Teachers’ Motivation and Its Association with Job Commitment and Work Engagement

Understanding the influencing factors of foreign language teachers' work engagement: A meta-analytic structural equation model

The work engagement of foreign language teachers enhances both their performance and the quality of instruction. Recent studies have highlighted the pivotal roles of emotional intelligence, job burnout, and self-efficacy in fostering teachers' work engagement. Despite this, the complex interactions between these factors in the context of foreign language teaching remain underexplored. This study conducts a meta-analytic review to examine the connections among work engagement, emotional intelligence, burnout, and self-efficacy among foreign language educators. Utilizing a chain mediation model, it probes the mechanism through which emotional intelligence impacts work engagement, based on an analysis of 49 scholarly articles. Results from the meta-analysis robustly validate the hypothesized model. Specifically, emotional intelligence contributes to work engagement via four distinct routes: direct mediation through self-efficacy, direct mediation through job burnout, and chain mediation involving self-efficacy and job burnout. Of the evaluated factors, emotional intelligence had the most significant impact on enhancing work engagement. Implications of these findings may facilitate the training and ongoing professional development of foreign language teachers.

Finding the Link Between Iranian EFL Teacher Motivation and Engagement via Ant Colony Optimization Algorithm and Fuzzy Decision Mode

Teacher motivation is considred as one of the most decisive factorts infulencing teacher functioing as well as students' achievement. Many variable can develop teacher motoivation. In this study, it is presumed that teacher engagement, comprising three facets of emotional, behavioral, and cognitive influence teacher motivation. To examine this hypothesis, this study takes the initiative to utiliuze an innovative artificial intelliengce (AI)-inspired approach called Ant Colony Optimization (ACO) technique. ACO is an artificial intelligence (AI) algorithm originating from natural phenomena. The concept originates from biology and physics and specifically from ants' movements. ACO has the ability to find the connections between inputs and outputs, and it can find the most influencing inputs. Motivation was the output of the study, and the inputs were three different engagement factors. Based on the results, ACO reached a high R-value meaning that it could predict the output with a high accuracy. The findings of this study substantiate the wide-ranging and multifacsted potentials of AI, in particular ACO, in studying and predicting human functioning in academic settings.

Workplace Spirituality and Employee Wellbeing in the Hospitality Sector: Examining the Influence of Fear of COVID-19

Drawing from the job demands-resources theory and the protection motivation theory, this study attempts to examine how the experience of workplace spirituality impacts employee wellbeing during times of remote work. This study also examines how fear of COVID-19 moderates the aforementioned relationship. Using a self-administered questionnaire, data were collected from 566 employees working in the hospitality sector in India. Results revealed that the experience of three dimensions of workplace spirituality (meaningful work, sense of community, and alignment with organisational values) had a significant positive influence on employee wellbeing, even during remote work. Fear of COVID-19 had an interesting significant moderating effect, and it was revealed that the higher the fear, the stronger the relationship between the workplace spirituality (in terms of meaningful work, sense of community, and alignment with organisational values) and employee wellbeing. The insights gained from this study will assist both the employees and the managers in devising personalised approaches towards individual wellbeing and the organisation at large during times of pandemics.

EFL teachers' coping strategies amidst the Covid-19 virtual education and their association with work engagement and teacher apprehension

Teaching can be considered as an extremely demanding and stressful occupation and being a language educator brings about its own distinctive challenges. In the wake of COVID-19 pandemic, teachers worldwide experienced fundamental changes in their profession and their lives as a whole. Coping with such an unprecedented situation and responses to it have created new and extra stressful factors for teachers to cope with, including the difficulties created by quick transition from direct teaching to virtual and remote teaching. This study examined EFL teachers' coping strategies during the Covid-19 virtual education and their association with work engagement and teacher apprehension. A total of 296 language instructors contributed to this study by participating in a survey in January 2021. To determine what coping strategies teachers use in virtual teaching during Covid-19, the researchers adapted the Brief-COPE scale designed and validated by Carver (Int J Behav Med 4:92-100, 1997) to make it appropriate for virtual education. The survey measured 11 coping strategies divided into two broad types, approach and avoidant. For measuring work engagement, the Work and Well-being Survey (UWES) scale designed and validated by Schaufeli and Bakker (Test manual for the Utrecht work engagement scale, vol 3. Utrecht University, The Netherlands, pp. 44-52, 2003. http://www.schaufeli.com) was utilized. To assess teachers' apprehension, the research employed the Sources of Teachers' Apprehension Scale (STAS) developed by Ghanizadeh et al. (Asia-Pac Educ Res 1-14, 2020). The result demonstrated that the adapted coping strategies scale enjoys acceptable reliability and validity indices. The results estimated via structural equation modeling (SEM) revealed that EFL teachers' approach coping strategies positively and significantly predicted work engagement (β = 0.72, t = 10.56). Work engagement was negatively predicted by avoidant coping strategies (β = - 0.29, t = - 3.36). Teacher apprehension was negatively influenced by approach coping strategies (β = - 0.44, t = - 5.57) and positively by avoidant coping strategies (β = 0.43, t = 5.29). The study proposes some practical recommendations for overcoming the Covid-19 related challenges which could further deliver valuable guidance for supporting future training of teachers.

Ability of neural network cells in learning teacher motivation scale and prediction of motivation with fuzzy logic system

We employed a new approach in the field of social sciences or psychological aspects of teaching besides using a very common software package that is Statistical Package for the Social Sciences (SPSS). Artificial intelligence (AI) is a new domain that the methods of its data analysis could provide the researchers with new insights for their research studies and more innovative ways to analyze their data or verify the data with this method. Also, a very significant element in teaching is teacher motivation that is the trigger that pushes the teachers forward, depending on some internal and external factors. In the current study, seven research questions were designed to explore different aspects of teacher motivation, and they were analyzed via SPSS. The current study also compared the results by using an adaptive neuro-fuzzy inference system (ANFIS). Due to the similarity of ANFIS to humans' brain intelligence, the results of the current study could be similar to humans regarding what happens in reality. To do so, the researchers used the validated teacher motivation scale (TMS) and asked participants to fill the questionnaire, and analyzed the results. When the inputs were added to the ANFIS system, the model indicated a high accuracy and prediction capability. The findings also illustrated the importance of the tuning model parameters for the ANFIS method to build up the AI model with a high repeatability level. The differences between the results and conclusions are discussed in detail in the article.