Learning in groups is a common feature of science classrooms. The three articles I have chosen to feature in this installment of Current Insights reflect recent research of group learning at different scales. The first examines within-group dynamics, identifying interactions among students that allow scientific sense-making discussions to begin and continue. The second proposes to study groups as the unit of analysis, asking why some groups are able to persevere in the face of challenging problems. The third considers the potential for learning to occur between groups, through connections in students’ extended social networks. Each brings new ideas and questions to the study of group learning.

Over the past two decades, science, technology, engineering, and mathematics (STEM) faculty have been striving to make their teaching practices more inclusive and welcoming to the variety of students who enter college. However, many STEM faculty, even those at teaching-focused institutions, have been educated in a traditional environment that emphasizes research and may not include classroom teaching. This can produce a deficit in training that leaves many STEM faculty feeling uncertain about inclusive teaching practices and their essential undergirding principles. This essay describes an online, evidence-based teaching guide (https://lse.ascb.org/evidence-based-teaching-guides/inclusive-teaching) intended to help fill this gap, serving as a resource for science faculty as they work to become more inclusive, particular with regard to differences in race, ethnicity, and gender. The guide describes the importance of developing self-awareness and empathy for students as a precursor to considering classroom practices. It also explores the role of classroom climate before turning to pedagogical choices that can support students’ sense of belonging, competence, and interest in the course. Finally, the guide suggests that true inclusivity is a community effort and that instructors should leverage local and national networks to maximize student learning and inclusion. Each of these essential points is supported by summaries of and links to articles that can inform these choices. The guide also includes an instructor checklist that offers a concise summary of key points with actionable steps that can guide instructors as they work toward a more inclusive practice. We hope that the guide will provide value for both faculty who are just beginning to consider how to change their teaching practices and faculty seeking to enrich their current efforts.

Advancement of the scientific enterprise relies on individuals conducting research in an ethical and responsible manner. Educating emergent scholars in the principles of ethics/responsible conduct of research (E/RCR) is therefore critical to ensuring such advancement. The recent impetus to include authentic research opportunities as part of the undergraduate curriculum, via course-based undergraduate research experiences (CUREs), has been shown to increase cognitive and noncognitive student outcomes. Because of these important benefits, CUREs are becoming more common and often constitute the first research experience for many students. However, despite the importance of E/RCR in the research process, we know of few efforts to incorporate E/RCR education into CUREs. The Ethics Network for Course-based Opportunities in Undergraduate Research (ENCOUR) was created to address this concern and promote the integration of E/RCR within CUREs in the biological sciences and related disciplines. During the inaugural ENCOUR meeting, a four-pronged approach was used to develop guidelines for the effective integration of E/RCR in CUREs. This approach included: 1) defining appropriate student learning objectives; 2) identifying relevant curriculum; 3) identifying relevant assessments; and 4) defining key aspects of professional development for CURE facilitators. Meeting outcomes, including the aforementioned E/RCR guidelines, are described herein.

An expanded investment in interdisciplinary research has prompted greater demands to integrate knowledge across disciplinary boundaries. Vision and Change similarly made interdisciplinary expectations a key competency for undergraduate biology majors; however, we are not yet synchronized on the meaning of interdisciplinarity, making this benchmark difficult to meet and assess. Here, we discuss aspects of interdisciplinarity through a historical lens and address various institutional barriers to interdisciplinary work. In an effort to forge a unified path forward, we provide a working definition of interdisciplinary science derived from both the perspectives of science faculty members and scientific organizations. We leveraged the existing literature and our proposed definition to build a conceptual model for an Interdisciplinary Science Framework to be used as a guide for developing and assessing interdisciplinary efforts in undergraduate science education. We believe this will provide a foundation from which the community can develop learning outcomes, activities, and measurements to help students meet the Vision and Change core competency of “tapping into the interdisciplinary nature of science.”

Data are becoming increasingly important in science and society, and thus data literacy is a vital asset to students as they prepare for careers in and outside science, technology, engineering, and mathematics and go on to lead productive lives. In this paper, we discuss why the strongest learning experiences surrounding data literacy may arise when students are given opportunities to work with authentic data from scientific research. First, we explore the overlap between the fields of quantitative reasoning, data science, and data literacy, specifically focusing on how data literacy results from practicing quantitative reasoning and data science in the context of authentic data. Next, we identify and describe features that influence the complexity of authentic data sets (selection, curation, scope, size, and messiness) and implications for data-literacy instruction. Finally, we discuss areas for future research with the aim of identifying the impact that authentic data may have on student learning. These include defining desired learning outcomes surrounding data use in the classroom and identification of teaching best practices when using data in the classroom to develop students’ data-literacy abilities.

Although burnout and mental health problems may adversely impact quality of scientific research, academic productivity, and attrition in biomedical doctoral training programs, very little research has been done on this topic. Recent studies have used brief survey methods to begin to explore burnout and mental health problems in biomedical doctoral students. In this pilot study, biomedical doctoral students (N = 69; 12% of enrolled biomedical doctoral students at a large research institution’s school of medicine in the United States) were administered standardized psychiatric interviews and self-report questionnaires focused on dimensions of burnout, mental health symptoms, and academic outcomes. We discovered high levels of burnout, depression, and anxiety. Additionally, we identified that burnout was significantly associated with thoughts related to dropping out, subjective appraisal of employment opportunities, functional impairment due to a mental health problem, and having at least one current psychiatric disorder. These findings extend prior research indicating the presence of significant emotional health challenges doctoral students in biomedical graduate programs face involving high burnout and difficulties with the training environment. We outline several recommendations and next steps to programmatically understand and address these emerging emotional wellness concerns in biomedical doctoral students.

Graduate students and postdocs in science, technology, engineering, and math fields are faced with a wide range of career paths to employment, but they are often not trained to effectively pursue these opportunities. The lack of career management skills implies long tenures in graduate school for many students, especially as tenure-track positions in academia dwindle. At our university, we used a cohort model in which graduate students and postdocs were encouraged to apply to the Broadening Experiences in Scientific Training program (BEST under the aegis of the National Institutes of Health) that provided opportunities to gain career management skills, engage in career exploration, and complete at least one formal internship. In this interview study of the BEST trainees, we investigated the efficacy of internships as career exploration tools and associated outcomes. Our findings show that internships were seen as effective career exploration and self-development vehicles that influenced participants’ long-term career goals. Graduate students and postdocs reported gaining transferable knowledge and skills, in addition to receiving valuable industry mentoring and networking opportunities.

Graduate students and postdoctoral researchers (postgraduates) in the life sciences frequently mentor undergraduate researchers, especially at research universities. Yet there has been only modest investigation of this relationship from the postgraduate perspective. We conducted an exploratory study of the experiences of 32 postgraduate mentors from diverse institutions, life sciences disciplines, and types of research to examine their motivations for mentoring and their perceived outcomes. Although some postgraduates reported feeling pressured to mentor undergraduate researchers, all expressed personal motivations, including both agentic (self-focused) and communal (community-focused) motivations. These postgraduates reported benefits and costs of mentoring that had both vocational and psychosocial elements. Given that our results indicated that even postgraduates who engaged in mentoring at the request of their faculty advisors had their own motivations, we conducted a second phase of analysis to determine the extent to which our results aligned with different theories of motivation (self-determination theory, social cognitive career theory, expectancy-value theory, social exchange theory). We end by proposing a model of postgraduate mentoring of undergraduate researchers that integrates the theories supported by our findings.

Problem solving is an integral part of doing science, yet it is challenging for students in many disciplines to learn. We explored student success in solving genetics problems in several genetics content areas using sets of three consecutive questions for each content area. To promote improvement, we provided students the choice to take a content-focused prompt, termed a “content hint,” during either the second or third question within each content area. Overall, for students who answered the first question in a content area incorrectly, the content hints helped them solve additional content-matched problems. We also examined students’ descriptions of their problem solving and found that students who improved following a hint typically used the hint content to accurately solve a problem. Students who did not improve upon receipt of the content hint demonstrated a variety of content-specific errors and omissions. Overall, ultimate success in the practice assignment (on the final question of each topic) predicted success on content-matched final exam questions, regardless of initial practice performance or initial genetics knowledge. Our findings suggest that some struggling students may have deficits in specific genetics content knowledge, which when addressed, allow the students to successfully solve challenging genetics problems.

Next-generation sequencing (NGS)-based methods are revolutionizing biology. Their prevalence requires biologists to be increasingly knowledgeable about computational methods to manage the enormous scale of data. As such, early introduction to NGS analysis and conceptual connection to wet-lab experiments is crucial for training young scientists. However, significant challenges impede the introduction of these methods into the undergraduate classroom, including the need for specialized computer programs and knowledge of computer coding. Here, we describe a semester-long, course-based undergraduate research experience at a liberal arts college combining RNA-sequencing (RNA-seq) analysis with student-driven, wet-lab experiments to investigate plant responses to light. Students derived hypotheses based on analysis of RNA-seq data and designed follow-up studies of gene expression and plant growth. Our assessments indicate that students acquired knowledge of big data analysis and computer coding; however, earlier exposure to computational methods may be beneficial. Our course requires minimal prior knowledge of plant biology, is easy to replicate, and can be modified to a shorter, directed-inquiry module. This framework promotes exploration of the links between gene expression and phenotype using examples that are clear and tractable and improves computational skills and bioinformatics self-efficacy to prepare students for the “big data” era of modern biology.

The CUNY Research Scholars Program (CRSP) provides a yearlong faculty-mentored research experience to associate’s degree students. The program takes place at all 10 associate’s degree–granting colleges within the City University of New York system. We report on a mixed-methods study of 500 students who participated in the program during its initial 3 years. Quantitative longitudinal assessments revealed that students who engaged in CRSP were more likely to be retained in a science, technology, engineering, and mathematics (STEM) discipline or to graduate with a STEM degree than their counterparts in a matched comparison group. Furthermore, students who participated in CRSP demonstrated an increased likelihood of transferring to the more research-intensive 4-year schools within the CUNY system and to R1 universities outside the CUNY system. CRSP students reported an increased sense of belonging in college based on survey data, and focus groups with their mentors provided insight into the factors that led to the gains listed above. These combined results—of student data analysis, student surveys, and mentor focus groups—provide evidence that early research experiences for associate’s degree students contribute to their academic success.

We analyzed effects of peer teaching on non–science major undergraduates’ knowledge, perceptions, and opinions about genetically modified (GM) crops and their use in agriculture. Undergraduates enrolled in an introductory nonmajors biology course participated in a service-learning program (SLP) in which they acted as cross-age peer teachers to high school students, teaching about the role of genetics in crop improvement through traditional breeding and GM approaches. Using pre/postassessments, we found that undergraduates’ opinions shifted to favor the use of GM organisms (GMOs) in agriculture after SLP participation, rising from 46 to 97%. Perceptions about risks and benefits of GMOs also shifted from 43% stating that GMOs are harmful or suspect to no students describing GMOs in that way. Knowledge about GMOs became more accurate after SLP participation. There were significant correlations between students who had negative perceptions of GMOs and negative opinions or inaccurate knowledge about them. Students recognized the effect of peer teaching on their knowledge and perceptions, identifying the repeated peer teaching as an important factor in knowledge gain. Our results suggest students developed an informed opinion about the use of GMOs through first learning the science of genetic engineering and then teaching this information to younger students.

Understanding student ideas in large-enrollment biology courses can be challenging, because easy-to-administer multiple-choice questions frequently do not fully capture the diversity of student ideas. As part of the Automated Analysis of Constructed Responses (AACR) project, we designed a question prompting students to describe the possible effects of a mutation in a noncoding region of DNA. We characterized answers from 1127 students enrolled in eight different large-enrollment introductory biology courses at three different institutions over five semesters and generated an analytic scoring system containing three categories of correct ideas and five categories of incorrect ideas. We iteratively developed a computer model for scoring student answers and tested the model before and after implementing an instructional activity designed to help a new set of students explore this concept. After completing a targeted activity and re-answering the question, students showed improvement from preassessment, with 64% of students in incorrect and 67% of students in partially incorrect (mixed) categories shifting to correct ideas only. This question, computer-scoring model, and instructional activity can now be reliably used by other instructors to better understand and characterize student ideas on the effects of mutations outside a gene-coding region.

Low-stakes testing, or quizzing, is a formative assessment tool often used to structure course work. After students complete a quiz, instructors commonly encourage them to use those quizzes again to retest themselves near exam time (i.e., delayed re-quizzing). In this study, we examine student use of online, ungraded practice quizzes that are reopened near exam time after a first graded attempt 1–3 weeks prior. We find that, when controlling for preparation (performance in a previous science, technology, engineering, and mathematics [STEM] course and incoming biology knowledge), re-quizzing predicts better performance on two cumulative exams in introductory biology: a course posttest and final exam. Additionally, we describe a preliminary finding that, for the final exam, but not the posttest, re-quizzing benefits students with lower performance in a previous STEM course more than their higher-performing peers. But unfortunately, these struggling students are also less likely to participate in re-quizzing. Together, these data suggest that a common practice, reopening quizzes for practice near exam time, can effectively benefit student performance. This study adds to a growing body of literature that suggests quizzing can be used as both an assessment tool and a learning tool by showing that the “testing effect” extends to delayed re-quizzing within the classroom.

Multistage collaborative exams are implemented to enhance learning and retention of course material. However, the effects of multistage collaborative exams on retention of course content are varied. These discrepancies may be due to a number of factors. To date, studies examining collaborative exams and content retention have used questions that all, or mostly, require students to select an answer, rather than generate one of their own. However, content retention can improve when students generate their own responses. Thus, we examined the effect of collaborative exams with open-ended questions on retention of course content. Retention was measured at two time periods; one relatively shortly (9 days) following a collaborative exam and another over a longer time period (23 days). Furthermore, we examined whether content retention differed for low-, mid-, or high-­performing students. Our results suggest that collaborative exams offer retention benefits at relatively long time periods between pre- and posttests, but not over shorter time periods. Retention varied across students in different performance categories. Our study, the first to use only open-ended questions, showed relatively small effects compared with studies using multiple-choice or fill-in-the-blank format, but still suggest that collaborative exams can aid in content retention.

Group work in science, technology, engineering, and mathematics courses is an effective means of improving student outcomes, and many different factors can influence the dynamics of student discussions and, ultimately, the success of collaboration. The substance and dynamics of group discussions are commonly examined using qualitative methods such as discourse analysis. To complement existing work in the literature, we developed a quantitative methodology that uses graph theory to map the progression of talk-turns of discussions within a group. We observed groups of students working with peer facilitators to solve problems in biological sciences, with three iterations of data collection and two major refinements of graph theory calculations. Results include general behaviors based on the turns in which different individuals talk and graph theory parameters to quantify group characteristics. To demonstrate the potential utility of the methodology, we present case studies with distinct patterns: a centralized group in which the peer facilitator behaves like an authority figure, a decentralized group in which most students talk their fair share of turns, and a larger group with subgroups that have implications for equity, diversity, and inclusion. Together, these results demonstrate that our adaptation of graph theory is a viable quantitative methodology to examine group discussions.

Accurate self-evaluation is critical for learning. Calibration describes the relationship between learners’ perception of their performance and their actual performance on a task. Here, we describe two studies aimed at assessing and improving student calibration in a first-semester introductory biology course at a 4-year public institution. Study 1 investigated students’ (n = 310) calibration (the difference between estimated and actual exam performance) across one semester. Students were significantly miscalibrated for the first exam: their predicted scores were, on average, significantly higher than their actual scores. The lowest-performing students had the most inaccurate estimates. Calibration improved with each exam. By the final exam, students underestimated their scores. We initiated a second study in the following semester to examine whether explicitly teaching students about self-evaluation strategies would improve their calibration and performance. Instruction in the experimental section (n = 290) focused on students’ tendency to overestimate their abilities and provided retrieval-practice opportunities. Students in the experimental section showed better calibration and performance on the first exam compared with students in a control section taught by a different instructor during the same semester (n = 251). These findings suggest that simple instructional strategies can increase students’ metacognitive awareness and improve their performance.

Metacognitive regulation occurs when learners regulate their thinking in order to learn. We asked how introductory and senior-level biology students compare in their use of the metacognitive regulation skill of evaluation, which is the ability to appraise the effectiveness of an individual learning strategy or an overall study plan. We coded student answers to an exam self-evaluation assignment for evidence of evaluating (n = 315). We found that introductory and senior students demonstrated similar ability to evaluate their individual strategies, but senior students were better at evaluating their overall plans. We examined students’ reasoning and found that senior students use knowledge of how people learn to evaluate effective strategies, whereas introductory students consider how well a strategy aligns with the exam to determine its effectiveness. Senior students consider modifying their use of a strategy to improve its effectiveness, whereas introductory students abandon strategies they evaluate as ineffective. Both groups use performance to evaluate their plans, and some students use their feelings as a proxy for metacognition. These data reveal differences between introductory and senior students, which suggest ways metacognition might develop over time. We contextualize these results using research from cognitive science, and we consider how learning contexts can affect students’ metacognition.

Students respond to classroom activities and achievement outcomes with a variety of emotions that can impact student success. One emotion students experience is anxiety, which can negatively impact student performance and persistence. This study investigated what types of classroom anxiety were related to student performance in the course and persistence in the major. Students in introductory biology classes self-reported their general class, test, communication, and social anxiety; perceived course difficulty; intention to stay in the major; and demographic variables. Final course grades were acquired from instructors. An increase in perception of course difficulty from the beginning to the end of the semester was significantly associated with lower final course grades (N = 337), particularly for females, non-Caucasians, and students who took fewer Advanced Placement (AP) courses. An increase in communication anxiety slightly increased performance. Higher general class anxiety at the beginning of the semester was associated with intention to leave the major (N = 122) at the end of the semester, particularly for females. Females, freshmen, and those with fewer AP courses reported higher general class anxiety and perceived course difficulty. Future research should identify which factors differentially impact student anxiety levels and perceived difficulty and explore coping strategies for students.

Various personal dimensions of students—particularly motivation, self-efficacy beliefs, and epistemic beliefs—can change in response to teaching, affect student learning, and be conceptualized as learning dispositions. We propose that these learning dispositions serve as learning outcomes in their own right; that patterns of interrelationships among these specific learning dispositions are likely; and that differing constellations (or learning disposition profiles) may have meaningful implications for instructional practices. In this observational study, we examine changes in these learning dispositions in the context of six courses at four institutions designed to scaffold undergraduate thesis writing and promote students’ scientific reasoning in writing in science, technology, engineering, and mathematics. We explore the utility of cluster analysis for generating meaningful learning disposition profiles and building a more sophisticated understanding of students as complex, multidimensional learners. For example, while students’ self-efficacy beliefs about writing and science increased across capstone writing courses on average, there was considerable variability at the level of individual students. When responses on all of the personal dimensions were analyzed jointly using cluster analysis, several distinct and meaningful learning disposition profiles emerged. We explore these profiles in this work and discuss the implications of this framework for describing developmental trajectories of students’ scientific identities.

Gender disparities in science, technology, engineering, and mathematics (STEM) are well documented, yet little is known about how women and men students establish social relationships with research mentors and peers and how that shapes their experiences in STEM. We conducted a series of interviews with 17 undergraduate students from a Hispanic majority institution regarding their participation in STEM-focused summer research programs at nine universities. Differences in levels of comfort in relationships were present when comparing men and women. Women students expressed comfort in relationships with mentors who provided psychosocial mentoring, were available to answer questions, and were of the same gender; they expressed some social discomfort in informal interactions with mentors. Men students felt comfortable with mentors who provided limited guidance, little psychosocial mentoring, and opportunities for informal interactions. In terms of peer relationships, women sought out the confidence of a few similar peers, while men were comfortable with a wide variety of peers. Men’s greater comfort with social relationships seemed to reflect their affinity with the masculine-dominated culture of STEM. For women, cultivating safe spaces through relationships with supportive peers and working with same-gender faculty mentors seemed to mitigate some of the discomforting aspects of their STEM research experiences.

Evidence-based teaching (EBT), such as active learning and formative assessment, benefits student learning but is not present in many college science classrooms. The choices faculty make about how to teach their science courses are influenced by their personal beliefs and motivations, as well as their departmental structures and institutional cultures. With data from 584 science, technology, engineering, and mathematics (STEM) faculty trained in EBT, we compare which of the following factors most relate to faculty’s use of EBT: 1) faculty’s personal motivations (e.g., teaching value, confidence, beliefs about intelligence); and 2) their experiences with their institutional teaching environments (e.g., departmental support, student enthusiasm). Faculty’s perceived supports in their teaching environments (e.g., having supportive colleagues, being able to access curricular resources) were by far most predictive of their use of EBT. Faculty’s personal motivations had little to no relationship when supports were included in these models. The effects were robust, even when controlling for faculty gender, minority status, and teaching experience. Much of the literature has focused on perceived barriers to EBT implementation (e.g., lack of time, constrained teaching space). The current data indicate that a focus on building supports for faculty may have the greatest impact on increasing the presence of EBT in college STEM courses.

This paper builds on previous studies of instructional practice in science, technology, engineering, and mathematics courses by reporting findings from a study of the relationship between instructors’ beliefs about teaching and learning and their observed classroom practices. Data collection took place across six institutions of higher education and included in-depth interviews with 71 instructors and more than 140 hours of classroom observations using the Teaching Dimensions Observation Protocol. Thematic coding of interviews identified 31 distinct beliefs that instructors held about the ways students best learn introductory concepts and skills in these courses. Cluster analysis of the observation data suggested that their observable practices could be classified into four instructional styles. Further analysis suggested that these instructional styles corresponded to disparate sets of beliefs about student learning. The results add momentum to reform efforts that simultaneously approach instructional change in introductory courses as a dynamic relationship between instructors’ subjective beliefs about teaching and learning and their strategies in the classroom.