More Like this

1. Student Behaviors and Interactions Influence Group Discussions in an Introductory Biology Lab Setting

   https://doi.org/10.1187/cbe.20-03-0054  

   Paine, Alex R.; Knight, Jennifer K. (December 2020, CBE—Life Sciences Education)

   Gardner, Grant Ean (Ed.)

   Past research on group work has primarily focused on promoting change through implementation of interventions designed to increase performance. Recently, however, education researchers have called for more descriptive analyses of group interactions. Through detailed qualitative analysis of recorded discussions, we studied the natural interactions of students during group work in the context of a biology laboratory course. We analyzed multiple interactions of 30 different groups as well as data from each of the 91 individual participants to characterize the ways students engage in discussion and how group dynamics promote or prevent meaningful discussion. Using a set of codes describing 15 unique behaviors, we determined that the most common behavior seen in student dialogue was analyzing data, followed by recalling information and repeating ideas. We also classified students into one of 10 different roles for each discussion, determined by their most common behaviors. We found that, although students cooperated with one another by exchanging information, they less frequently fully collaborated to explain their conclusions through the exchange of reasoning. Within this context, these findings show that students working in groups generally choose specific roles during discussions and focus on data analysis rather than constructing logical reasoning chains to explain their conclusions. 

   more » « less
2. Are there metacognitive benefits of learner‐ and instructor‐generated visualizations?

   https://doi.org/10.1002/acp.4135  

   Fiorella, Logan; Jaeger, Allison J. (September 2023, Applied Cognitive Psychology)

   Abstract This study explored how different formats of instructional visuals affect the accuracy of students' metacognitive judgments. Undergraduates (n = 133) studied a series of five biology texts and made judgments of learning. Students were assigned randomly to study the texts only (text only), study the texts with provided visuals (provided visuals group), study the texts and generate their own visuals (learner‐generated visuals group), or study the texts and observe animations of instructor‐generated visuals (instructor‐generated visuals group). After studying the texts and making judgments of learning, all students completed multiple‐choice comprehension tests on each text. The learner‐generated and instructor‐generated visuals groups exhibited significantly higher relative judgment accuracy than the text only and provided visuals groups, though this effect was relatively small. The learner‐generated visuals group also required more study time and was more likely to report the use of visual cues when making their judgments of learning. 

   more » « less
3. Characterizing student argument justifications in small group sociotechnical discussions

   https://doi.org/10.18260/1-2--43187  

   Andrews, Chelsea; Rahman, Fatima (June 2023, Annual ASEE Conference and Exhibition)

   There have been increased calls to include sociotechnical thinking–grappling with issues of power, history, and culture–throughout the undergraduate engineering curriculum. One way this more expansive framing of engineering has been integrated into engineering courses is through in-class discussions. There is a need to understand what students are attending to in these conversations. In particular, we are interested in how students frame and justify their arguments in small-group discussions. This study is part of an NSF-funded research project to implement and study integrating sociotechnical components throughout a first-year computing for engineers course. In one iteration of the revised course, each week students read a news article on a current example of the uneven impacts of technology, then engaged in in-class small-group discussions. In this study, we analyze students’ discourse to answer the research questions: What arguments do students use to argue against the use of a technology? How do these arguments relate to common narratives about technology? In this qualitative case study, we analyzed videorecordings of the small group discussions of two focus groups discussing the use of AI in hiring. We looked closely at the justifications students gave for their stated positions and how they relate to the common narratives of technocracy, free market idealism, technological neutrality, and technological determinism. We found all students in both groups rejected these common narratives. We saw students argue that (1) AI technology does not solve the hiring problem well, (2) it is important to regulate AI, (3) using AI for hiring will stagnate diversity, and (4) using AI for hiring unfairly privileges some groups of people over others. While students in both groups rejected the common narratives, only one group explicitly centered those who are harmed and how this harm would likely occur, and this group did so consistently. The other group managed to consistently reject the narratives using vague, safe language and never explicitly mentioned who is harmed by the technology. As a result, only one group’s discussion was clearly centered on justice concerns. These results have implications for how to scaffold small group sociotechnical discussions, what instructors should attend to during these discussions, and how to support students to orient toward systemic impacts and sustain a focus on justice. 

   more » « less
4. Initial evidence that peer instruction is less effective for physics students with lower quantitative preparation in a highly heterogeneous class

   https://doi.org/10.1119/5.0070669  

   Smith, Eric N; Helwig, Jacob; Hendren, Kathryn; Huk, Jane M; Gordon, Vernita (July 2025, American Journal of Physics)

   Historically, physics education primarily consisted of lectures in which students have a largely passive role. Proponents of educational reform have rallied around active learning to increase engagement and retention in STEM fields, particularly advocating peer interactions to build a foundation of deep understanding. However, little is known about how students' prior preparation for introductory courses impacts their mastery of course material when instructors incorporate active learning. In the present study, we examine learning outcomes in two sections of an introductory mechanics course at an institution with a wide range of students' prior mathematics preparation as assessed by quantitative SAT scores. For each of three years, one section was taught using peer instruction in which much of the class time was spent in small-group discussions between students. The other section was taught by the same instructor using interactive lectures in which discussions primarily took place between volunteers from the class and the instructor. We find that students enrolled in the peer instruction sections earned lower grades in the course than did students in the interactive sections. We also find students in the peer instruction sections with lower quantitative SAT scores showed lower gains in understanding foundational concepts as assessed by the Force Concept Inventory and were less likely to earn an A in the course than comparable students in the interactive sections. While further research is needed to confirm these results, this study suggests that peer instruction might not be the optimal pedagogy for heterogeneous populations. 

   more » « less
5. Ecological Forecasting and Dynamics: A graduate courseon the fundamentals of time series and forecasting in ecology

   https://doi.org/10.21105/jose.00198  

   Ernest, S. K.; Ye, Hao; White, Ethan P. (August 2023, Journal of Open Source Education)

   Ecological Dynamics and Forecasting' is a semester-long course to introduce students to the fundamentals of ecological dynamics and forecasting. This course implements paper-based discussion to introduce students to concepts and ideas and R-based tutorials for hands-on application and training. The course material includes a reading list with prompting questions for discussions, teachers notes for guiding discussions, lecture notes for live coding demonstrations, and video presentations of all R tutorials. This course material can be used either as self-directed learning or as all or part of a college or university course. Individual learners have access to all of the necessary material - including discussion questions and instructor notes - on the website. The course focuses on papers with an open-access or free-to-read version where possible, though some materials still rely on access to closed-access papers. The course is structured around two sessions per week, with most weeks consisting of a one hour paper discussion session and a 1-2 hour session focused on applications in R. R tutorials use publicly available ecological datasets to provide realistic applications. Because the material is organized around content themes, instructors can modify and remix materials based on their course goals and student levels of background knowledge. These course materials have been taught for several years at the authors’ university and have also generated significant online engagement with course videos tens of thousands of times. 

   more » « less