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1. The Inclusive Classroom

   https://doi.org/10.1038/scientificamerican0524-52  

   Killen, Melanie (May 2024, Scientific American)
2. Conversation in the classroom

   https://doi.org/10.1177/0031721720903824  

   Holmberg, Carrie; Muwwakkil, Jamaal (February 2020, Phi Delta Kappan)

   Linguists have found that certain dynamics of conversation are consistent across languages worldwide, and these dynamics can affect the classroom discussions that teachers use to assess student understanding and make instructional decisions. Carrie Holmberg and Jamaal Muwwakkil discuss how conversational pauses, for example, might lead questioners to infer meaning that isn’t overtly expressed. Yes/no questions tend to elicit quick yes responses, while no answers and expressions of uncertainty come more slowly. This means that in whole-class discussions, students who are fast processors or are inclined to answer yes to teacher questions tend to dominate conversations, leaving teachers with less information about students who respond more slowly. The authors urge teachers to think about how these dynamics affect conversations in their classrooms and to use tools and policies that create more equitable discussions. 

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3. Reproducibility in the Classroom

   https://doi.org/10.1146/annurev-statistics-112723-034436  

   Dogucu, Mine (March 2025, Annual Review of Statistics and Its Application)

   Difficulties in reproducing results from scientific studies have lately been referred to as a reproducibility crisis. Scientific practice depends heavily on scientific training. What gets taught in the classroom is often practiced in labs, fieldwork, and data analysis. The importance of reproducibility in the classroom has gained momentum in statistics education in recent years. In this article, we review the existing literature on reproducibility education. We delve into the relationship between computing tools and reproducibility through visiting historical developments in this area. We share examples for teaching reproducibility and reproducible teaching while discussing the pedagogical opportunities created by these examples as well as challenges that the instructors should be aware of. We detail the use of teaching reproducibility and reproducible teaching practices in an introductory data science course. Lastly, we provide recommendations on reproducibility education for instructors, administrators, and other members of the scientific community. 

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4. Rethinking the classroom science investigation

   https://doi.org/10.1002/tea.21625  

   Manz, Eve; Lehrer, Richard; Schauble, Leona (February 2020, Journal of Research in Science Teaching)

   Abstract There is now a significant research literature devoted to reconceptualizing scientific activities, such as modeling, explanation, and argumentation, to realize a vision of science‐as‐practice in classrooms. As yet, however, not all scientific practices have received equal attention.Planning and Carrying out Investigationsis one of the eight scientific practices identified in the Next Generation Science Standards, and there is a long line of research from both psychological and science education traditions that addresses topics about investigation, such as the generation and interpretation of evidence. However, investigation has not been subject to concerted reconceptualization within recent research and instructional design efforts focused on science‐as‐practice. In this article, we propose a framework that centers the investigation as a key locus for constructing alignments among phenomena, data, and explanatory models and makes visible the work that scientists engage in as they develop and stabilize alignments. We argue that these alignments are currently under‐theorized and under‐utilized in instructional environments. We explore four opportunities that we argue are both accessible to students from a young age and can support conceptual innovation. These are (a) developing empirical systems, (b) getting a grip on empirical systems, (c) determining, defining and operationalizing data as “evidence,” and (d) making sense of what the results of empirical systems do and do not help us understand. 

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5. Engaging classroom observation: A brief measure of active learning in the college classroom

   https://doi.org/10.1177/14697874241229421  

   Young, Chase; Mitchell-Yellin, Benjamin; Randall, George Kevin (February 2024, Active Learning in Higher Education)

   The purpose of this study was to develop a valid, reliable, and brief measure of active learning in college classrooms that is cheap and easy to complete and yields results that faculty can easily use to inform their development as instructors. Initial construct and face validity was achieved by modifying existing instruments and creating a draft of a brief measure of active learning for external expert review. Following the suggested revisions, the engaging classroom observation was then piloted and revised as necessary. Reliability was tested and measures of internal consistency and interrater reliability were acceptable. A principal component analysis showed two components that were moderately correlated, which indicated the potential they could be combined. An Exploratory Factor Analysis confirmed the instrument is measuring one factor, which we propose as active learning. This study is significant because it offers a brief instrument based on students’ perceptions that can be used formatively by faculty. 

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