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Article

Comparing STEM and Non-STEM Instructor Motivation Suggests Directions for STEM Education Innovations

by
Deborah South Richardson
1 and
Robert S. Bledsoe
2,*
1
Department of Psychological Sciences, Augusta University, Augusta, GA 30912, USA
2
Department of English and World Languages, Augusta University, Augusta, GA 30912, USA
*
Author to whom correspondence should be addressed.
Educ. Sci. 2025, 15(6), 720; https://doi.org/10.3390/educsci15060720
Submission received: 21 March 2025 / Revised: 1 June 2025 / Accepted: 4 June 2025 / Published: 9 June 2025
(This article belongs to the Special Issue Pathways to Progress and Practice: Leading the Way in STEM Education Innovations and the Workforce)
Versions Notes

Abstract

:
This study compares the motivation and teaching practices of STEM and non-STEM instructors with the aim of discovering factors that may contribute to and can be applied to improve the retention and persistence of STEM students in higher education. Instructors completed measures of mindset, motivation, and teaching practices. We found that non-STEM instructors reported being more motivated by belief in student efficacy than STEM instructors, and they reported a greater use of evidence-based teaching practices. Subsequent analyses revealed that mastery goals predicted the use of evidence-based teaching practices for both sets of instructors; growth mindset was also a significant predictor of evidence-based teaching practices for non-STEM instructors. We propose that a pathway to STEM student success may be training and incentivizing instructors, especially those who may question students’ abilities to learn difficult STEM material.

1. Introduction

It is widely recognized that student characteristics and experiences are related to their academic success (Glass & Westmont, 2014; Zajacova et al., 2005). Student motivation and mindset, for example, have been related to successful academic performance in higher education (Burnette et al., 2013; Limeri et al., 2023; Paunesku et al., 2015). However, the college classroom is a dynamic environment where students, with their unique characteristics and motivations, interact with instructors, who bring their own motivations and mindsets (Murphy et al., 2021). Clearly, what happens in the classroom may encourage or hinder student motivation, and, ultimately, student success; instructors can be said to set the stage for student success. An instructor’s attitudes and motivation regarding teaching and learning are likely to impact their teaching practices, and, ultimately, student success (Carroll et al., 2023). In this study, we examined instructor mindset, motivation, and teaching practices, comparing reports from instructors in STEM disciplines to those in non-STEM disciplines. In view of the concerns about student persistence in STEM disciplines (Sithole et al., 2017; Strenta et al., 1994), examining differences between instructors in STEM and non-STEM disciplines may provide some understanding about mechanisms for improving student success and retention.

1.1. Instructor Motivation

Daumiller et al. (2020) questioned why faculty motivation has received relatively little research attention despite evidence that effective instruction has been connected to student engagement, learning, and persistence. For example, Sithole et al. (2017), who mentioned a rather extensive list of factors that contribute to STEM student persistence and retention, did not include instructor motivation—although they did consider pedagogy to be an important factor, and they noted the need for professional development aimed at improving teaching practice.
A variety of theories can be used to examine instructor motivation (Daumiller et al., 2020). We use Dweck’s social-cognitive model of motivation (C. S. Dweck & Leggett, 1988; C. S. Dweck et al., 2004), which identifies mindsets regarding the characteristics of individuals (e.g., personality, intelligence) as precursors to motivation. A growth or incremental mindset reflects the belief that individuals can change; a fixed or entity mindset reflects the belief that characteristics are not malleable. In the context of education, the individual’s mindset for intelligence is an important predictor of outcomes (Burnette et al., 2013; Paunesku et al., 2015). The individual who adopts a fixed mindset for intelligence believes that intelligence is static, that it is not able to be improved; the individual who has a growth mindset for intelligence believes that intelligence can be improved. Mindset, then, determines motivation. Growth mindset is associated with mastery goal orientation; the individual who has a growth mindset for intelligence is likely to be motivated to learn and to persist in view of challenges. Fixed mindset, on the other hand, is associated with performance goal orientation; the individual who has a fixed mindset for intelligence is motivated to seek positive and avoid negative feedback rather than take a chance on failing.
Research consistently demonstrates relationships of faculty attitudes/mindset with student outcomes. Mindset researchers have started to focus on the impact of the learning environment on individual mindset and on the efficacy of mindset interventions (Carroll et al., 2023; Murphy et al., 2021). Much of this research has examined faculty mindset and motivation in the context of STEM education, and the findings consistently demonstrate that instructor mindset is related to teaching practice and student motivation and outcomes (Fuesting et al., 2019; Rattan et al., 2012; Richardson et al., 2024). For example, LaCosse et al. (2021) found that students anticipated more psychological vulnerability, lower expectations and lower performance in classes taught by STEM faculty with a fixed mindset. Muenks et al. (2020) documented that these anticipated effects were born out in field studies. Canning et al. (2019) found that STEM instructor fixed mindset was related to student reports of less motivation and poorer performance. Students also reported that those instructors who believed that student ability was fixed were relatively unlikely to use teaching practices that encourage learning and development.
The link of instructor attitudes and mindset with teaching behaviors has also been examined in several investigations, again most frequently in investigations of STEM instructor practices. For example, Gibbons et al. (2018) reported that chemistry faculty who endorsed student-centered learning beliefs employed more engaging teaching practices. Aragon et al. (2018), in a study seeking to determine instructor characteristics that might relate to willingness to adopt evidence-based teaching practices, found that STEM instructors with a relatively fixed mindset for intelligence were less likely to employ or advocate for active learning practices.
Several investigations suggest that faculty mindset and motivation may underlie the extent to which instructors adopt evidence-based effective teaching practices (Daumiller et al., 2020, 2022; Muenks & Yan, 2022). Stupinsky et al. (2018) reported that instructors with autonomous motivation for teaching (i.e., found teaching to be enjoyable) reported using teaching best practices. Richardson et al. (2020) found that STEM instructor mindset and motivation were linked to teaching behaviors; instructors with a more growth-oriented mindset were more motivated to master teaching and learning and more likely to employ evidence-based teaching practices. In that investigation, instructor motivation mediated the relationship between mindset and teaching practices. Nilipay et al. (2021) found that math teachers’ mindset towards teaching predicted their motivation and work engagement. Bayanova et al. (2023) have pointed to a need for further research on STEM faculty motivation.
Although there is extensive research on STEM instructors and instruction, there are relatively few STEM and non-STEM comparisons. The present study aims to fill that gap in the existing literature. Studies that have examined the relationship between instructor motivation and teaching practice tell part of the story. A comparative approach that considers STEM faculty relative to others may shed more light on the process. Are there differences in the mindset or motivation of STEM and non-STEM instructors that might provide a clearer picture of the stage that STEM instructors set for their students? If, for example, non-STEM instructors were more likely than STEM instructors to evidence a growth mindset or more likely to be motivated to master teaching and learning, then we might have a window into mechanisms that may underlie challenges for STEM student persistence.

1.2. Disciplinary Differences

Building on the research of Becher (1994) and others, Neumann (2001), in a review of research that identified disciplinary differences in teaching practices, curriculum, and assessment, reported that disciplinary affiliation exercises a strong influence on instructor beliefs and teaching practices, and on student learning. She noted that instructors may adopt teaching practices specific to their discipline, and she called for additional research to examine teaching variations across disciplines and search for explanations for these differences. “The strong influence of disciplines on academics’ beliefs, on teaching and on students’ learning, would suggest that disciplines need to be subjected to greater systematic study, especially regarding their effect on the quality of teaching and learning in higher education” (p. 144).
Multiple investigations have revealed differences among disciplines in teaching attitudes and experience (e.g., Smeby, 1996). Milutinovic et al. (2024), in a study of teaching perspectives of instructors at the University of Novi Sad in Serbia, found that social science instructors were more likely to address social reforms in their teaching and more likely to report that their teaching effort “comes from the heart” than technology science instructors. Similarly, studies have found that, relative to instructors in the humanities and social sciences, instructors in the natural sciences report more focus on how they teach rather than on how students learn (Kemp, 2012; Lindblom-Ylänne et al., 2006). Seymour (2001) also found that natural science instructors are likely to emphasize covering content rather than student learning, and Fong et al. (2019) reported that graduate students teaching in non-STEM disciplines (humanities and social sciences) reported more teaching self-efficacy about their ability to foster a positive learning environment than did graduate student instructors in STEM disciplines.
In sum, although some studies have addressed disciplinary differences in approaches to teaching, Neumann’s (2001) argument that more attention should be given to disciplinary differences in instructors beliefs and the relationship of those beliefs to teaching and learning still holds. The present study aims to address the gap that Neumann (2001) and others (e.g., Becher, 1994) have identified by examining mindset, motivational, and teaching practice differences between STEM and non-STEM instructors.

1.3. Current Study

Addressing the theme of this Special Issue, our examination of differences between STEM and non-STEM instructor motivation and mindset suggests a possible pathway for promoting student interest and persistence. Although previous research has provided some support for the notion that STEM and non-STEM instructors differ in their approach to teaching and learning (e.g., Kemp, 2012; Lindblom-Ylänne et al., 2006; Seymour, 2001), those differences have not been linked to motivational differences. We aim to fill this gap by examining differences in practice and connecting them to possible differences in motivation.
The differences between STEM and non-STEM instructors that have been revealed in previous research (Milutinovic et al., 2024; Smeby, 1996) suggest that STEM instructors may be more likely to endorse a fixed mindset for student ability than their non-STEM peers. Similarly, the learner-centered approach that non-STEM instructors practice to a greater extent than their STEM colleagues (Kemp, 2012; Seymour, 2001) suggests that they may be more concerned than STEM instructors with mastering teaching and learning and employing evidence-based teaching practices.
Thus, we examined differences between STEM and non-STEM faculty in their mindset, their motivation, and their teaching practices. Our hypotheses are as follows:
  • In view of the differences in teaching vs. learning focus (Kemp, 2012; Lindblom-Ylänne et al., 2006), we predict that non-STEM instructors will report more growth-oriented mindsets than STEM instructors.
  • Following those differences in mindset and consistent with C. S. Dweck’s (2000) motivational model, we predict that non-STEM instructors will report more motivation to master teaching and learning.
  • In view of focus on the coverage of content and teaching-focused practices of STEM instructors (Seymour, 2001), we predict that non-STEM instructors will endorse more evidence-based teaching practices than STEM instructors.
In order to gather an understanding of motivations that may underlie expected differences in teaching practices, we also addressed a research question: To what extent do mindset and motivation predict effective teaching practices for STEM and non-STEM instructors?

2. Materials and Methods

This study was reviewed and approved by the Institutional Review Board at the authors’ university.

2.1. Participants

All participants were faculty members at a public comprehensive research university in the southeastern United States with approximately 9000 students. Most of the undergraduate courses are taught by fulltime faculty members. Of the 130 faculty members who participated in this study, 55 (42%) were affiliated with STEM disciplines (Biological Sciences, Mathematics, Chemistry, Kinesiology, Physics), and 75 (58%) taught in the Humanities (Art and Design, Communication, English and World Languages, History, Music) or Social Sciences (Political Science, Psychological Sciences, Sociology, Teaching and Leading). A majority (60%) of the respondents indicated that their responses referred to a lower-level undergraduate course; 35% reported that they were referring to an upper-level undergraduate course, and 5% referred to both upper-and lower-level courses. To maintain participant anonymity, no additional background information was collected.

2.2. Measures

All variables were assessed in a self-report survey. Respondents indicated their agreement with statements that measured the extent of their growth mindset, their motivations for teaching, and their evidence-based teaching practices. Responses to all measures were made on 6-point scales ranging from 1 (strongly disagree) to 6 (strongly agree). Detailed conceptual and operational definitions of each of the variables and sources of measures follow.
Mindset. Cook et al.’s (2018) adaptation of C. S. Dweck’s (2000) mindset measure assessed the extent to which faculty members endorsed a growth mindset about intelligence/ability (e.g., “No matter how much intelligence/ability you have, you can always change it quite a bit”). Cronbach’s alpha of 0.89 indicated good internal consistency. The responses for the two items that were stated so that agreement would indicate a fixed mindset were reversed before calculating mean scores; thus, higher scores indicate stronger growth mindset.
Teaching Motivation. Thirteen items were adapted from Midgley and colleagues’ (Midgley et al., 2000) Pattern of Adaptive Learning Scales (PALS) to reflect instructors’ motivations regarding teaching. The 5 items reflecting mastery goals for students (e.g., “I stress the importance of trying hard to my students”) were adapted from PALS items that measured how school structure supports mastery. The 4 items reflecting personal teaching efficacy (e.g., “If I try hard, I can get through to most of the students in my class”) measured instructor beliefs that they contribute to student success. The 4 items that assessed instructor perception of student academic efficacy (e.g., “I am confident that students can master the material taught in my class”) were originally intended for student response; we modified them to reflect instructor belief in student efficacy. Higher scores indicated greater motivation to master teaching and learning.
Teaching Behaviors. Twenty-three items were selected from the Teaching Practices Inventory (Wieman & Gilbert, 2014; Wieman, 2015) to assess the extent to which faculty members endorsed evidence-based effective teaching behaviors. Respondents were instructed to “Think about an undergraduate course that you taught last semester or are teaching this semester. As you answer the following questions, think about the course that was most challenging for your students”. Higher scores indicate the use of more evidence-based teaching practices. This measure was internally consistent (Cronbach’s alpha = 0.84). The themes that address teaching practices and a sample item from each theme can be found in Table 1.

2.3. Procedure

The investigators invited instructors to participate in the study during meetings of departments that offered undergraduate degrees. After reading the instructions and answering questions, the investigator distributed the survey. Instructions directed the faculty members to return their survey (completed, or not completed if they did not wish to participate) in the envelope provided. They were informed that all responses would be anonymous, and the investigator and the department chair left the room while faculty members did or did not complete the survey.

3. Design and Analysis1

The specified comparative (STEM vs. non-STEM) hypotheses were tested in a multivariate analysis of variance (MANOVA), with subsequent univariate analyses. Exploratory analyses testing the relationships among motivations and practices were examined with Pearson correlation coefficients and multiple regression analyses.

4. Results

4.1. Preliminary Analyses2

Teacher motivation. Internal consistency of the three scales adapted from the PALS did not reach acceptable levels (α = 0.44–0.66, less than the accepted 0.70 value for internal consistency). Thus, we conducted a principal components factor analysis in an attempt to find more suitable, internally consistent scales. KMO (0.74) and Bartlett’s Test of Sphericity were significant, suggesting suitability of the data for factor analysis. This analysis produced a four-factor solution with eigenvalues greater than 1; however, three of the factors produced unreliable and uninterpretable scales.
Because the scree plot suggested a two-factor solution, we forced two factors. This analysis revealed two meaningful, internally consistent scales when we dropped two items (Items 5 and 6) that did not load adequately (>0.40) on either factor. Factor 1 (Belief in Student Efficacy) includes items that address the instructor’s belief that students can master the material in the course (α = 0.71). Factor 2 (Instructor Mastery Goals) emphasizes the instructor’s motivation to design a course for and interact with students in a manner that will encourage their success (α = 0.71). Table 2 displays the results of the factor analysis. We created scores for each factor by averaging responses on the relevant items. Higher scores reflect more belief in student efficacy or stronger mastery goals.
Descriptive data and simple correlations. Table 3 presents descriptive data and correlations among the measures for both groups. The mean values on the scales indicate that respondents generally reported high levels of mastery goals, belief in student efficacy, growth mindset, and evidence-based teaching practices.
The outcomes of the correlational analyses indicate that teaching behaviors were associated significantly (p < 0.01) with mindset, belief in student efficacy, and mastery goals for non-STEM instructors; non-STEM instructors who reported using more evidence-based teaching practices also reported more growth-oriented mindsets, more mastery goals, and more belief in student efficacy. Although belief in student efficacy was not significantly related to any of the other variables for STEM instructors, both mindset and instructor mastery goals were significantly correlated with teaching behavior for those instructors. That is, STEM instructors with relatively strong growth mindset and mastery goals reported using more evidence-based teaching practices. The strongest correlation (r = 0.60), indicating a large effect size, for both sets of instructors was between instructor mastery goals and effective teaching practices, suggesting that instructors who are strongly motivated to master teaching are also relatively likely to use evidence-based teaching practices.

4.2. Hypothesis Testing Analyses: Disciplinary Differences

To test hypotheses 1 through 3, namely that STEM instructors would report less growth mindset, less motivation, and less use of evidence-based teaching practices than non-STEM instructors, we conducted a MANOVA that included the measures of mindset, mastery goals, belief in student efficacy, and teaching behaviors as dependent variables. The analysis revealed a main effect of disciplinary area, F (4, 125) = 10.50, p < 0.001, η2p = 0.25 (Wilks’ Lambda), indicating a large effect size. Subsequent univariate tests revealed significant medium effects of disciplinary areas for teaching behaviors and belief in student efficacy. Means, standard deviations, confidence intervals, and univariate F values appear in Table 4.
Contrary to our hypotheses, STEM and non-STEM instructors did not differ in growth mindset (Hypothesis 1) or mastery goals (Hypothesis 2). However, the predicted difference for teaching practice was found, with STEM instructors reporting fewer evidence-based teaching practices (Hypothesis 3). STEM instructors also reported having less belief in student efficacy than did instructors from non-STEM disciplines.

4.3. Analyses to Address Research Question: Does Motivation Predict Effective Teaching Practice?

To answer our question about the relative ability of mindset, motivation, and belief in self-efficacy to predict teaching behaviors, we conducted regression analyses for both disciplinary groups. Growth mindset, belief in student efficacy, and instructor mastery goals were entered as predictors of use of effective teaching practices. Results of those analyses appear in Table 5.
The models were significant (p < 0.001) and the R2 values reveal that a notable amount of variance in teaching behaviors (39% for STEM and 44% for non-STEM instructors) can be explained by the three predictors. These values also represent a large effect size. Both instructor growth mindset and mastery goals significantly (p < 0.01) predicted teaching behaviors for the non-STEM instructors with the standardized beta (B) suggesting small (0.2–0.5) to medium (0.5–0.8) effect sizes. Only instructor mastery goals significantly, and with a medium effect size, predicted teaching behaviors of STEM instructors. In no case did belief in student efficacy serve as a significant individual predictor of teaching behaviors.

5. Discussion

5.1. Comparing STEM and Non-STEM Instructors

This study aimed to discover some “pathways to practice” in STEM education consistent with the theme of this Special Issue by examining differences between motivations and practices of STEM and non-STEM instructors. Our hypotheses that there would be differences between STEM and non-STEM instructors in mindset (Hypothesis 1) and mastery goals (Hypothesis 2) were not supported by our findings. However, our hypothesis that non-STEM instructors would endorse more evidence-based teaching practices than STEM instructors (Hypothesis 3) was supported, and the analysis revealed an unpredicted finding that non-STEM instructors reported greater belief in student efficacy than STEM instructors. These findings are summarized in Table 4 and the review of the results of the comparative analysis.
If it is the case that there is little difference between STEM and non-STEM instructors when it comes to growth mindset, more research on the disconnect between mindset beliefs and the endorsement of more evidence-based teaching practices is called for. As we suggested earlier, the differences might be due to disciplinary practices and cultures, as well as institutional structures. This lends support to calls for exploring mindset and the institutional practices that impact it (Carroll et al., 2023; Murphy et al., 2021). On the other hand, it is possible that instructors do not fully understand mindset (C. Dweck, 2016), or that they may express ideas that have been endorsed by their institution without accepting them (Duckworth & Yeager, 2015).
Although we only identified beliefs about student efficacy as a coherent set of attitudes about student learning through the factor analysis (presented in Table 2), it provided information about an important distinction between STEM and non-STEM instructors. As reported in Table 3 and the review of those results, we found that STEM instructors were less likely than non-STEM instructors to believe that students can learn what they teach. Indeed, there is evidence that students receive lower grades in STEM courses than in non-STEM courses (Tomkin & West, 2022). One might infer that giving lower grades suggests that, compared with non-STEM instructors, STEM instructors think that the students in their courses are not learning what they are teaching.
STEM instructors also were less inclined to endorse teaching best practices (see Table 4 for the report of the relevant analysis). The original version of the measure of evidence-based teaching practices that we employed was intended for STEM courses; the version we used was designed to address teaching practices across natural and social sciences (Wieman, 2015; Wieman & Gilbert, 2014). It is possible that STEM instructors do not endorse some of the evidence-based practices because they do not consider them to be appropriate for their teaching context. However, we attempted to select carefully practices that would be effective for both disciplinary groupings. Furthermore, the finding is consistent with other research that has examined differences in teaching strategies among STEM and non-STEM instructors (Kemp, 2012; Lindblom-Ylänne et al., 2006). The evidence that STEM instructors have been found to use more teacher-focused (i.e., instructor transmits information; students learn facts and skills) than learner-focused (i.e., students are active and collaborative in their learning) teaching approaches (Kim et al., 2024) may reflect an attitude about what students must learn to be successful in STEM disciplines (i.e., facts).
It is possible that STEM faculty are less motivated to master the art of teaching; however, mastery goals did not differentiate the two groups (Hypothesis 2). This implies a mismatch between inclination and practice. Some of this is likely based in disciplinary habits and training (Oleson & Hora, 2014); however, the mismatch points to the significance of continued efforts to understand individual, disciplinary, and institutional factors that make STEM faculty less likely than their non-STEM colleagues to adopt evidence-based teaching practices (Apkarian et al., 2021; Archie et al., 2024; Lund & Stains, 2015; Murphy et al., 2021; Sansom et al., 2023).
One explanation suggested by our findings would be that if an instructor does not believe that students are able to learn the material being taught (i.e., do not believe in student efficacy), then they may be less motivated to adopt effective teaching practices. That might be a reasonable expectation in view of the differences between STEM and non-STEM instructors in belief in student efficacy and adoption of effective teaching practices. However, as revealed in Table 3 and Table 5, belief in student efficacy was not related to endorsement of evidence-based teaching practices for STEM instructors, even in simple correlation analyses. So, STEM instructors think that students are challenged to learn what they teach, and they are relatively unlikely to adopt evidence-based teaching practices, but those two phenomena are not statistically significantly correlated with one another. Perhaps these attitudes and behaviors exist independently as parts of the larger culture of STEM instruction (Murphy et al., 2021). Further research may define the components that make up a disciplinary culture and explore the nature of the relationships among those components.
Our findings about differences between STEM and non-STEM instructor motivation and practices are consistent with, and extend, previous findings about differences between STEM and non-STEM instructors (Fong et al., 2019; Kemp, 2012; Seymour, 2001). Although we defined our concepts differently from previous work, the differences between STEM and non-STEM instructors were consistent with the earlier research (e.g., Kemp, 2012).

5.2. Predicting STEM and Non-STEM Instructor Teaching Practices

The correlation and regression analyses presented in Table 3 and Table 5 reveal that both STEM and non-STEM instructors who reported being motivated to design a course and interact with students in ways that encourage student success (i.e., motivated by mastery goals) were more likely to adopt effective teaching practices. That is, instructors who reported that they wanted to master effective teaching and wanted students to master the course material were relatively likely to use active, engaging teaching practices. One might expect that those mastery-oriented instructors would also be more likely to seek faculty development opportunities that would inform them about effective teaching practices, and there is some evidence that they do. For example, Richardson et al. (2024) reported that faculty members with more of a mastery goal orientation were also more likely to seek feedback about their teaching.
For non-STEM instructors, mindset regarding the malleability of ability also predicted their use of evidence-based teaching practices (see Table 4 for relevant analysis); those who believed that ability can be “grown” were also relatively likely to report that they adopt teaching practices that have been demonstrated to contribute to student learning. It is notable that this relationship between mindset and teaching practices did not show up for STEM instructors, especially since other researchers have established that relationship. For example, Nilipay et al. (2021) found mindset and teaching practice were related for math instructors. On the other hand, Richardson et al. (2020) found that the relationship between mindset and teaching practice was indirect, mediated by motivational factors. It may be the case that varied definitions of mindset, motivation, or teaching practice may account for some of these differences in these findings, in which case a review of the literature might be called for. Or, perhaps this is another case in which disciplinary culture may be defined in a way that disconnects these factors.

6. Implications

Instructors who are motivated by mastery goals are more likely to seek information and training in effective teaching practices (Richardson et al., 2024). Those instructors are likely to be the ones who show up for teaching workshops or seek feedback from their students and peers. Faculty developers and academic administrators should seek ways to attract less motivated instructors to engage in opportunities to learn how to use learner-centered, evidence-based teaching practices. Attracting STEM instructors may be especially challenging. The results of the comparative analyses indicate that STEM instructors, although they do not differ from their non-STEM colleagues in terms of the extent of their growth mindset, are more inclined to question student ability to master the material they teach (i.e., belief in student efficacy). If they believe the poor performance of students is due to the students’ inability to master learning, then they are not likely to be motivated to develop their teaching practices.
So how can one change STEM faculty members’ perception of their students? It is likely that words alone will not do it, although a discussion on the role of setbacks and even failure in learning and in scientific research could be a starting point. Proof that their students can succeed would be better. One might help the instructor target just one of those items that “students never get”. If the students’ learning does improve, then one has opened a path for further changes. The main point is that when trying to persuade faculty to adopt evidence-based teaching practices, one cannot just focus on their own motivation, we also need to be attuned to their perceptions of student efficacy.
As this Special Issue considers pathways to student success in STEM disciplines, the results of the present study suggest that it would be advisable for academic administrators, educational developers, and faculty members, themselves, to examine attitudes, motivations, and institutional factors that may interfere with or promote the adoption of evidence-based teaching practices. The pathway to student success needs to have an intersection that connects instructor motivation to knowledge of good teaching practices.

7. Limitations and Suggestions for Future Research

The cross-sectional design of this investigation precludes confident causal inference. Nevertheless, it seems more reasonable that instructor internal disposition/attitude (i.e., motivation) would lead to willingness to invest time and energy in developing evidence-based teaching practices than that the adoption of such teaching practices would lead to instructor motivation. On the other hand, one might argue that experiencing the success of evidence-based teaching practices could motivate instructors to master teaching and learning. Clearly, there is a need for additional, ideally longitudinal, research on this issue.
Our mindset measure did not ask specifically about the instructors’ mindset for student intelligence/ability. Asking about the extent to which they believe their students’ intelligence/ability is malleable or set would be likely to demonstrate a stronger relationship between mindset and teaching practices. A replication of the current study with a more directed measure of domain specific mindset is called for.
This study was conducted at one medium-sized, primarily undergraduate university with limited investment in training and development of teaching practices as instructors were facing expectations of increasing research effort. Several questions are raised by this context. Would the results change if the administration encouraged evidence-based teaching practices, for example? In that case, the differences between STEM and non-STEM instructors might be less.
The nature of the university raises an issue of context. As we have noted throughout the discussion, the culture of a discipline also defines the context in which motivations, attitudes, and teaching practices are developed. Research that explores and defines different disciplinary cultures is called for. It would be interesting to know, for example, whether the local culture of an academic institution or size or type of institution (e.g., research, primarily undergraduate) has more or less impact on teaching practices than disciplinary culture.
Measurement also presents some limitations. Our measure of teacher motivation did not produce two clear motivational factors; rather it produced one motivational factor (i.e., mastery goals) and one attitudinal factor (i.e., belief in student efficacy). It is interesting to note that the attitudinal factor distinguished between STEM and non-STEM instructors better than the motivational factor. Future research should aim to clearly identify measures that assess central motivational and attitudinal concepts that differentiate between the disciplines. The other measurement issue involves our dependence on self-report. Self-report is appropriate when measuring motivation and attitudes; however, observation of actual teaching materials and practices by a peer reviewer might provide a better measure of the extent to which effective teaching behaviors are practiced. This would be a very time- and effort-intensive undertaking as it would require review of course materials and multiple observations of classroom interactions.

8. Conclusions

This study aimed to identify mindset, motivation, and teaching practice differences between STEM and non-STEM instructors that may help explain challenges to student persistence in STEM disciplines. Our initial hypotheses that STEM and non-STEM instructors would express different levels of belief in a growth mindset and their motivation to adopt mastery goals were not sustained. Yet, this study has provided evidence that STEM and non-STEM instructors differ in their expressed use of evidence-based teaching practices and their belief in student efficacy. The first finding is consistent with findings in previous literature; STEM instructors used fewer learner-oriented, effective, evidence-based teaching practices than non-STEM instructors. The second finding has not been examined as fully in previous research but is worthy of further investigation.
The exploratory, predictive analyses revealed that there are some differences in the factors that predict the adoption of evidence-based teaching practices by STEM and non-STEM instructors. Whereas mastery goals predict teaching practice for both sets of instructors, mindset only predicted teaching practice for non-STEM faculty. We suggest that the definition of growth mindset may differ for the two groups of instructors and that the success of faculty development efforts at improving teaching practice may depend to some extent on the disciplinary background of instructors.
One of the primary findings of this study is that further research is needed to address several questions that were inspired by our results. Our understanding of disciplinary cultures, differences in understanding of growth mindset, and effective conceptual and operational definitions of instructor motivation would benefit from careful reviews of the literature in order to determine consistencies, inconsistencies, and the establishment of important research questions.

Author Contributions

Conceptualization, D.S.R. and R.S.B.; Methodology, D.S.R. and R.S.B.; Formal analysis, D.S.R.; Investigation, D.S.R. and R.S.B.; Writing—original draft, D.S.R.; Writing—review and editing, D.S.R. and R.S.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was reviewed and approved by the Institutional Review Board at Augusta University (protocol 1343435, 4.1.2019].

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Contact the first author for access to the research data.

Conflicts of Interest

The authors declare no conflict of interest.

Notes

1
Data from STEM instructors in these analyses were also used in Richardson et al. (2020).
2
Three individual scores that were identified as outliers on the measure of teaching attitudes were replaced with average scores.

References

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Table 1. Table of themes and sample items regarding teaching behaviors.
Table 1. Table of themes and sample items regarding teaching behaviors.
ThemesSample Items
Active learningMost class sessions include small group work or problem-solving activities.
Providing feedback to studentsI assure that students receive feedback on homework assignments and quizzes.
Adopting practices aimed at motivating studentsI employ distinct activities that encourage students to make connections between the course material and their own lives.
Providing opportunities for student reflectionI ask students to evaluate and reflect on their own learning and study habits.
Reflecting on their own teachingI use student feedback (and results) to improve course delivery.
Using effective summative assessment practiceI assign multiple homework assignments or problem sets that (in total) contribute significantly to the course grade.
TransparencyAssignments describe clear criteria for successful completion.
Table 2. Factor analysis of motivation items.
Table 2. Factor analysis of motivation items.
ItemFactor 1:
Belief in Student Efficacy
(α = 0.71, CI: 0.59, 0.77) *		Factor 2:
Instructor Mastery Goals
(α = 0.71, CI: 0.62, 0.78)
1I am certain that students can figure out how to do the most difficult class work.0.78−0.15
2If I try hard, I can get through to most of the students in my class.0.650.01
3I am confident that students can master the material taught in my classes.0.640.18
4Students can do almost all of the work in class if they don’t give up.0.620.25
5Some students are not going to make progress, no matter what I do. (reversed)0.330.05
6Factors beyond my control have a greater influence on my students’ achievement than I do. (reversed)0.24−0.11
7I design my courses to ensure that most students will learn.0.130.71
8I make an effort to show students how the work they do in my classes is related to their lives outside of school.−0.170.67
9I emphasize really understanding the material, not just memorizing it. −0.040.63
10I make an effort to recognize students for effort and improvement.−0.070.60
11Even if it takes hard work, students can learn the material in my class. 0.430.46
12I stress the importance of trying hard to my students.0.170.45
13I tell students that it’s okay to make mistakes as long as they are learning and improving.0.380.42
* α—Cronbach’s alpha (internal consistency); CI—confidence interval of alpha.
Table 3. Descriptive data and correlations among measures for STEM and non-STEM instructors.
Table 3. Descriptive data and correlations among measures for STEM and non-STEM instructors.
MeasureMSDGrowth MindsetBelief in Student EfficacyInstructor
Mastery Goals
STEM Instructors (n = 55)
Teaching Behaviors4.390.550.34 *0.230.60 **
Growth Mindset4.581.04 0.210.36 **
Belief in Student Efficacy4.350.76 0.21
Instructor Mastery Goals5.220.41
Non-STEM Instructors (n = 75)
Teaching Behaviors4.810.560.47 **0.33 **0.60 **
Growth Mindset4.401.01 0.31 **0.35 **
Belief in Student Efficacy4.860.68 0.54 **
Instructor Mastery Goals5.350.47
* p < 0.05, ** p < 0.01. Correlations between 0.3 and 0.5 suggest a medium effect size; >0.5 is interpreted as a large effect size (Cohen, 1992).
Table 4. Differences between STEM and non-STEM instructors’ motivations and behaviors.
Table 4. Differences between STEM and non-STEM instructors’ motivations and behaviors.
STEM
(n = 55)		Non-STEM
(n = 75)		F (1, 128)
M (SD)
[CI]		M (SD)
[CI]
Growth Mindset4.58 (1.04)
[4.30, 4.85]		4.40 (1.01)
[4.17, 4.64]		0.92, p = 0.34, η2p = 0.01
Belief in Student Efficacy4.35 (0.76)
[4.16, 4.54]		4.86 (0.68)
[4.70, 5.02]		16.34, p < 0.001, η2p = 0.11
Instructor Mastery Goals5.22 (0.41)
[5.10, 5.34]		5.35 (0.47)
[5.25, 5.45]		0.10, p = 0.10, η2p = 0.02
Teaching Behaviors4.39 (0.55)
[4.24, 4.54]		4.82 (0.56)
[4.69, 4.94]		18.38, p < 0.001, η2p = 0.13
Table 5. Mindset and motivations as predictors of instructor teaching behaviors.
Table 5. Mindset and motivations as predictors of instructor teaching behaviors.
STEMNon-STEM
BSE BβBSE Bβ
Growth Mindset0.070.060.130.160.050.30 *
Belief in Student Efficacy0.070.080.09−0.050.09−0.05
Instructor Mastery Goals0.710.160.53 *0.630.130.53 *
R2 (SE)0.39 (0.44)0.44 (0.43)
F (3, 51) = 10.65, p < 0.001F (3, 71) = 18.71, p < 0.001
Asterisks (*) indicate significance at p < 0.01. B = unstandardized beta; SE B = standard error for unstandardized beta; β = standardized beta.
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Richardson, D.S.; Bledsoe, R.S. Comparing STEM and Non-STEM Instructor Motivation Suggests Directions for STEM Education Innovations. Educ. Sci. 2025, 15, 720. https://doi.org/10.3390/educsci15060720

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Richardson DS, Bledsoe RS. Comparing STEM and Non-STEM Instructor Motivation Suggests Directions for STEM Education Innovations. Education Sciences. 2025; 15(6):720. https://doi.org/10.3390/educsci15060720

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Richardson, Deborah South, and Robert S. Bledsoe. 2025. "Comparing STEM and Non-STEM Instructor Motivation Suggests Directions for STEM Education Innovations" Education Sciences 15, no. 6: 720. https://doi.org/10.3390/educsci15060720

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Richardson, D. S., & Bledsoe, R. S. (2025). Comparing STEM and Non-STEM Instructor Motivation Suggests Directions for STEM Education Innovations. Education Sciences, 15(6), 720. https://doi.org/10.3390/educsci15060720

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