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1. Designing Computational Models as Emergent Systems Microworlds to Support Learning of Scientific Inquiry

   Dabholkar, S.; Wilensky, U. (January 2020, eipSTEM8)

   Emergent Systems Microworlds (ESMs) are a special kind of computational models. Design of ESMs involves a combination of two approaches in Learning Sciences, namely agent-based modelling of complex systems and constructionism. ESMs and ESM-based curricula are frameworks for designing learning environments to foster the learning of complex scientific phenomena by engaging students in authentic scientific inquiry practices. In this paper, we discuss our approach in the context of an ESM called GenEvo that we designed for the learning of molecular genetics and evolution. We further discuss how agent-based representations and constructionist design principles mediated students’ expansive learning, as students collaboratively con- structed knowledge by engaging in authentic scientific inquiry practices. 

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2. Mediating Students’ Fixation with Grades in an Inquiry-Based Undergraduate Biology Course

   https://doi.org/10.1007/s11191-020-00161-3  

   DeFeo, Dayna Jean; Tran, Trang C.; Gerken, Sarah (September 2020, Science education)

   Abstract The paper analyzes focus group data to explore student perceptions of an inquiry-based undergraduate biology course. Though the course was designed to mimic the scientific process by incorporating uncertainty, peer review, and self-reflection, students came to class focused on getting As and with a developed schema for didactic instruction and passive learning. They perceived the autonomy and self-directedness of the learning experience as a threat to their grades, and responded with strategies that protected their grades and ego, but were deleterious to learning. Students could identify merits of the inquiry-based approach; however, they made clear: they prioritized grades, and were unwilling to trust an unfamiliar pedagogy if they perceived it jeopardized their grades. In the framework of self-regulated learning, the discussion considers how to scaffold students to foreground learning over achievement. 

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3. Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning

   Taub, M.; Azevedo, R. (December 2018, Journal of educational data mining)

   Self-regulated learning conducted through metacognitive monitoring and scientific inquiry can be influenced by many factors, such as emotions and motivation, and are necessary skills needed to engage in efficient hypothesis testing during game-based learning. Although many studies have investigated metacognitive monitoring and scientific inquiry skills during game-based learning, few studies have investigated how the sequence of behaviors involved during hypothesis testing with game-based learning differ based on both efficiency level and emotions during gameplay. For this study, we analyzed 59 undergraduate students’ (59% female) metacognitive monitoring and hypothesis testing behavior during learning and gameplay with CRYSTAL ISLAND, a game-based learning environment that teaches students about microbiology. Specifically, we used sequential pattern mining and differential sequence mining to determine if there were sequences of hypothesis testing behaviors and to determine if the frequencies of occurrence of these sequences differed between high or low levels of efficiency at finishing the game and high or low levels of facial expressions of emotions during gameplay. Results revealed that students with low levels of efficiency and high levels of facial expressions of emotions had the most sequences of testing behaviors overall, specifically engaging in more sequences that were indicative of less strategic hypothesis testing behavior than the other students, where students who were more efficient with both levels of emotions demonstrated strategic testing behavior. These results have implications for the strengths of using educational data mining techniques for determining the processes underlying patterns of engaging in self-regulated learning conducted through hypothesis testing as they unfold over time; for training students on how to engage in the self-regulation, scientific inquiry, and emotion regulation processes that can result in efficient gameplay; and for developing adaptive game-based learning environments that foster effective and efficient self-regulation and scientific inquiry during learning. 

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4. A web-based tool for participatory science learning in the context of human psychology research

   Dikker, S.; Yetman-Michaelson, L.; Shevchenko, Y.; Burgas, K.; Chaloner, K.; Sole, M.; Davidesco, I.; Martin, R.; Matuk, C. (April 2022, International Conference on Computer-Supported Collaborative Learning)

   We describe an online citizen science platform (www.mindhive.science) for human brain and behavior research that uses a participatory science learning approach to engage learners in the full spectrum of scientific inquiry. Building on an open science philosophy, it features a collaborative study design environment comprising an experiment builder, a database of validated tasks and surveys, and a public-facing study page; a peer review center where students are able to engage with and reflect on studies designed by peers from their own schools and schools around the globe; and GDPR-compliant data collection, data management, and data visualization and interpretation functionality. We describe student-initiated research generated during the COVID-19 pandemic to illustrate how the platform supports student-teacher-scientist community partnerships for participatory learning in authentic inquiry. 

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5. Preprint articles as a tool for teaching data analysis and scientific communication

   https://doi.org/10.1371/journal.pone.0261622  

   Scheifele, Lisa Z.; Tsotakos, Nikolaos; Wolyniak, Michael J. (December 2021, PLOS ONE)

   Aslam, Muhammad Shahzad (Ed.)

   The skill of analyzing and interpreting research data is central to the scientific process, yet it is one of the hardest skills for students to master. While instructors can coach students through the analysis of data that they have either generated themselves or obtained from published articles, the burgeoning availability of preprint articles provides a new potential pedagogical tool. We developed a new method in which students use a cognitive apprenticeship model to uncover how experts analyzed a paper and compare the professional’s cognitive approach to their own. Specifically, students first critique research data themselves and then identify changes between the preprint and final versions of the paper that were likely the results of peer review. From this activity, students reported diverse insights into the processes of data presentation, peer review, and scientific publishing. Analysis of preprint articles is therefore a valuable new tool to strengthen students’ information literacy and understanding of the process of science. 

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