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Abstract

This study modelled the influence of lecturers’ feedback and students’ psychological variables on students’ engagement as moderated by students’ gender. The data were collected from 400 students who responded to feedback, self-efficacy, motivation, interest, and engagement questionnaires. Path diagrams and path coefficients were used to answer research questions. Hypotheses were tested with the average path coefficient (APC), average R-squared (ARS), and average adjusted R-squared (AARS). This study found that the causal model for explaining students’ engagement is a recursive model involving lecturers’ feedback and students’ psychological variables. The study found that the composite effects of lecturers’ feedback and students’ psychological variables explained 69% of the variation in students’ engagement in learning economics. In addition, this study revealed that the most significant variable that affected students’ engagement in this model was self-efficacy, followed by interest and motivation. In addition, lecturers’ feedback positively and significantly affected all psychological variables as well as students’ engagement in this model. Finally, the moderating effect of gender was not significant with respect to students’ engagement. Based on this study’s findings, lecturers should consider students’ psychological variables when providing feedback. Also, seminars and workshops should be organized for economics lecturers to improve feedback quality.

Keywords

lecturers’ feedback motivation interest self-efficacy engagement economics gender

Introduction

Students’ engagement is a psychomotor variable that is concerned with students’ active participation in both classroom and outside classroom activities. Engagement is the extent of inquisitiveness displayed by students, the level of cooperation that they exhibit with others in the course, and their inspiration to learn the course (Briggs, 2015). According to Student Engagement (2014), engagement is the amount of curiosity, optimism, enthusiasm, attention, and passion that students display during the instructional process, which extends to the level of inspiration they have to learn and progress in their education. Contextually, students’ engagement is the extent of efforts and time devoted to participating in classroom activities and extra time dedicated to learning Economics contents among peers outside the classroom. Engagement of Economics Education students could be anticipated based on the view that learning improves when students are inquisitive and pay attention, and learning dwindles when students are bored, disappointed, displeased, and disaffected. Students’ engagement becomes enhanced when they comprehend that the school setting fulfils their requirements for proficiency, independence, and relatedness (Wang & Eccles, 2013). Economics Education students’ engagement could be ascertained by the time they devote to working on their assignment, discussing economic terms, building models, and interpreting economic data.

These students’ engagements have dimensions in teaching and learning processes. These dimensions have been outlined by Kraft and Dougherty (2013), which include behavioral, cognitive, and emotional engagements. Students are behaviorally engaged when they tend to adhere to behavioral rules and display a dearth of disruptive behavior; students are cognitively engaged when they are committed to investing in the rightful ways of learning and also work toward achieving the setup objectives; and when students portray affectionate reactions as a sense of closeness and inquisitiveness, such students are considered emotionally engaged (Kraft & Dougherty, 2013). All these dimensions of engagement are crucial for students’ active participation in the instructional process. It has been observed that measuring levels of student engagement allows lecturers to readjust their instructional pedagogies as well as provide feedback with regards to changes in students’ participation, inspiration, and approach to their course and educational pursuits (Bowden et al., 2021). Students’ engagement in the learning activities at a higher institution could be influenced by the kinds of feedback lecturers provide in the classroom.

Feedback has been acknowledged as one of the rudiments of teaching as well as a vital practice by lecturers in the classroom. Feedback is all responses concerning the current state of learning to re-adjust the learning activities toward specified standards (Narciss, 2013). In line with this definition, feedback could be a comment, gesture, or spoken word from a lecturer that comes after teaching and assessments and feeds back on what has been taught. Feedback creates an enabling environment for lecturers and students to re-evaluate the techniques they have formally adopted. Vattøy and Smith (2019) defined feedback as the presentation of facts about students’ performance, comprehension, and goals to help students distinguish between what they know and what they want to accomplish. Hence, feedback is designed to help students improve themselves with respect to their’ efforts. The following principles can be used to provide appropriate feedback in the classroom: clarify presumed achievement; ease self-assessment in learning; deliver high-quality facts; promote lecturers’ and students’ discussion about learning; strengthen motivational beliefs and self-esteem; offer chances to reach the desired goal; and provide facts to help lecturers shape instructional activities. Feedback plays a significant role in instructional delivery (Er et al., 2021). Feedback plays a significant role in instructional delivery. Hattie (2013) revealed that feedback has more impact on facilitating high academic success when effectively utilized in an instructional process. It is a vital element of effective instruction in lower and higher education (Onuigbo & Eze, 2010), and it invigorates interest, self-efficacy, and performance at all educational levels (Hattie, 2013). Even though literature has shown that feedback has a significant impact on students’ engagement, there is a lack of literature that establishes the direction and magnitude of the relationship that exists between lecturers’ feedback and students’ engagement.

Furthermore, students’ engagement in learning activities could be influenced by their psychological variables. The students’ psychological variables this study investigated include interest, motivation, and self-efficacy. These variables have been found to be related to students’ engagement in learning processes. Interest is one of the most researched students’ psychological variables as well as a vital latent variable that influences students’ engagement. van Rooij et al. (2017) described interest as individual liking and willful engagement in an activity. Hence, interest is conceptualized as individual students’ attention, concentration, curiosity, and display of concern toward a particular course or academic activity, which could be noticed in their characteristics. Furthermore, students’ motivation in teaching and learning could be seen as a form of cognitive and emotional academic arousal that drives students to do something or attain a result, as Cava (2011) described it as one of the vital variables that help in promoting students’ construction and re-construction of their conceptual understanding in learning. Motivation is a pressure that assists students in learning activities as regards to achieving their goals, and this pressure can be easily identified by the extents of students’ excitement, interest, and commitment toward learning (Senjahari et al., 2021). Such a desire and wiliness in students usually accompany deciding to act based on their effort to achieve academic goals. Motivation is a significant factor that facilitates students’ learning autonomy (Nafiati, 2017). Students’ motivation in high school is influenced by self-efficacy and achievement goals, and motivation enhances self-efficacy and makes students actively engaged in their academic activities, which translates to high achievement (Chan & Norlizah, 2017).

Self-efficacy is the students’ conviction in their ability to coordinate and conduct the sequence of actions necessary to achieve a stipulated objective (Pike & Donnell, 2010). Bandura in Simorangkir and Rohaeti (2019, p. 79) revealed that self-efficacy is “individual belief in their competence to organize and execute the courses of action required to produce given attainments, regulate, and carry out the actions needed to produce a given achievement.” Therefore, self-efficacy is the students’ belief and confidence in their ability to learn and understand an academic task. A student with higher self-efficacy is found to make extreme efforts in accomplishing a difficult task, and despite the difficulty, they are insistent on completing it (Pike & Donnell, 2010). In the same way, economics students who have a high self-efficacy conviction would select a difficult task and view obstruction encountered as a chance rather than a threat. Therefore, the students’ self-efficacy is likely to affect their’ engagement in the learning process, especially in the economics classroom, which involves critical thinking skills.

The influence of students’ psychological variables (interest, motivation, and self-efficacy) as well as lecturers’ feedback on students’ engagement can be moderated by students’ gender. The issue of the influence of gender on student engagement in higher education has substantially changed over the years, and this shift was highlighted in the last century (Guramatunhu, 2015). Gender has been observed to be an active moderator that enhances students’ engagement. Shao and Chen (2021)revealed that perceived synchronicity influences male students’ engagement more than their female counterparts. The gender variable has also been observed as a significant variable that influence students’ psychological variables like self-efficacy in higher education. Saadat and Sultana (2023) revealed that male students with a better socioeconomic background tend to have better self-efficacy than their female counterparts; however, female students have found actively engaged in pursuing higher education which result in gender imbalance in higher education. Gender influence in higher school has engender male students to “prove and protect” and female students to “doubt and try to improve,” which inclined males to develop high self-efficacy, value selfless accomplishment, pursue self-esteem at the cost of engaging in their learning activities, while females are inclined to invest and sustain effort in engaging in learning activities, acknowledge difficulties, utilize constructive learning strategies, and value effortful accomplishment (Butler, 2019). With respect to students’ interest, Carlsson Nilsson (2020) revealed that students’ gender has a significant direct effect on students’ interest in choice of quantitative and theoretical courses at the higher education level. With respect to teachers’ digital literacy, Edeh et al. (2022) found that gender is not a substantial variable that influences teachers’ digital literacy skills for assisting students with functional diversity.

Literature has established the significant role of gender in teaching and learning processes; however, the majority of these studies were done either on lecturers’ skills or students’ psychological variables. This current study is unique in the sense that it incorporates the influence of lecturers’ feedback and students’ psychological variables on students’ engagement as moderated by the gender of the students. This current study aimed to utilize the path model to establish multiple moderating roles of gender factors in a recursive model: teachers’ feedback and students’ psychological variables influence students’ engagement in an attempt to reveal the decisive factor that influences students’ engagement using the partial least squares structural equation model (PLS-SEM). This study is essential because it is the first of its kind to establish the multiple moderating roles of students’ gender factors on the influence of lecturers’ feedback and students’ psychological factors on students’ engagement in learning at higher education in south-east Nigeria. The findings of this study would substantially assist higher education curriculum implementers in prioritizing students’ psychological variables and moderating the effect of gender when implementing the contents of curriculum in the classroom.

Research Questions and Hypotheses

What is the causal model for explaining the influence of lecturers’ feedback and students’ psychological variables on Economics students’ engagement as moderated by gender?

There is a significant model fit between the theoretical causal proposed model and empirical observed data for explaining the influence of lecturers’ feedback and students’ psychological variables on economics students’ engagement as moderated by students’ gender.

What is the effect of the parameter estimates of the decomposed variables on students’ achievement in economics as moderated by gender?

There is a significant effect of the parameter estimates of the decomposed variables on students’ engagement in economics.

Empirical Literature

Feedback is a substantial part of higher education, mostly from lecturers. Research has linked feedback from instructors to some of the students’ variables. A study conducted by Vattøy and Smith (2019), on English as a second language learners indicated that teacher feedback is positively related to students’ learning goals, and students who are aware of their goals found teachers’ feedback more useful. It was also found that students’ self-efficacy substantially predicted students’ perceptions of teachers’ feedback. In addition, another study conducted on English students’ emotional perceptions toward teacher feedback revealed that students’ motivation and interest were strongly correlated with teacher feedback in a cloud classroom learning environment (Li et al., 2023). This study further revealed that English students with higher scores favored thoughtful and inoffensive comments; students with medium scores preferred teacher attention and received positive feedback; and students with low scores preferred direct and explicit evaluation. Still on language learners, a study conducted by Zhang and Hyland (2022) found that students’ engagement with lecturers’ feedback in higher education can be facilitated through an integrated approach. Furthermore, a study investigated the impact of teachers’ feedback modes and characteristics on students revisions in writing, and it was found that feedback features like suggestions, questions, and imperatives were mostly more integrated by students than others (Alharbi, 2022). All these empirical studies on teachers’ or lecturers’ feedback established a linear relationship among students’ variables in isolation. However, the current study established the composite effects of both teacher feedback and students’ psychological variables on students’ engagement.

Regarding the relationship between students’ psychological variables and their academic engagement, Nayir (2017) found that students’ motivation is related to class engagement in learning activities. A study also revealed that students’ motivation and engagement were significantly related to students’ achievement in reading, and teachers’ perceptions of the association between motivation and engagement were more pronounced in the first years (Brandmiller et al., 2023). According to the research conducted on secondary school students, academic motivation was strongly correlated with students’ engagement, and students’ academic engagement and motivation differed by gender (Muhammad et al., 2023). Among struggling students, a study revealed that maladaptive motivation and engagement were linked to low students’ achievement in the mathematics test (Xia et al., 2022).

Study results indicate that students’ learning self-efficacy is a key variable for online learning and is positively related to online engagement (Tseng et al., 2020). As well, another study revealed that students’ self-efficacy was found to be directly related to engagement as well as moderate the effect of motivation on student’ engagement (Alemayehu & Chen, 2023). With respect to the link between students’ interest and engagement, a study revealed that situational interest enhances behavioral and cognitive engagement, which translates into better grades for students (Kahu et al., 2021). Also, a study found that students’ situational and personal interests were positively correlated with behavioral and emotional engagement in school physical education (Otundo & Garn, 2019). Additionally, a study reported that interest-tailored lectures substantially enhanced students’ engagement in lectures and students exposed to interest-tailored lecture had a meaningful increase in their achievement score (Pliner et al., 2020). On the moderating role gender plays in influencing student engagement, the results of a study indicate that students’ gender positively influenced their engagement in a real classroom setting (Ikram et al., 2023). Also, students’ engagement strategies varied based on their gender and access to technology (Abou-Khalil et al., 2021). Additionally, another study found that girls were more engaged in digital learning during Covid-19 than boys (Korlat et al., 2021). Students’ gender influences the relationship between students’ motivation and engagement (Muhammad et al., 2023).

Most empirical studies on teachers’ and lecturers’ feedback were conducted on language courses, and they only found a linear relationship between feedback and one or two variables of students that predict performance. Also, studies on the relationship between students’ psychological variables and engagement were done in isolation of either feedback or students’ gender. In this study, we used a structural equation model to determine the effects of lecturers’ feedback and students’ psychological variables on engagement as moderated by gender.

Theoretical Literature

This study is anchored on the counterfactual theory of causation propounded by David Lewis in 1973 (Rahimi et al., 2023). This theory tenet explores what ought to have happened in the most common “parallel world” if, contrary to reality, a certain aspect of perceived reality had been different (Rahimi et al., 2021). The counterfactual approach to causality begins with the idea that some of the information required for inferring causal relationships is and will always be unobserved, and assumptions must be made (Morgan & Winship, 2015). This methodology is drawn from a new stream of causal analysis in geospatial science, which models the causal-effects relationships among parameters underlying a spatiotemporal mechanism (Christiansen et al., 2022). The counterfactual theory explained cases of pre-emption (Northcott, 2021), overdetermination case (Won, 2014), and causation by absence (Sartorio, 2023).

This theory strives to exploit this insight by creating an exploration of the causal associations with regards to counterfactual dependence. The paramount intention behind a counterfactual exploration of causation is that the association of counterfactual reliance between endogenous variables and exogenous variables ensures that there is a causal relationship between the events (Northcott, 2021). However, since there are accurate and clear conditions of causation without simple counterfactual reliance, counterfactual reliance is not necessary for causation; rather, it means that reliance on an endogenous variable on exogenous variables can infer causation. Lewis’ counterfactual assumption that causation is transitive depicts the indirect effect of exogenous variables on other exogenous variables, which results in its effect on endogenous variables (Rahimi et al., 2023).

Lewis says counterfactual analysis, when coupled with a sophisticated approach to empirical research, solves problems with common causes. Common causes, for example, in this study are where lecturers’ feedback and psychological variables of students, such as self-efficacy, affect students’ interest, and interest affects students’ engagement. Lewis described it as an ancestral form of counterfactual dependence as a result of stepwise counterfactual dependence between exogenous and endogenous variables (Sartorio, 2023). A stepwise case in the model is the indirect effect of feedback on engagement through the interest that is unobservable. This study is anchored on the counterfactual theory of causation propounded by David Lewis in 1973 (Rahimi et al., 2023). This theory tenet explores what ought to have happened in the most common “parallal world” if, contrary to reality, a certain aspect of perceived reality had been different (Rahimi et al., 2021). The counterfactual approach to causality begins with the idea that some of the information required for inferring causal relationships is and will always be unobserved and assumptions must be made (Morgan & Winship, 2015). This methodology is drawn from a new stream of causal analysis in geospatial science which models the causal-effects relationships among parameters underlying a spatiotemporal mechanism (Christiansen et al., 2022). The counterfactual theory explained cases of pre-emption (Northcott, 2021), overdetermination (Won, 2014), and causation by absence (Sartorio, 2023).

This theory strives to exploit this insight by creating an exploration of the causal associations with regards to counterfactual dependence. The paramount intention behind a counterfactual exploration of causation is that the association of counterfactual reliance between endogenous variables and exogenous variables ensures that there is a causal relationship between the events (Northcott, 2021). However, since there are accurate and clear conditions of causation without simple counterfactual reliance, counterfactual reliance is not necessary for causation, rather it means that reliance on an endogenous variable on exogenous variables can infer causation. Lewis’ counterfactual assumption that causation is transitive depicts the indirect effect of exogenous variables on other exogenous variables, which results in its effect on endogenous variables (Rahimi et al., 2023).

Lewis says counterfactual analysis, when coupled with a sophisticated approach to empirical research, solves problems with common causes. Common causes, for example, in this study are where Lecturers’ feedback and psychological variables of students such as self-efficacy, affect students’ interest and interest affects students’ engagement. Lewis described it as an ancestral of counterfactual dependence as a result of stepwise counterfactual dependence between exogenous and endogenous variables (Sartorio, 2023). A stepwise case in the model is the indirect effect of feedback on engagement through an interest that is unobservable. Detecting direct and indirect causal relations between variables using counterfactual theory is one of the most reliable methods of determining whether there exists a causal mechanism. An important inspiration for counterfactual theory is that it can reinforce causal claims that there are a series of events that are not captured by the model and are cause and effect. This situation in causation involves omissions that can cause; hence, there can be causation without a causal relation that is captured as an error term in the model. Thus, the counterfactual theory of causation relates to the present study because not all the variables of the study are observable.

Methodology

The ethics committee of the School of Postgraduate Studies authorized this study. A correlational survey research design was adopted for the study. The target population of the study was 3,314 undergraduate economics education students in five federal and five state universities in the southeast, Nigeria. Through a multistage sampling procedure, 400 Economics Education students were selected. The ethical guidelines of the American Psychological Association were strictly followed. All the sample students who participated in this study filled out the consent form. The instruments for data collection were the Feedback Questionnaire (FQ) made up of 19 items, the Students’ Motivation Questionnaire (SMQ) made up of 14 items, the Students Self-efficacy (SSQ) made up of 15 items, the Students’ Interest Questionnaire (SIQ) made up of 15 items, and the Students Engagement Questionnaire (SEQ) made up of 13 items. These instruments were developed by the researcher based on the empirical literature and were structured on modified four-point Likert rating scales of “Strongly Agree” (4), “Agree” (3), “Disagree” (2), and “Strongly Disagree” (1).

These instruments were subjected to face and construct validity. Five experts validated the instruments. The final instruments were restructured based on the expert’s suggestions and observations. The instruments were also subjected to construct validity using principal component factor analysis to detect impure and complex items. In line with Pallant (2020) a factor loading of 0.3 and above was used for pure items. The output of factor analysis indicated that the Kaiser Meyer Olkin coefficient was above .60 and Bartlett’s Test of Sphericity was significant at 0.05, denoting that the sample used for construct validity was adequate. The output also indicated that 66 items were pure, 10 items were found complex, which were reconstructed by the researcher, and five items were factorial impure and were eliminated from the instrument. A Cronbach alpha reliability estimate was used to estimate the internal consistency of the instruments. The following reliability coefficients of internal consistency were obtained: FB = 0.756, SEQ = 0.735, SMQ = 0.685, SSQ = 0.720, and SIQ = 0.751 respectively. These reliability coefficients were all above .6, indicating that they are highly reliable.

The data were collected with the help of two research assistants in administering and retrieving of the research instrument from the respondents. Sample schools for the study were visited by the researcher and two research assistants, who sought the approval of the school authority. With the consent of the school authority, we assured students of their confidentiality and the instruments were administered accordingly and retrieved on the spot.

The data collected were imputed in SPSS version 25, and the coded data were transferred to WarpPLS version 8.0 program. WarpPLS was used for data analysis since it is used for data analyze both normally or not normally distributed data in Partial least square structural equation modeling (Kock, 2016; Kock, 2020). Path diagram and path coefficients were used to answer research questions. Path coefficients of 0.05 and above were accepted as opined by Kerlinger in 1979, (Kline, 2011). Path diagram that shows the association among these variables in causal model are represented.

The structural model in Figure 1 above shows the variables in the model. In the diagram, engagement (Eng) is a dependent variable, while lecturers’ feedback (FB) and students’ psychological variables (students’ motivation [SM], students’ interest [SI], and students’ self-efficacy [SS]) are the independent variables. However, gender is a moderating in the model. Paths in doted lines indicate moderating effects of gender, while paths in solid lines indicates direct effect of exogenous variable on an endogenous variable. The model fit indices were established using average path coefficient (APC), average R-squared (ARS), and average adjusted R-squared (AARS), average block VIF (AVIF), Average full collinearity VIF (AFVIF), tenenhaus GoF (GoF), simpson’s paradox ratio (SPR), R-squared contribution ratio (RSCR) and Statistical suppression ratio (SSR) were used to test hypothesis 1 because the data collected were not normally distributed. According to Kline (2011) and Kock (2016), these are the commonly used statistical measures adopted for testing model fit in the partial least square structural equation model.

Figure 1.

Hypothetical structural model.

Result

RQ1: What is the causal model for explaining the influence of lecturers’ feedback and students’ psychological variables on Economics students’ engagement?

The path model below shows a good model fit (APC = 0.203, p < .001; ARS = 0.500, p < .001; AARS = 0.494, p < .001) at p < .001 level of significant. Also, the coefficients of other fitness indices were within acceptable range (AVIF = 1.535, AFVIF = 2.115, SPR = 0.882, RSCR = 0.994, SSR = 1.00, NLBCDR = 0.941). The coefficient of GoF was large (GoF = 0.547). This showed there is significant model fit between the theoretical structural model proposed for the study and the empirically observed model.

Figure 2 equally showed that all the exogenous variables (lecturers’ feedback, students’ motivation, interest, and self-efficacy) as moderated by gender affected the endogenous variable (engagement). This figure equally showed that this causal model exhibited multiplier effects as some exogenous variables affected each other to transmit indirect effect on students’ engagement. Therefore, students’ psychological variables played a dual role as endogenous variables for mediating effects of lecturers’ feedback on students’ engagement as well as having direct effect on students’ engagement. The path coefficients of these exogenous variables were significant at 0.05 level of significance at 0.05 level of significance. Finally, this causal model depicted unidirectional interrelationship among feedback and these psychological variables to affect students’ engagement, hence, the model is recursive. The recursive causal model indicates that the combined effects of these exogenous variables on students’ achievement explained 69% of variation in students’ engagement. Therefore, the most meaningful recursive structural model for providing an explanation of students’ engagement in Economics as moderated by students’ gender is a recursive model involving lecturers’ feedback and students’ psychological variables (self-efficacy, motivation, and interest).

Figure 2.

Path analysis result.

Research equation 2: What is the effect of the parameter estimates of the decomposed variables on students’ achievement in Economics as moderated by gender?

Table 1 shows the path coefficients of the association that exists among variables in the recursive model as moderated by gender. Lecturer feedback significantly positively affected students’ psychological variables: interest (β = .22, p < .001), self-efficacy (β = .29, p < .001), and students’ engagement (β = .16, p < .001). Furthermore, students’ psychological variables significantly affected their engagement in learning economics: interest (β = .23, p < .001), self-efficacy (β = .31, p < .001), and motivation (β = .27, p < .001). In addition, students’ motivation equally significantly positively affected students’ interest (β = .48, p < .001) and self-efficacy (β = .53, p < .001). Moderating effects of gender were found insignificant in moderating the effect of teachers’ feedback and students’ psychological variables on students’ engagement, feedback (β = −.04, p = .25), interest (β = −.0.05, p = .18), self-efficacy (β = .0, p = .47), and motivation (β = .04, p = .25). In addition, gender moderated students’ psychological variables on students’ engagement: self-efficacy (β = .04, p = .22), and motivation (β = .10, p = .05). Furthermore, a moderating effect of gender was found on students’ interest (β = .03, p = .33). The most significant variable that affects students’ engagement in learning Economics in higher education is students’ self-efficacy, followed by students’ interest and motivation. However, lecturers’ feedback significantly affected students’ psychological variables, such as students’ motivation, interest, and self-efficacy, when moderated by gender. And students’ gender was found to be insignificant in moderating the effects of lecturers’ feedback and students’ psychological variables on students’ engagement.

Table 1.

Paths Coefficients and P-values of Directs and Moderation Effects.

Variables	Paths coefficients	p-Value
Direct effects
Feedback → Interest	.22	.001*
Feedback → Self-efficacy	.29	.001*
Feedback → Motivation	.58	.001*
Feedback → Engagement	.16	.001*
Interest → Engagement	.23	.001*
Self-efficacy → Engagement	.31	.001*
Motivation → Engagement	.27	.001*
Motivation → Interest	.48	.001*
Motivation → Self-efficacy	.53	.001*
Moderation effects
Gender × Feedback → Interest	.08	.080***
Gender × Feedback → Self-efficacy	.00	.47***
Gender × Feedback → Motivation	.04	.25***
Gender × Feedback → Engagement	−.04	.25***
Gender × Self-efficacy → Engagement	.04	.22***
Gender × Motivation → Engagement	.10	.05**
Gender × Interest → Engagement	−.05	.18***
Gender × Motivation → Interest	.03	.33***

Note:* = significant at less than 0.001; ** = significat at 0.05; *** = not signifiant at 0.05.

Discussion

This study found that the most meaningful causal model for the explanation of the influence of lecturers’ feedback and students’ psychological variables on students’ engagement is a recursive model involving lecturers’ feedback, students’ motivation, self-efficacy, and interest as moderated by gender. The data used for the study fit the model. This indicates that lecturers’ feedback and students’ psychological variables have a unidirectional effect on students’ engagement in economics. Students’ psychological variables, such as motivation, interest, and self-efficacy, interact with one another and with the feedback they receive from lecturers to affect their engagement in learning activities. As a result of this study’s findings, it can be concluded that regardless of the sort of feedback lecturers provide, it has a significant effect on students’ psychological variables, which translate to students’ engagement. The unidirectional effect of lecturers’ feedback supported the transitivity tenet of Lewis counterfactual theory of causation Lewis (1973) which states that causation is transitive from exogenous variables through some mediating variables to endogenous variables (Rahimi et al., 2023). This depicts the indirect effect of lecturers’ feedback through students’ psychological variables on their’ engagement.

Psychological variables such as motivation, self-efficacy, and interest may have contributed to this students’ engagement. Motivation is the driving force that propels students to engage in an activity for the pleasure and fulfilment inherent in academic activities. In addition, self-efficacy, which is positively related to interest, enables students to cope with challenging tasks and be confident that they will succeed. Students’ interest enables them to pay attention with regard to their academic activities. Students’ engagement in economics is greatly influenced by these psychological variables when they interact with lecturers’ feedback. Thus, lecturers’ feedback as well as students’ psychological variables are crucial in the instructional process in order to enhance students’ engagement in economics. Consequently, this finding supported the transitivity tenet of the Lewis Counterfactual Theory of Causation (1973), according to which causation occurs from exogenous variables through certain mediating variables to endogenous variables (Northcott, 2021). It illustrates the indirect effects of self-efficacy on engagement as well as motivation through interest. As a result of this study, the Bandura cognitive theory of learning (1993) was strengthened as it was observed that self-efficacy has a significant impact on students’ engagement in learning economics (Azila-Gbettor et al., 2021). The findings of this study are in line with those of previous research. According to Ugwuanyi (2017), the most meaningful causal model for explaining the achievement of students in physics is the recursive model, which takes into account teachers and students’ variables. Similarly, Orji et al., (2023) found that a model explaining students’ conceptual changes in sound waves is recursive.

Similarly, this study revealed that the composite effects of lecturers’ feedback and students’ psychological variables accounted for 69% of the variation in students’ engagement in economics. This implies that some unobserved variables not included in this model could affect students’ engagement in economics. These unobserved variables (error terms) accounted for 31% of the variation in students’ engagement. In other words, lecturers’ feedback and students’ psychological variables had a significant causal effect on students’ engagement by explaining 69% of the variation in students’ engagement. This could be attributed to the effectiveness of lecturers’ feedback in stimulating students’ psychological variables such as interest, motivation, and self-efficacy, which manifest in students’ active participation in learning economics. Lecturers’ feedback is the driver of learning activities because it has the potential to eliminate students’ misconceptions that conflict with their learning activities. For instance, through oral feedback, a lecturer could interact with students face-to-face with regard to concepts they misunderstood. Through this process, the abstract nature of economics and misconceptions about certain Economics terms could be resolved as lecturers would provide convincing answers to those conflicting terms using local examples, which could enhance their engagement in learning activities. This research confirms the overdetermination tenet of Lewi’s counterfactual theory of causation (1973), which states that two exogenous variables may simultaneously cause endogenous variable (Won, 2014). The symmetrical causal contributions of lecturers’ feedback and students’ psychological variables to students’ engagement is consistent with the counterfactual theory of causality (Northcott, 2021). Furthermore, the error term that accounted for 31% of the variation in students’ engagement supported the causation by absence tenet of the counterfactual theory of causality (Sartorio, 2023). This theory states that variables that are not included in the model could affect students’ engagement.

The findings of this study confirmed the findings of Zhang and Hyland (2022), who revealed that the high level of students’ engagement levels was positively affected by lecturers’ interactions and can be facilitated through an integrated approach. The findings of this study also affirmed the findings of Lee et al. (2019),who found that students’ engagement in learning activities is composed of six factors: interaction with instructors (feedback), psychological motivation, cognitive problem solving, and learning management. Additionally, this study is in agreement with Devito (2016), who revealed that interaction between students and teachers, involvement in learning activities, collaboration, and enriching educational experiences were factors that influenced students’ engagement.

The results indicated that lecturers’ feedback and students’ psychological variables (interest, motivation, and self-efficacy) had significant direct structural links with students’ engagement in economics. Likewise, lecturer feedback had a significant causal effect on students’ self-efficacy and motivation, while motivation had a significant structural influence on students’ interest. Therefore, lecturers’ feedback had significant multiplier effects on both students’ psychological variables as well as two major endogenous variables and students’ engagement. This finding is in accordance with findings of Vattøy and Smith (2019), which reported that there is link between teacher feedback and students’ self-efficacy since students’ self-efficacy substantially predicted students’ perceptions of teachers’ feedback. Furthermore, this finding aligned with a result of Alharbi (2022) who found that feedback features like suggestions, questions, and imperatives were mostly more integrated by students than others.

Motivation had a significant direct causal effect on students’ interest and engagement, which denotes that students are more engaged in economics learning activities when they are motivated. This finding is in consonance with the report of previous researchers. Nayir (2017) found that students’ motivation is related to class engagement in learning activities. Muhammad et al. (2023) revealed that academic motivation was strongly correlated with students’ engagement, and students’ academic engagement and motivation differed by gender.

Self-efficacy had a direct and significant relationship with students’ engagement. As a consequence, this implies that students’ self-efficacy and level of engagement in Economics activities are a function of their self-efficacy. In the teaching and learning of Economics, lecturers should focus on promoting students’ self-efficacy as it contributes to their interest, which in turn enhances their engagement. This finding aligned with reports of previous studies. Tseng et al. (2020) revealed that students’ self-efficacy is a key variable for online learning and is positively related to online engagement (Tseng et al., 2020). As well, another study revealed that students’ self-efficacy was found to be directly related to engagement as well as moderate the effect of motivation on student’ engagement (Alemayehu & Chen, 2023). Also, the students’ interest was significantly associated with students’ engagement. This finding conforms with the findings of previous research. Kahu et al. (2021) reported that situational interest enhances behavioral and cognitive engagement, which translates into better grades for students. Also, Otundo and Garn (2019) found that students’ situational and personal interests were positively correlated with behavioral and emotional engagement in school physical education Additionally, a study revealed that an interest-tailored lectures substantially enhanced students’ engagement in lectures and students exposed to interest-tailored lecture had a meaningful increase in their achievement score (Pliner et al., 2020).

Finally, the moderating effects of gender was not significant with respect to students’ engagement. This mean students’ gender does not affect the link among lecturers’ feedback and students’ psychological variables on their engagement in learning economics. This finding disagree with the findings of the previous research. Ikram et al. (2023) revealed that students’ gender positively influenced their engagement in a real classroom setting. Korlat et al. (2021) found that girls were more engaged in digital learning during Covid-19 than boys. Muhammad et al. (2023) who found that students’ gender influences the relationship between students’ motivation and engagement.

As a result, the current study was a valuable one and the first of its kind to develop a structural model to explain students’ engagement in economics in higher education using lecturers’ feedback and students’ psychological variables. As a result, it is envisaged that Economics education educators in higher education will use the study’s findings to tackle instructional issues in the classroom. Researchers in the teaching and learning of economics at the university level should use this study as a model to develop other structural models to provide answers to issues in the teaching and learning of economics at the university level.

The major implication of this study is that students’ levels of engagement in economics are determined by the multiplier effects of their psychological variables and feedback from their lecturers. For the lecturers’ feedback to be useful in engaging students in learning activities, it must be structured in a manner that stimulates their psychological variables. Additionally, in order to promote students’ effective engagement in learning activities, instructional feedback should be structured so that psychological variables will be induced in the students. In light of the findings and implications of this study, it is recommended that lecturers who implement economics materials in the classroom take into account students’ psychological variables when providing oral and written feedback during and after instruction. Additionally, seminars, workshops, and conferences should be organized for economics lecturers in order to improve the quality of feedback that facilitates student psychological variables that promote engagement in higher education.

This study is limited by the direction formation path diagram of the structural model, which is the result of researchers’ ideas; any change in the direction of these paths will produce a different result. Furthermore, the instruments for data collection were questionnaires; thus, peer interaction could have influenced the opinions of respondents while responding to the questionnaire item. Moreover, this study purposively sampled final-year students from the selected universities; therefore, inclusion of other years could affect the result of this study.

In light of the limitations of the study, it was suggested that further research should be conducted to examine the effects of lecturers’ feedback variables (oral, written, and reflective) and students’ affective variables (attention, locus of control, effort, situational interest, and personality) on students’ engagement and conceptual change. Future research should expand the sample size and augment quantitative data with qualitative data.

Conclusion

In this study, a recursive causal model was developed to explain students’ engagement in economics in higher education using lecturers’ feedback and students’ psychological variables (self-efficacy, motivation, and interest) as moderated by students’ gender. It is clear that feedback from lecturers and students’ psychological variables explains students’ engagement in economics due to their symmetrical causal contributions. This study demonstrates the existence of multiplier effects between students’ psychological variables and the feedback provided by lecturers, which has a cumulative impact on students’ engagement in learning economics at higher educational institutions. However, students’ gender was found to be insignificant in moderating the influence of lecturers’ feedback and students’ psychological variables on students’ engagement in learning economics. This implies that lecturers’ feedback and students’ psychological variables contribute significantly to students’ engagement in learning activities. Therefore, lecturers should provide feedback that stimulates students’ psychological variables positively to enhance their engagement in learning activities. Moreover, since the gender effect is not significant, lecturers should provide students with different modes of feedback, regardless of their gender.

Footnotes

Acknowledgements

Special thanks to Dr. Mrs. Njide Dorathy Eneogu, Social Science Education, Dr. Ikeh, Elochukwu Francis, Science education department, Dr. Christian Ugwuanyi science education department and Dr. Osita Ossai, Education foundation all from university of Nigeria Nsukka for validating research instruments.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Approval

School of Post-graduate Studies research ethics Committee authorized this study through their institutional review board with ID No: REC/SPG/20/000967. In addition, the ethical standard of America psychological Association (APA, 2017) was strictly followed.

ORCID iD

Amos Nnaemeka Amedu

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Lecturers’ Feedback,Students’ Psychology Variables and Students’ Engagement in Economics: The Moderating Role of Students’ Gender

Abstract

Keywords

Introduction

Research Questions and Hypotheses

Empirical Literature

Theoretical Literature

Methodology

Result

Discussion

Conclusion

Footnotes

Acknowledgements

Declaration of Conflicting Interests

Funding

Ethical Approval

ORCID iD

Data Availability Statement

References