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. 2025 Fall 1;24(3):ar39. doi: 10.1187/cbe.24-07-0199

The Power of Peer Experiences: Shifts in Science Motivation and Impacts on Performance

Joshua Premo †, William B Davis ‡, Brittney N Wyatt †,*
Editor: Sheela Vemu
PMCID: PMC12415602  PMID: 40892978

Abstract

Interacting with others is an important aspect of life. Especially in education, collaborations can help students learn. Unfortunately, there are often systemic barriers of science being perceived as individualistic, which may impact student success in science. Therefore, this study investigated how college students’ (n = 672) social experiences (including learning benefits from peer ideas, LBP) in an introductory biology laboratory course was related to their science motivation and performance. Initial correlational analysis showed positive associations amongst students’ social experiences, science motivation, and course performance. Regression analysis demonstrated a change in LBP and interaction of this with first-generation student (FGS) status, were important predictors of final science motivation. Science motivation, in turn, was able to predict student performance in the course. Interestingly, although science motivation was predictive of performance for all students, FGS status interacted with science motivation to predict performance only in the laboratory that featured a more collaborative curriculum. Results suggest that experiencing LBP may impact all students’ science motivation and through this course performance. Yet these relationships may be more critical for FGS in collaborative classroom environments. Implications for optimizing LBP in introductory life science courses will be explored.

INTRODUCTION

As with all communities, the science community is subject to stereotypes. There are perceptions of scientists and stereotypes that may lead to a predetermined sense of belonging in science. Common examples include the contention that scientists are white men, scientists wear laboratory coats, and scientists are only analytical and not emotive (Ferguson and Lezotte, 2020). An important stereotype is that science promotes and only rewards individual achievement over group achievement (Clark et al., 2016). This stereotype stems from institutions, systems, and historical accounts of science in the United States that encourage individualism (Tebes et al., 2014). For instance, when students are learning science, an individual grade is assigned and their work compared with others (Schinske and Tanner, 2014). Another contributor is the view that when one is doing science, one does so in isolation (i.e., a scientist is an expert, a principal investigator) (Carli et al., 2016). Although not all science is based on individualism (see discussion), it is reasonable for students to have perceptions of science that do not include solving problems with others or working in a team-based setting.

As scientists and/or educators, we must actively work against these stereotypes to engage students in science, specifically with students entering college whose parents/guardians have not obtained a 4-y degree (first-generation students, FGS). FGS are more likely to have higher communal values (i.e., they place a higher value on collaboration; Stephens et al., 2012) that may conflict with stereotypes associated with a more individually oriented science discipline (Allen et al., 2015; Jackson et al., 2016; Boucher et al., 2017; Belanger et al., 2020). Evidence for the existence of this systemic barrier can be seen in findings that FGS are more likely to want to pursue a science career when the collaborative nature of science is emphasized (Allen et al., 2015; Ives and Castillo-Montoya, 2020). Although precollege preparation is critical for FGS success, recreating those experiences for all FGS is not reasonable once they start college. A more reasonable target would be to scaffold supports as part of their college science experiences. As such, an important motivational pathway for FGS may be to experience an increase in science collaboration during their college education, which highlights and is designed to reinforce the importance of collaboration in science.

Little research has focused on how the value of FGS’ collaborative experiences in classrooms may impact their science motivation and grade performance. Specifically, the extent to which FGS feel like their learning was increased by their classmates (i.e., learning benefit from peers; Premo et al., 2018b). The current study argues that this is a critical gap to be addressed. We hypothesized that the learning benefit from peers experienced by FGS in an introductory biology classroom (with lecture-laboratory components) would be a unique predictor of science motivation for FGS. It was also hypothesized that learning benefit from peers would remain an important predictor of FGS science motivation even when considering additional characteristics (e.g., if they were pursuing a science career, a student's course performance, etc.). Finally, it was hypothesized that FGS science motivation would be more impactful on FGS’ grades in the laboratory component (more collaborative) compared with the lecture component (less collaborative).

Literature Review

Importance of Social Connections in Academia.

Connection to other individuals is critical for the well-being of all humans, including students in the classroom (Allen et al., 2021b; Richard et al., 2022). Not only is socialization and interaction with peers a primary desire for first-year college students (Adams et al., 2017), it has been directly tied to their success in college (Brouwer et al., 2016). Specifically in science, faculty accessibility is especially important for FGS connection to science (Espinoza, 2013) and in maintaining a higher grade point average (GPA) (Almeida et al., 2021). FGS also rely on peer connections to be successful in college. For example, lack of peer support in college has been shown to predict lower college integration and a lower GPA in FGS’ first-year in college (Dennis et al., 2005).

Although collegiate socialization is important for fostering a sense of belonging and success, the social dynamics of FGS often extend beyond the campus, reflecting their unique roles and responsibilities. In addition to collegiate socialization, FGS tend to enter college with higher social responsibilities. Many FGS provide childcare and financial/emotional support for their families, (Covarrubias et al., 2019) and this has been found to be a motivational source to perform well and persist in college (Espinoza, 2013). These findings show that the social sphere in which FGS function may be more diverse and connected than continuing generation students (students who have had at least one parent or legal guardian graduate from college, CGS).

Importance of Collaborative Value to Science Career Motivation.

Not only is social connection critical to FGS college success, but it has also been found to be a major factor in their career choice. Goal Congruity Theory (Diekman et al., 2020) argues that individuals will seek out careers that provide opportunities for them to pursue goals they value. This theory has been applied to better understand why students may, or may not, pursue a college major. Klussman et al., (2021) found positivity toward a major is predicted by alignment between student values and those of their major. As such, FGS typically experience significantly higher disconnect between the stereotypical view of science as an individualistic career and their personal value of collaboration (Allen et al., 2015). If the scientific community and educational structures do not effectively communicate or demonstrate opportunities for collaboration, students who highly value collaborative work may perceive science as misaligned with their goals, reducing their motivation to pursue a science degree and career.

Consequently, the misalignment between FGS’ collaborative values and the perceived individualistic nature of science careers not only influences their major choices but also affects their sense of belonging in science, technology, engineering, and mathmatics (STEM) fields. FGS have been found to have a higher communal orientation than their CGS peers (i.e., collaboration has a higher value). However, this higher communal orientation is associated with a lower sense of belonging in STEM (Isenegger et al., 2023). FGS entering college often believe that science careers are individualistic (Allen et al., 2018) unless they have a science experience demonstrating science can be team-based, such as collaborative research. This suggests that students’ view of science as individualistic is relatively stable unless shaped by collaborative experiences in science.

Connecting Collaborative Value to Motivation.

Research has established a significant connection between FGS’ communal orientation and their motivation for science careers. However, most studies have focused on general FGS’ experiences in science (Allen et al., 2015; Brown et al., 2015a; Belanger et al., 2020). Relatively fewer studies have examined classroom-specific associations. Those that have done so focus on value affirmation interventions aimed at promoting FGS’ recognition of the value of science classes. As an example: students reflected on the value and relevance of curriculum, which showed smaller variability in academic outcomes in earlier cases (Harackiewicz et al., 2014) but not later (Harackiewicz et al., 2016). However, when this intervention was redesigned to emphasize the collaborative value of science, positive impacts were again observed for FGS (Harackiewicz et al., 2023) most likely due to the importance of collaboration to FGS.

Meta-analysis of value affirmation interventions has shown a greater impact in classroom settings compared with research-focused studies (Wu et al., 2021), indicating the importance of classroom-centric examinations of value-focused research. This aligns with findings from Belanger et al., (2017) who demonstrated service-learning interventions in classrooms could increase students’ recognition of a STEM course, satisfying communal goals. Given the importance of these goals to FGS, this could be a pathway for changing student STEM perceptions.

To address some of these gaps, the current study takes a course-specific view of how naturalistic (i.e., nonintervention) collaborative interactions in lecture-laboratory courses impact FGS’ science motivation and grade performance. Prior research has found that experiencing certain types of curricula like CURES (course-based undergraduate research experiences) can result in an increase in science motivation (Olimpo et al., 2016). However, the role of student-student social interactions as a driver of science motivation, particularly for FGS, has been underexplored. Thus, this study aims to investigate how collaboration value within a lecture-laboratory (which includes a CURE-like environment [SEA-Phages; Caruso et al., 2009; Staub et al., 2016]) may drive FGS’ science motivation.

Connections Between Student Science Motivation and Performance.

Not all forms of motivation are equally linked to student classroom performance, including grades. Evidence indicates that self-driven or autonomous motivation (stemming from student interest, identity-sourced motivation, value-centered motivations, etc.) is a much better predictor of grades than controlled motivation (arising from psychological pressures, avoidance of guilt, pursuit of external rewards, avoidance of punishment, etc.), which relies on coercion rather than student-sourced desire (Mouratidis et al., 2021). Autonomous motivation in the classroom also positively impacts out-of-class contexts (Hagger and Chatzisarantis, 2016), suggesting that motivational forms based on interest, identity, and values have widespread effects. The Science Motivation Questionnaire II (SMQ-II; Glynn et al., 2011) is an instrument used to measure various aspects of students’ science motivation. The SMQ-II primarily assesses autonomous motivation, including intrinsic motivation (science interest), self-efficacy (view of their ability in science), self-determination (actions to ensure success in science), and career motivation. Grade motivation is the only aspect measured that is more control oriented. Prior research using the SMQ-II, as is used in this study, has established correlations between student scores on this measure and their science grade point averages (Glynn et al., 2009; Glynn et al., 2011). Therefore, it is hypothesized that if high collaborative value is experienced by FGS, there will be shifts in their science motivation, subsequently impacting their grades in the laboratory and/or lecture components of the course.

Research Questions

Given the importance of collaborative value to FGS’ science motivation, understanding the impact of beneficial peer experiences in lecture-laboratory classrooms could reveal key areas of needed FGS support. Drawing on prior research, the current study examined students’ social perception of their laboratory classroom with a specific focus on the learning benefits they experienced from peers. As laboratory classrooms are typically the most authentic exposure college students have to scientific practices, we hypothesized that experiencing learning benefit from one's peers will impact FGS’ science motivation. Therefore, we sought to answer three research questions: 1) How do social experiences, science motivation, and performance vary across students’ higher education generational status (i.e., first-generation vs. continuing generation students)? 2) What factors predict final science motivation? and 3) To what extent do shifts in science motivation predict course performance?

MATERIALS AND METHOD

Course Description

Data were collected from college students enrolled in an introductory biology course for science majors (which included a required laboratory component) at both the start (first laboratory meeting) and end of the semester (final laboratory meeting). All students in the study completed SEA-PHAGES, which is a CURE, in their laboratory (Caruso et al., 2009; Staub et al., 2016). CUREs focus on students participating in more authentic science where science skills are critical, and outcomes are unknown (Buchanan and Fisher, 2022). CURE environments have the potential to increase exposure to collaborative science. Thus, the laboratory environment of the current study may be more science aligned than other laboratory environments and have greater impact on FGS.

The curriculum for the laboratory portion of the course included the SEA-PHAGES program with the addition of weekly sessions that encouraged collaborative behavior between students. During each session, students worked in groups (maximum four students) to think more deeply about either topics of the SEA-PHAGES program or another topic relevant for scientific inquiry (e.g., reading a scientific paper, plagiarism, scientific writing, etc.). These sessions were scaffolded so that students first worked with their peers on a question set before a whole-class exploration of the topics led by the section's laboratory instructor. For more details on the laboratory curriculum see Premo et al. (2018a). All students completed a graded quiz on the material covered in a session at the start of their next class. These were meant to hold students accountable for their learning during the sessions.

The lecture component of the course included one section a semester capped at 525 students. The laboratory component of the course was capped at 24 students per section each semester. Participants in the current study were from three lecture sections and 58 laboratory sections spread across three semesters. In contrast with the highly collaborative nature of the laboratory curriculum, the lecture portion was interactive (some student collaboration but also instructor focused lecturing). The lecture was taught completely by one instructor and this instructor supervised the coordination of each section of the course laboratory.

Participant Information

A total of 672 students consented to be part of the study (IRB#, 15680) and completed all required assessments to be retained in the analysis. Collection took place at a doctoral granting university with high research activity (R1) in the Pacific Northwest of the United States and participants were enrolled in one of 58 laboratory classrooms over three academic semesters. Demographics of the sample included 75% women, 24% FGS, 92% English as a first language, and 17% were from ethnicities historically minoritized and underrepresented in science. Students self-reported their ethnicities. Those that reported being black/African American, Hispanic/Latino, American Indian, Alaskan Native, and Pacific Islander were considered under this definition. It is important to note that this group showed intersectionality by generational status. A total of 23% of FGS were from ethnicities historically minoritized, while only 7% of CGS met this definition. Although the course was introductory in nature, first-year students constituted only 32% of the sample. Of the remainder, 39% were sophomores, 21% juniors, and 8% seniors. A total of 87.6% of participants indicated that they were planning on pursuing a science career in the future. The course was a prerequisite course for many of the courses needed for a variety of science programs at the university. Thus, performance in the course has critical implication for students’ future success in science.

Measures

Due to the importance of performance in an introductory biology course, grades were provided for the study. The instructor provided two grades for each student who consented to be part of the study. These included: the students’ final grade in the laboratory component and the students’ final grade for the lecture. When looking at students’ overall performance (in RQ#2 and RQ#3) these were combined. In addition to grades, a series of demographic questions in the laboratory component of the course were collected. These included: first-generation student status, number of prior undergraduate science courses completed, if they were pursuing a science career (“Are you interested in a future career in science? Yes, Maybe, and No options), biological sex (Male, Female, Prefer Not to Answer options), ethnicity, and English as a first language status. Additionally, pre- and postsurvey responses were collected. These included both the Cooperative Classroom Environment Measure (CCEM; Premo et al, 2018a; Premo et al, 2018b) and the SMQ-II (Glynn et al., 2011). See Supplemental Material for more details on instruments. Students were given oral and written instructions to answer the survey questions given their experience in the laboratory classroom environment. Thus, for Research Questions #1 and #2, postsurvey minus presurvey change was calculated and considered as the student experience in the laboratory classroom environment during the semester.

To assesses both students’ dispositions toward cooperative engagement and their perceptions of factors that drive cooperative behavior (Premo et al., 2018b), the CCEM survey was used. The survey was selected due to its ability to measure student perception of the social environment of a classroom (Premo et al., 2018c) and its items include: reputational concern (RC) (“My reputation in class is something I value”), cooperative norms (CN) (“I feel that I need to cooperate with my classmates”), investment in cooperation (IC) (“I spend a greater amount of time helping classmates than I get helped”), friendship (frie.) (“I know my classmates from outside of class.”), willingness to help peers (WHP) (“I spend time helping classmates during class.”), and reciprocity (rec.) (“Classmates I help tend to help me back.”). Finally, a second-order construct was used to determine the extent to which students felt like their learning was increased by their classmates (i.e., learning benefit from peers, Premo et al., 2018b). “Learning benefit from peers” consisted of the common pattern of variance between the preference for cooperation subscale (example questions included: “Class is more enjoyable when I work with other students,” “I learn best when working with classmates,” and “I receive better grades when working with other students.”) and benefit from classmate ideas subscale (example questions included: “I understand more when my classmates participate in college science classroom discussions,” “The amount I understand is increased by classmates’ ideas in my college science classes,” and “When classmates share their ideas in college science classes this helps me learn.”) Use of this second-order construct (learning benefit from peers, LBP) was due to its demonstrated ability to connect social dynamics to one's disposition toward working collaboratively (Premo et al., 2018c). Students responded to all items on a Likert scale from one (strongly disagree) to five (strongly agree). For a complete view of the CCEM instrument and scales see Supplemental Table S1.

Before CCEM results were used for analysis, a confirmatory factor analysis was performed on both the start of semester data and end of semester data using a robust maximum likelihood estimator. These were both modeled in mPlus and included an eight-factor model (reciprocity, willingness to help others, preference for cooperation, benefit from classmate ideas, friendship, RC, CN of the classroom, and IC) with the single second-order construct, learning benefit from peers. Upon running the initial models, it was found that one item of the IC subscale (“I put more energy into working cooperatively than my classmates.”) was loading below 0.30, indicating poor relationship to the subscale factor. Thus, this item was removed for both the purposes of CFA and analysis. All other items loaded at 0.40 or above on the start of the semester survey and 0.50 or above on the final survey. Final CCEM CFA results for the start of the semester survey showed that the model had good fit to the data across indices (RMSEA = 0.043, CFI = 0.91, SRMR = 0.068). This was similar to final survey fit indices RMSEA = 0.043, CFI = 0.93, SRMR = 0.061) and indicated evidence of structural validity in measurement with the CCEM. All factors from the CCEM were used in the initial regression model (see regression methods below) to examine potential contributions to student science motivation in Research Question #2.

The second start of the semester survey/final survey given was the SMQ-II (Glynn et al., 2011). This was collected from students to measure science motivation. The survey assesses overall student science motivation with five factors each measured with five questions: intrinsic motivation (“The science I learn is relevant to my life.”), self-determination (“I use strategies to learn science well.”), self-efficacy (“I am sure I can understand science.”), career motivation (“Learning science will help me get a good job”), and grade motivation (“Scoring high on science tests and labs matters to me.”). We used the same items as previously published with the minor change of “science” to “biology” to make it more specific to the course.

A confirmatory factor analysis was used to model SMQ-II data separately for both start of the semester survey and final survey data using a robust maximum likelihood estimator. The model included a five-factor model with one second-order factor connecting all five. This was done due to the purpose of the current study, to look at students’ overall science motivation, and to ensure that the change in wording from “science” to “biology” did not invalidate the instrument. Thus, the second-order factor of “overall science motivation” included the common pattern of variance across all science motivation subscales and is the focus of the current study. All items for both the start of the semester survey and the final survey loaded on their specified constructs at above a 0.42 and 0.54, respectively. Final SMQ-II CFA results for the start of the semester survey showed that the model had acceptable to good fit to the data across indices (RMSEA = 0.057, CFI = 0.90, SRMR = 0.063). This was similar to the final survey fit indices RMSEA = 0.058, CFI = 0.92, SRMR = 0.058) and indicated evidence of structural validity in measurement with the SMQ-II. Unlike with the CCEM, where most subscales were used and only one smaller second-order factor was used (learning benefit from peers), our research questions used only overall student science motivation and thus the larger second-order construct was used for analysis.

Analyses

The current study examined the relationship between FGS peer social experience in science and changes in their science motivation. For Research Question #1, Pearson's correlations were used to assess bivariate relationships and independent t tests with Bonferroni corrections were used to compare groups. For Research Question #2, multiple regression was used to determine which factors best predicted students’ final science motivation at the end of the semester. A combined model selection process was completed for predicting all students’ science motivation at the end of the semester. We first generated a highly inclusive initial model before beginning the model selection process. The initial regression model for Research Question #2 included start of semester science motivation, science career interest, identification with an minoritized ethnicity in science, biological sex, start of semester and change in CCEM subscales, and course performance. This initial model was established, and then backwards stepwise regression was used to remove nonsignificant predictors until the best supported model remained based on Akaike information criterion (AIC) validation (Burnham and Anderson, 2004). We adopted the model with the lowest AIC with the more parsimonious model being accepted if ΔAIC was 2 > |x| > 0 in alignment with recommendation by Jordt et al. (2017).

For Research Question #3, two separate regression models were constructed to predict students’ performance in the laboratory and overall course grades. Both models included final science motivation, initial science motivation, generational status, and the interaction between final motivation and generational status. This approach was used to evaluate whether changes in science motivation and its relationship to performance varied by generational status. All predictors were initially included, and nonsignificant predictors were removed for the final models. This approach allowed for a more complete understanding of the role of generational status and motivation, including interaction effects when appropriate.

For all analyses that included change predictors (start of the semester and end of the semester), the authors chose to use change scores as predictors in models (e.g., instead of using “Final benefit of learning from peers,” we used “Change in benefit of learning from peers”). This approach was chosen to capture student experience during the semester, not across the lifetime of one's science education. If we had used an end of semester score without controlling for a matched presemester variable, our results would have to be interpreted through years of student experiences in science classrooms. Although the results are more bounded by the smaller variance in change, they are also more specific to the time in which the change occurred (i.e., the semesters in which the study was being conducted). Thus, the use of change variables for analysis may be more authentic to immediate student experiences in the course and more appropriate for this study that is looking at the learning benefit from peers within a collaborative learning environment.

RESULTS

Research Question (1): How Do Social Experiences, Science Motivation, and Performance Vary Across Student Generational Status?

Pearson's correlations were used to examine the relationship between students’ self-reported collaborative experiences in their laboratory classroom (CCEM), overall science motivation (SMQ-II), and final semester grades (Table 1). We hypothesized that if FGS experienced greater learning benefit from classmates, they would be more science motivated. Our results supported this hypothesis. According to the correlation table, the more learning benefit FGS experienced from peers, the more motivated in science they became (roverall = 0.40, p < 0.05, medium effect, see Table 1). CGS did not show this same pattern as their learning benefit from peers was not significantly correlated with their final science motivation. A follow-up regression was used to inferentially test this comparison. Regression results showed that the interactions term FGS x learning benefit from peers predicted significant variance in students’ change in science motivation (p <0.05). This confirmed a statistical difference for the relationship between benefit experienced from peers and changed science motivation by generational status. This finding aligns with previous studies that have found exposing students to a collaborative view of science in a noncourse context can result in FGS shifting their perception and thus increase in their science motivation (Brown et al., 2015a; 2015b), particularly their intrinsic science motivation (Allen et al., 2015).

TABLE 1.

Correlations between end of semester student science motivation (SMQ-II), change in social perceptions of their laboratory classroom, and performance by generational status (FGS top values, CGS bottom values)

Factor FSM Rec. Frie. WHP RC CN IC LBP Lect. Lab.
Final Science Motivation (FSM) — 0.14
0.05		 0.12
0.16		 0.02
0.03		 0.18a
0.13a 		0.04
0.11a 		0.06
0.02		 0.40a
0.05		 0.26a
0.41a 		0.19a
0.10a
Reciprocity (Rec.) — — 0.15
0.17a 		0.05
0.15a 		0.17a
0.20a 		0.03
0.06		 0.06
0.00		 0.26a
0.30a 		0.02
−0.11a 		0.08
0.00
Friendship (Frie.) — — — 0.12
0.13a 		0.17a
0.13a 		0.01
0.00		 0.08
−0.10		 0.25a
0.15a 		0.20a
0.05		 0.03
0.17a
Willingness to Help Peers (WHP) — — — — 0.19a
0.24a 		0.04
0.32a 		0.07
0.11a 		0.17a
0.20a 		0.01
−0.01		 0.04
0.07
Reputational Concern (RC)  — — — — — 0.19a
0.35a 		0.08
0.04		 0.37a
0.28a 		0.03
−0.07		 0.02
0.10a
Cooperative Norms (CN)   — — — — — — 0.01
0.00		 0.19a
0.26a 		−0.04
−0.02		 0.04
0.02
Investment in Cooperation (IC)  — — — — — — — 0.03
0.03		 −0.05
0.09		 −0.05
−0.05
Learning benefit from peers (LBP) — — — — — — — — 0.12
−0.02		 0.05
0.05
Lecture course performance (Lect.) — — — — — — — — — 0.43a
0.31a
Laboratory course performance (Lab.) — — — — — — — — — —
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Note: n = 161 (FGS, top values), 511 (CGS, bottom values).

ap < 0.05.

Comparing correlations between science motivation and the classroom environment revealed additional differences and commonalities between FGS and CGS. One difference was how the relationship between students’ perception of CN correlated to their WHP in the classroom. CGS were more willing to help their peers if they perceived there to be higher expectations for cooperation (r = 0.32, p < 0.05, small-medium effect, see Table 1). There was not a significant correlation between these factors for FGS. A follow-up regression was used to inferentially test this comparison. Regression results showed that the interactions term CGS x CN predicted significant variance in students’ WHP (p <0.05). This confirmed a statistical difference for the relationship between classroom CN and students’ WHP by generational status specifically for CGS.

There were also common correlations regardless of generational status. RC was positively correlated with science motivation in both groups and learning benefit from peers also was correlated with many other aspects of the classroom social environment.

Several factors correlated to student performance in both the laboratory portion of the course and overall. For example, final science motivation was significantly correlated with student performance overall (rFGS = 0.26, p < 0.05, small-medium effect; rCGS = 0.41, p < 0.05, medium effect) and in the laboratory (rFGS = 0.19, p < 0.05, small effect; rCGS = 0.10, p < 0.05, small effect). Interestingly, gaining greater friendship during the semester was negatively correlated with lecture performance but only for FGS (r = −0.20, p < 0.05, small effect). In contrast, a small correlation was seen between increased in friendship and greater performance for CGS in laboratory performance (r = 0.17, p < 0.05) that was not observed for FGS. A follow-up regression was used to inferentially test this comparison. Regression results showed that, in contrast with observed correlational differences, there was no significant difference for the relationship between friendship formation and performance (lecture or laboratory) by generational status.

Next, we compared social perceptions, science motivation, and performance by time (start or end of the semester) and generational status. Differences in social perception and science motivation can be seen in Figures 1 and 2. These figures show surprising consistency for all students with the only significant difference being in CN. CGS perceived significantly higher CN throughout the semester (though the difference was small in magnitude). It should also be noted that science motivation significantly decreased for all students throughout the semester (mFGS-Pre = 4.09, SD = 0.40, mFGS-Post = 3.83, SD = 0.44, Cohen's d = 0.62, medium-large effect; mCGS-Pre = 4.15, SD = 0.41, mCGS-Post = 3.88, SD = 0.45, Cohen's d = 0.63, medium-large effect). This showed that all students left the course less motivation in science than when they began it.

FIGURE 1.

FIGURE 1.

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Comparison of start of the semester science motivation (SMQ-II) and aspects of the laboratory class environment between first-generation students (FGS, n = 161) and continuing generation students (CGS, n = 511); *p < 0.006 (after Bonferroni correction).

FIGURE 2.

FIGURE 2.

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Comparison of final science motivation (SMQ-II) and aspects of the laboratory class environment between first-generation students (FGS, n = 161) and continuing generation students (CGS, n = 511). *p < 0.006 (after Bonferroni correction).

Finally, FGS performed significantly lower than CGS overall (mFGS = 78.8%, SD = 10.8, mCGS = 83.7%, SD = 8.9; Cohen's d = 0.51, medium effect) and in the laboratory portion of the course (mFGS = 86.4%, SD = 8.2, mCGS = 88.5%, SD = 7.4; Cohen's d = 0.27, small effect). This indicates generational status may play a role in course performance. Investigating potential variation in student performance related to generational status is important as there are opportunities to implement targeted strategies to promote the success of all students in science.

Research Question (2): What Factors Predict Final Science Motivation?

Although correlations allow one to look at basic relationships across groups, they do not account for intercorrelation across multiple variables. In other words, they do not allow one to see what factor, or a combinations of factors, can best account for the variance in a particular outcome. Thus, we expanded our analysis to multiple regression models for Research Question #2. Using backwards regression with AIC validation, we systematically generated an optimal model best able to predict final semester science motivation for all students. The final regression model included both FGS and CGS to allow a direct comparison of the groups and was able to predict 53% of the variance in students’ final semester science motivation (Table 2).

TABLE 2.

Final model predicting final science motivation (average overall SMQ-II score) for all students (n = 672)

Factor B (SE) Beta
Intercept •0.41 (0.26)
Starting science motivationa 0.71 (0.05) 0.57
Change in RCa 0.08 (0.03) 0.10
Change in the learning benefit from peersa 0.41 (0.17) 0.40
FGS x Change in the learning benefit from peersa 0.19 (0.02) 0.34
Pursuing a science careera 0.13 (0.05) 0.10
Overall performance in the coursea 0.02 (<0.01) 0.30
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Note: Model adjusted R2 = 0.53.

ap < 0.05.

Factors retained in the final model included students’ science motivation at the start of the semester, some changes in the classroom social environment, whether they were pursuing a science career, and their performance in the course. A student's science motivation at the start of the semester had the most impact on their final semester science motivation (Beta = 0.57, p <0.05) suggesting that students’ science experiences before the course were impactful throughout it.

In contrast with our hypothesis, (learning benefit from peers would be primarily important for FGS) learning benefit from peers had the second strongest impact on students’ final science motivation for all students (Beta = 0.40, p <0.05). When students experienced an increased learning benefit from peers in their classroom, they were more science motivated. This impact can be quantified by looking at the unstandardized B values in the model. The unstandardized B values show that for every one unit of change in learning benefit from peers (this is measured on a five-unit scale) there was a 0.41 (SD = 0.17) shift in a student's final semester science motivation (also on a five-unit scale). To highlight the scale, if a student went from neutral to agree (Likert scale) on their learning benefit from peers and started at a neutral science motivation (“3”), this would correspond to a 14% increase compared with their initial science motivation. It is important to note that this change goes both ways. If a student experienced a decrease in learning benefit from peers, science motivation would be predicted to decrease by the same degree.

An interaction effect between FGS status and change in the learning benefit from peers was also retained in the final model. The interaction effect was positive and the third highest beta in the model (Beta = 0.34, p <0.05). As indicated by the positive interaction effect, FGS may receive an additional gain to their final semester science motivation when they experience an increase in the learning benefit from peers (beyond that experienced by all students). To see a graphical representation of this interaction effect see Figure 3. This figure shows how final semester science motivation varies by generational status and whether learning benefit from peers increases or decreases from the start to the end of the semester.

FIGURE 3.

FIGURE 3.

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Interaction between change in science motivation and learning benefit from peers by generational status.

Additional factors were found to predict final science motivation such as RC, pursuit of a science career, and overall performance in the course. Students who cared more about how their peers viewed them in the classroom were more science motivated at the end of the semester, but the relative impact of this was minor (Beta = 0.10, p <0.05) compared with the previously discussed factors. If a student planned to pursue a science career, a minor science motivation gain occurred (0.13 higher on the five-point motivation scale) (Beta = 0.10, p <0.05). The impact of overall course performance (combined lecture and laboratory grade) was three times higher (Beta = 0.30, p <0.05) in comparison with if they were planning to pursue a science career. Students had higher final science motivation if they had higher performance in the course.

Research Question (3): To What Extent Do Shifts in Science Motivation Predict Course Performance?

Results presented so far suggest that the learning benefit from peers was a predictor of science motivation for all students but had an additional interaction effect with FGS. This suggests that, while learning benefit from peers is important for all students, FGS appear to receive an additional gain to their motivation. Because prior research using the SMQ-II has established correlations between student science motivation and their science grade point averages (Glynn et al., 2009; Glynn et al., 2011), the final research question sought to determine the extent to which final science motivation predicted students’ course performance. To do so we ran two regression models, one predicting students’ laboratory grade and the second predicting their overall course grade (Laboratory + Lecture). The first regression used science motivation (starting and final), student generational status, and science motivation by generational status interaction to predict student laboratory grade performance. The same factors were initially entered into a second regression that predicted student overall performance in the course, though not all factors were retained.

Factors predicting students’ final laboratory grade (Table 3) were only able to predict a small amount of overall grade variance (Adjusted R2 = 0.03). Factors that predicted higher laboratory grades included initial science motivation (Beta = 0.35), final science motivation (Beta = 0.49), and an interaction between FGS and a change in science motivation (Beta = 0.89). The only factor that predicted a lower final grade was FGS status (Beta = −0.87). These can be contextualized in terms of “percentage points” in the laboratory course using the unstandardized (B) values. For example, each unit of science motivation change (e.g., m = 4 “agree” to m = 5 “strongly agree”) predicted ∼8.4 percentage-point shift in grade (this would correspond in most instances to a one letter grade increase) when controlling for initial science motivation and generational status.

TABLE 3.

Model using generational status and science motivation to predict students’ final laboratory grade

Factor B (SE) Beta
Intercept 59.47 (10.72)
Starting science motivationa 6.60 (2.77) 0.35
FGSa −15.45 (5.70) −0.87
Final science motivationa 8.44 (2.80) 0.49
FGS x Final science motivationa 3.74 (1.47) 0.89
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Note: All factors retained in the final model.

n = 672.

ap < 0.05.

Model adjusted R2 = 0.03.

The second model was run the same way but predicted students’ overall grade in the course (lecture plus laboratory performance). When backwards regression was used, the interaction effect between FGS and change in science motivation did not significantly add to the model so it was removed in the final version (Table 4). The final model was able to predict 25% of the variance in students’ overall final course grade (Adjusted R2 = 0.25). Other than the interaction effect, the same patterns for positive and negative predictors were found in this model. Increases in students’ starting science motivation (Beta = 0.28) and final science motivation (Beta = 0.43) both predicted higher final grade in the course. For example, each unit of higher final science motivation (e.g., m = 4 “agree” to m = 5 “strongly agree”) predicted ∼9.2 percentage-point increase with each unit of starting science motivation predicting a 6.7 percentage-point increase in final grade. Also, in alignment with the previous model, identifying as an FGS was associated with a decrease in final grade, specifically a decrease of 4.8 percentage-points.

TABLE 4.

Model using generational status and science motivation to predict students’ final grade overall in the course (lecture plus laboratory).

Factor B (SE) Beta
Intercept 49.40 (3.50)
Starting science motivationa 6.73 (0.82) 0.28
FGSa −4.77 (0.75) −0.21
Final science motivationa 9.23 (0.75) 0.43
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Note: Factor not retained in the final model include “FGS x Final science motivation.”

n = 672.

ap < 0.05.

Model Adjusted R2 = 0.25.

CONCLUSIONS

Research Question (1): How Do Social Experiences, Science Motivation, and Performance Vary Across Student Generational Status?

Given FGS are more likely to have higher communal values (i.e., they place a higher value on collaboration; Stephens et al., 2012) that may conflict with stereotypes associated with a more individually oriented science discipline (Allen et al., 2015; Jackson et al., 2016; Boucher et al., 2017; Belanger et al., 2020), we predicted FGS would value factors that contribute to a cooperative classroom environment more than CGS. Factors that were measured included: RC, CN, peer investment in collaboration, friendship, WHP, reciprocity, and learning benefit from peers. Contrary to our predictions, FGS and CGS did not vary greatly on these factors both at the start and the end of the semester. CGS perceived significantly higher CN throughout the semester though the difference was small in magnitude. Additionally, FGS performed significantly lower than CGS in the lecture and laboratory. When looking at trends for all students over the course of a semester, a decrease in science motivation, slight decrease in reciprocity, and increase in friendship was observed.

To further investigate any potential differences between FGS and CGS, correlations were determined for cooperative classroom environment factors. There were numerous significant correlations for CGS only (9 total) and for both CGS and FGS (12 total). However, there were only one exclusive significant correlation for FGS. This was that learning benefit from peers was significantly correlated with increased final science motivation. Among the various correlations, the highest ones exclusively involving CSG included: final science motivation and CN. For both FGS and CGS: final science motivation and lecture grade, reciprocity and learning benefit from peers, RC and learning benefit from peers, and lecture course performance and lab course performance.

These results indicate there are differences between how FGS and CGS are engaging in collaborative classroom environments. However, contrary to our predictions, the differences do not necessarily support FGS valuing all the factors of a collaborative classroom environment more than CGS. Although learning benefit from peers does seem to be especially important for FGS. If FGS experienced more learning benefit from peers, they had significantly higher final science motivation. If both FGS and CGS had higher final science motivation, they had significantly higher lecture grades. Overall, results indicate that collaborative classrooms have the potential to benefit all students regardless of generation status (although to varying degrees).

Research Question (2): What Factors Predict Final Science Motivation?

Research has established a significant connection between FGS’ communal orientation and their motivation for science careers. However, the role of student-student social interactions as a driver of science motivation, particularly for FGS, has been underexplored. Thus, this study aimed to investigate how collaboration value within a lecture-laboratory (which includes a CURE-like environment [SEA-Phages, Caruso et al., 2009; Staub et al., 2016]) may drive FGS’ science motivation. Contrary to our predictions that collaboration-related factors would be the largest predictor for FGS, our results indicate that initial science motivation was the greatest predictor of final science motivation. Learning from peers was the second highest predictor for all students. However, FGS do get a slight gain in their final science motivation when there is learning benefit from peers.

Given that science motivation at the start of the semester had the most impact on final science motivation, it would suggest prior experiences influence science motivation and can have a lasting impact throughout an introductory biology course. This could be especially important to consider for FGS as their prior experiences with science may look different compared with CGS. FGS often have lower preparation for college science courses due to fewer math and science classes taken in high school and less exposure to science at home (DeFreitas and Rinn, 2013; Dika and D'Amico, 2016). Additionally, FGS are less likely to have parents in STEM careers, meaning that they have less science-specific support at home (Bettencourt et al., 2020). Finally, FGS performed significantly lower than CGS overall (mFGS = 78.8%, SD = 10.8, mCGS = 83.7%, SD = 8.9; Cohen's d = 0.51, medium effect) and in the laboratory portion of the course (mFGS = 86.4%, SD = 8.2, mCGS = 88.5%, SD = 7.4; Cohen's d = 0.27, small effect).

Despite this, based on results from Research Question #1, FGS did not have significantly lower start of the semester science motivation compared with CGS. As instructors, this information may be beneficial to consider as we want to avoid making assumptions about FGS entering introductory biology courses. Based on previous literature, one may assume that FGS have had less exposure to science and therefore would be less motivated to pursue a science career. This may not be the case. Alternatively, FGS may be just as motivated as CGS when entering an introductory biology course and that learning from peers helps maintain motivation throughout the semester. However, if there are limited opportunities to engage in collaboration throughout the semester, FGS may be impacted more. It is important to recognize that learning from peers may beneficially impact motivation for all students regardless of generational status. Therefore, focusing on collaborative environments in the classroom likely will not detract from student motivation, but potentially support science motivation for all with a potential greater benefit to FGS.

Research Question (3): To What Extent Do Shifts in Science Motivation Predict Course Performance?

As prior research has established correlations between student science motivation and science grade point averages (Glynn et al., 2009; Glynn et al., 2011), the final research question sought to determine the extent to which end of semester science motivation predicted students’ course performance. We hypothesized that a change in student motivation would be a significant predictor of course performance. The results of this study indicate science motivation was a positive predictor of laboratory grade performance for all students. Specifically, increases in students’ starting science motivation and final science motivation both predicted higher final grade in the course. FGS received an additional bonus to their grade when their science motivation increased. The only factor that predicted a lower final grade was FGS status. Given that FGS performed significantly lower in the laboratory, it may follow that increased science motivation for FGS (potentially driven by increased learning benefit from peers) may be able to counteract lower performance in laboratory class settings.

When interpreting results from Research Question #3, it is important to keep in mind that all the models’ factors are dynamically interacting. For example, if two students (FGS and CGS) had entered the course with the same initial science motivation and their change in science motivation was equivalent, the FGS would still be predicted to end up with a significantly lower final grade in the course. Yet if, under the same circumstances, the FGS experienced a 0.5 increase in their science motivation (out of 5) the model predicts that both the FGS and the CGS would have around the same grade. A greater than 0.5 change in science motivation would predict the FGS to perform higher than the CGS. This can help to interpret the model results in the context of Research Question #2 findings. The model for Research Question #2 found learning benefit from peers predicted higher science motivation for all students, but with an additional benefit (via interaction effect) for FGS. When taken together, the results of these models suggest, in alignment with our prediction, that learning benefit from peers could be a mechanism by which greater science motivation, and subsequent performance, may be able to be encouraged for FGS (though all students would benefit).

DISCUSSION

Although colleges have limited ability to impact FGS before they enter college, they have a responsibility to provide support upon enrollment. This is especially important for science-related majors as FGS may feel pursuing a science-related career will not support the communal values they hold. Therefore, college science experiences could be potential targets to increase FGS success. The results of the present study suggest that FGS collaborative experiences in science classrooms matter. These experiences have the potential to shift students’ science motivation and thus have impacts in student performance that are intimately tied to retention. First-semester grade point averages for FGS have been found to be predictive of their persistence in STEM (Dika and D'Amico, 2016). Even single semester grade shifts observed in the current study could have larger ramifications for FGS, and all students’, science degree completion.

Cultivating Collaborative Classrooms

Given the results of this study, cultivating collaborative classrooms may be appropriate and measurable interventions to better support FGS. These interventions can happen in any classroom setting, lecture or laboratory, and appear to matter. Canning et al., (2020) found competitive (individually oriented) classroom environments exacerbate FGS imposter syndrome, negatively impacting FGS attendance and course performance. This in turn, increases the likelihood of dropping out of college. Therefore, classroom environments centered on lecturing and limitation of student-centered learning and interactions, may be largely isolating for FGS.

In contrast, active learning classrooms are centered on student engagement the with content and some forms of active learning can promote collaborations within the classroom. Theobald et al. (2020) found high-intensity active learning in college STEM classrooms narrowed the achievement gap between overrepresented students and underrepresented students in STEM (including FGS). Given this, active learning strategies in the classroom likely support FGS in science. However, some active learning strategies are more collaborative and thus better situated to support FGS.

To specifically support collaborative active learning in the classroom, it would be recommended to align active learning strategies with the collaborative nature of science. This may include daily group-work (e.g., use of case studies that student complete in groups), collaborative projects, service learning that connects to the local community, or consistent think-pair-shares throughout a lesson. Less effective active learning techniques might include those that are typically completed by individuals and may be more isolating to FGS. These include individual writing assignments or projects, individually answered iClickers or other response systems (where students are not required to interact), individually completed simulations or laboratory exercises, gamification of learning (scores are individualized), or building individual concept maps. Instructors may want to consider transitioning more individualized active learning techniques to be more collaborative in the classroom.

Based on the results of this study, transitioning a classroom to a more collaborative environment could support not only FGS, but CGS as well. Increased opportunities for collaboration are needed if students are to receive greater exposure to the collaborative nature of science. However, it is important to recognize the type of collaboration being implemented in a course. Specifically, collaborations must include students experiencing a learning benefit from peers. For example, when there was an increase in learning benefit from peers, students’ motivation was significantly higher. This also went in the opposite direction. When the quality of learning from peers was lower (they learned less than they thought they would from peers), students’ motivation was significantly lower. These trends did not hold true for all aspects of a collaborative environment. Therefore, a critical factor that needs to be maximized specifically is learning benefit from peers.

Not All Collaborations Benefit Learning

It is often assumed that all collaborations are inherently valuable and will benefit learning. Although the results of this study indicate the learning benefit from peers can increase science motivation, it also holds true nonvaluable collaborations (decreased learning benefit from peers), decreases science motivation. For example, if a student experienced a decrease in learning benefit from peers, science motivation would be predicted to also decrease.

This raises the question: what makes a collaboration valuable? According to the research presented here, a collaboration was considered valuable when students recognized they were learning more because of collaboration (learning benefit from peers). Specifically, learning benefit in this study was calculated as an average between items such as: “I understand more when my classmates participate in college science classroom discussions,” “The amount I understand is increased by classmates’ ideas in my college science classes,” and “When classmates share their ideas in college science classes, this helps me learn.” Learning benefit from peer ideas was both significantly and positively related to changes in their science motivation for all students. However, optimizing collaboration to ensure students experience significant learning gains can be challenging.

Several studies have explored the differences between collaborations that result in greater learning and those that do not. It is evident not all collaborative classrooms promote increased student learning (Premo et al., 2018b). Instead, the structures guiding the collaboration and the nature of student interactions are critical. One effective technique to foster valuable collaboration is by generating interdependence through jigsaw activities, where students must rely on one another. This approach has been shown to improve the accuracy of science discussions and increase the likelihood that students justify their ideas, both of which are linked to greater learning in groups (Premo et al., 2023). Moreover, students spend more time considering accurate ideas, a critical component of peer learning (Cavagnetto et al., 2022). Besides accuracy and idea consideration, the amount of time students engage with science ideas is another essential factor for effective science collaboration (Premo et al., 2022).

Thus, encouraging accurate, content-specific interactions with justification during collaboration appear to be a key to ensuring greater student learning. These may be optimized through judicious instructor use of several instructional tools. For example, curating predetermined resources to be used by students during collaboration may can reinforce interdependence without relying only on prior knowledge (Premo and Cavagnetto, 2018). This means that even students who enter the collaboration with less understanding have a higher likelihood of contributing to learning. Also providing specific response stems may be useful. These stems can guide students’ interactions and potentially make conversations more productive (Anderson and Dobie, 2022). Justification of ideas in science has been found to be an element of increased learning through discussion (Premo et al., 2023). Thus, conversational stems that require students to justify their ideas may also increase the potential learning benefit that students experience from peers.

Although structured collaboration that emphasizes science-related discussions enhances learning, the context of peer interactions also plays a crucial role. Peer relationship-building exercises have been suggested to increase students’ sense of community in science classrooms and their persistence (Allen et al., 2021a). However, increased engagement in nonscience talk during collaborative discussions has been shown to predict lower student performance in science (Premo et al., 2022). Therefore, it may be necessary to compartmentalize relationship-building activities, such as team-building exercises, from collaborative learning or application of science ideas to maximize the learning benefits of collaboration.

Science Careers are Collaborative

Publicly and explicitly acknowledging the communal opportunities in science could help FGS reconceptualize the collaborative nature of the discipline (Boucher et al., 2017). Often, undergraduate science classes focus primarily on conceptual understanding, neglecting the time needed to develop students’ grasp of science practices and cross-cutting concepts, which are essential to three-dimensional science learning (NRC, 2012; Bain et al., 2020; Cooper et al., 2024). Because the collaborative aspects of science are a critical component of science practice (Tanner et al., 2003; Ford, 2008), three-dimensional approaches to teaching undergraduate biology may be better aligned with the collaborative nature of science and, consequently, more supportive of FGS’ science motivation.

LIMITATIONS AND FUTURE DIRECTIONS

The current study aimed to explore the theoretical potential of peer interactions in the classroom on FGS’ science motivation and performance. As such, it shares limitations common to similar studies, including the lack of an explicit intervention with expected shifts based on this (i.e., a theoretically grounded exploratory approach). For example, this can be seen in the more exploratory approach taken with the correlation matrix where patterns of difference were identified and then confirmed via regression analysis. Exploratory results, like this, are limited due to post-hoc rationalization of findings. The authors believe these findings should be noted for future consideration but are more limited in scope within the current study. Additionally, this was a single-institution study, which may be more generalizable to FGS attending larger R1 institutions, as other institution types were not represented in the data. Therefore, replicating the study with a larger and more diverse sample of institutions and students would be a natural next step to enhance generalizability.

The study's results are limited by examining students within only one course, making it surprising that social experiences within this single class could track changes in science motivation. If similar findings are observed in subsequent courses, collaborative value alone could significantly contribute to FGS’ science motivation across entire programs. Future research should expand on this study to examine how collaborative value impacts FGS’ science motivation longitudinally across course sequences and programs.

Another limitation is the lack of direct measurement of the communal affordances for FGS/CGS. Although theoretical support from prior research suggests this mechanism of action, confirmation requires collecting data specifically on this construct and its function for students within these classroom settings. Despite these limitations, we believe that the current study provides nuanced insights into how collaborative value may impact FGS in college science and offers potential recommendations to better support them. Diversity in science is critical for growth in the field, and FGS have the potential to contribute to science in diverse ways as researchers and educators. Increased support in college science classrooms is essential for them to realize this potential.

Supplementary Material

cbe-24-ar39-s001.pdf (259KB, pdf)

ACKNOWLEDGMENTS

This study was funded by Washington State University's College of Veterinary Medicine Teaching Academy.

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