Background The extent to which students view their intelligence as improvable (i.e., their mindset) influences students' thoughts, behaviors, and ultimately their academic success. Thus, understanding the development of students' mindsets is of great interest to education scholars working to understand and promote student success. Recent evidence suggests that students' mindsets continue to develop and change during their first year of college. We built on this work by characterizing how mindsets change and identifying the factors that may be influencing this change among upper-level STEM students. We surveyed 875 students in an organic chemistry course at four points throughout the semester and interviewed a subset of students about their mindsets and academic experiences. Results Latent growth modeling revealed that students tended to shift towards viewing intelligence as a stable trait (i.e., shifted towards a stronger fixed mindset and a weaker growth mindset). This trend was particularly strong for students who persistently struggled in the course. From qualitative analysis of students' written survey responses and interview transcripts, we determined that students attribute their beliefs about intelligence to five factors: academic experiences, observing peers, deducing logically, taking societal cues, and formal learning. Conclusions Extensive prior research has focused on the influence of mindset on academic performance. Our results corroborate this relationship and further suggest that academic performance influences students' mindsets. Thus, our results imply that mindset and academic performance constitute a positive feedback loop. Additionally, we identified factors that influence undergraduates' mindset beliefs, which could be leveraged by researchers and practitioners to design more persuasive and effective mindset interventions to promote student success.

Background Large achievement and motivation gaps exist in science between students from higher and lower socioeconomic status (SES) backgrounds. Middle and high school are an important time to address these disparities, as science motivation typically declines for all students at this time, leading to particularly low science interest and achievement for lower SES students on average when the gaps are left unaddressed. Students' control over their free time also increases at this time, providing opportunities for optional science experiences that may improve science attitudes and skills to combat these achievement and motivation gaps. Using a longitudinal dataset of 2252 middle and high school students from two regions in the USA, we investigate (1) disparities between higher and lower SES students in participation in optional summer science experiences and post-summer science attitudes and skills; (2) whether the child and family characteristics that predict participation in home-related, nature-related, and STEM camp experiences in the summer differ for higher and lower SES students; and (3) how participation in these types of optional summer science experiences contribute to post-summer science attitudes and skills when controlling for self-selection biases. Results Higher SES students reported greater participation in optional summer science experiences and higher post-summer science attitudes and sensemaking skills. Fascination for science was more important for participation in home-related and nature-related experiences for higher SES participants, whereas science competency beliefs were more important for lower SES participants. For STEM camp experiences, higher SES participants with higher competency beliefs and lower SES participants with lower scientific sensemaking skills were more likely to participate. After controlling for self-selection biases that may influence participation in these experiences, we found that home-related and nature-related experiences had a positive impact on students' attitudes toward science. Conclusions Our findings suggest two pathways for increasing participation in optional summer science experiences for higher SES and lower SES students. Specifically, it may be helpful to support interest in science for higher SES students and competency beliefs for lower SES students. Greater participation in home-related and nature-related summer science experiences can also increase science attitudes during middle and high school.

Background Characteristics of both teachers and learners influence mathematical learning. For example, when teachers use hand gestures to support instruction, students learn more than others who learn the same concept with only speech, and students with higher working memory capacity (WMC) learn more rapidly than those with lower WMC. One hypothesis for the effect of gesture on math learning is that gestures provide a signal to learners that can reduce demand on working memory resources during learning. However, it is not known what sort of working memory resources support learning with gesture. Gestures are motoric; they co-occur with verbal language and they are perceived visually. Methods In two studies, we investigated the relationship between mathematical learning with or without gesture and individual variation in verbal, visuospatial, and kinesthetic WMC. Students observed a videotaped lesson in a novel mathematical system that either included instruction with both speech and gesture (Study 1) or instruction with only speech (Study 2). After instruction, students solved novel problems in the instructed system and transfer problems in a related system. Finally, students completed verbal, visuospatial, and kinesthetic working memory assessments. Results There was a positive relationship between visuospatial WMC and math learning when gesture was present, but no relationship between visuospatial WMC and math learning when gesture was absent. Rather, when gesture was absent, there was a relationship between verbal WMC and math learning. Conclusion Providing gesture during instruction appears to change the cognitive resources recruited when learning a novel math task.

Student clickstream data-time-stamped records of click events in online courses-can provide fine-grained information about student learning. Such data enable researchers and instructors to collect information at scale about how each student navigates through and interacts with online education resources, potentially enabling objective and rich insight into the learning experience beyond self-reports and intermittent assessments. Yet, analyses of these data often require advanced analytic techniques, as they only provide a partial and noisy record of students' actions. Consequently, these data are not always accessible or useful for course instructors and administrators. In this paper, we provide an overview of the use of clickstream data to define and identify behavioral patterns that are related to student learning outcomes. Through discussions of four studies, we provide examples of the complexities and particular considerations of using these data to examine student self-regulated learning.

In the laboratory-based disciplines, selection of a principal investigator (PI) and research laboratory (lab) indelibly shapes doctoral students' experiences and educational outcomes. Framed by the theoretical concept of person-environment fit from within a socialization model, we use an inductive, qualitative approach to explore how a sample of 42 early-stage doctoral students enrolled in biological sciences programs made decisions about fitting with a PI and within a lab. Results illuminated a complex array of factors that students considered in selecting a PI, including PI relationship, mentoring style, and professional stability. Further, with regard to students' lab selection, peers and research projects played an important role. Students actively conceptualized trade-offs among various dimensions of fit. Our findings also revealed cases in which students did not secure a position in their first (or second) choice labs and had to consider their potential fit with suboptimal placements (in terms of their initial assessments). Thus, these students weighted different factors of fit against the reality of needing to secure financial support to continue in their doctoral programs. We conclude by presenting and framing implications for students, PIs, and doctoral programs, and recommend providing transparency and candor around the PI and lab selection processes.

Recent calls in biology education research (BER) have recommended that researchers leverage learning theories and methodologies from other disciplines to investigate the mechanisms by which students to develop sophisticated ideas. We suggest design-based research from the learning sciences is a compelling methodology for achieving this aim. Design-based research investigates the learning ecologies that move student thinking toward mastery. These learning ecologies are grounded in theories of learning, produce measurable changes in student learning, generate design principles that guide the development of instructional tools, and are enacted using extended, iterative teaching experiments. In this essay, we introduce readers to the key elements of design-based research, using our own research into student learning in undergraduate physiology as an example of design-based research in BER. Then, we discuss how design-based research can extend work already done in BER and foster interdisciplinary collaborations among cognitive and learning scientists, biology education researchers, and instructors. We also explore some of the challenges associated with this methodological approach.

Two foundational concepts in biology education are 1) offspring are not identical to their parents, and 2) organisms undergo changes throughout their lives. These concepts are included in both international and U.S. curricular standards. Research in psychology has shown that children often have difficulty understanding these concepts, as they are inconsistent with their intuitive theories of the biological world. Additionally, prior research suggests that diagrams are commonly used in instruction and that their features influence student learning. Given this prior work, we explored the characteristics of life cycle diagrams and discuss possible implications for student learning. We examined 75 life cycle diagrams from books, including five biology or general science textbooks and 25 specialized trade books focusing on biology for children. We also examined 633 life cycle diagrams from a publicly available online database of science diagrams. Most diagrams failed to show any within-species variability. Additionally, many diagrams had perceptually rich backgrounds, which prior research suggests might hinder learning. We discuss how the design characteristics of diagrams may reinforce students' intuitive theories of biology, which might make it difficult for students to understand key biological concepts in the future.

This article describes an equity-focused professional learning community that used the EQUIP observation protocol to provide data analytics to instructors. The learning community met during Spring 2020, and due to the global coronavirus pandemic, it moved online midsemester. This article describes patterns of student participation and how they were impacted in moving online. We found that student participation dropped significantly in moving online, but instructors were able to implement new teaching strategies to increase participation. We document seven concrete strategies that instructors used to promote equitable participation in their online classes and that can be incorporated by biology educators into their online teaching. The strategies were: 1) re-establishing norms, 2) using student names, 3) using breakout rooms, 4) leveraging chat-based participation, 5) using polling software, 6) creating an inclusive curriculum, and 7) cutting content to maintain rigor. In addition, we describe the faculty learning process and how EQUIP data and the learning community environment supported instructors to change their practices.

Many studies have examined children's understanding of playing and learning as separate concepts, but the ways that children relate playing and learning to one another remain relatively unexplored. The current study asked 5- to 8-year-olds (N = 92) to define playing and learning, and examined whether children defined them as abstract processes or merely as labels for particular types of activities. We also asked children to state whether playing and learning can occur simultaneously, and examined whether they could give examples of playing and learning with attributes either congruent or incongruent with those activities. Older children were more likely to define both playing and learning in terms of abstract processes, rather than by describing particular topics or activities. Children who defined both playing and learning in this way were able to generate more examples of situations where they were simultaneously playing and learning, and were better able to generate examples of learning with characteristics of play, and examples of playing with characteristics of learning. These data suggest that children develop an understanding that learning and playing can coincide. These results are critical to researchers and educators who seek to integrate play and learning, as children's beliefs about these concepts can influence how they reflect on playful learning opportunities.

When allocating resources, people often diversify across categories even when those categories are arbitrary, such that allocations differ when identical sets of options are partitioned differently (partition dependence). The first goal of the present work (Experiment 1) was to replicate an experiment by Fox and colleagues in which graduate students exhibited partition dependence when asked how university financial aid should be allocated across arbitrarily partitioned income brackets. Our sample consisted of community members at a liberal arts college where financial aid practices have been recent topics of debate. Because stronger intrinsic preferences can reduce partition dependence, these participants might display little partition dependence with financial aid allocations. Alternatively, a demonstration of strong partition dependence in this population would emphasize the robustness of the effect. The second goal was to extend a high transparency modification to the present task context (Experiment 2) in which participants were shown both possible income partitions and randomly assigned themselves to one, to determine whether partition dependence in this paradigm would be reduced by revealing the study design (and the arbitrariness of income categories). Participants demonstrated clear partition dependence in both experiments. Results demonstrate the robustness of partition dependence in this context.