Student Perspectives of Success and Failure in Biology Lecture: Multifaceted Definitions and Misalignments
Abstract
Investigating definitions of success and failure among introductory biology students is essential for understanding what underlies their self-efficacy; a student who gets a B on an exam may lose self-efficacy if they define failure as anything less than an A. Yet, whether students have the same definitions for success as they have for failure in these classes is unknown, nor how those definitions relate to course performance. To better understand student definitions for success and failure and their implications, this mixed-methods study collected survey data from students in two introductory biology courses about their definitions of success and failure and their self-reported grades. Coding of open-ended responses revealed four broad themes related to both success and failure: Performance, Content, Preparation, and Attitude. Although there were common themes in how students defined success and failure overall, individual students often (65%) described success or failure in relation to different standards. We also found some definitions of success and failure were predicted by grades. These results highlight the complexity of building self-efficacy in introductory biology and suggest the need for greater awareness and acknowledgment of the different standards students use to judge their success and failure.
INTRODUCTION
Approximately 50% of undergraduate students in Science, Technology, Engineering, and/or Mathematics (STEM) programs do not graduate with STEM degrees (Chen and Ho, 2012; President’s Council of Advisors on Science and Technology, 2012; Riegle-Crumb et al., 2019). Empirical evidence has highlighted that students leave their programs due to perceptions of poor instruction, inadequate preparation, interest in other subjects, and/or a lack of self-efficacy (Seymour and Hewitt, 1997; Chemers et al., 2011; McKenzie and Schweitzer, 2001; Bandura and Locke, 2003; Shaw and Barbuti, 2010; Wright et al., 2013). Students’ decisions to leave or switch degree programs often occur at the early stages of their college career, with first- and second-year STEM students being at the highest risk of switching out of their majors (Watkins and Mazur, 2013). One challenge to this ongoing attrition crisis is an incomplete understanding of the factors that underlie students’ motivation to stay in their programs or leave.
There is an integral connection between students’ motivation and their feelings of self-efficacy (Bandura, 1997). When a student succeeds at a task, their self-efficacy increases, which in turn increases their motivation to practice and succeed at that task again (Bandura, 1977; Schunk, 1991). Students with high self-efficacy exhibit greater persistence in their degree and excel in coursework compared with their peers with lower self-efficacy (Pajares, 2005; Britner and Pajares, 2006; Wright et al., 2013). However, increasing self-efficacy hinges on our knowing what “succeeding,” or by corollary, “failing” on a task means to a student. Despite our knowledge of self-efficacy and its interaction with student motivation, little research has been done on how individual students define both success and failure, thus leaving the standards underlying self-efficacy unknown.
Previous work has examined constructs related to success and failure (e.g., perceptions [Burger and Naude, 2020; Hoare and Goad, 2022], coping [Corwin et al., 2022; Shim and Pelaez, 2022]), but few studies have asked students to explicitly define success or failure. Furthermore, while studies have focused on facets of student perceptions of failure in lab courses (Gin et al., 2018; Henry et al., 2019; Henry et al., 2021; Corwin et al., 2022), few have examined student definitions of success or failure in lecture courses, which often comprise the larger portion of a students’ grade and are centered on different learning outcomes (e.g., learning foundational science concepts vs. gaining scientific process skills). Lab courses typically expect and allow for mistakes to be made during investigations; however, in lecture courses, failure on assessments affect a student’s grade and is often not normalized as part of learning. Thus, there is a need to better understand how students define both success and failure within STEM lecture courses.
We contend that studying definitions of success and failure simultaneously is important because although failure is commonly perceived as not achieving success (Cannon and Edmondson, 2005; Henry et al., 2019; Corwin et al., 2022) there is little empirical evidence supporting the assumption that the definitions are inherently aligned for students. Given the foundational nature of introductory courses and their outsized impact on students’ retention and motivation to persist, this study sought to explore student definitions of success and failure in introductory biology lecture courses. Our goal was to use these data to make inferences about student motivations in introductory biology lecture classes to achieve success or avoid failure.
Self-Efficacy Theory
Self-efficacy theory is part of social cognitive theory (Bandura, 1986) and states that confidence to perform some task (i.e., self-efficacy for that task) is integral to success on that task. A students’ self-efficacy influences their motivation to perform a task and their ability to complete that task successfully (Bandura, 1977; Schunk, 1991; Figure 1a). So according to self-efficacy theory, a student with low self-efficacy for performance on an exam may have low motivation to study for the exam and will likely do poorly on the exam; this reinforces their low self-efficacy (Boekaerts and Rozendaal, 2010). We will be calling this a “low self-efficacy feedback loop.” In contrast, if a student has high self-efficacy for exam performance, they will be motivated to study, and may succeed at that task. According to theory, success will reinforce and increase their self-efficacy (Bandura, 1991). We will be calling this a “high self-efficacy feedback loop.” Inherently important to the theoretical outcomes explained above is that the standard for success and failure is the same and is in reference to the same task.
FIGURE 1: Diagrams detailing self-efficacy theory as described by Bandura 1977. In 1a, high or low self-efficacy is determined by success or failure on the same task and using the same standard (defined as doing well or not well on an exam). Accordingly, high or low motivation is determined by the same standard. We consider this an example of aligned definitions of success and failure. In 1b, high or low self-efficacy is determined by two different standards (misaligned): this student defines success as doing well on an exam but defines failure as not learning the content. This means that high and low-self-efficacy (and motivation) is being driven by performance on two different tasks. Each student definition is composed of both the task they associate with success and failure (e.g., the exam) and the standard by which they are judging their outcomes (e.g., doing well).
Self-efficacy theory commonly focuses on standards for success for specific tasks, and infers that failure is not meeting the standard, thus implying that success and failure are seen by students as opposite outcomes of a single standard. This assumption means that high and low motivation and self-efficacy are also based on the same standard (Figure 1a). However, there has been little empirical exploration of this assumption. Hypothetically, if a student judges success by one standard (e.g. doing well on an exam), and failure by a different standard (e.g. not learning the content), these “misaligned” standards make self-efficacy in introductory biology more complicated. High self-efficacy would be reinforced by doing well on an exam, while low self-efficacy would be reinforced by not understanding the content (Figure 1b); self-efficacy for this student would be influenced by one standard for success and an entirely different standard for failure. Our study explicitly sought to determine whether student definitions of success and failure (which articulate both the task and the standard) were aligned or misaligned for individual students.
Since student definitions of success and failure set a standard upon which they judge their outcomes, different definitions could also impact students’ performance and ultimately their grades. Bandura (1977) documented several ways to build self-efficacy: vicarious experiences, verbal persuasion, physiological states/emotional arousal, and performance accomplishments. The most impactful mechanism is through performance accomplishments (Bandura, 1977; Britner and Pajares, 2006; Usher and Pajares, 2006; van Dinther et al., 2011). Performance accomplishments are when a person succeeds at a task; in introductory biology, performance on exams, tests, and quizzes typically form the largest part of their grade in the course. While the link between high self-efficacy and course performance is known (McKenzie and Schweitzer 2001; Pajares, 2005; Britner and Pajares, 2006), how student definitions of success and failure might be linked with grades is unknown. If a student defines success as getting a C on an exam, they are more likely to increase their self-efficacy than some of their peers with higher standards. Therefore, this study examined whether student grades were related to definitions of success and failure.
Recognizing and considering the definitions of success and failure among students in introductory biology courses is essential for understanding the intricacies of self-efficacy development. Each student’s unique perspective influences how they define success or failure, shaping the feedback loops that impact their self-efficacy and motivation. This nuance informs the need to understand the criteria students use to evaluate their performance. By acknowledging individual differences, educators can help students attain and succeed at the tasks that increase their self-efficacy.
Success and Failure
Studies have explored the concepts of success and failure, yet to our knowledge that work has not examined students’ definitions of success and failure in introductory biology lectures. Here we highlight some studies on success and failure in the sciences while noting the gaps applicable to this study.
Historically, researchers and instructors have been interested in increasing student success, yet few have investigated how students define success (Weatherton and Schussler, 2021). One study utilized focus groups to understand undergraduate student perceptions of success and found that success was defined as engagement, relationships and empowerment, health and wellbeing, economic, academic, navigating institutional process, and personal growth and resilience (Hoare and Goad, 2022). In a review of the literature (N = 52 articles) on how success was defined in the life sciences, Weatherton and Schussler (2021) found four themes: academic, persistence, career, and social. They emphasized that most of these definitions were conceptualized by experts conducting research on student success, thus perpetuating expert ideas of student success rather than learning how students define success. In a later study, Weatherton and Schussler (2022) examined graduate student definitions of success in life science programs and found graduate students viewed success as encompassing many facets of success such as “being happy” instead of only “academic achievement.” This work highlights that students have varied definitions of success yet, little work has focused on learning about those definitions.
Historically, “failure” has been assumed to simply be a lack of success. Therefore many scientists, especially those in training (i.e., students in STEM disciplines), have negative connotations associated with failure (Corwin et al., 2022; Shim and Pelaez, 2022). It also means that little research has explicitly asked about definitions of failure. Corwin and colleagues (2022) explored students’ coping responses to failures and challenges in a course-based undergraduate research experience. They found that when students encountered a challenge or failure, students often experienced negative emotions, but then used adaptive coping mechanisms such as repetition and emotional regulation to work through the challenge. Despite negative feelings about failure, some undergraduate biology students felt their classwork was “more authentic” when they engaged with opportunities for failure within lab courses (Goodwin et al., 2021; Von der Mehden et al., 2023). Thus, while some undergraduates may have negative perceptions of failure initially, experiencing this aspect of the scientific process is seen as critical for their development as scientists. While perceptions of failure have been explored in the context of lab classrooms and research experiences (Gin et al., 2018; Corwin et al., 2022; Shim and Pelaez, 2022), little is known about how undergraduate biology students define failure in their lecture courses, and whether these conceptions are diametrically opposed to their perceptions of success.
Currently, instructors design courses based on their own, and likely administrative, definitions of what it means to be a successful introductory biology student. Yet, students enter courses with diverse expectations and motivations (Galotti and Umscheid, 2019; Lee et al., 2021), and their definitions of success and failure may vary widely. One might assume that a student who does not achieve a particular grade in a course has experienced failure. However, because definitions of failure are rarely explicitly articulated, there is little to support this claim. It may be that the student achieved success in terms of their personal standard for a grade but failed in some other course aspect; without an understanding of this nuance, educators will not be equipped to help this particular student. Without more clarity about how students define success and failure, educators and researchers may be making assumptions that could negatively impact student success.
Study Rationale
This work will elucidate student definitions of both success and failure in two introductory biology classes. We undertook this study because student definitions of success and failure could provide insight into supporting and increasing motivation and self-efficacy in introductory biology. To do so, we need to know the standards by which students judge success or failure. We also suggest that students with misaligned standards of success and failure may have more complicated routes toward motivation and self-efficacy, and that knowing more about this complexity is potentially important. Finally, given the literature linking self-efficacy and grades, we wondered whether certain standards of success and failure may be more likely if a student has one grade or another. Specifically, this study sought to answer the following research questions:
How do undergraduate students define success and failure in their introductory biology lectures?
Are individual student definitions of success and failure aligned?
How do students’ self-reported grades in the course relate to student definitions of success and failure?
Answering these questions may help identify student motivational factors that can be integrated into course design in order to promote a more positive and effective experience for students.
MATERIALS AND METHODS
Course Descriptions and Recruitment
The participants for this study were enrolled in several large lecture sections of introductory organismal biology and cellular biology courses (typically ∼90% STEM majors) at a research-intensive institution in the southeastern United States. Both courses are typically taken by biology students in their first or second year and are requirements of the biology major. The courses are highly coordinated across sections and use common learning objectives based on Vision and Change Core Concepts and Competencies (American Association for the Advancement of Science, 2011). Course assessment is typically 30–40% exams or quizzes, 25% discussion (coordinated across sections), and 35–45% homework and in-class assignments. Assessments, lecture content, homework, and assignments are instructor-created, but instructors meet regularly and generally strive toward similarity in course delivery.
The survey was disseminated in spring 2023 to six lecture sections. The organismal course (first in the sequence) had three lecture sections with 232, 230, and 127 students enrolled, with the first two sections taught by the same instructor. The cellular course had sections with 149, 156, and 232 students enrolled, and all sections were taught by different instructors. There were 1126 total students enrolled in the courses who received the survey.
The survey was distributed April 3, 2023, by instructors to students via Qualtrics (Supplemental Materials S1). Student responses were anonymous to the researchers, but students submitted receipts of completion to their instructors to receive one extra credit point in the course, out of a potential 1000 points total. This study was approved under Institutional Review Board protocol #22-07163-XM.
Development of the Survey Instrument
During the fall 2022 semester, we created a pilot survey and distributed it via Qualtrics to students in two large biology lecture courses. These questions asked, “What does success in your biology lecture course look like to you?” and “What does failure in your biology lecture course look like to you?” To provide validity evidence for the success and failure questions, two researchers coded the open-ended responses from the pilot survey (N = 40) using inductive coding methods (Saldaña, 2021). Inductive coding uses ideas mentioned in student responses as codes, so that the original meaning from the group of interest is not altered (Saldaña, 2021). The two researchers (B.V.D.M. and M.S. [see Acknowledgments]) independently read the dataset in its entirety multiple times, looking for common ideas. They each individually converged on categories that they thought represented the student responses about success and failure; these were represented by the codes. The researchers then met to compare codes and found that they had identified similar ideas. In addition, the codes for the success definitions also adequately captured student ideas about failure. This information was formalized into a codebook and used to fully code the pilot data.
We found that the survey questions from the pilot elicited student definitions of success and failure as intended. We altered one word (changed “to” to “for”) in each question for clarity as suggested by an expert in the field. The pilot responses and expert advice on survey questions served as two pieces of evidence of validity for our survey questions (American Educational Research Association, 2014).
The survey for this study (spring 2023) had 14 questions total. Three of those questions were used for analysis, the remainder of the questions were either used to collect sociodemographic information or were not used in this study. One question was multiple choice and two were open-ended (Supplemental Materials S1). The multiple choice question asked, “At this point in the semester, what grade do you think you have in your biology lecture course?” to which students could select a letter grade (A–F). The open-ended survey questions asked, “What does success in your biology lecture course look like for you?” and “What does failure in your biology lecture course look like for you?” The survey also included questions about students’ sociodemographic information (gender, race/ethnicity, age, first-generation college student status, year in college, grade point average, and major).
Positionality Statement
Although every effort was made to bracket any researcher biases, qualitative data analysis can be subjective because the researcher is the tool by which the data are interpreted (Patton, 2014). Two of the authors conducted the analyses: B.V.D.M. and K.W. The first author (B.V.D.M.) is a Ph.D. student focused on discipline-based education research while situated in a traditional biology department. She has an undergraduate and master’s degree in biology and has taught several introductory biology courses. The second coder (K.W.) was an undergraduate student enrolled in introductory biology at the time of coding, though K.W. did not participate in the survey. Neither author was involved in course delivery.
Qualitative Analyses
Given that the open-ended questions asking about student definitions of success and failure were almost the same as the pilot survey, the codebook from the pilot study was used as the starting point for analysis for this study. Coding procedures were similar, with independent initial coding of portions of the data, comparison of results, iterative improvement of the codebook, and then coding of all data. B.V.D.M. and K.W. independently coded the same 100 selected responses spanning multiple biology lectures and both success and failure responses using the pilot codebook. B.V.D.M. then calculated coder agreement, discussed the results with K.W., and they adjusted the codebook together. Changes to the codebook consisted of small verbiage refinements and adding additional examples to further clarify the code definitions. Each continued independently coding new sets of 100 responses, coming together to check agreement, and adjusting the codebook until coder agreement was above 70%. Coder agreement was calculated by the number of times both coders had identical codes for the entire coding frame (each students’ response to one question), divided by the total number of coding frames multiplied by 100. Once coder agreement was above 70%, the codebook was not altered any further (Supplemental Materials S2). At that time, both researchers coded the entire dataset, and then B.V.D.M. calculated coder agreement (77%) on the completed dataset. All coding disagreements were resolved through discussion between B.V.D.M. and K.W. until consensus was reached. After coding was complete, B.V.D.M. categorized codes into broader themes. These themes were discussed with a group of biology education researchers as a form of validity evidence (American Educational Research Association, 2014).
Analyses for RQ 1: How do Undergraduate Students Define Success and Failure in Their Introductory Biology Lectures?
We calculated descriptive statistics to summarize the frequency of success and failure codes for the entire dataset. We did the same for the themes.
Sociodemographic characteristics are known to influence student perceptions of self-efficacy (Britner and Pajares, 2006; D’Lima et al., 2014; Edman and Brazil, 2007). To explore whether students with different sociodemographic characteristics mentioned different codes, we calculated the frequency of code incidence for the following sociodemographic characteristics: gender identity, college generation status, course, major, race/ethnicity, and year in college. To further examine the effects of sociodemographic characteristics on code incidence in students’ definitions, we included the sociodemographic characteristics as predictors in the binomial regression analyses described below (see RQ 3).
Analyses for RQ 2: Are Individual Student Definitions of Success and Failure Aligned?
The previous analysis allowed us to explore success and failure definitions across all students, but we also wanted to understand how individual students defined success and failure to understand whether they conceptualized them the same way. To do so, we decided an “aligned” definition was when a student response included the exact same codes to define success and failure. When there were differences in the success and failure codes, they were considered “misaligned.” We undertook the same analysis using the themes as well to provide evidence of reliability of the findings (American Educational Research Association, 2014). We calculated descriptive statistics to summarize the number of students with aligned and misaligned code and theme definitions.
Analyses for RQ 3: How do Students’ Self-reported Grades in the Course Relate to Student Definitions of Success and Failure?
We used binomial regression models with a logit link function to test whether student self-reported grades or sociodemographic characteristics (gender, college generation, course, major, race, year in college) were related to the incidence of specific success or failure codes. We grouped certain subcategories of sociodemographic characteristics together because of small numbers in some of the groups. In the self-reported grade category, we combined students that reported having D’s in the course with students that reported having F’s in the course; thus our self-reported grade categories were A, B, C, and D’s and F’s. Within the gender category, we removed students who did not identify themselves as either men or women because of the small number of participants in those groups. College generation was either first generation or continuing generation. Course was organismal or cellular. Within the major category, we could only differentiate whether students were biology majors or not. Within the race and/or ethnicity category, we removed the students that identified as Hispanic or Latine or other Spanish origin because of small sample size. We did not feel that combining groups for this category was appropriate, so our categories were Asian, Black, Multiracial, and White. Last, in the year category, students in the third, fourth, and fifth+ years of their degree were combined due to small numbers in each of those groups, so our subcategories were first, second, and third or above.
To ascertain model fit, we started with a full model (example below) and used a backward, stepwise model selection approach, dropping one variable at a time until the most parsimonious model (lowest Akaike information criterion [AIC]; ∆ AIC ≥ 2) was reached (Supplemental Table S3; Johnson and Omland, 2004). This approach was used to determine the combination of self-reported grades and sociodemographic characteristics that were most predictive of each code students mentioned in their definitions of success and failure. This approach produced a unique set of predictors for each model. An example of the full model, before model selection, is below.
All predictor variables in the models returned a variance inflation factor (VIF) factor lower than the accepted cutoff (VIF < 5), thus we concluded that there was no multicollinearity present among our predictor variables (Dupuis and Victoria-Feser, 2013). All statistical tests were performed in R v. 4.3.1 (R Core Team, 2023) using the packages stats (R Core Team, 2023), MASS (Venables and Ripley, 2002), bblme (Bolker and R Development Core Team, 2023).
RESULTS
Overall, we found that many students had misaligned definitions of success and failure that sorted broadly into four themes (Performance, Content, Attitude, and Preparation) and that there was a relationship between students’ self-reported grades in the course and some definitions of success and failure. These findings are detailed below.
Participants
The response rate for the survey was 79%, with 890 completed survey responses. Participants were primarily women (72.9%), White (75.2%), continuing-generation college students (81.3%), who were non-biology majors (63.9%), and in their first year of college (64.2%; Table 1).
| Sociodemographic characteristics | % (N = 890) | |
|---|---|---|
| Gender | Women | 72.9% |
| Men | 25.7% | |
| Non-binary | 1.4% | |
| Race and/or Ethnicity | Asian | 7.0% |
| Black | 5.3% | |
| Hispanic or Latine or Spanish origin | 2.6% | |
| Multiracial | 7.3% | |
| White | 75.2% | |
| College generation | Continuing gen | 81.3% |
| First-gen | 16.5% | |
| Does not know or preferred not to respond | 2.2% | |
| Year in college | First year | 64.2% |
| Second year | 21.4% | |
| Third year | 11.6% | |
| Fourth year | 1.9% | |
| Fifth year and beyond | 0.9% | |
| Major | Non-Biology | 66.2% |
| Biology | 33.8% |
1) How do Undergraduate Students Define Success and Failure in their Introductory Biology Lectures?
Across the entire dataset, students defined success and failure broadly using standards related to Performance, Content, Preparation, and Attitude. These four themes were composed of eight codes (Table 2).
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The theme Performance was the most frequent way that students in this study talked about both success (82%) and failure (81%) (Figure 2; Supplemental Figure S1). Student standards within the theme Performance had three separate conceptions, each representing a code in this study: passing the course, excelling in the course, and performance on assessments. “Pass,” “excel,” and “assessment” codes were used when students talked about how their performance in the course dictated their perception of success or failure. The code “pass” was used when students mentioned that success or failure was defined by passing the course or earning a grade of C in the course. One student response coded as “pass” said, “Failure is not being able to pass.” The code “excel” was used when students described success or failure using the benchmark of either earning less than or greater than 80% in the course. For example, one student response coded as “excel” said “Success for me is getting good grades which is a B or higher.” The code “assessment” was used when students mentioned their performance on homework, quizzes, or exams. One student response coded as “assessment” said “[Success is] [p]assing all exams and doing well in (sic) assignments given.” All of these examples mention that the students’ performance was a key component of their definition of success or failure.
FIGURE 2. Student responses to Success and Failure questions by code. Success responses are depicted by lighter hues and are labeled “S”. Failure responses are depicted by darker hues and are labeled “F”. Codes within the theme Performance are shown in blue. Codes within the theme Content are shown in orange. The code Preparation is the only code in the theme Preparation and is shown in green. The code Attitude is the only code in the theme Attitude and is shown in purple.
The theme Content had the second-highest frequency in the dataset (Figure 2). Content standards that were mentioned by students and separated into separate codes were: comprehending the material, applying concepts to future situations, and recalling information. “Comprehension,” “application,” and “recall” were all codes that were used when a student mentioned understanding or using the knowledge they learned from the course. Students were more likely to mention the theme Content when defining success (71%) than when they defined failure (52%). The code “comprehension” was used when students mentioned understanding the material in the course. One example of a response coded with “comprehension” was, “If I understand the concepts we cover in lecture or develop a better understanding of a certain topic I previously struggled with, I consider that a success.” The code “application” was used when students mentioned using the material they learned in future scenarios. One example of a response coded with application was, “Success in this course would allow me to have the knowledge to move on to a higher-level course and use knowledge in real world interactions.” The code “recall” was used when students mentioned the ability to remember the information from the course. One example of a response coded with recall was, “Failure in this course looks like not retaining any information learned in this course.” In these examples, students mentioned that some form of understanding the material in the course was an important component of their perceived success or failure.
Students mentioned the themes of Preparation and Attitude less frequently compared with the other themes (Figure 2), and there were no separate codes for each. The theme/code “Preparation” was used when students mentioned studying, or notetaking as an important aspect that related to success and/or failure. One student said, “[Failure for me is] not attending class and not reviewing notes.” The theme/code “Attitude” was used when students mentioned their confidence or how hard they were trying in the class. For example, one student said, “Success in my biology lecture course looks like pushing myself to my maximum capabilities and doing as best in the class as I know I can do.” These themes emphasized that preparation in the course and the effort a student went through were both important aspects in their definitions of success and failure.
Although we found the common codes students used to define success and failure in the dataset, we did not find an equal frequency of those codes overall; some codes were mentioned more frequently when defining success, and others more frequently when defining failure (Figure 2). For example, students mentioned the code “excel” more frequently when they were defining success (34%) compared with when they were defining failure (18%). In addition, students mentioned the code “pass” more frequently when defining failure (40%) compared with when they were defining success (24%).
Code incidence also varied by sociodemographic characteristics (Supplemental Tables S1 and S2). Results from the binomial regressions (RQ 3) indicated that women were more likely than men to mention the code “assessment” when defining success (Table 3). Students in the organismal course were more likely to mention “recall” when defining success than students in the cellular course. Women and Biology majors were more likely than men and non-biology majors to mention “comprehension” in their definitions of success. Students in the organismal course mentioned “Preparation” more often in their definitions of success than students in the cellular course.
| Final “Success” models | Significant predictor | Predictor category | Reference | Odds ratio | Standard error | 95% CI | P-value |
|---|---|---|---|---|---|---|---|
| Pass ∼ Grade + Gender + Course + Major | Self-reported grade | B | A | 1.674 | 1.221 | (1.14–2.49) | <0.05 |
| C | A | 2.039 | 1.299 | (1.22–3.40) | <0.05 | ||
| D and F | A | 3.592 | 1.469 | (1.67–7.60) | <0.001 | ||
| Excel ∼ Grade + Gender + Generation + Race + Course + Major | Self-reported grade | B | A | 0.512 | 1.179 | (0.38–0.72) | <0.001 |
| C | A | 0.446 | 1.278 | (0.27–0.72) | <0.05 | ||
| D and F | A | 0.163 | 1.732 | (0.05–0.43) | <0.001 | ||
| Assessment ∼ Grade + Gender + Year + Race + Course + Major | Gender | Men | Women | 0.584 | 1.234 | (0.38–0.87) | <0.05 |
| Recall ∼ Grade + Gender + Generation + Course + Major | Self-reported grade | B | A | 3.916 | 1.588 | (1.69–10.69) | <0.05 |
| Course | Cellular | Organismal | 0.379 | 1.485 | (0.17–0.79) | <0.05 | |
| Comprehension ∼ Gender + Course + Major | Gender | Men | Women | 0.683 | 1.176 | (0.50–0.94) | <0.05 |
| Major | Not Biology | Biology | 0.696 | 1.162 | (0.52–0.94) | <0.05 | |
| Preparation ∼ Race + Course + Major | Course | Cellular | Organismal | 0.611 | 1.266 | (0.38–0.96) | <0.05 |
| Final “Failure” Models | Significant predictor | Predictor category | Reference | Odds ratio | Standard error | 95% CI | P-value |
| Pass ∼ Grade + Gender + Race + Course + Major | Self-reported grade | B | A | 1.901 | 1.182 | (1.37–2.64) | <0.001 |
| C | A | 2.139 | 1.255 | (1.37–3.34) | <0.001 | ||
| D and F | A | 2.289 | 1.450 | (1.10–4.75) | <0.05 | ||
| Course | Cellular | Organismal | 1.485 | 1.159 | (1.11–1.99) | <0.05 | |
| Excel ∼ Grade + Gender + Race + Course + Major | Self-reported grade | B | A | 0.442 | 1.221 | (0.30–0.65) | <0.001 |
| C | A | 0.312 | 1.395 | (0.16–0.58) | <0.001 | ||
| D and F | A | 0.173 | 2.103 | (0.03–0.59) | <0.05 | ||
| Race | Black/African American | White | 0.205 | 2.083 | (0.03–0.68) | <0.05 | |
| Assessment ∼ Grade + Gender + Year + Course + Major | Gender | Men | Women | 0.531 | 1.244 | (0.34–0.80) | <0.05 |
| Recall ∼ Gender + Generation + Year + Race + Course | Course | Cellular | Organismal | 0.468 | 1.480 | (0.21–0.98) | <0.05 |
| Comprehension ∼ Gender + Year + Course + Major | Gender | Men | Women | 0.650 | 1.183 | (0.47–0.90) | <0.05 |
| Application ∼ Race + Course | Race | Multiracial | White | 5.504 | 1.698 | (1.83–15.1) | <0.05 |
| Preparation ∼ Gender + Generation + Course + Major | Course | Cellular | Organismal | 0.573 | 1.256 | (0.36–0.89) | <0.05 |
Similarly, sociodemographic characteristics impacted students’ definitions of failure. Students in the cellular course were more likely than students in the organismal course to mention “pass” when defining failure (Table 3). White students were more likely than Black students to mention “excelling” in their definition of failure. Women were more likely than men to mention “assessment” in their failure definitions. Students in the organismal course were more likely than students in the cellular course to mention “recall” in their definitions of failure. Women were more likely than men to mention “comprehension” in their definitions of failure. Multiracial students were more likely than White students to mention “application” in their definitions of failure. Students in the organismal course were more likely than students in the cellular course to mention “Preparation” in their definitions of failure.
2) Are Individual Student Definitions of Success and Failure Aligned?
Using the standard of perfect alignment between success and failure codes for a student response, we found that 34.5% of students defined success and failure using the same codes (Table 4). Correspondingly, we found that 65.5% of students had misaligned definitions for success and failure. Looking only at alignment in themes, 59.5% of students had definitions that were perfectly aligned by theme and 40.5% were misaligned by theme.
3) How do Students’ Self-Reported Grades in the Course Relate to Student Definitions of Success and Failure?
| Alignment by codes and themes | Definition of success | Success codes (Themes) | Definition of failure | Failure codes (Themes) |
|---|---|---|---|---|
| Code alignedTheme aligned | Success in my biology course looks like passing the exams and making good scores on all my assignments while also having a great understanding of the course’s material. | Assessment, Comprehension (Content) | Failure in my biology course looks like not passing the exams and scoring poorly on the assignments. It also looks like having little to no understanding of the course material. | Assessment, Comprehension (Content) |
| Code alignedTheme aligned | Passing all my assignments. | Assessment (Performance) | When I get less than a 75 on assignments. | Assessment (Performance) |
| Code alignedTheme aligned | Getting good grades and understanding the material. | Pass, Comprehension (Content, Performance) | Getting not so good grades and not being able to understand all of the material. | Pass, Comprehension (Content, Performance) |
| Code misalignedTheme aligned | Getting an A or a B in the class. | Excel (Performance) | Not passing. | Pass (Performance) |
| Code misalignedTheme aligned | Success in my biology lecture to me is getting good grades on my exams and in the class. | Assessment, Pass (Performance) | Failure in this course to me would look like getting a C in the class. The lowest grade I would want would be a B. | Excel (Performance) |
| Code misalignedTheme misaligned | Understanding the content after lecture and throughout bio lit groups. | Comprehension (Content) | Bad grades. | Pass (Performance) |
| Code misalignedTheme misaligned | Success in my biology class to me looks like getting a good grade after trying my hardest and working towards it. Also, working to understand the concepts and getting help when I need it. | Pass, Attitude, Comprehension, Preparation (Content, Performance, Attitude, Preparation) | Failure looks like doing bad on the exams and not getting help, not understanding concepts and struggling to understand the class. | Assessments, Preparation, Comprehension (Content, Performance, Preparation) |
The models that included students’ self-reported grades better predicted code incidence for the codes: “pass,” “excel,” and “recall” (Table 3).
Definitions of Success.
Students that self-reported having “D’s and F’s” at week 8 had the highest likelihood of defining success as passing the course (i.e., getting a C in the course was the definition; Table 3). One student that reported having a D said, “It looks like passing for me, science isn’t my strong suit.” Students with A grades had the highest likelihood of defining success as excelling (i.e., an A or B in the course was defined as success). One student that reported having an A said, “To me success would look like getting an A in the course.” Students with B grades had the highest likelihood of defining success as being able to recall the material (i.e., being able to recall the knowledge learned in the course was defined as success). One student that reported having a B said, “Success for me is being able to retain the information I learn so that I am able to carry it into later courses.”
Definitions of Failure.
Students that self-reported having “D’s and F’s” had the highest likelihood of defining failure as not passing the course (i.e., earning a D or an F in the course was defined as failure). One student that reported having an F said, “I would classify failure in this course as failing the class. Since [the cellular course] specifically is filled with so much content, it is easy to fail one test or assignment, that happens to everyone. However, failing the course would be total failure.” Students with A grades had the highest likelihood of defining failure as not excelling (i.e., anything below earning a B in the course was defined as failure). For example, one student that reported having an A said, “[Failure in my biology course is] getting below a B− in the class.”
The code “attitude” could not be predicted by self-reported grades or sociodemographic characteristics for both definitions of success and failure. The code “application” could not be predicted by self-reported grades or sociodemographic characteristics when students mentioned it in their definition of success.
DISCUSSION
This study investigated student definitions of success and failure in introductory biology lecture courses to understand more about students’ perspectives and factors driving self-efficacy in introductory biology. We found that students defined success and failure in their introductory biology courses using four broad themes: Performance, Content, Preparation, and Attitude. Although there was broad alignment in the definitions of success and failure across the dataset, individual student definitions of success and failure were often misaligned, which has potential implications for what drives self-efficacy. We also found that in some cases, the codes describing success and failure were significantly related to students’ perceptions of course performance. Overall, these results add nuance to our understanding of student definitions of success and failure in introductory biology and have implications for our understanding of student motivations and pedagogical practices.
RQ 1: How do Undergraduate Students Define Success and Failure in their Introductory Biology Lectures?
We found gender, race and/or ethnicity, course, and major were related to some definitions of success and failure. Students of different genders and races and/or ethnicities are likely affected by societal and cultural differences which may shape their perceptions of various aspects of a course including their definitions of success or failure. Other studies have found that race and/or ethnicity and gender relate to students’ perceptions of self-efficacy (Britner and Pajares, 2006; Edman and Brazil, 2007), motivation (D’Lima et al., 2014), and definitions of success (Weatherton and Schussler, 2021). Our study suggests that perceptions of assessments and comprehension of the material may drive some perceptual differences between genders, but White and non-White students only differed in their definitions of failure when they mentioned the codes “excel” and “application”. This adds to the literature regarding what aspects of a course might be perceived differently by each group. Students at this institution typically take the organismal course their first semester, followed by the cellular course. This means students in the organismal course are likely to have less experience in biology courses, which has been shown to influence perceptions of failure (Von der Mehden et al., 2023). In this study, we found that students in the organismal course were more likely to mention recall of material and preparation when defining both success and failure compared with students in the cellular course. Last, students in biology majors compared with non-biology majors likely have different goals for themselves in introductory biology which would influence their perceptions (Knight and Smith, 2010). In our study, we found the difference to be in perceptions of success. Specifically, biology majors were more likely to mention comprehension in their definitions of success than non-biology majors. Ultimately, more work is needed to understand how and why sociodemographic characteristics influence definitions of success and failure in introductory biology courses, but this study hints at course aspects to investigate in the future.
The themes Performance and Content were the most frequently mentioned by students when they defined success and failure. Most studies that use expert definitions of success also define it based on student performance in their courses and content understanding (Dean et al., 1998; York et al., 2015; Weatherton and Schussler, 2021). Therefore, students defining success and failure in terms of their course performance and content understanding is not surprising and likely reflects the reality that students exist within a structure that values those outcomes. These two themes can also be linked to goal orientation theory, where the performance theme aligns with performance orientation and the content theme aligns with mastery orientation (Pintrich and Schunk, 1996). Performance orientation is when students are motivated by achieving an external reward or validation (e.g., grades) and mastery orientation is when students are motivated by intrinsic learning for the sake of gaining new knowledge or skills (Pintrich, 2000a, b). Studies suggest that students with mastery orientations rather than performance orientations perform better in their courses (Mattern, 2005), do better in the face of failure (Niiya et al., 2004) and are highly motivated (Mikail et al., 2017). In the future it may be interesting to investigate how students that mention performance versus content in their definitions of success and failure compare in their self-efficacy and motivation. But beyond Performance and Content themes, our results revealed that student definitions were multidimensional and included more facets than grades and content understanding alone.
Students also defined success and failure related to their effort (Attitude) and the practices they engaged in to perform well and understand the content (Preparation). This reinforces findings from previous studies that suggest students care about additional dimensions of success and failure compared with experts (Dean et al., 1998; Brauer et al., 2022; Hoare and Goad, 2022; Weatherton and Schussler, 2022). These findings could have implications for course design; courses that help build motivation toward performance and content understanding, but neglect other factors important to students, such as attitudes toward learning and learning how to study, may foster inequitable outcomes among students. By acknowledging other forms of success, courses could be designed to help students build self-efficacy in multiple ways. For example, creating a welcoming and inclusive classroom environment (Ambrose, 2010) could be motivating for students who define success as having a positive attitude toward learning. Similarly, the use of active learning has been shown to increase performance and self-efficacy (Wilke, 2003; Freeman et al., 2014) likely through its use of formative feedback that encourages peer discussion and student reflection of their understanding of the material. Perhaps one way to increase self-efficacy and motivation across all students is by integrating course design features that build self-efficacy in each of the four themes identified in this study. However, more research is needed on these research findings and how they might be used in the classroom.
RQ 2: Are Individual Student Definitions of Success and Failure Aligned?
Overall, students in the study defined success and failure using the same codes and themes, which may suggest broad alignment across an introductory student population. However, when we examined definitions by individual students, more than half of the students defined success as one standard and failure as a different standard. Even when using a less stringent definition of alignment, theme alignment, we still found a large proportion of the students had misaligned definitions. We chose two student responses to illustrate how these aligned and misaligned definitions may impact their self-efficacy feedback loops. One example of an aligned student response (Figure 3) shows Dorothy whose self-efficacy in introductory biology is driven by both excelling and comprehension; she defines both success and failure using those as her standards. This means Dorothy’s self-efficacy feedback loops are aligned. We suggest this means she has a lack of dissonance in her goals for introductory biology success. However, most students in this study did not have aligned definitions.
FIGURE 3. An example of one student’s aligned definitions for success and failure and how those definitions interact with self-efficacy theory and self-efficacy outcomes.
Violet is one of the students with misaligned definitions from this study and she represents the majority of our participants. Violet defines success as understanding content, while she defines failure as not performing well in the course (Figure 4). In Violet’s case, she may be doing well in understanding the content, which would contribute to feelings of high self-efficacy, but at the same time, she could be getting low grades which would contribute to feelings of low self-efficacy. This leaves Violet in a complicated situation where self-efficacy in introductory biology is driven by multiple, concurrent standards. She may experience misaligned self-efficacy feedback loops, which we suggest might lower her self-efficacy overall.
FIGURE 4. An example of one student’s misaligned definitions for success and failure and how those definitions interact with self-efficacy theory and self-efficacy outcomes.
These findings may have implications for how student definitions of success and failure fit into the context of self-efficacy theory. Self-efficacy theory implies that if one reaches their goal, they succeed, and, implicitly, if they do not reach the goal, they fail (Bandura, 1997). Yet, we found that many students have explicitly different standards for defining success and failure. This introduces an interesting question of the salience of explicit and implicit standards. If a student has completely misaligned definitions of success and failure, does not attaining success impact self-efficacy as an implicit failure, or is it less salient than the explicitly articulated definitions of failure? Although our results suggest that one standard is not necessarily the lack of the other, the extent to which this is true needs to be further explored. If the misaligned standards are most salient to the student, we suggest the need for research on inherent conflicts between balancing different success and failure goals and how this impacts self-efficacy in introductory biology.
Misaligned definitions potentially make building self-efficacy in introductory biology much more complicated than previously assumed. We posit that this may be because the misaligned definitions result in different motivators for success and failure, and that those motivators may sometimes be in conflict. However, more research needs to be conducted to probe potential conflicts between definitions and motivators. It may be that, in practice, one of the standards for success or failure is more important than the other and overrides any inherent conflict.
RQ 3: How do Students’ Self-reported Grades in the Course Relate to Student Definitions of Success and Failure?
Students’ self-reported grades at week 8 emerged as significant predictors for the incidence of three codes (excel, pass, and recall). It is unclear whether students mentioned certain codes more or less frequently due to their performance up to week 8 (when the survey was distributed) or if their initial ideas of success and failure at the beginning of the course influenced their performance. We posit that students likely had a definition of success and failure going into the course, but that those definitions may have changed as students began to experience the course and receive grades. Changes in student definitions of success and failure could be due to performance in the course which is known to affect self-efficacy (Bandura, 1977; Britner and Pajares, 2006; Usher and Pajares, 2006; van Dinther et al., 2011), changes in students’ goal orientations (Pintrich and Schunk, 1996), and/or students’ interactions with faculty in the course (Dresel et al., 2013). Future studies should examine student goals and motivations before and after receiving course performance feedback to further understand the malleability of these perceptions over time.
The relationship between students’ grades and their definitions of success and failure reinforces and adds to the principles of self-efficacy theory, as it highlights how definitions of success and failure shape judgments of outcomes (grades) and in turn shapes self-efficacy in introductory biology. However, grades did not predict code frequency for many of the codes. This suggests that while grades can be indicative of performance-related aspects like excelling and passing, they may not comprehensively represent the broader spectrum of factors that students consider when defining their own success. One strategy to validate student definitions of success and failure is the practice of ungrading (Blum and Kohn, 2020). Ungrading often honors each students’ goals through one-on-one conversations between student and instructor and usually includes opportunities for students to iterate and retest challenging objectives. Opportunities to iterate and retest allows students to the opportunity to fail, but includes the flexibility for them to adapt and change their study methods. Instructors can then evaluate students based on an individualized scale and assign a grade based on a student’s improvement and other factors important to the student and instructor. The connection between students’ grades and their definitions of success and failure highlights the potential benefits of alternative approaches like ungrading to better recognize and validate individual student goals.
Limitations
This study was carried out at one research-intensive institution in the southeastern United States; thus, these results may not be generalizable to other institutions. Because our participants share largely homogenous sociodemographic factors, the results would likely be different for other student populations. In addition, we surveyed students at week 8 during the semester, so it is unclear what their definitions and motivations may have been at the beginning of the semester. It is essential to acknowledge the constraints of our study and the inability to draw extensive generalizations from our dataset; future work is needed to expand and confirm these results.
CONCLUSION
Students in our study defined success and failure in their introductory biology courses using four themes: Performance, Content, Preparation, and Attitude. We found that, for most students, success and failure were not aligned, but instead, students had different standards for each. We also found that in some cases students defined success and failure differently according to their course performance. Our study suggests that building self-efficacy in introductory biology is likely more complex than theory predicts and learning more about student perspectives is crucial. This study has the potential to expand the way researchers and instructors conceptualize students’ goals and motivations in introductory biology. This study not only adds valuable voices to the discourse on success and failure in academia, but also serves as a focal point for informing pedagogical practices, while offering a launchpad for further investigations into student goals, motivation, and self-efficacy within the realm of introductory biology.
ACKNOWLEDGMENTS
The authors thank Morgan Smith for her contributions to coding the pilot data and establishing the first version of the codebook. The authors thank Nadejda Sero M.S., Laurel Philpott, and Orou Gaoue for their statistical consultation and guidance on this manuscript. The authors thank Maryrose Weatherton for her feedback and support throughout the research and manuscript preparation stages. The authors thank the reviewers and editor for their helpful feedback that made this manuscript stronger.
