X-DBER 2021 Conference

Over the past few years, there has been an overwhelming interest in exposing students to data and graphing. Our work adds to this body of literature because we provide a set of tested homework assessments for gradually implementing these skills in a large introductory biology lecture course, without sacrificing time away from content. The aim of this research is to understand how repeated exposure to data and practice with graphing affects student quantitative literacy and perception of graphing over the course of the semester. In our talk we will elaborate on creation and implementation of these graphical skill building exercises. Using freely available HHMI BioInteractive modules we adapted four modules to include a portion on biology content knowledge and graphing and piloted them in an introductory lecture course in Fall 2019 as homework assignments. Preliminary findings reveal significant (p<.05) student improvement over the semester in the categories of writing a research question, hypothesis, making an appropriate graph, providing appropriate reasoning for graph choice, and in providing the take-home message. End-of-the-semester student reflections were positive, and, overall, students felt the assignments improved their overall graphing abilities. This work has implications for instructors teaching high school biology or introductory biology. A future area of research will focus on giving students opportunities to create informative graphs using graphing software.

The reform movement in statistics education has led to a revitalization of the undergraduate introductory statistics course. However, many students satisfy their degree requirements by taking statistics courses in “client departments” such as business, the social sciences, and the lab sciences, typically taught by non-statisticians. This talk will present the findings of a metasynthesis of the existing literature on teaching statistics in these client disciplines to learn (a) what is currently being taught and how and (b) the most important challenges for statistics teachers in other departments. Articles were reviewed using qualitative axial coding and quantitative text analysis to identify common research themes and ideas in the literature for each discipline. Research themes, attitudes toward statistics instruction, and pedagogical techniques were found to vary from discipline to discipline. Collaboration with instructors in other disciplines is a needed first step toward improving statistics instruction across the university.

This talk in particular will highlight the need for greater collaboration in education research across disciplines. Statistics is somewhat unique in that many disciplines offer their own “flavor,” but there are implications for DBER in other fields. For example, as data science becomes more integrated into the STEM disciplines, there are lessons that can be shared across fields.

Advancements in organic chemistry depend upon chemists’ ability to interpret NMR spectra, though research demonstrates that cultivating such proficiency requires years of graduate-level study. The organic chemistry community thus needs insight into how this expertise develops to expedite learning among its newest members. This study investigated undergraduate and doctoral chemistry students’ understanding and information processing during the interpretation of 1H NMR spectra and complementary IR spectra. Eye movements were measured to identify differences in cognitive processes between undergraduate and doctoral participants, and interviews were conducted to elucidate the assumptions that guided participants’ reasoning. Results suggest five areas of understanding are necessary for interpreting spectra, and progress in understanding corresponds to increasing knowledge of experimental and implicit chemical variables. Undergraduate participants exhibited uninformed bidirectional processing of all information, whereas doctoral participants exhibited informed unidirectional processing of relevant information. These findings imply the community can support novices’ development of expertise by cultivating relevant understanding and encouraging use of informed interpretation strategies, including preliminary evaluation of relevant variables, prediction of expected spectral features, and search for complementary data across spectra. These findings also provide insight into the potential relationship between conceptual understanding and information possessing across scientific representations.

The biological field is increasingly interdisciplinary and requires students to build individual concepts into complex understanding. It is important to understand how best to support students in this process and provide them with the tools necessary to succeed. One way for students to consider their own understanding, and determine what steps to take next, is by engaging in metacognition. Students engaging in metacognition have better learning outcomes, and past work has shown students can be supported to become more metacognitive. However, development of more advanced and targeted supports depends on a greater understanding of how metacognition develops as students learn increasingly complex material. In the current study, we examined students’ development of metacognition and conceptual understanding from their first introductory biology course through each of the five required core courses for the biology major. Data collection consisted of surveys, semester grades, and interviews with a subset of students. We performed statistical analysis on the metacognitive score generated from each survey and grades collected at the end of each semester. We performed qualitative trend and case study analysis on the open-ended survey questions and on the transcripts from the semi-structured interviews. We found stark differences in metacognition engagement between introductory students and seniors. We also found interesting patterns of development among those students we followed through multiple semesters. Results from this study will help to structure how future scaffolds and instructional tools are created and utilized to best support students in learning and understanding scientific concepts across disciplines.

Co-author: Megan Nagel, Associate Professor of Chemistry, Penn State Greater Allegheny

Researchers in physics education have recently been applying dual-process theories of reasoning and decision-making (DPToR) as a guide to inform the development of research-based instructional materials. This approach is particularly well-suited for tasks and topics for which a strong incorrect intuitive model interferes with a student’s ability to successfully apply their conceptual understanding. In this talk, we will describe an interdiscipinary research project designed to identify topics and questions in the general chemistry curriculum for which we expect that an intervention approach rooted in DPToR would help students to improve their reasoning. This project adapts a novel tool from the physics education research literature known as the reasoning chain construction task. In this tool, students are provided with a limited number of true statements from which they must generate a reasoning chain to support their answer to a question. We will illustrate our approach and the utility of this tool with results from a task on the ideal gas law.

* This material is based upon work supported by the National Science Foundation under Grant Nos. DUE-1821390, DUE-1821123, DUE-1821400, DUE-1821511, and DUE-1821561.

The Queer in STEM study provides an unprecedented look at the personal stories and workplace experiences of LGBTQ-identified STEM professionals. Quantitative analysis of 1,427 participant responses to an online survey allowed us to describe the diversity of LGBTQ identities and STEM fields represented by survey participants, and to examine the relationship between workplace climate and openness about LGBTQ identities in professional settings. Qualitative analysis of 151 open-response questionnaires and transcripts of 54 semi-structured interviews identified common themes among participants’ diverse personal experiences. A grounded theory framework allowed us to develop a model of queer STEM identity with three main stages: (1) defining individual queer identity, (2) forming a personal STEM identity, and (3) navigating queer identity at work. A truly inclusive DBER program must take into account the unique experiences of queer individuals in STEM to make the products of our academic endeavors work in the benefit of as many individuals as possible.

This longitudinal, qualitative study explored and critically interrogated how the culture, climate, and structure of graduate education impacted the mental health of underrepresented minority (URM) graduate students as they completed doctoral degrees at primarily White institutions (PWIs). Recent research has shown that graduate students are substantially more likely to have mental health challenges than the U.S. population as a whole. URM students face many challenges on the path to degree completion and are likely to be at risk for a higher incidence of mental health challenges. This research project followed 17 URM graduate students at three different Midwestern PWIs to understand their experiences with mental health as they progressed through their STEM doctoral programs. Using critical race theory and constructivist grounded theory analysis perspectives, we explored the incidence, timing, and factors that impacted students’ experiences of mental health issues, with a critical focus on departmental/program structure and power dynamics. It is clear that ongoing research into the experiences of graduate student with mental health issues is urgently needed in order to support graduate students, provide appropriate resources, and more critically, to identify and address the cultural and institutional environments that exacerbate and perpetuate the experience of mental health challenges for graduate students. Initial findings indicate that mental health challenges are exacerbated by a lack of social support, solo status, and aversion to seeking out professional help due to cultural stigma or lack of diverse counselors. This work takes important first steps toward understanding mental health among URM doctoral students at PWIs.

Graduate school is the primary location where students are socialized into the norms, behaviors, and attitudes of their selected discipline, and this socialization has been hypothesized to be a gendered process. Women are likely to experience challenges related to gender in masculine-normed fields, such as physics and computer science; however, research on fields that are considered gender-equal is limited. Despite nearly equal numbers of women enrolled in biological sciences programs at the undergraduate and graduate levels, studies have reported persistent gender gaps in academic achievement, sense of belonging, publishing, and grant funding. The gendered nature of STEM academic disciplines is largely driven by gendered structures within the organization itself. Therefore, this study examined the institutional practices of graduate STEM education from the standpoint of women graduate students in the biological sciences. Data collection and analysis focused on describing the day-to-day work of six women graduate students in biological sciences at a Southern research university and examined if and how those day-to-day experiences informed the socialization of women into scientists. Preliminary analyses point to internal and external challenges experienced by women related to their graduate education. Internal challenges included themes of time management and anxiety, whereas external challenges were related to a lack of clear expectations for graduate students and the transition to remote work and isolation caused by the COVID-19 pandemic. This presentation will discuss women’s experiences and challenges associated with the COVID-19 pandemic.

Students’ academic success is influenced by how they think about intelligence (termed “mindset”). Students who view intelligence as something they can improve tend to persist through challenges and realize better academic outcomes than students who view intelligence as an unchangeable trait. Although mindset beliefs are important for understanding student success, there is currently no high-quality survey to measure undergraduates’ mindset beliefs. The existing survey for measuring mindset was designed for use with elementary school children, and subsequently used with students of all ages, without rigorous validation. Our prior work suggests that this scale does not work well with undergraduates because it includes vague language. We are developing a new survey to measure mindset in undergraduate STEM students. First we interviewed a diverse, national sample of STEM students to identify language about intelligence that STEM undergraduates would interpret consistently. Based on these results, we drafted a set of items. Then, we collected revised the items based on feedback from STEM undergraduates and experts in mindset theory and survey design. We are currently collecting responses to the item set from a large, diverse national sample of STEM undergraduates to further evaluate and refine the survey. This new survey will be useful to instructors in understanding and responding to their students’ mindsets and to education researchers in studying mindset interventions. This work will have broad appeal to DBERs in any discipline because students’ mindset beliefs are relevant in all their fields of study.

The use of 2D images to teach students about 3D molecules continues to be a prevalent issue in many classrooms. As affordable visualization technologies continue to advance, there has been an increasing interest to utilize novel technology, such as augmented reality (AR), in the development of molecular visualization tools (Sirakaya & Alsancak, 2018). Existing evaluations of the implementation of these visual-spatial learning tools focus primarily on student performance and attitude, with little attention toward equitable implementation. Our study adds to the current literature on implementing molecular visualization technology in biochemistry classrooms by examining the different situations of equity and inequity in a group activity mediated by AR technology. Adapting the participatory equity framework to our specific context, we view equity in terms of access to the technological conversational floor, a social space created when people enter into technology mediated joint endeavors (Shah & Lewis, 2019). We explore two questions: What are the different ways students interact with an augmented reality model of the KcsA channel? What are some patterns of interaction that may signify inequity in accessing the learning opportunities afforded by the technology?

Cameras and microphones were placed across the classroom to record video and audio data. In the first stage of data analysis, classroom video recordings were coded using an inductive coding method inspired by grounded theory. In open coding, analytical memos and preliminary codes were constructed for how each student interacted with the AR activity (Corbin & Strauss, 1990; Strauss, 1987). In axial coding, analytical memos and preliminary codes were compared and grouped into categories of different types of interaction that students had with the AR activity. Using the constant comparative method, descriptions and definitions for each category were compared with those from previously analyzed video excerpts to confirm or disconfirm conjectures (Corbin & Strauss, 1990). In the second stage of data analysis, types of interactions identified in the first stage were used as discourse dimensions to quantify students’ participation in the AR activity. Quantified participatory analysis was compared both within group and across group to gain insight into the participatory patterns in AR technology mediated group work. Our findings highlight the implications that participatory patterns have for equitable access to learning opportunities in the context of technology mediated group work activity. These insights can inform educators who are interested in implementing visualization technology in their classroom about the potential inequities that may arise.

High structure courses are designed to have students be active participants in the learning process with pre-class content acquisition through reading or videos, in-class practice with active learning exercises, and after-class review assignments. While faculty may suggest certain strategies for success in high structure courses, it is unknown what study methods and general strategies students perceive as most helpful and therefore what kinds of advice they would give future students. The goal of the study was therefore to survey students from various high structure biological sciences and engineering courses about advice they would give to future students of the same course. Students ranging from freshman to seniors (n = 840) in eight different STEM courses from two different universities gave advice to future students by writing advice on a notecard on the last day of class. The advice comments were coded using an iterative qualitative methodology and five large categories and 18 total sub-categories were identified as types of advice that students gave. The most common types of advice, in order of prevalence, were general study tips, course expectations, and not procrastinating. Differences were observed in frequencies of types of advice between universities and course types. This student advice can now be built into these and other similar courses to provide suggestions for how students can succeed.

An augmented reality application (H NMR MoleculAR) and worksheet have been developed as part of a first-semester Organic Chemistry II laboratory to help students visualize the concepts involved when problem solving with 1H NMR spectroscopy. This activity was designed to encourage conceptual problem-solving and prevent memorization by eliminating the use of chemical shift tables. The augmented reality models aid students in visualizing 3D structures, molecular orbitals, and electrostatic potential maps. The worksheet uses the compare-predict-observe-explain (CPOE) framework so students can abstract principles about how proton equivalency, electronegativity, and anisotropy impact the number, intensity, splitting, and shift of signals on a spectrum. Student user experience and classroom observations will be discussed to evaluate the application.

Many undergraduate students find it difficult to visualize 3D concepts in physics and engineering courses. There is a need for context-specific spatial visualization activities to help students connect 2D representations to 3D models. We have assembled an interdisciplinary team of undergraduate physics and computer science research students and faculty to create 3D augmented reality (AR) models of physics concepts including centripetal force, torque, electric and magnetic fields and forces, and electromagnetic induction. Utilizing software including Unity, Vuforia, and Merge SDK, research students create the 3D physics models. Once developed, researchers upload the AR apps to the Apple and Android stores, making them publicly available. To use, students download the apps to their smartphone or tablet and view the Merge cube through their device, the 3D physics model appears on the Merge cube. By rotating the Merge cube, the students can view the 3D model from different perspectives. This provides an opportunity for students to envision many 2D representations of the same 3D concept. Students and instructors can utilize these AR apps in a variety of situations including for use within lectures, problem-solving sessions, or labs, either within in-person or remote instruction. Augmented reality 3D models have applications in STEM disciplines including mathematics and chemistry where strong spatial visualization improves student performance in STEM.

Problem-based learning (PBL) advocates self-directed learning by students, needing them to generate laboratory procedure by themselves. Students in the learning phase need support, especially when they are transitioning to the inquiry-based labs such as PBL. This talk will be based on the strategies for developing scaffolds to help students carry out a meaningful laboratory investigation. Multiple scaffolding strategy should be used for a more effective outcome, and these scaffolds should be distributed across the entire task, i.e., the pre-lab, lab, and the post-lab work. The talk will include the strategies for use of a combination of soft and hard scaffolds so that the laboratory investigation in a PBL environment is accomplished as intended. Without appropriate support, the cognitively complex of the task may lead students to frustration. I will give examples of the PBL module on indigo dyeing wastewater treatment in undergraduate chemistry laboratory and the scaffolds that were introduced for this task.