Exploring the Impact of Emotional Intelligence on University Academics’ Job Performance ()
1. Introduction
Global discourse on education increasingly recognizes the critical role of emotional intelligence in improving academic performance and educational outcomes (Mohamad & Jais, 2016). For instance, in Malaysia, the Strategic Plan for Higher Education emphasizes the transformative role of academics in addressing contemporary educational challenges, facilitated by emotional intelligence (Chong et al., 2020). Nguyen et al. (2019) further supports this argument by associating emotional intelligence in educational settings to improved job performance and organizational communication (Nguyen et al., 2019). However, research within the framework of China, particularly in higher education, is scant. A search on the Chinese National Knowledge Network (CNKI) revealed minimal attention to the emotional intelligence of university academics, with existing research focused primarily on nursing educators and the non-educational sector.
Empirical studies have demonstrated a significant correlation between emotional intelligence dimensions and improved job performance. Effective self-awareness enables professionalism and calmness in the face of stress or challenges, leading to improved work performance (Rechberg, 2020). Self-regulation helps individuals maintain a positive disposition in a condition of criticism or frustration, facilitating problem-solving and conflict resolution (Sarani et al., 2020). Individuals with high levels of intrinsic motivation are more likely to succeed in their long-term professional goals (Mukokoma, 2020). Social skills, including effective communication, teamwork, and leadership, are also key to workplace success (Kusché et al., 2020). Finally, empathy—understanding issues from others’ perspectives—is particularly important for developing relationships and team management (Giménez-Espert et al., 2020).
The application of emotional intelligence and its effects in the specific context of the Chinese university environment, especially in a culturally and historically rich province like Henan, requires further research. The study attempts to fill this gap by combining global research findings with the unique dynamics of higher education in China. Considering the large student population and enthusiasm for education in Henan Province, this study offers a unique perspective that contributes to regional educational development and a broader understanding of emotional intelligence among Chinese academic stakeholders.
2. Research Context Pertaining to Emotional Intelligence and Job Performance
Firstly, there is a lack of consensus in the academic community regarding the definition and measurement of emotional intelligence. Some researchers emphasize the intrinsic aspect of emotional regulation, while others focus on extrinsic social interaction skills. Additionally, there is ongoing debate about the long-term effects and sustainability of emotional intelligence training. Some studies suggest that such training can have a lasting positive impact on teachers’ career development (Valente, 2019). However, others argue that these effects may be short-lived or limited due to the dynamic nature of social interactions and the fluidity of emotional intelligence (Kovalchuk et al., 2022).
Many studies support the positive impact of emotional intelligence on enhancing teachers’ classroom management skills, student engagement, and teaching effectiveness (Alam & Ahmad, 2018; Taseer, 2020). Nevertheless, this relationship varies across different educational settings and cultures, highlighting the need for more cross-cultural research to deepen our understanding and inform the ongoing discussion.
As higher education increasingly shapes the future of society, the role of university academics has become more critical. While previous studies have extensively explored the effects of emotional intelligence across various industries, its specific impact on higher education remains largely overlooked (Halimi et al., 2021). For example, Tănăsescu and Leon (2019) examined the impact of emotional intelligence on the Romanian banking system. Clearly, these studies focused on different sectors and objectives, highlighting the diverse applications of emotional intelligence across various fields.
Moreover, while García-Martínez et al. (2021) claimed that emotional intelligence influences teacher performance in his study of secondary school teachers, he did not extend his research to an in-depth analysis of this relationship in higher education. This gap in the literature highlights the need for further investigation into how emotional intelligence affects the job performance of university academics. Understanding this relationship is crucial for developing strategies that enhance educator effectiveness and, consequently, the overall quality of higher education.
A growing body of research suggests that enhancing the emotional intelligence of educators can be a strategic approach to improving the quality of education (Pérez-González & Qualter, 2018). This study highlights the need for targeted emotional intelligence training programs aimed at enhancing teachers’ interpersonal and emotional skills as part of their professional development.
Research focusing on higher education teachers in Henan Province increasingly acknowledges the critical role of emotional intelligence in shaping educational outcomes. Studies conducted in the region have demonstrated significant correlations between emotional intelligence attributes such as self-awareness, empathy, and interpersonal skills, and educator performance (Chen & Guo, 2020). These attributes significantly impact classroom management, student engagement, and overall teaching effectiveness.
In Henan, where the educational landscape is vast and diverse, the application of emotional intelligence by academics has a clear impact on their ability to manage varied classroom dynamics and respond effectively to educational challenges. For instance, Zhang et al.’s (2023) study highlights that teachers with high levels of emotional intelligence are better equipped to handle stress and conflict, thereby enhancing their performance. This connection suggests that emotional intelligence is crucial not only for improving individual teachers’ performance but also for fostering a more positive learning environment (Dolev & Leshem, 2017).
The concept of emotional intelligence is based on the model proposed by Mayer and Salovey, which defines it as the ability to identify, understand, regulate, and utilize one’s own emotions as well as those of others (Opengart, 2005). It consists of four components: recognizing emotions, using emotions to facilitate thinking, understanding emotions, and managing emotions (Rosete & Ciarrochi, 2005). This model is particularly relevant to educational settings, where emotional intelligence is crucial for academics to effectively manage classroom dynamics and promote student engagement.
In this context, job performance is defined as the effectiveness with which higher education teachers fulfil their teaching, research, and administrative duties (Iqbal et al., 2017). This study considers job performance to encompass aspects such as instructional effectiveness, student engagement, research output, and contributions to administrative tasks. Performance assessments will be based on a combination of self-evaluations, peer reviews, and student feedback, providing a comprehensive view of each teacher’s professional effectiveness.
The relationship between emotional intelligence and job performance is critical, and it is predicted that higher emotional intelligence can enhance job performance across various dimensions. The goal of this study is to explore this relationship within the unique cultural and institutional context of the higher education system in Henan Province, addressing a gap in the existing literature that often overlooks this geographic region.
3. Theoretical Framework
This study aims to examine the relationship between emotional intelligence and job performance among university academics in Henan Province, China. Emotional intelligence, defined as the ability to recognize, understand, and manage one’s own emotions as well as those of others, has been identified as a critical factor influencing teachers’ job performance in education (Mayer et al., 2000). In recent years, educational research has increasingly highlighted the importance of emotional intelligence in enhancing pedagogical approaches and improving student learning outcomes (Su et al., 2022).
Despite the growing recognition of emotional intelligence’s role in education, the academic community has not reached a consensus on the specific mechanisms linking emotional intelligence to job performance. Some studies suggest that emotional intelligence directly enhances teachers’ teaching effectiveness and student satisfaction (Taseer, 2020). In contrast, other research indicates that emotional intelligence indirectly improves job performance by positively influencing teachers’ work attitudes, teaching methods, and interpersonal skills (Hamid et al., 2019). These discrepancies may result from variations in research methodologies and differing interpretations of emotional intelligence dimensions and job performance measures.
The theoretical framework developed in this study aims to provide insights into how emotional intelligence affects the job performance of university academics. It will explore the impact of emotional intelligence on self-management, student interaction, and the ability to handle teaching challenges (Figure 1).
Figure 1. Theoretical framework.
The following research objectives underpin this study: (a) to ascertain information related to university academics’ demographic backgrounds in Henan Province, China, (b) to determine the level of emotional intelligence and job performance among teachers at Chinese college, and (c) to investigate the relationship between emotional intelligence and job performance among teachers at Chinese college.
4. Methodology
A quantitative approach, specifically a correlational design, was the most appropriate method for this study. Data were collected through a questionnaire survey administered to academics at five universities in Henan Province. Using Krejcie and Morgan’s formula, the sample size was determined to be 293. Stratified random sampling was employed to proportionately distribute the sample according to the number of academics at each institution, ensuring representativeness.
The questionnaire was designed with self-report scales and objective performance indicators to quantify various aspects of emotional intelligence and job performance. It consisted of three sections: Section A collected demographic data from respondents, Section B assessed levels of emotional intelligence using a standardized scale adapted from the Meyer-Salovey Caruso Test of Emotional Intelligence (MSCEIT), and Section C evaluated job performance through items measuring teaching effectiveness, student engagement, and professional development.
The reliability of the scales was validated using Cronbach’s alpha, calculated with IBM SPSS version 29. Descriptive analysis was used to assess each dimension of emotional intelligence. Pearson correlation analysis was then employed to determine the relationship between emotional intelligence and job performance, addressing research objectives related to each dimension of emotional intelligence (Figure 2).
Figure 2. Map of the five universities.
5. Results and Discussion
The researcher received all 293 completed questionnaires. Table 1 presents the descriptive analysis of the respondents’ profiles. Regarding age distribution, young academics (25 - 35 years old) accounted for 10.24% of the total. The majority were middle-aged, with 40.27% aged 36 - 45 and 40.61% aged 46 - 55, while those close to retirement (56 - 65 years old) made up 8.87%. This distribution suggests that a significant portion of the participating academics are in the middle of their careers, likely bringing extensive teaching experience and emotional intelligence skills to their roles.
In terms of gender ratio, 60.07% of the respondents were female and 39.93% were male, indicating a higher proportion of female academics in five higher education institutions in Henan Province.
Table 1. Demographic profiles of respondents (n = 293).
Profile |
Frequency |
Percentage (%) |
Age |
|
|
25 - 35 |
30 |
10.24 |
36 - 45 |
118 |
40.27 |
46 - 55 |
119 |
40.61 |
56 - 65 |
26 |
8.87 |
Gender |
|
|
Male |
117 |
39.93 |
Female |
176 |
60.07 |
Teaching experience |
|
|
less than 5 years |
29 |
9.9 |
6 - 10 years |
119 |
40.61 |
11 - 20 years |
93 |
31.74 |
more than 20 years |
52 |
17.75 |
Academic qualification |
|
|
Bachelor’s degree |
47 |
16.04 |
Master’s degree |
88 |
30.03 |
Doctorate degree |
124 |
42.32 |
Other |
34 |
11.6 |
Regarding teaching experience, 9.90% of the respondents had less than 5 years of experience, 40.61% had 6 to 10 years, 31.74% had 11 to 20 years, and 17.75% were senior academics with more than 20 years of experience. This data reflects a broad distribution of teaching experience among the respondents.
In terms of academic qualifications, 16.04% of the academics held a bachelor's degree, 30.03% held a master’s degree, 42.32% had a doctorate degree, and 11.60% had other qualifications. This indicates that most of the academics who participated in the survey had a high level academic degree.
This demographic and diversity within the respondent group offers a critical foundation for examining how emotional intelligence may affect the work performance of university academics, especially in the context of different age groups, genders, levels of experience, and academic credentials.
5.1. Descriptive Analysis of Emotional Intelligence
Table 2 presents the analysis of the emotional intelligence levels of university academics, as assessed by the survey. Emotional intelligence was evaluated across five key dimensions: self-awareness, self-regulation, motivation, empathy, and social skills. Each dimension was measured using a Likert scale from 1 to 5, where 1 indicates strongly disagree and 5 indicates strongly agree with the statements provided. This analysis includes frequencies and percentages, central tendency (mean) and variability (standard deviation). The variability is continuous, low (1.00 - 2.33); medium (2.34 - 3.67) and high (3.68 - 5.00) for further clarification.
As shown in Table 2, the level of self-awareness was shown that 64 academics, or 21.8%, had a low level, 130 academics, or 44. 4%, had a medium level, and 99 academics, or 33.8%, had a high level. The mean self-awareness score for the overall sample was 3.36 with a standard deviation of 0.916, the group of academics studied had a high level of self-awareness and the data distribution was relatively concentrated.
Seventy-one academics (24.2 per cent) were at a low level of self-regulation which implies that this group of academics may need additional support and training in the management of their emotions. The number of academics at the medium level was 90 (30.7%), indicating that these academics were able to manage their emotions effectively in most situations. In contrast, 132 academics (45.1%) at the high level demonstrated a high level of self-regulation and were able to maintain emotional stability in stressful situations. Overall, these academics had a mean self-regulation score of 3.14 with a standard deviation of 1.01, reflecting a broad distribution from low to high. This distribution pattern suggests that although most academics can control their emotions effectively, there are still some teachers who need more attention and support in self-regulation.
There were 61 or 20.8% of academics at the low level and these academics may need additional support to boost their motivation. Academics at the medium level, 116 or 39.6%, show some motivation but may still need support to maintain or enhance their motivation in certain situations. High level motivated academics at 116 or 39.6%, demonstrate a high degree of motivation, which could improve student learning outcomes and their efficacy as academics. The mean motivation score of the academics was 3.42 with a standard deviation of 0.92 which reflects some motivational dispersion. Even though the majority of academics demonstrate good motivation, the uneven level of motivation suggests that individual differences need to be taken into account in academic training and development strategies in order to more effectively enhance each academic motivation to teach and, in turn, optimize the teaching process and students’ learning experience.
Moreover, there were 62 teachers with a low level of empathy, which accounted for 21.2%. moderate levels of empathy encompassed 127 teachers, or 43.3% of the total, and these academics were able to understand the emotions of others to some extent but may still lack sufficient sensitivity in certain situations. In addition, 104 academics, or 35.5% of the total, showed high levels of empathy, and this group demonstrated a strong ability to empathize with emotions and effectively understand and respond to the needs of their students. The overall mean score for these data was 3.34 with a standard deviation of 0.94, and although most academics were able to demonstrate some empathy, there is still room for improvement in overall levels.
Finally, the distribution of levels of social skills among the 293 academics is stated in Table 2. Low level of social skills (1.00 - 2.33) had 52 academics or 17.7% of the total. Medium level social skills (2.34 - 3.67) had 101 academics, or 34.5% of the total, and this group of academics had some social skills and were able to meet basic instructional and professional communication needs. The high level of social skills (3.68 - 5.00), with 140 academics or 47.8% of the total, indicates that nearly half of the academics excelled in social skills and were able to communicate and collaborate effectively. The overall mean social skills were 3.33 with a standard deviation of 0.94, indicating that while most the academics’ social skills were in the upper middle range.
Table 2. Descriptive analysis of dimensions under emotional intelligence.
Dimension |
Level |
Frequency |
Percent |
Mean |
SD |
Self-awareness |
Low |
64 |
21.8 |
3.36 |
0.92 |
Medium |
130 |
44.4 |
|
|
High |
99 |
33.8 |
|
|
Self-regulation |
Low |
71 |
24.2 |
3.14 |
1.01 |
Medium |
90 |
30.7 |
|
|
High |
132 |
45.1 |
|
|
Motivation |
Low |
61 |
20.8 |
3.42 |
0.92 |
Medium |
116 |
39.6 |
|
|
High |
116 |
39.6 |
|
|
Empathy |
Low |
62 |
21.2 |
3.34 |
0.94 |
Medium |
127 |
43.3 |
|
|
High |
104 |
53.5 |
|
|
Social skill |
Low |
52 |
17.7 |
3.33 |
0.94 |
Medium |
101 |
34.5 |
|
|
High |
140 |
47.8 |
|
|
Total (n) |
|
293 |
100 |
|
|
5.2. Descriptive Analysis of Job Performance
Table 3 describes the analysis of the assessed job performance of university academics in Henan Province, China. The study consists of four main dimensions: teaching effectiveness, research output, academic and administrative engagement, and guidance and support. Each dimension was measured using a Likert scale of 1 - 5, where 5 denotes strongly agree with the statements provided, and 1 denotes strongly disagree. The analysis included frequencies and percentages, central tendency (mean) and variability (standard deviation). Variability was continuous and was low (1.00 - 2.33), medium (2.34 - 3.67) and high (3.68 - 5.00).
The data shows that 57 academics, or 19.5%, had low teaching effectiveness, 120 academics, or 40.9%, had moderate teaching effectiveness, while 116 academics, or 39.6%, had high teaching effectiveness. 3.47 was the average score overall, and the standard deviation was 0.81, indicating that the majority of the academics possessed good teaching effectiveness, but there were still close to one-fifth of the academics who needed to improve on their teaching.
Of 293 academics, 47, or 16%, had a low level of research output, 118 academics, or 40.3%, had a medium level of research output, while 128 academics, or 43.7%, had a high level of research output. The overall mean research output score was 3.53 with a standard deviation of 0.85, indicating that the majority of academics were performing well in research, but there were a proportion of academics with low research output.
In addition, the results show that 55 academics, or 18.8%, had a low level of engagement, 133 academics, or 45.4%, had a moderate level of engagement, while 105 academics, or 35.8%, had a high level of engagement. The overall mean score of 3.41 with a standard deviation of 0.86 reflects that academics are generally active in this area, but the distribution of engagement still shows some variation.
The final dimension depicts 27% (79) of the academics had low levels of guidance and support, indicating significant room for improvement in effectively supporting students; 28.6% (84) of the academics demonstrated moderate levels of performance, suggesting that despite being able to provide basic support, there is still room for improvement; while 44.4% (130) of the academics demonstrated high levels of performance, making them leaders among their peers. The overall mean was 3.41 with a standard deviation of 0.99, reflecting differences in academics’ perceptions of their own performance in this key competency.
Table 3. Descriptive analysis of dimensions under job performance.
Dimension |
Level |
Frequency |
Percent |
Mean |
SD |
Teaching effectiveness |
Low |
57 |
19.5 |
3.47 |
0.81 |
Medium |
120 |
40.9 |
|
|
High |
116 |
39.6 |
|
|
Research output |
Low |
47 |
16 |
3.53 |
0.85 |
Medium |
118 |
40.3 |
|
|
High |
128 |
43.7 |
|
|
Academic and administrative engagement |
Low |
55 |
18.8 |
3.41 |
0.86 |
Medium |
133 |
45.4 |
|
|
High |
105 |
35.8 |
|
|
Guidance and support |
Low |
79 |
27 |
3.41 |
0.99 |
Medium |
84 |
28.6 |
|
|
High |
130 |
44.4 |
|
|
Total (n) |
|
293 |
100 |
|
|
5.3. Correlation Analysis between Emotional Intelligence and Job Performance
Table 4 reports that all dimensions of emotional intelligence (self-awareness, self-regulation, motivation, empathy, and social skills) are significantly and positively related to the job performance of university academics in Henan Province, so it can be inferred that improving teachers’ emotional intelligence can significantly improve their teaching effectiveness and professional achievement.
In this study, the independent variable Emotional Intelligence (Self-awareness, Self-regulation, Motivation, Empathy, Social Skills) was positively correlated with the dependent variable Job Performance. According to Pearson’s correlation analysis, the correlation coefficient between self-awareness and job performance was 0.778 (p < 0.01), showing a strong association between high self-awareness and high job performance (Côté & Yip, 2021). The correlation coefficient between self-regulation and job performance was 0.773 (p < 0.01), indicating that the better the teachers were at emotion regulation, the better their job performance was (Joseph et al., 2015). Furthermore, the correlation coefficient between motivation and job performance was 0.760 (p < 0.01), emphasizing that high levels of motivation are significant predictors of superior job performance (Miao et al., 2017). The correlation coefficient between empathy and job performance was 0.755 (p < 0.01), pointing out that teachers who are able to understand and empathize with the emotions of their students and colleagues usually perform better (Brasseur et al., 2013). Finally, the correlation coefficient between social skills and job performance was 0.734 (p < 0.01), confirming that good social skills have a positive impact on enhancing teachers’ professional performance (O’Boyle et al., 2011).
Table 4. Pearson’s correlation between emotional intelligence and job performance.
Emotional Intelligence Dimension |
Job performance |
p |
Self-awareness |
0.778 |
0.000 |
Self-regulation |
0.773 |
0.000 |
Motivation |
0.760 |
0.000 |
Empathy |
0.755 |
0.000 |
Social skills |
0.734 |
0.000 |
*correlation is significant at p < 0.01.
6. Discussion
Self-awareness, self-regulation, motivation, empathy, and social skills are the dimensions of emotional intelligence that researchers identified in this study, along with the level of job performance. The researcher then thoroughly examined the relationship between emotional intelligence and the job performance of university academics in Henan Province. The analyses’ findings demonstrated a significant and positive correlation between academics’ job performance and all dimension of emotional intelligence, including self-awareness, self-regulation, motivation, empathy, and social skills (r = 0.778, 0.773, 0.760, 0.755, 0.734; p < 0.01). These results are consistent with those found in the body of literature, including studies by Goleman (1995) and Mayer and Salovey (2007), which highlight the vital role that emotional intelligence plays in improving professional performance.
From a practical perspective, these findings emphasize the need to incorporate emotional intelligence development in academic training and CPD programs. Educational institutions can enhance the quality of teaching and learning by adding necessary modules on emotional intelligence, such as emotional awareness and risk management. The results of this study support the theoretical basis of emotional intelligence in improving job performance and provide educational policy makers with empirical data to support strategies for incorporating emotional intelligence into academics’ professional development. As the educational environment continues to change, continued exploration of how emotional intelligence plays a vital role in specific educational practices.
The results of this study highlight the importance of integrating emotional intelligence training in academic or CPD training programs. By systematically enhancing academics’ competence in emotional awareness and management, the teaching and learning process can be effectively optimized to increase student engagement and satisfaction. Educational administrators can accordingly adjust their academic development program to focus more on the development of academics’ emotional competence, thereby creating a more positive and supportive teaching and learning environment on campus.
Higher education institutions have a crucial role to play in enhancing the emotional intelligence of academics, and they should develop and implement customized emotional intelligence training programs to meet the needs of academics at different levels. For new academics, such training should focus on foundational skills such as ways to understand and manage students’ emotions, while more advanced training on leadership and team management should be offered to experience senior academics. Such tiered training would help academics to receive the necessary support and enhancement at different stages of their careers.
Educational policy makers should support the development of emotional intelligence by creating incentive policies, such as providing career advancement opportunities or other forms of rewards for academics who demonstrate excellent emotional management skills. At the same time, policies should include financial support for emotional intelligence training in order to reduce the cost of implementing such training in educational institutions and to encourage more educational institutions to participate.
In addition, in order to ensure the timeliness and effectiveness of the training content, higher education institutions should work closely with educational researchers to collect and analyze data on the effectiveness of training on a regular basis, and to adjust the training programs according to the feedback. By establishing a continuous research and feedback mechanism, it can be ensured that training programs are always in line with the actual needs of academics and the latest educational research findings, and such a strategy will greatly enhance the usefulness and effectiveness of training.
The study also revealed some potential limitations in that the sample was limited to a number of higher education institutions in Henan Province and was not fully representative of other regions or countries. Future research should consider using a wider sample and possibly incorporating qualitative research methods to gain insight into how emotional intelligence affects academics’ performance in different cultural and educational contexts.
Because of its cross-sectional research design, the current study’s investigation of causality was limited in its ability to pinpoint the precise relationship between emotional intelligence and job performance. Future research could validate how changes in emotional intelligence affect academics’ job performance over time through a longitudinal research design, which would contribute to a more accurate understanding of the long-term effects of emotional intelligence and its specific application in educational practice.
The unique culture of Henan Province, characterized by traditional values and a large population with a high level of education, suggests that the standard emotional intelligence model may need to be adapted to adequately account for the complex interactions between emotional intelligence skills and job performance indicators in different regions. This is in line with the results discussed in previous chapters, which emphasize the differences in the level of awareness and emphasis on emotional intelligence in different cultural contexts. Incorporating these cultural factors allows for more effective implementation of emotional intelligence development programs tailored to the specific needs of educators in different settings.
This study acknowledges its limitations, with a primary geographic focus on Henan Province, and suggests that a broader, possibly cross-cultural, study be conducted to validate and extend these findings. Through a careful understanding of how emotional intelligence influences educational environments, this study provides valuable perspectives for the fields of educational psychology, leadership, and faculty development, as well as for policymakers and educational administrators who create supportive and effective educational environments.
Future research should investigate new technologies that involve interactive software tools to simulate real classroom scenarios, a method that more realistically assesses academics’ ability to apply emotional intelligence in actual teaching. Through such simulations, teachers can practice their emotional management skills in a controlled and safe environment, thus better preparing them to face various challenges in teaching.
7. Conclusion
The study serves as a foundation for developing policies in educational establishments. Policy makers are encouraged to incorporate regular assessments of emotional intelligence into academic performance evaluations and to consider incentivizing academics to further develop these skills. Such policies will not only improve academic performance, but also create a more supportive learning environment, which will improve a wide range of educational outcomes. Ongoing research is also crucial. Further research could explore which elements of emotional intelligence most affect performance and propose interventions that would explore the long-term impact of these interventions on the education system, providing ongoing feedback to help refine and adapt educational practices and academic training methods to effectively meet changing educational needs.
Acknowledgements
The publication fee was paid by the Faculty of Educational Studies, Universiti Putra Malaysia in supporting postgraduate student experiences on publication purposes. Appreciation also goes to the academics at five universities in Henan Province, China who contributed as respondents for this study.
Appendix
Questionnaire form
Part A: Background of Respondent
There is no right or wrong in the questions of this questionnaire. Please give them the appropriate options according to your school and your personal basic information “√”.
1) Your age: ____
2) Your gender: □ male / □ female
3) Your teaching age: less than □ 5 years / □ 6 - 10 years / □ 11 - 20 years / □ more than 20 years
4) Your degree: □ undergraduate / □ master / □ doctorate / □ other
Part B: Emotional Intelligence Assessment
This section contains 15 items, each with five response options ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). Please respond according to your actual situation and truthfully. Select the most appropriate option for each item and mark “√” in the corresponding box.
B1 |
I can recognise my emotions accurately |
1 |
2 |
3 |
4 |
5 |
B2 |
I can understand the source of complex emotions when they arise |
1 |
2 |
3 |
4 |
5 |
B3 |
I can recognise the impact of changes in my mood on others |
1 |
2 |
3 |
4 |
5 |
B4 |
I can manage my emotions effectively, even in stressful situations |
1 |
2 |
3 |
4 |
5 |
B5 |
I can remain calm in conflict and solve problems effectively |
1 |
2 |
3 |
4 |
5 |
B6 |
I can find appropriate ways to calm down in times of emotional stress |
1 |
2 |
3 |
4 |
5 |
B7 |
I can stay positive and motivated in the face of setbacks |
1 |
2 |
3 |
4 |
5 |
B8 |
I set goals and stick to them |
1 |
2 |
3 |
4 |
5 |
B9 |
I feel passionate about my profession and translate that passion into action |
1 |
2 |
3 |
4 |
5 |
B10 |
I can understand the emotions and positions of others |
1 |
2 |
3 |
4 |
5 |
B11 |
I can sense and respond appropriately to the emotional needs of others |
1 |
2 |
3 |
4 |
5 |
B12 |
I demonstrate a high degree of empathy in my interactions with students and colleagues |
1 |
2 |
3 |
4 |
5 |
B13 |
I am an active collaborator in the team |
1 |
2 |
3 |
4 |
5 |
B14 |
I can effectively influence and motivate others |
1 |
2 |
3 |
4 |
5 |
B15 |
I can solve problems at work and in relationships through communication. |
1 |
2 |
3 |
4 |
5 |
1 = Strongly Disagree; 2 = Disagree; 3 = Moderately Agree; 4 = Agree; 5 = Strongly Agree.
Part C: Job performance assessment
This section contains 15 items, each with five response options ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). Please respond according to your actual situation and truthfully. Select the most appropriate option for each item and mark “√” in the corresponding box.
C1 |
I have a teaching methodology that can be adapted to students with different
learning styles |
1 |
2 |
3 |
4 |
5 |
C2 |
I regularly update course content to ensure information is current and relevant |
1 |
2 |
3 |
4 |
5 |
C3 |
I often get positive feedback from students about my teaching |
1 |
2 |
3 |
4 |
5 |
C4 |
I can manage the classroom effectively to ensure that teaching and learning activities run smoothly |
1 |
2 |
3 |
4 |
5 |
C5 |
The management strategies I implement in my classroom are effective in sustaining student interest and engagement in learning |
1 |
2 |
3 |
4 |
5 |
C6 |
I can identify and solve problems in the learning process in a timely manner |
1 |
2 |
3 |
4 |
5 |
C7 |
I am involved in a research project that makes a significant contribution to my field of expertise |
1 |
2 |
3 |
4 |
5 |
C8 |
I have cited the results of my research, proving its academic impact |
1 |
2 |
3 |
4 |
5 |
C9 |
I participate in the decision-making process of the school and contribute to
educational policies and college development |
1 |
2 |
3 |
4 |
5 |
C10 |
I have good collaborative relationships with colleagues to promote academic and
educational programs |
1 |
2 |
3 |
4 |
5 |
C11 |
I regularly engage in out-of-classroom mentoring with students to help them progress academically and professionally |
1 |
2 |
3 |
4 |
5 |
C12 |
I have positively impacted the student's academic achievement and personal
development through my guidance |
1 |
2 |
3 |
4 |
5 |
C13 |
I participate in the decision-making process of the school and contribute to
educational policies and college development |
1 |
2 |
3 |
4 |
5 |
C14 |
I play a role in dealing with faculty and student issues and institutional policy
implementation |
1 |
2 |
3 |
4 |
5 |
C15 |
I take a role in enhancing school community co-operation and partnerships,
promoting collaboration between the school and external organizations |
1 |
2 |
3 |
4 |
5 |
1 = Strongly Disagree; 2 = Disagree; 3 = Moderately Agree; 4 = Agree; 5 = Strongly Agree.