The influence of lecturer non-verbal cues on student perceptions of teaching quality: the role of gender and age

Abstract

This study investigates the influence of lecturer non-verbal cues on student perceptions of teaching quality, considering the moderating effects of gender and age. Non-verbal communication is crucial in instructional settings, impacting students’ engagement, learning experiences, and overall perceptions of teaching effectiveness. Drawing on the social identity theory, the study explores the relationships between specific non-verbal cues displayed by lecturers and students’ perceptions of teaching quality. A quantitative research design is employed, comprising questionnaires. The participants include 614 students from various Technical University education programs, selected using the Google Form. The questionnaires assess students’ perceptions of lecturer non-verbal cues and teaching quality, with Likert scale items (i.e. 1. Strongly disagree to 5. Strongly agree) providing quantitative data. Quantitative analysis reveals significant associations between non-verbal cues such as facial expressions and gestures, and positive teaching quality perceptions. Moreover, gender and age are identified as moderators, affecting students’ interpretations of non-verbal cues and subsequent evaluations of teaching quality; however, students do not place much emphasis on these two variables. The findings contribute to instructional communication theory, advancing the understanding of how non-verbal communication influences teaching effectiveness.

Keywords:

• Age
• gender
• higher education
• non-verbal communication
• teaching quality

Reviewing Editor:

• Serafina Pastore
• Università degli Studi di Bari Aldo Moro
• Bari, Italy

Subjects:

• Higher Education
• Teachers & Teacher Education
• Educational Psychology

1. Introduction

Non-verbal communication is a fundamental aspect of human interaction that influences how messages are perceived, understood, and responded to Matsumoto et al. (2021; Hall, 2022). In the educational context, non-verbal cues displayed by lecturers during instructional sessions have been recognized as important factors that impact student perceptions of teaching quality and overall learning experiences (Mazer et al., 2020; Schnall et al., 2022). Understanding the role of non-verbal communication in the teaching-learning process is crucial for enhancing pedagogical practices and creating a positive and engaging learning environment for students. Research has shown that lecturer non-verbal cues, such as facial expressions, gestures, and body language, can significantly influence student engagement, attention, and comprehension (Le Hunte et al., 2021; Taylor & Sobel, 2022). Students often form impressions of their instructors based on these cues, shaping their evaluations of teaching effectiveness and overall satisfaction with the course (Chen et al., 2023; Wei et al., 2023). However, the specific influence of lecturer non-verbal cues on student perceptions of teaching quality, focusing on the moderating effects of gender and age, remains an area that requires further exploration. Gender has been a subject of increasing interest in non-verbal communication, with research indicating potential differences in how men and women exhibit and interpret non-verbal cues (Weisbuch et al., 2020). These gender differences may influence how students perceive and evaluate lecturers’ non-verbal behaviors, leading to biased evaluations and gender-based stereotypes in teaching quality perceptions (LaFrance & Mayo, 2021). Therefore, it is essential to investigate the potential role of gender as a moderator in the relationship between lecturer non-verbal cues and student perceptions of teaching quality. Similarly, age-related differences in non-verbal communication have also been explored in recent literature (Lavín & Castillo, 2023; Nowakowska & Weytknecht, 2023). Different age groups may exhibit distinct non-verbal behaviors due to generational differences and variations in socialization (Shen & Cui, 2022; Trevisan et al., 2021). Considering age as a moderator can provide valuable insights into how students of different age groups perceive and evaluate lecturers’ non-verbal cues, contributing to a more comprehensive understanding of effective instructional communication.

Given the potential impact of non-verbal cues on teaching quality perceptions and the need to explore the role of gender and age as moderators, the present study aims to investigate the influence of lecturer non-verbal cues on student perceptions of teaching quality in higher education. Using a mixed-methods sequential explanatory design, the study addresses the research questions comprehensively, using quantitative data analysis. The findings from this study have the potential to enrich the existing scholarship on instructional communication, inform evidence-based pedagogical practices, and promote a more inclusive and equitable learning environment for students of all backgrounds.

Non-verbal communication in educational settings has been recognized as a significant factor influencing student perceptions of teaching quality and academic outcomes (Mazer et al., 2020; Schnall et al., 2022). Lecturer non-verbal cues, including facial expressions, gestures, and body language, can shape students’ evaluations of teaching effectiveness, engagement, and overall satisfaction with the course (Chen et al., 2023; Wei et al., 2023). However, despite the growing interest in non-verbal communication and its impact on education, limited research has explored the specific influence of lecturer non-verbal cues on student perceptions of teaching quality, particularly concerning the moderating effects of gender and age. Existing studies have highlighted the importance of non-verbal cues in the educational context, but they often lack a comprehensive analysis of the types of non-verbal cues exhibited by lecturers and their implications for students’ perceptions of teaching quality (Le Hunte et al., 2021; Taylor & Sobel, 2022). Moreover, the role of gender in non-verbal communication has been the subject of recent research, revealing potential gender differences in facial expressions and body language that could affect how lecturers are perceived by students (LaFrance & Mayo, 2021). However, the specific impact of gender on lecturer non-verbal cues and students’ interpretations of these cues remains underexplored in the educational context. Similarly, the role of age in non-verbal communication has also garnered attention, with studies suggesting that different age groups may exhibit distinct non-verbal behaviors (Lavín & Castillo, 2023; Nowakowska & Weytknecht, 2023). However, little research has examined how age influences the display and recognition of non-verbal cues in the educational setting and whether age-related differences affect students’ perceptions of teaching quality. Consequently, there exists a significant research gap in understanding the nuanced relationship between lecturer non-verbal cues, student perceptions of teaching quality, and the moderating effects of gender and age. Addressing these gaps is crucial for optimizing pedagogical practices, promoting inclusive and equitable educational environments, and enhancing the overall learning experience for students in higher education.

Therefore, this study seeks to investigate the following research questions:

1. What are the specific types of non-verbal cues exhibited by lecturers during instructional sessions in the higher education context?

2. How do these lecturer non-verbal cues influence student perceptions of teaching quality, including evaluations of teaching effectiveness and overall satisfaction with the course?

3. To what extent does gender moderate the relationship between lecturer non-verbal cues and students’ interpretations of these cues in the context of teaching quality perceptions?

4. How does age moderate the association between lecturer non-verbal cues and student perceptions of teaching quality in the higher education classroom?

By addressing these research questions, the study aims to provide a comprehensive understanding of the influence of lecturer non-verbal cues on student perceptions of teaching quality, while considering the potential moderating effects of gender and age. The findings will contribute to evidence-based instructional practices and inform strategies to foster inclusive and effective communication between lecturers and students in higher education.

2. Theoretical framework

This study draws on the social identity theory to explore the role of non-verbal communication in educational contexts and how gender and age may moderate its effects on student perceptions of teaching quality. Social identity theory (SIT) posits that individuals categorize themselves and others into social groups, and their behavior and attitudes are influenced by their identification with these groups (Tajfel & Turner, 1979). According to the theory, individuals categorize themselves and others into social groups based on shared characteristics. The theory emphasizes the role of perceived in-group and out-group differences. The tenet of the theory suggests that individuals engage in social comparison and may employ stereotypes to understand and evaluate members of different social groups. By incorporating gender and age as variables, the study intends to examine how these factors contribute to social comparisons and potential stereotyping in the context of teaching quality. In the educational setting, both lecturers and students have distinct social identities based on factors such as gender and age. These social identities can influence their communication patterns, including non-verbal cues, and may shape how they perceive and interpret each other’s non-verbal behaviors. In the context of the study, students and lecturers can be seen as distinct social groups, and factors such as gender and age contribute to group categorization. The study aims to explore how non-verbal cues from lecturers may shape students’ perceptions of teaching quality. Gender and age, as social categories, can influence the perceived differences between students (in-group) and lecturers (out-group). Lecturer non-verbal cues are likely to be interpreted through the lens of social identity. For instance, students may subconsciously associate certain non-verbal behaviors with gender or age-related expectations, influencing their perceptions of teaching quality. SIT provides a framework to explore these cognitive processes by emphasizing the role of social context in shaping group dynamics. Concerning a university setting where students and lecturers interact within an academic environment, understanding the social context is crucial. Gender and age are salient social categories that can influence interactions, and SIT helps in examining their impact within this context. This study’s goal is grounded on its relevance to understanding how individuals categorize themselves and others into social groups, influencing perceptions and behaviors. Thus, the SIT is relevant to understanding this phenomenon and provides a deeper understanding of the complex dynamics involved in instructional communication and its impact on the teaching and learning process (Figure 1).

Figure 1. The proposed conceptual framework.

3. Literature

3.1. Non-verbal communication

Non-verbal communication refers to the transmission of information, feelings, and meaning through cues other than words or spoken language (Andersen, 2021). It encompasses a wide range of non-linguistic signals, including facial expressions, gestures, body language, eye contact, tone of voice, and physical proximity. These non-verbal cues often complement, contradict, or reinforce verbal messages, playing a crucial role in conveying emotions, attitudes, and intentions during interpersonal interactions. Non-verbal cues are particularly significant in instructional settings due to their impact on student engagement, comprehension, and overall learning experiences (Le Hunte et al., 2021). Research has shown that non-verbal cues displayed by lecturers can influence students’ attention, interest, and motivation to teach (Mazer et al., 2020). Facial expressions and gestures, for example, can provide visual cues that aid in clarifying complex concepts and reinforcing key points, leading to improved information retention and understanding. Non-verbal communication also contributes to the establishment of a positive learning environment and the formation of strong teacher-student relationships (Uttl et al., 2023). Warm and approachable non-verbal behaviors by lecturers can foster a sense of trust and rapport, creating a safe space for students to actively participate and ask questions.

Several studies have investigated the role of non-verbal communication in teaching quality and student evaluations of instructors. Research by Gorham and Christophel (1992) found a significant relationship between instructor non-verbal immediacy (cues that signal approachability and warmth) and student perceptions of teaching effectiveness. Instructors who displayed higher non-verbal immediacy were rated more positively by students in terms of their teaching ability and overall likability. Similarly, in online instructional settings, Mazer et al. (2020) reported that instructor non-verbal immediacy, conveyed through video lectures, positively influenced students’ perceptions of teaching presence and engagement. Students perceived instructors who displayed more non-verbal cues as more accessible, interactive, and attentive. While previous research has established the significance of non-verbal communication in teaching quality, several gaps in the literature warrant further exploration. Firstly, limited attention has been given to the role of gender and age as potential moderators in the relationship between non-verbal cues and student perceptions of teaching quality. Secondly, many existing studies have focused on specific aspects of non-verbal communication (e.g., non-verbal immediacy) and may not have captured the full range of non-verbal cues exhibited by lecturers. Understanding the different types of non-verbal cues and their impact on teaching quality perceptions is essential for a comprehensive analysis. Lastly, the majority of the existing research has been conducted in traditional face-to-face instructional settings. As educational delivery methods continue to evolve, with the increasing use of online and blended learning formats, it is essential to investigate the role of non-verbal communication in various instructional contexts. Therefore, the current study aims to address these gaps by investigating the influence of a wide range of lecturer non-verbal cues on student perceptions of teaching quality in both face-to-face and online instructional settings, while considering the moderating effects of gender and age. By employing a mixed-methods approach, the study aims to provide a more holistic and nuanced understanding of the complex interplay between non-verbal communication, teaching quality perceptions, and the potential influence of gender and age factors. The findings from this research can inform evidence-based pedagogical practices, contributing to improved instructional communication and enhanced learning experiences for students in higher education.

Research has identified certain non-verbal cues that are positively associated with teaching effectiveness and student engagement. For instance, warm and approachable non-verbal behaviors, such as smiling, nodding, and maintaining eye contact, contribute to higher perceived teacher immediacy and engagement (Mazer et al., 2020). Also, Effective use of gestures and visual aids can enhance students’ understanding and retention of course material (Smith et al., 2022). Again, Lecturers who use vocal variation and enthusiasm while delivering lectures are often perceived as more engaging and dynamic (Burklund et al., 2019).

3.2. Non-verbal cues and teaching quality perceptions

Non-verbal cues exhibited by lecturers can significantly shape student perceptions of teaching quality and influence the outcomes of student perceptions of teaching. Non-verbal immediacy, characterized by warmth, friendliness, and accessibility, has been associated with higher teaching effectiveness ratings (Klooster et al., 2018). Effective use of non-verbal cues, such as maintaining eye contact, using gestures, and displaying enthusiasm, can create a positive and engaging learning environment, leading to more favorable evaluations from students. Moreover, non-verbal cues can also impact students’ emotional responses during instructional sessions. Lecturers who display positive and engaging non-verbal behaviors can evoke positive emotions in students, enhancing their overall satisfaction with the course and teaching quality (Gao et al., 2021). Student perceptions of teaching quality can be influenced by various factors, such as teaching style, where the lecturer’s instructional approach, communication style, and use of teaching strategies can impact how students perceive the quality of instruction. Clarity and organization refer to a clear presentation of course content and well-organized lectures contribute to positive perceptions of teaching effectiveness. The lecturer’s interpersonal skills, including approachability, responsiveness to students, and willingness to provide support, can also influence teaching quality perceptions. Moreover, lecturers who actively engage students, encourage class participation, and foster interactive learning experiences are often perceived as effective instructors. Student evaluations of teaching are a valuable feedback mechanism used in higher education to assess teaching quality and instructional effectiveness (Spooren et al., 2013). Student evaluations of teaching provide lecturers with insights into their strengths and areas for improvement, allowing them to make pedagogical adjustments and enhance their teaching practices. Additionally, student evaluations of teaching play a role in faculty evaluation, tenure, and promotion decisions, making them an essential component of assessing teaching performance in academia (Göllner et al., 2014). Institutions use student evaluations of teaching as part of a comprehensive approach to evaluate teaching effectiveness and ensure accountability in the delivery of quality education.

3.3. Gender and age differences in non-verbal communication

Recent research has explored gender and age differences in non-verbal communication, shedding light on potential variations in non-verbal behaviors exhibited by lecturers based on gender and age. Studies have found gender differences in the use of non-verbal cues. For example, women tend to display more facial expressiveness, while men may use more expansive body language (Dunne et al., 2022). These differences may influence how lecturers are perceived and evaluated by students. Age-related changes in non-verbal communication have been observed, particularly in facial expressions and body language (Rosenberg et al., 2022). Lecturers of different age groups may display distinct non-verbal cues that can impact students’ perceptions and responses. Understanding these gender and age differences in non-verbal communication can offer valuable insights into how students perceive and interpret lecturers’ non-verbal behaviors, and how these perceptions may influence teaching quality evaluations.

3.4. Gender and age differences in student evaluations of teaching

Research has explored gender and age differences in student evaluations of teaching, with some studies indicating potential biases and variations in evaluations based on several factors. For example, Gender biases may influence how students evaluate lecturers. Studies have found that female lecturers, especially those in male-dominated fields, tend to receive lower teaching evaluations compared to male lecturers (Boring et al., 2016). These gender-based biases can impact teaching quality perceptions and have implications for gender equity in academia. Student evaluations may also vary based on the age of the lecturer. Research has shown that younger and older lecturers may receive different evaluation scores, with younger lecturers often receiving more favorable evaluations (Carrell & West, 2010). Age-related perceptions and stereotypes may influence students’ evaluations of teaching quality. Understanding these gender and age differences in student evaluations of teaching is crucial for ensuring fair and unbiased assessments of teaching effectiveness and for implementing policies to promote equitable evaluations.

3.5. The moderating role of gender on non-verbal cues and teaching quality perceptions

Numerous studies have explored gender effects on non-verbal communication, both in general interpersonal interactions and in educational settings. For instance, Dunne et al. (2022) investigated vocal and facial femininity’s association with teaching evaluations in academia. They found that higher vocal and facial femininity in female lecturers was linked to more favorable teaching evaluations. Moreover, research by Hall et al. (2019) examined gender differences in non-verbal immediacy behaviors displayed by lecturers. They discovered that female lecturers tended to exhibit higher non-verbal immediacy, characterized by warmth and approachability, compared to male lecturers. Gender biases and stereotypes can significantly impact teaching evaluations, with female lecturers often facing unique challenges. One study by MacNell et al. (2015) found that male and female lecturers received different evaluations even when delivering identical course content. Female lecturers received more comments related to their appearance and communication style, while male lecturers received comments focused on their expertise and knowledge. Gender differences in interpreting non-verbal cues have also been studied. Researchers have found that individuals may interpret the same non-verbal behaviors differently based on the communicator’s gender. For example, Hall et al. (2021) investigated gender differences in the interpretation of lecturer facial expressions. They observed that participants attributed more positive emotions to female lecturers’ facial expressions, while male lecturers’ expressions were perceived as more neutral. Additionally, a study by Judd et al. (2019) explored gender differences in the perception of non-verbal cues related to assertiveness and dominance. They found that assertive non-verbal behaviors were more likely to be positively evaluated in male lecturers, while the same behaviors were sometimes negatively perceived in female lecturers.

3.6. The moderating role of age on non-verbal cues and teaching quality perceptions

Studies have examined age-related differences in non-verbal communication, both within and outside educational contexts. For example, research by Rosenberg et al. (2022) investigated age-related changes in non-verbal behavior and expressive style. They found that older individuals tend to display less intense and more positive facial expressions compared to younger individuals. Additionally, studies by Carstensen et al. (2016) explored age-related differences in body language and physical gestures. They observed that older adults may use more subdued and controlled body movements, while younger adults may exhibit more dynamic and expressive gestures. Age-based expectations can influence how students perceive and interpret lecturers’ non-verbal cues. Students may have preconceived notions about how instructors of different age groups should behave during lectures. For instance, older lecturers may be expected to display more maturity and wisdom, while younger lecturers may be expected to exhibit more energy and enthusiasm. Research by Klooster et al. (2018) examined age-based differences in students’ perceptions of lecturer non-verbal immediacy. They found that students’ expectations of non-verbal immediacy varied based on the lecturer’s age, with different age groups displaying non-verbal cues that aligned with their age-based stereotypes. Age-related variations in teaching quality perceptions have been observed in student evaluations of teaching. Carrell and West (2010) conducted a study on the impact of instructor age on student evaluations and found that younger lecturers often received more favorable evaluations than older lecturers, even when controlling for other factors. This suggests that students’ perceptions of teaching quality may be influenced by the age of the instructor. Moreover, age-related differences in non-verbal cues displayed by lecturers may also impact teaching quality perceptions. Students’ interpretations of non-verbal behaviors may vary based on the age of the lecturer, influencing their overall evaluations of teaching effectiveness and engagement.

4. Materials and methods

4.1. Sampling and data collection

One essential component of human interaction that affects how messages are received, interpreted, and responded to is nonverbal communication. Non-verbal cues used by lecturers during class have been identified as crucial elements in the educational setting that influence students’ opinions of the caliber of instruction and their overall learning experiences. To achieve its goal, the study employed a quantitative methodology. To accomplish the study’s goal, a structured questionnaire was designed and administered to the respondents (university students) using an online approach. The online approach to data collection has been used extensively in recent times (Amoah et al., 2023; Amoah & Jibril, 2020; Bruce et al., 2023). The structured questionnaire includes information about the research constructs as well as the participant profiles (such as age, gender, and educational background) to enable a comprehensive analysis of the data. The respondents of this study were university students in various higher educational courses across different disciplines. It was very prudent to use university students as the respondents for this study since the needed information is within their domain. The respondents (students) were chosen from various faculties of Takoradi Technical University such as the Engineering, Business School, Arts and Technology, and Applied Science. Takoradi Technical University is noted as one of the best Technical University in Ghana. The researchers first sought permission verbal permission from the various Deans of the faculties used in the present study concerning the usage of the students as respondents. To fully execute the objectives of this present study, the researchers resorted to the adoption of a purposive sampling technique to ensure a diverse representation of students from different age groups and genders. Purpose sampling technique is a method used to choose a particular group of people or units for analysis. The selection of participants is done “on purpose,” not at random. It is also referred to as selective sampling or judgmental sampling. One usefulness of purposive sampling is that researchers can extract a great deal of information from their collected data. This enables researchers to explain the significant influence their findings have on the general public. Researchers and scholars within the domain of the educational fraternity have extensively applied purposive sampling techniques (Ariyanti, 2023; Fitriani, 2023; Franzhardi et 2022). The sample size was determined based on the study’s power analysis to ensure sufficient statistical power for meaningful analysis.

To ascertain the level of respondents’ understanding of the research questions (perceptions of teaching quality, lecturer non-verbal cues, and other relevant factors), the study deployed the usage of the five-point Likert-scale method ranging from (1. strongly disagree, 2. Disagree, 3 neutral, 4. Agree, and 5. Strongly agreed). All of the measurements in the study were taken from relevant literature. To measure and minimize common method bias, we used Harman’s single-factor test (Harman, 1976). For the respondents to guess the survey’s outcome, additional controls were used, including the way the questionnaire’s items were arranged, the use of unambiguous questions, and the inclusion of superior scale items (Podsakoff et al., 2003). For ethical reasons (anonymity and confidentiality), the students who served as respondents were given the highest level of assurance regarding confidentiality. Six hundred and fourteen questionnaires were domineered through the online version of data collection. To reduce duplications, respondents have the chance to answer only one question, and also, the questions were tagged such that each respondent cannot jump over to answer another question. On average, each respondent used a minimum of eight to ten minutes to answer the questionnaire. Respondents were strictly advised not to write their details like course offering, age, and level of study among others on the questionnaire to adhere to research ethical standards. Four months were used in the data collection processes.

5. Data results and analysis

The quantitative data obtained from the questionnaires were analyzed using appropriate statistical techniques. Descriptive statistics were employed to summarize the demographic characteristics of the participants and the frequency of specific non-verbal cues exhibited by lecturers. To address the research questions and test the hypotheses, inferential statistics were applied. Specifically, correlation analysis was used to examine the moderating effects of age and gender on the relationship between lecturer non-verbal cues and teaching quality perceptions. Moreover, regression analysis was employed to explain the predictor variables (facial expression, gesture, engagement, Age moderator, and gender moderator) on the dependent variable (teaching effectiveness). This analysis helped identify which non-verbal cues have a more significant impact on teaching quality evaluations.

6. Findings and discussion

This session presents the findings and the discussion of data collected from students on the influence of lecturer non-verbal cues on student perceptions of teaching quality. This study aimed to fulfill the objective of exploring the relationships between specific non-verbal cues displayed by lecturers and the students’ perceptions of teaching quality. The results are presented in a tabular form followed by their interpretation.

Table 1 depicts the demographic characteristics of the respondents who participated in the survey. From the table, out of 614 (100%) total respondents, the majority (377) of respondents representing 61.4% were between the ages of 18 years to 24 years, 196 respondents representing 31.9% were between the ages of 25 years to 34 years whiles 41 respondents representing 6.7% belongs to the ages of 35 years and above.

Table 1. Demographic characteristics of respondents.

Variables	Frequency	Percentage
Age
Under 18	0	0%
18-24	377	61.4%
25-34	196	31.9%
35 and above	41	6.7%
Gender
Male	234	38.1
Female	380	61.9
Educational Level
Diploma	117	19.1
HND	299	48.7
Bachelor’s Degree	190	30.9
Master’s Degree	8	1.3
Academic program
Engineering	107	17.4
Business	231	37.6
Applied Arts	80	13.0
Applied Science	196	31.9
Total	614	100%

Source: Field survey, 2023.

Concerning gender, 380 of the respondents representing 61.9% were female while 234 respondents representing 38.1% were male.

Also, in the aspect of the educational level of the respondents, the majority (299) of respondents 48.7% were HND holders, 190 of the respondents representing 30.9% were bachelor’s degree holders, 117 respondents representing 19.1% were diploma holders 8 respondents representing 1.3% were master’s degree holders.

Moreover, concerning the academic program of the respondents, 231 respondents representing 37.6% had their credentials in the field of business, 196 respondents representing 31.9% had credentials in the field of applied science, 107 respondents representing 17.4% had their credentials in the field of engineering, and whiles 80 respondents representing 13.0% had their credentials in the field of applied arts.

The given OLS regression results (Table 2) provide insights into a statistical model’s performance in explaining the variation in the dependent variable, denoted as “effective teaching.” The model seems to be a good fit as indicated by the R-squared value of 0.698, which implies that approximately 69.8% of the variability in the dependent variable (effect teaching) is accounted for by the independent variables (Gestures, facial expressions, engagement, gender, and age) in the model. The adjusted R-squared value is also high at 0.696, suggesting that the model’s explanatory power remains strong even after considering the number of predictors.

Table 2. Overall significance of ordinary least square regression (OLS) model.

Facial expression	Gestures	Teaching Effectiveness	Engagement	Gender Moderator	Age_ Moderator
OLS Regression Results
Dep. Variable:	The_dependent_variable	R-squared:	0.698
Model:	OLS	Adj. R-squared:	0.696
Method:	Least Squares	F-statistic:	281.4
Date:	Thu, 24 Aug 2023	Prob (F-statistic):	1.49e-155
Time:	08:47:46	Log-Likelihood:	−319.22
No. Observations:	614	AIC:	650.4
Df Residuals:	608	BIC:	677.0
Df Model:	5
Covariance Type:	No robust

Source: field survey, 2023.

The F-statistic of 281.4 is significant with a very low associated p-value (1.49e-155), which indicates that the overall model is statistically significant, meaning that at least one of the independent variables in the model is having a significant effect on the dependent variable. This is further supported by the Prob (F-statistic) value, which is well below the typical significance level of 0.05. The date and time information suggest that the analysis was conducted on August 24, 2023, at 08:47:46.

The model’s goodness of fit can also be evaluated using the information criteria AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion). Lower values of AIC and BIC indicate better model fit. In this case, the AIC is 650.4 and the BIC is 677.0, which suggests that the model is reasonably parsimonious and provides a good balance between fit and complexity.

The model consists of 5 predictor variables, and the number of observations in the dataset is 614. The residual degrees of freedom (Df Residuals) is 608, while the degrees of freedom associated with the model (Df Model) is 5. It’s worth noting that the analysis assumes a non-robust covariance type, which means that the model does not account for potential heteroscedasticity (unequal variances of the residuals) or other forms of data deviations that might require more robust statistical techniques. The overall fit of the model can be evaluated through the omnibus test, which tests for the normality of residuals. The small p-value (p < 0.001) indicates that the residuals might not be normally distributed. The Durbin-Watson statistic of 1.869 suggests that there might be some positive autocorrelation in the residuals. The bark-beta test, with a very low p-value (p < 0.001), indicates that the residuals do not follow a normal distribution and may be skewed and have excess kurtosis.

The provided regression output (Table 3) presents the estimated coefficients and associated statistics for a multiple linear regression model. Each coefficient represents the estimated change in the dependent variable (The_dependent_variable) for a one-unit change in the corresponding predictor variable while holding other predictors constant.

Table 3. Result of the ordinary least square regression for the model.

	coef	std err	t	P>|t|	[0.025	0.975]
const	0.0182	0.111	0.164	0.870	−0.200	0.236
Facial expression	0.2092	0.032	6.632	0.000	0.147	0.271
Gestures	0.1810	0.035	5.181	0.000	0.112	0.250
Engagement	0.4757	0.036	13.227	0.000	0.405	0.546
Gender_Modera	0.0648	0.028	2.310	0.021	0.010	0.120
Age_Moderator	0.0647	0.029	2.249	0.025	0.008	0.121
Omnibus:		179.199		Durbin-Watson 1.869
Prob(Omnibus):		0.000		Jarque-Bera (JB):1109.315
Skew:		−1.142		Prob(JB): 1.30e-241
Kurtosis:		9.176		Cond. No. 58.4

Source: field survey, 2023.

The constant term (intercept) is estimated to be 0.0182 with a standard error of 0.111. However, the p-value associated with the constant term is 0.870, which is not statistically significant at conventional significance levels (e.g., 0.05). However, since teaching effectiveness isn’t likely to be meaningful at zero values of the other variables, this coefficient might not hold a significant interpretation in this context.

Moving to the predictor variables, “facial expression,” “gestures,” “engagement,” “gender moderator,” and “age moderator,” each of these variables has a coefficient associated with it, indicating their impact on the dependent variable (Teaching Effectiveness). All of these coefficients are positive, suggesting that increases in these predictor variables are associated with higher values of the dependent variable.

The “facial expression” predictor has a coefficient of 0.2092, which is statistically significant (p < 0.001). This implies that a one-unit increase in facial expression is associated with an increase of 0.2092 units in the dependent variable, all else being equal. This suggests that more positive facial expressions are linked to higher perceived teaching effectiveness by students. These findings confirm the findings of Mazer et al. (2020), who stated that non-verbal cues displayed by lecturers can influence students’ attention, interest, and motivation to learn facial expressions and gestures, for example, can provide visual cues that aid in clarifying complex concepts and reinforcing key points, leading to improved information retention and understanding. Moreover, several studies have investigated the role of non-verbal communication in teaching quality and student evaluations of instructors

Similarly, “gestures” and “engagement” have coefficients of 0.1810 and 0.4757, respectively, both of which are statistically significant with p-values less than 0.001. This indicates that one-unit increases in these predictors correspond to increases of 0.1810 and 0.4757 units in the dependent variable, respectively. This indicates that incorporating gestures while teaching positively impacts perceived teaching effectiveness and higher engagement levels contribute significantly to higher perceived teaching effectiveness. Effective use of gestures and visual aids can enhance students’ understanding and retention of course material (Smith et al., 2022). According to Klooster et al. (2018), effective use of non-verbal cues, such as maintaining eye contact, using gestures, and displaying enthusiasm, can create a positive and engaging learning environment, leading to more favorable evaluations from students. Lecturers who display positive and engaging non-verbal behaviors can evoke positive emotions in students, enhancing their overall satisfaction with the course and teaching quality (Gao et al., 2021)

“Gender moderator” and “Age moderator” are binary predictor variables. The positive coefficients of 0.0648 and 0.0647. If gender is present (coded as 1), it’s associated with an increase of 0.0648 units in teaching effectiveness, holding other variables constant. The p-values of gender imply that gender moderation has a minor influence on teaching effectiveness. Again, Similar to the gender, age moderator (likely coded as 1 when present) is associated with a 0.0647 increase in teaching effectiveness, while keeping other variables constant. Its p-value indicates a slight impact of age moderation on teaching effectiveness. Women tend to display more facial expressiveness, while men may use more expansive body language (Dunne et al., 2022). These differences may influence how lecturers are perceived and evaluated by students. Age-related changes in non-verbal communication have been observed, particularly in facial expressions and body language (Rosenberg et al., 2022). Lecturers of different age groups may display distinct non-verbal cues that can impact students’ perceptions and responses. Notwithstanding the above literature, this study also confirmed that students take the age and gender of lecturers into consideration (in terms of non-verbal communication) when evaluating teaching effectiveness however, students don’t place much emphasis on these two variables.

The correlation output illustrates the kind of relationships between the variables, particularly focusing on the variables “facial expression,” “gestures,” “effective teaching,” “engagement,” “gender moderator,” and “age moderator.” the Pearson correlation coefficients measure the strength and direction of linear relationships between pairs of variables.

Starting with “facial expression,” it has a strong positive correlation with “gestures” (r = 0.686**), “effective teaching” (r = 0.667**), “engagement” (r = 0.600**), “gender moderator” (r = 0.428**), and “age moderator” (r = 0.423**). All of these correlations are statistically significant at the 0.01 level (2-tailed), indicating that as “facial expression” increases, the other variables tend to increase as well.

Similarly, “gestures” exhibit strong positive correlations with “effective teaching” (r = 0.717**), “engagement” (r = 0.713**), “gender moderator” (r = 0.479**), and “age moderator” (r = 0.464**). Again, all of these correlations are statistically significant at the 0.01 level.

“Effective teaching” is strongly positively correlated with “engagement” (r = 0.784**), “gender moderator” (r = 0.521**), and “age moderator” (r = 0.497**), with all correlations being significant at the 0.01 level.

Likewise, “engagement” is strongly positively correlated with “gender moderator” (r = 0.544**) and “age moderator” (r = 0.505**), both significant at the 0.01 level.

"Gender moderator” and “age moderator” also exhibit positive correlations, with a correlation coefficient of 0.450**, significant at the 0.01 level.

Overall, these correlation results suggest that there are strong positive relationships among the variables under consideration. Specifically, higher levels of “facial expression,” “gestures,” “effective teaching,” “engagement,” “gender moderator,” and “age moderator” are associated with each other.

In conclusion, the regression model and the correlation analysis suggest that “facial expression,” “gestures,” “engagement,” “gender moderator,” and “age moderator” are important predictors of effective teaching. This implies that a positive change in any of the above variables (Non-verbal cues) corresponds to highly effective teaching (Table 4).

Table 4. Correlation analysis of variables (Non-Verbal Cues).

Correlations	Facial expression	Gestures	Effective teaching	Engagement	Gender_ Moderator	Age_ Moderator
Facial expression
Pearson Correlation	1	.686**	.667**	.600**	.428**	.423**
Sig. (2-tailed)		.000	.000	.000	.000	.000
N	614	614	614	614	614	614
Gestures
Pearson Correlation	.686**	1	.717**	.713**	.479**	.464**
Sig. (2-tailed)	.000		.000	.000	.000	.000
N	614	614	614	614	614	614
Effective teaching
Pearson Correlation	.667**	.717**	1	.784**	.521**	.497**
Sig. (2-tailed)	.000	.000		.000	.000	.000
N	614	614	614	614	614	614
Engagement
Pearson Correlation	.600**	.713**	.784**	1	.544**	.505**
Sig. (2-tailed)	.000	.000	.000		.000	.000
N	614	614	614	614	614	614
Gender Moderator
Pearson Correlation	.428**	.479**	.521**	.544**	1	.450**
Sig. (2-tailed)	.000	.000	.000	.000		.000
N	614	614	614	614	614	614
Age Moderator
Pearson Correlation	.423**	.464**	.497**	.505**	.450**	1
Sig. (2-tailed)	.000	.000	.000	.000	.000
N	614	614	614	614	614	614

** Correlation is significant at the 0.01 level (2-tailed).

7. Recommendations and limitations

The study’s findings have practical implications for educators and policymakers in the following ways. For example, educational institutions should invest in faculty development programs that focus on improving lecturers’ non-verbal communication skills. Training on the effective use of non-verbal cues, especially non-verbal immediacy behaviors, can enhance teaching effectiveness and student engagement. Also, educators should be mindful of gender and age-based expectations and stereotypes when displaying non-verbal cues. Encouraging inclusive teaching practices that cater to the diverse preferences of students can lead to fairer teaching evaluations. Furthermore, policymakers can revise student feedback mechanisms to include evaluation of lecturer non-verbal cues. Incorporating this aspect in teaching evaluations can provide a more comprehensive assessment of teaching quality. Finally, teacher training programs can incorporate insights from this study to prepare future educators with effective non-verbal communication strategies. Equipping new teachers with these skills can enhance their teaching experiences and student outcomes.

Despite the valuable contributions, the study has some limitations that warrant consideration for future research. The study’s sample size may limit the generalizability of the findings. Future research with larger and more diverse samples from various institutions and disciplines can enhance the external validity of the results. Also, the study focused on non-verbal communication in a specific cultural and educational context. Exploring cross-cultural variations in the perception and interpretation of non-verbal cues can provide additional insights into instructional communication. Again, the study adopted a cross-sectional design, capturing data at a specific point in time. Conducting longitudinal studies that follow students and lecturers over an extended period can reveal how perceptions of teaching quality and non-verbal cues may evolve. Furthermore, the study focused on a specific set of non-verbal cues. Exploring additional non-verbal behaviors, such as paralinguistic features or physical gestures, can offer a more comprehensive understanding of their impact on teaching quality perceptions.

8. Conclusions

This study sheds light on the crucial role of non-verbal communication in shaping student perceptions of teaching quality. The findings underscore the significance of lecturer non-verbal cues in creating a positive and engaging learning environment. Moreover, the study contributes to the field of instructional communication by exploring the moderating effects of gender and age on the relationship between non-verbal cues and teaching quality perceptions. Understanding how gender and age influence students’ interpretations of non-verbal behaviors is essential for promoting fair and equitable teaching evaluations. By recognizing and addressing potential biases, educators, and policymakers can create inclusive educational practices that cater to the diverse needs and expectations of students. This study holds significance in the broader educational context as it highlights the multidimensional nature of teaching quality evaluations. It calls for a comprehensive approach to assessing teaching effectiveness, encompassing both instructional content and non-verbal communication. The study contributes valuable insights to the field of educational research and provides practical recommendations to improve teaching practices. By recognizing the impact of non-verbal cues, gender, and age on teaching quality perceptions, this research paves the way for more effective and inclusive educational experiences, benefitting both educators and students in the pursuit of quality teaching and learning.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Solomon Abekah Keelson

Solomon Abekah Keelson is an Associate Professor of Marketing and Strategy at the Business Faculty of Takoradi Technical University, Ghana. He holds a PhD in Marketing and Strategy from Open University, Malaysia, an MBA in Marketing, and a Bachelor of Arts in Economics from the University of Cape Coast, Ghana. Prof Keelson also holds a professional certificate in Marketing from the Chartered Institute of Marketing UK. He is a Chartered member of Marketing - UK and Ghana and a fellow of the Chartered Institute of Management Consultants. Keelson is a renowned academic with several publications and taught for over 25 years. He has also worked as theses Assessor (internationally and externally) for various tertiary institutions in Ghana and other countries. Keelson has also gotten industry experience from working with the Electricity Company of Ghana for about 10 years and consulting for companies such as Ghana Rubber Estate and Laine Service. He is currently the Dean of the Faculty of Business, at Takoradi Technical University.

Jacob Odei Addo

Dr. Jacob Odei Addo is a Ghanaian accomplished academician .With a PhD from Open University of Malaysia (OUM), in Business Administration. Currently, a Senior Lecturer at Takoradi Technical University. Has a diverse portfolio of published works which shows author's versatility and ability to explore a wide range of subjects

Ann Dodor

Dr. Ann Dodor, a Ghanaian scholar of Marketing and Entrepreneurship, holds PhD in Management Science & Engineering from Giangsu University, China. She is a Senior Lecturer at Takoradi Technical University; with many publications to her credit and the ability to explore a wide range of subjects in Marketing and Entrepreneurship