotional intelligence? In: Salovey P, Sluyter DJ (editors). Emotional development and emotional intelligence: Educational implications. New York; Basic Books; 1997. pp. 3–34.

[54] Chen J, Lin C, Lin F. The interplay among EFL teachers’ emotional intelligence and self-efficacy and burnout. Acta Psychologica. 2024; 248: 104364. doi: 10.1016/j.actpsy.2024.104364

[55] Gkonou C, Mercer S. Understanding emotional and social intelligence among English language teachers. London: British Council; 2017.

[56] Liu H, Chu W, Duan S, et al. Measuring language teacher resilience: Scale development and validation. International Journal of Applied Linguistics. Published online May 29, 2024. doi: 10.1111/ijal.12562

[57] Bronfenbrenner U. The Ecology of Human Development. Published online December 31, 1979. doi: 10.4159/9780674028845

[58] de Bot K, Larsen-Freeman D. Researching Second Language Development from a Dynamic Systems Theory perspective. A Dynamic Approach to Second Language Development. Published online 2011: 5–24. doi: 10.1075/lllt.29.01deb

[59] Liu H, Li H, Fang F. “Behind the Screen, I Still Care about my Students!”: Exploring the emotional labour of English language teachers in online teaching during the COVID‐19 Pandemic. International Journal of Applied Linguistics. 2023; 34(2): 450–465. doi: 10.1111/ijal.12501

[60] Liu H, Chen B, Li X, et al. Exploring the Predictive Role of Self-Efficacy in Engagement Among EFL Teachers in Online Teaching: The Mediation of Buoyancy. The Asia-Pacific Education Researcher. 2024; 33(4): 879–888. doi: 10.1007/s40299-024-00820-x

[61] Liu H. Uncovering the mediating role of resilience between EFL teacher turnover intention and wellbeing: A conservation-of-resources theory perspective. System. 2024; 124: 103394. doi: 10.1016/j.system.2024.103394

[62] Liu H, Li Y, Liu B. Exploring the relationship between emotional intelligence and emotion regulation: Evidence from junior high school EFL teachers in China. Forum for Linguistic Studies. 2024; 5(2). doi: 10.59400/fls.v5i2.1641

[63] Gass SM, Mackey A. Stimulated recall methodology in second language research. Mahwah, NJ: Lawrence Erlbaum; 2000.

Published

2024-11-06

How to Cite

Cai, Y., & Liu, H. (2024). Language teacher emotional intelligence: A scoping review. Forum for Education Studies, 2(4), 1599. https://doi.org/10.59400/fes.v2i4.1599

Download Citation

Issue

Vol. 2 No. 4 (2024)

Section

Review

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it for any purpose, even commercially, under the condition that the authors are given credit. With this license, authors hold the copyright.

Editor-in-Chief

Dr. Surapati Pramanik

Nandalal Ghosh B.T. College, India

eISSN

3029-2956

Publication Frequency

Quarterly

About the Publisher

Academic Publishing insists on taking academic exchange and publication as the main line, carrying out comprehensive management based on science and technology, and fully exploring excellent international publishing resources. Within 5 years, it will form a strategic framework and scale with science (S), technology (T), medicine (M), education (E), and humanities and arts (H) as the main publishing fields. Academic Publishing is headquartered in Singapore and based in Malaysia, with the United States and China providing the main scientific and academic resources. At the same time, it has established long-term good cooperative relations with other publishing companies, scientific research communities, and academic organizations in more than a dozen countries and regions. Academic Publishing uses English and Chinese as its main publishing languages, mainly publishing books, journals, and conference papers in print and online. The vast majority of publications follow the international open access policy, providing stable and long-term quality and professional publications. With the joint efforts of the expert team and our professional editorial team, our publications will gradually be indexed by international databases in stages to provide convenient and professional retrieval for various scholars. At the same time, manuscripts we accept will be subject to the peer review principle, and cutting-edge and innovative research articles will be preferentially accepted for peer reference and discussion. All kinds of our publications are welcome for peer to contribute, access, and download.

more

Volume Arrangement

2024

2023

Featured Articles

Diachronic analysis of Spanish scientific production on teaching methodologies in Primary Education: A scientometric and conceptual perspective (2000–2023)

The evaluation of science is essential to ensuring the quality, validity, and reliability of scientific results. Science needs to undergo a review process to ensure the rigorousness of scientific output. This evaluation provides a solid basis for political and economic decisions related to the design and execution of research projects, the establishment of new lines of research, or the identification of areas of specialization. This paper analyses diachronically the Spanish scientific production related to the implementation and development of teaching methodologies in primary education and indexed in the Scopus and Web of Science databases during the period 2000–2023. This analysis is carried out on the one hand, from a scientometric perspective, based on the analysis of indicators such as diachronic production, the journals with the highest scientific productivity, and the most productive institutions, and, on the other hand, from a conceptual perspective, trying to define its relationship with other areas of education. The general results of this study reveal two clear stages: the first, up to 2010, with little scientific production; and the second, from 2011 onwards, characterised by a general growth. The relationship between this field and others such as initial teacher training, ICT, and didactics is also evident.

The relationship between teachers’ self-efficacy and classroom management practices in secondary schools

This study explores the intricate relationship between teacher self-efficacy and classroom management practices in secondary schools in the Mansehra district of Pakistan. Teacher self-efficacy, defined as the belief in one’s ability to manage and influence classroom environments effectively, has been identified as a critical factor influencing both teaching performance and student outcomes. The research employed a mixed-method approach, gathering data from 62 teachers and 310 students using both online surveys (via Google Forms) and physical questionnaires to ensure a diverse and inclusive participant pool. Data analysis was conducted using two complementary tools: SPSS and Smart PLS. SPSS was used for descriptive statistics and inferential analyses, such as t-tests, chi-square tests, and measures of central tendency, to offer an overview of group differences and relationships between variables. Meanwhile, Smart PLS was employed for Partial Least Squares Structural Equation Modeling (PLS-SEM), a technique suited for complex models and smaller sample sizes. This method allowed for the analysis of both direct and indirect relationships between the study variables—teacher self-efficacy, teaching practices, and classroom management. The findings reveal a significant positive correlation between teacher self-efficacy and classroom management practices. Additionally, teaching practices were found to mediate this relationship, indicating that higher levels of self-efficacy not only directly improve classroom management but also enhance teaching performance, which in turn contributes to better-managed classrooms. These results suggest that interventions aimed at enhancing teacher self-efficacy can have far-reaching effects on educational outcomes. The study highlights the need for focused teacher development programs that foster self-efficacy, thereby improving classroom management, student engagement, and overall academic success.

Artificial Intelligence in education: A comprehensive study

This comprehensive study delves into the multifaceted role of AI in education, exploring its applications, benefits, challenges, and future implications. The purpose of the study is to show how AI in education helps educators identify gaps in student knowledge and provide targeted feedback to improve learning outcomes. As a methodology, the library method and the study and review of various documents have been used in this research. The study examines the diverse range of AI technologies employed in educational settings, including intelligent tutoring systems, personalized learning platforms, educational chatbots, and virtual reality simulations. Furthermore, the study delves into the numerous benefits that AI brings to education. It highlights how AI-powered analytics and data-driven insights enable educators to gain deeper insights into student learning patterns, identify areas for improvement, and tailor instructional strategies accordingly. Additionally, AI-driven tools promote inclusivity by providing personalized support to learners with diverse needs and learning styles. Despite its transformative potential, the study also acknowledges the challenges and ethical considerations associated with integrating AI into education. Data privacy, algorithmic bias, and the digital divide are examined in detail, emphasizing the importance of responsible AI deployment and ethical guidelines. Looking ahead, the study explores the future implications of AI in education and the evolving role of educators in AI-enabled classrooms. It discusses how AI technologies will continue to evolve, offering new opportunities for collaborative learning, skill development, and lifelong education. In conclusion, this comprehensive study underscores the profound impact of AI on education and the need for thoughtful implementation strategies that prioritize equity, inclusivity, and ethical considerations. By harnessing the potential of AI, education systems can better prepare learners for the challenges and opportunities of the future.

Enhancing adult learner success in higher education through decision tree models: A machine learning approach

This article explores the use of machine learning, specifically Classification and Regression Trees (CART), to address the unique challenges faced by adult learners in higher education. These learners confront socio-cultural, economic, and institutional hurdles, such as stereotypes, financial constraints, and systemic inefficiencies. The study utilizes decision tree models to evaluate their effectiveness in predicting graduation outcomes, which helps in formulating tailored educational strategies. The research analyzed a comprehensive dataset spanning the academic years 2013–2014 to 2021–2022, evaluating the predictive accuracy of CART models using precision, recall, and F1 score. Findings indicate that attendance, age, and Pell Grant eligibility are key predictors of academic success, demonstrating the strong capability of the model across various educational metrics. This highlights the potential of machine learning (ML) to improve data-driven decision-making in educational settings. The results affirm the effectiveness of Decision Tree (DT) models in meeting the educational needs of adult learners and underscore the need for institutions to adapt their strategies to provide more inclusive and supportive environments. This study advocates for a shift towards nuanced, data-driven approaches in higher education, emphasizing the development of strategies that address the distinct challenges of adult learners, aiming to enhance inclusivity and support within the sector.