Proximal Social Interactions in the Social Cognitive Model of Task Performance: An Investigation of Students’ Experiences in an Introductory Chemical Engineering Course

Self-Efficacy in Engineering Education Research

In addressing issues of persistence in engineering, a number of scholars have used the construct of self-efficacy judgments to understand how students achieve their educational goals across different student populations and contexts (Concannon & Barrow, 2009; Lent et al., 2008; Seymour & Hunter, 2019). A person’s self-efficacy judgment is their perceived likelihood of success on a specific task (Bandura, 1986; Lent et al., 1994). In addition to persistence, self-efficacy judgments have also been associated with student outcomes like academic performance (Hunsu et al., 2021; Lent et al., 2008; Power et al., 2025; Won et al., 2024), academic major satisfaction (Gerçek & Elmas-Atay, 2024), and a sense of belonging (Won et al., 2024) in engineering student populations. Self-efficacy beliefs are not necessarily accurate measures of ability; therefore, people can over- or underestimate their ability based on prior learning experiences (Bandura, 1986; Lent et al., 1994). Slightly overestimating one’s capacity can allow learners to challenge themselves and develop new skills. However, a gross miscalculation may lead to discouragement and even abandonment of goals if students cannot achieve their desired results. Therefore, without moderate to high self-efficacy, learners are unlikely to attempt a task, even when they possess the necessary skills, thereby excluding otherwise competent individuals from consequential opportunities.

Purpose and Research Questions

Many studies in engineering education have examined self-efficacy in relation to persistence across different engineering majors. However, several scholars have highlighted the differences between engineering disciplines (Potvin et al., 2013) and have argued for more discipline-based education research (Reinholz et al., 2019; Singer & Smith, 2013). Within self-efficacy research, in particular, Marra et al. (2009) cite the multidisciplinary nature of their study of self-efficacy among women engineering students as a limitation and argue that self-efficacy should be studied in more specific contexts. While some scholars have adopted a discipline-based approach (e.g., Lewis & Matusovich, 2020), such work remains limited. Thus, this study examines the impact of the social learning environment on students’ learning experiences in an introductory chemical engineering course. We conducted five interviews, exploring the relationship between students’ learning experiences, motivation (e.g., self-efficacy judgements and goals), and performance in a chemical engineering materials and energy balances (MEB) course using the following research questions:

Bandura’s Social Cognitive Theory (SCT)

Bandura’s Social Cognitive Theory (SCT) views learning as a dynamic, reciprocal interaction between a person’s attributes, behavior, and environment (Bandura, 1986; Lent et al., 1994) (Figure 1). Two complementary concepts from SCT are human agency and self-regulated learning (Boekaerts et al., 2000; Panadero, 2017; Pintrich, 2000; Schunk & Greene, 2017; Schunk & Zimmerman, 2008). Together, they explain how individuals exert control over themselves and their environment to achieve desired outcomes.

Figure 1

Social Cognitive Career Theory (SCCT)

Social Cognitive Career Theory (SCCT) extends Bandura’s Social Cognitive Theory (SCT) to career development: developing career and academic interests, making career-relevant choices, and achieving success in career-relevant tasks (Lent et al., 1994, 2000). We focused on the task performance model, highlighting the continuous feedback between self-efficacy, outcome expectations, and academic performance for specific career and academic tasks (e.g., completing an introductory chemical engineering course) in this study. (Bandura, 1986; Lent et al., 1993, 1994; Lent & Brown, 2019). According to this model, students’ ability/past performance, self-efficacy judgements, and goals directly influence their academic performance.

Outcome expectations are an individual’s beliefs about the likely consequences of goal achievement (e.g., financial reward, recognition, and self-satisfaction) (Bandura, 1986). According to SCCT, students set academic and career goals to regulate their behavior and academic performance. These goals are informed by their self-efficacy judgements and outcome expectations. As a result, students are more likely to set, pursue, and attain their goals when they are confident in their ability to succeed and anticipate favorable outcomes. In addition to the direct relationships between constructs (e.g., the effect of self-efficacy on performance), SCCT also considers indirect relationships. For instance, students modify their goals based on their self-efficacy beliefs when interpreting their prior learning experiences (Lent et al., 1994). Finally, this model does not exist in a vacuum. Contextual factors, often beyond a student’s control, also inform career decisions and goals (Bandura, 1986; Lent et al., 1994, 2000). For example, engineering programs often use grades or other requirements (e.g., a GPA of 3.0 or higher) to screen incoming students.

SCCT in Engineering Education

Researchers have studied the career intentions and pathways of engineering student populations using SCCT at public and private institutions, Hispanic-Serving Institutions (HSIs), Predominantly White Institutions (PWIs), and Historically Black Colleges and Universities (HBCUs) (Flores et al., 2014; Lent et al., 2003, 2005, 2007, 2008, 2013). These prior research studies provide evidence that self-efficacy or engineering self-efficacy, self-efficacy judgments towards engineering tasks (Lent et al., 1984), are linked to outcome expectations (Lent et al., 2003, 2005, 2007, 2008), career-related interests (Lent et al., 2003), goals (Lent et al., 2003, 2005, 2007, 2008, 2010), and goal attainment and academic satisfaction (Flores et al., 2014; Lent et al., 2007, 2013). However, other studies have suggested additional or contrary findings. For instance, in a 2014 study of engineering students at an HSI, Flores and colleagues found strong evidence for a bidirectional relationship between students’ self-efficacy, outcome expectations, and interest. Thus, self-efficacy judgments can be directly influenced by students’ interest and favorable outcome expectations associated with specific engineering tasks. In summary, SCCT is a practical framework for understanding how students regulate their learning and navigate their careers in introductory engineering classes (Boekaerts et al., 2000; Panadero, 2017; Pintrich, 2000; Schunk & Greene, 2017; Schunk & Zimmerman, 2008).

Researcher Positionality

First author: I am an engineering education researcher with a background in chemical engineering. My experiences with the MEB course as an undergraduate student, as a graduate teaching assistant (Fall 2020), and as an instructor inspired this research study. This familiarity with the course was an asset, for instance, in building rapport with students during the interviews. My view of learning as a dynamic, cognitive, social, and contextual process aligns with SCCT and informed my interpretation of the results.

Second author: I am an engineering education researcher and a professor of chemical and biomolecular engineering. In the past, I redesigned and taught the MEB course. My research focuses on how engineering environments shape students’ identities, motivations, and sense of belonging, and on the impact of these psychosocial factors on student outcomes, including recruitment, retention, academic success, and overall well-being. As a White ciswoman, I have some experiences of marginalization within chemical engineering, predominantly in peer-to-peer interactions and co-curricular learning experiences. In writing and making sense of data, I focused on portraying the complex nature of motivation and shaping forces in the engineering environment. I was not involved in data collection for this study.

Course Context

The institutional context for this research study is a large public research university in the Midwestern United States. All engineering students affiliate with their engineering discipline in the second year after a general first-year engineering (FYE) program.

The MEB course is the first required, discipline-specific course for chemical engineering majors at this institution. Most students enroll in the MEB course during the first (Fall) semester of their second year. However, this four-credit course is offered in the Spring and Summer semesters for off-cycle students (e.g., students who want to get ahead over the summer, need to retake the course, or were absent in the Fall due to an internship or cooperative education (co-op) during the semester). Thus, in these semesters, students’ academic experiences are more heterogeneous relative to the Fall semester. To pass the course and progress through the chemical engineering program, students must earn a grade of “C” or higher. The demographic trends of MEB students from Fall 2019 to Fall 2021 are presented in Table 2.

This course covers the principles of mass and energy conservation and the first law of thermodynamics. In most semesters, the course is co-taught by two instructors with support from two or three graduate teaching assistants. One notable exception occurred during the COVID-19 pandemic when there was only one course instructor. Historically, the course instructors delivered the course content through lectures, while students practiced problems during the recitation sessions. However, in Spring 2019, the course was redesigned as a flipped class to support students by allocating more class time to solving mass and energy balance problems (Godwin & Boudouris, 2020). In this course, students are primarily assessed through homework assignments, summative midterm exams, and a final comprehensive exam at the end of the semester; however, the percentage assigned to each category varies depending on the instructor (Godwin & Boudouris, 2020). Most departments expect students to have taken a minimum of General Chemistry and Calculus courses before enrolling in the MEB course (Ford et al., 2022). The MEB course’s gatekeeping role, course content, and assessment structure at this institution are typical of those in most chemical engineering programs in Canada and the United States (Ford et al., 2022).

In response to restrictions on face-to-face interactions due to the COVID-19 pandemic, the MEB course was temporarily delivered online in the Spring, Summer, and Fall semesters in 2020. The Summer semester was fully online, and students were not assigned homework groups. Then, in Fall 2020, the course was delivered asynchronously online (Adaramola et al., 2022). Many students were on campus, but some were at home. The course instructor assigned students to homework groups and encouraged them to meet as often as they were comfortable, either in person (with masks) or virtually. The course instructor assigned online homework, released weekly pre-recorded lessons, and recorded virtual office hours for students who could not attend the live sessions. Despite the instructional team’s efforts to support the students, many struggled with the absence of in-person interactions and managing their learning independently (Adaramola et al., 2022).

Research Participants

In Spring 2022, we recruited research participants from the population of students who had completed the course by emailing students and student organizations and posting flyers in the chemical engineering building. Then we used a survey to screen chemical engineering majors who were at least 18 years old. Six of the ten responses we received were eligible, and five students consented to participate in the study. One student was enrolled in this course during the Fall 2019 semester, while the other students were enrolled during the initial response to the COVID-19 pandemic and subsequent restrictions on in-person meetings: one in the Summer 2020 semester and the remaining three in the Fall 2020 semester. The Fall 2019 course was in person, while the other two were online.

Despite the low response rate from our recruitment efforts, we captured a variety of different perspectives within the social reality of interest (i.e., students’ experience in the MEB course). Table 3 presents the participants’ pseudonyms, term enrolled in the MEB course, and additional information about the participants (Table 3). To protect the identity of our participants, we present students’ self-identified demographic information in aggregate. The research participants included four women and one man—two participants identified as White, two as Asian, and one as Black. There were no international students in the sample. For Shaan, Kavi, and Carol, their involvement in a chemical engineering student organization was essential to their chemical engineering journey. Finally, all participants completed the course on their first attempt.

Data Collection

We used a semi-structured interview protocol to collect data from the research participants (Supplementary Information, S2). The interview questions were developed based on the Self-Regulated Learning Theory (Boekaerts et al., 2000; Panadero, 2017; Pintrich, 2000; Schunk & Greene, 2017; Schunk & Zimmerman, 2008). We asked participants about their learning experiences using questions such as “What do you consider to be the most important part of your learning experience in this course?” “What was particularly helpful for your learning experience in this course?” “Did you feel motivated towards your goal of earning a chemical engineering degree in this course?” and “Did you feel supported to succeed in this course?” At the end of the interviews, we transcribed the audio recordings using a transcription software (Otter.AI). We checked the transcripts for accuracy and deidentified the data to protect the participants’ identities.

One often-mentioned limitation of interviews is the overreliance on participants’ retellings of events, which can introduce errors and biases that affect the accuracy of the information (Morris, 2015; Walther et al., 2013). The interview methodology we used in this study was appropriate because we were interested in the research participants’ perceptions of their experiences in the MEB courses and chemical engineering. In addition, using the same interviewer and interview protocol ensured relative consistency across all interviews. All interviews were conducted via Zoom, lasted approximately 30 to 90 minutes, and took place during the spring and summer semesters.

Data Analysis

We analyzed the interview transcripts using deductive thematic analysis. Braun and Clarke (2006) define thematic analysis as “a method for identifying, analyzing, and reporting themes within the data” (Braun & Clarke, 2006, p. 79). After familiarizing ourselves with the data, we primarily used in vivo coding, drawing on participants’ words (verbatim quotes) and descriptions, to identify and label salient information from the participants’ perspective (Saldaña, 2016). Next, we leveraged SCCT to refine and group the codes, focusing on the constructs and relationships outlined in the research questions. Finally, we collapsed the codes into broader categories to arrive at the final themes, sentences that organize patterns and relationships specified by the researchers into a theoretical claim about the population of interest (Saldaña, 2016). The coding process was conducted in NVivo.

The Social Cognitive Model of Task Performance in an Introductory Chemical Engineering Course

Our social cognitive model of task performance (Figure 2) proposes that proximal social interactions within the course learning environment directly inform students’ self-efficacy beliefs, academic goals, and behavior. The direct influence of proximal contextual factors (e.g., supports, opportunities, and barriers) on choice goals and the relationship between choice goals and performance, as they apply to career choice behaviors, is well documented in the SCCT literature (Lent et al., 1994; Lent & Brown, 2019). However, less attention has been paid in the SCCT literature to their effects on task performance through peer networks and student-faculty interactions within the classroom environment, as we do in this study. For both the in-person and online versions of the MEB course, students described how academic standards, such as an acceptable course grade, and behaviors, like participating in a homework group, were socially propagated through peer networks. At the same time, social experiences within the MEB course environment that students perceived as being unhelpful for their learning led them to minimize or completely abandon these potentially valuable resources.

In addition, the primary source of self-efficacy information is past performance, except when a learner has limited relevant prior experience (Bandura, 1977; Bong & Clark, 1999; Lent et al., 1994). Our results illustrate that proximal social interactions were a source of self-efficacy beliefs for students in the MEB course, enabling learning and fostering a sense of community. In the interviews, the students discussed how their peer groups and professors provided timely feedback and support, which bolstered their confidence in their abilities, guided their efforts toward the appropriate problem-solving approach, and encouraged them to persist when facing challenges. In particular, students mentioned faculty members and peers with shared visible identities in the classroom who encouraged and inspired them to succeed, promoting positive self-efficacy beliefs. In contrast, learning experiences that diminished students’ perceived self-efficacy beliefs and hindered their academic development were detrimental. Students felt isolated from their peers when they unfavorably compared themselves to their peers, which negatively affected their self-efficacy beliefs. These results suggest that students’ prior academic experiences were less relevant for initially forming self-efficacy judgements in the MEB context. Instead, they relied more on feedback from and observing others. Hutchison-Green et al. (2008) observed similar results in first-year engineering students who focused on performance comparisons with their classmates as a source of their self-efficacy beliefs.

We also found that students expected to form deep connections with their peers and professors in the MEB course context. The only social dimension of students’ academic development that the SCCT model of task performance considers is social rewards one can expect from completing academic tasks (Lent et al., 1994). However, engineering students’ desire to make friends in their classes, collaborate with peers, and receive support from faculty is well documented among undergraduate STEM students (Geisinger & Raman, 2013; Seymour & Hunter, 2019). Recent studies have provided evidence that positive support from faculty and peers positively influences the development of engineering students’ self-efficacy beliefs and outcome expectations (Buckley et al., 2023; Chen et al., 2023; Hsu et al., 2021). On the other hand, social interactions can lead to unhelpful comparisons that undermine learning. We observed how the decision to randomize exam questions and use third-party software for an online exam exacerbated the perceived division between students and their peers in Fall 2020. From Shaan’s interview, we see that this perception existed in the in-person version of the course due to what he described as a lack of preparation in previous classes and interacting with “people who (looked) like they knew what they were doing”. Notably, the source of the difference is not the issue; rather, it is how students interpret its impact on their learning experiences and performance. Canning et al. (2020) demonstrated that first- and second-year STEM students who view their classroom climate as competitive are more likely to have lower confidence in their abilities to succeed (and feel like an imposter), compared to the same students in the same class who view the climate as less competitive, especially for first-generation STEM students. One of the reasons students leave engineering programs is the perceived lack of support, especially from professors, a lack of personal encouragement, and competitive classroom environments (Geisinger & Raman, 2013; Seymour & Hunter, 2019).

Finally, as mentioned in the interviews, technological tools can unintentionally exacerbate some unfavorable social comparisons that some students are already experiencing in a classroom environment, such as the MEB course. As we observe with Eleanor, the detrimental effects of social comparison on efficacy judgments can last long after the course is completed.

Our findings from the MEB course context align with prior literature on students’ academic development in introductory engineering courses. As students continually evaluated their performance against their set goals, they adjusted their goals as needed, illustrating the triadic reciprocal relationships among a person, their behavior, and their context, as proposed by Social Cognitive Theory.

The Impact of the COVID-19 Pandemic

This study contributes to the extensive literature on the pandemic’s impact on students’ learning by providing detailed descriptions of how students navigated an online chemical engineering course. Four of the five participants were enrolled in the MEB course during the COVID-19 pandemic: Kavi (Online, Fall 2020) and Maya (Online, Summer 2020), while Eleanor and Carol (Online, Fall 2020) were on campus, where social distancing was heavily encouraged. As a result, they experienced varying levels of success in adapting to and navigating different obstacles in the online learning environment. The widespread restrictions on face-to-face interactions forced an unprecedented, rapid transition to online and socially distanced learning, which exacerbated existing inequalities in higher education systems (Arday, 2022; Bartolic et al., 2022; Faura-Martínez et al., 2022; Khawaja et al., 2023; McClure et al., 2023). Especially in the online learning environments, students had to adjust to less interactions with their peers and instructors, lower academic motivation, more stress, increased workload, technical difficulties (e.g., poor internet connection), unconducive home learning environments, and online assessments (Buckley et al., 2023; Clark et al., 2023; Iqbal et al., 2022; McIntyre et al., 2023; Misra et al., 2023). At the same time, students found the online nature of classes and office hours more convenient, accessible, and less intimidating for asking questions, and they could learn at their own pace (e.g., rewatching videos if needed) (Iqbal et al., 2022; Misra et al., 2023).

In our study, some students described the challenges they faced in advocating for their needs and receiving support from the instructor, especially during the asynchronous version of the MEB course in Fall 2020. Additionally, the lack of in-person class meetings made it more challenging for them to meet with their group, manage group dynamics, and receive feedback on their performance. They were able to overcome these obstacles by heavily relying on their existing peer network. Further, the changes resulting from the pandemic were not all negative. The virtual learning environment provided anonymity, reducing barriers to initiating conversations with professors. The educational changes made during the pandemic reminded us that education is ultimately a human activity, revealing the relational gaps in the engineering classroom environment and opportunities to leverage technological tools to support learning.

Limitations

While the current study enhances our understanding of how social interactions impact students’ academic development in an introductory chemical engineering class, it is important to acknowledge its limitations. First, we interviewed five participants from the same institution, and four of them took the course during the COVID-19 pandemic. Therefore, the results may not be generalizable to other introductory engineering contexts. Additionally, due to our limited sample size, we were unable to examine differences between student groups (e.g., gender). For instance, prior studies have documented gendered differences in self-efficacy beliefs (Chen et al., 2023; Concannon & Barrow, 2009; Power et al., 2025; Seymour & Hunter, 2019).

For this study, we recruited students who had already completed the course rather than those currently enrolled. Also, we did not triangulate the interview data with other data sources (e.g., exam scores). As a result, we relied on students’ reflections of their past experiences, which could have introduced some bias and inaccuracies into this study. We obtained similar results to those of an earlier pilot study of students who took this course in Fall 2021 (Adaramola & Godwin, 2022). Additionally, because we focused on students who had completed the course and were in the chemical engineering program, we missed the perspective of students who left the major after taking the MEB course. Further, we did not consider the role of power dynamics in influencing students’ social interactions in the MEB course. Future work can address these limitations.

Future Work

Future studies can further examine the intersection of career development and academic performance in introductory engineering classes across a broader sample of chemical engineering programs and other engineering disciplines, to refine and expand the relationships proposed by our model and to explore potential differences and similarities across disciplines. We hope that the results from this study and future research on engineering students’ experiences in introductory classes will inform the development of interventions that target their self-efficacy judgments and goal-setting abilities.