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1. A Deep Look into Designing a Task and Coding Scheme through the Lens of Causal Mechanistic Reasoning

   https://doi.org/https://doi.org/10.1021/acs.jchemed.1c00959  

   Noyes, K.; Carlson, C. G.; Stoltzfus, J. R.; Schwarz, C. V.; Long, T. M.; Cooper, M. M. (January 2022, Journal of chemical education)

   The purpose of this paper is to share the iterative process we used to design a task that elicits causal mechanistic reasoning and how the subsequent student responses can be analyzed. Our goal in this task is to strike a balance between eliciting as much student knowledge as possible without providing so much structure that the answer becomes obvious. The task development was approached using (1) a resources perspective of learning, (2) principles of scaffolding, and (3) evidence-centered design, for which we specified evidence that would be considered a fully causal mechanistic explanation. That is, an explanation which pays explicit attention to the properties, interactions, and behaviors of entities that are involved at a scalar level below the phenomenon under consideration. Since our eventual goal is to characterize how students use knowledge across disciplinary boundaries, the phenomenon of protein–ligand binding was chosen as the context for this task, because it requires students to apply ideas learned in chemistry courses to a biological phenomenon. After three rounds of iterative refinement, a final task was developed. To characterize students’ responses to this task, we developed a coding scheme which can be used to code explanations based on the presence or absence of three key ideas relevant to this phenomenon. In this paper, we share the detailed processes and approaches used in task development, which we hope will provide insight into instructors and researchers as they, too, develop such tasks to explore student reasoning. 

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2. Right but Wrong: The Independence of Mechanistic Reasoning and Canonical Understanding in Studying Diffusion

   Fuhrmann, T.; Rosenbaum, L.; Eloy, A; Wagh, A.; Wolf, A.; Blikstein, P.; Wilkerson, M. (January 2023, Annual International Conference of the National Association for Research in Science Teaching (NARST))

   This study explores how the interplay between data analysis and model design shifts 6th-grade students' understanding of diffusion from simple to sophisticated mechanistic reasoning and from non-canonical to canonical ideas about diffusion. Using mixed-methods qualitative analysis, we determine students' mechanistic reasoning and ideas about diffusion at five different points in a curricular sequence using a new tool for computational modeling called MoDa. With this data, we present a framework for the relationship between students' developing mechanistic reasoning and their canonical understanding, suggesting that they develop independently. Further, we illustrate how the computational modeling environment, MoDa, used in this study pushed students' mechanistic reasoning toward sophistication. Moreover, in allowing them to explore non-canonical mechanisms, MoDa supported their convergence on canonical scientific ideas about diffusion. 

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3. The Ripple Effect of Epistemological Framing: Linking Epistemology, Teacher Moves, and Student Learning

   Southerland, S. A.; Schellinger, J.; Jaber, L.Z.; Morandi, S.; Krishnan, H.; & Tekkumru-Kisa, M. (January 2021, Annual meeting program American Educational Research Association)

   null (Ed.)

   This work explores epistemological framing dynamics in a middle school biology classroom and how such dynamics shape student engagement and learning opportunities. Our data sources include student and teacher interviews, classroom videos of three multi-day lessons with a focus on argumentation, and work products collected across one academic year. Our analysis reveals that while the teacher made room for students to generate and negotiate ideas, brief but influential moves emphasizing single correct answers undermined students’ sensemaking. These instructional moves, while only occupying a small amount of instructional time, framed students’ sensemaking efforts not as a process to seek the strongest explanation from a number of possibilities, but rather to wait for the correct explanation to be revealed from an authority. 

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4. Confrustion in learning from erroneous examples: Does type of prompted self-explanation make a difference?

   https://doi.org/10.1007/978-3-030-23204-7_37  

   Richey, J. Elizabeth; McLaren, Bruce M.; Andres-Bray, Miguel; Mogessie, Michael; Scruggs, Richard; Baker, Ryan; Star, Jon (June 2019, 20th International Conference on Artificial Intelligence in Education)

   Confrustion, a mix of confusion and frustration sometimes experienced while grappling with instructional materials, is not necessarily detrimental to learning. Prior research has shown that studying erroneous examples can increase students’ experiences of confrustion, while at the same time helping them learn and overcome their misconceptions. In the study reported in this paper, we examined students’ knowledge and misconceptions about decimal numbers before and after they interacted with an intelligent tutoring system presenting either erroneous examples targeting misconceptions (erroneous example condition) or practice problems targeting the same misconceptions (problem-solving condition). While students in both conditions significantly improved their performance from pretest to posttest, students in the problem-solving condition improved significantly more and experienced significantly less confrustion. When controlling for confrustion levels, there were no differences in performance. This study is interesting in that, unlike prior studies, the higher confrustion that resulted from studying erroneous examples was not associated with better learning outcomes; instead, it was associated with poorer learning. We propose several possible explanations for this different outcome and hypothesize that revisions to the explanation prompts to make them more expert-like may have also made them – and the erroneous examples that they targeted – less understandable and less effective. Whether prompted self-explanation options should be modeled after the shorter, less precise language students tend to use or the longer, more precise language of experts is an open question, and an important one both for understanding the mechanisms of self-explanation and for designing self-explanation options deployed in instructional materials. 

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5. Confrustion in Learning from Erroneous Examples: Does Type of Prompted Self-explanation Make a Difference?

   https://doi.org/10.1007/978-3-030-23204-7_37  

   J. Elizabeth Richey, Bruce M. (May 2020, S. Isotani et al. (Eds.): AIED 2019, LNAI 11625)

   Confrustion, a mix of confusion and frustration sometimes experienced while grappling with instructional materials, is not necessarily detrimental to learning. Prior research has shown that studying erroneous examples can increase students’ experiences of confrustion, while at the same time helping them learn and overcome their misconceptions. In the study reported in this paper, we examined students’ knowledge and misconceptions about decimal numbers before and after they interacted with an intelligent tutoring system presenting either erroneous examples targeting misconceptions (erroneous example condition) or practice problems targeting the same misconceptions (problem-solving condition). While students in both conditions significantly improved their performance from pretest to posttest, students in the problem-solving condition improved significantly more and experienced significantly less confrustion. When controlling for confrustion levels, there were no differences in performance. This study is interesting in that, unlike prior studies, the higher confrustion that resulted from studying erroneous examples was not associated with better learning outcomes; instead, it was associated with poorer learning. We propose several possible explanations for this different outcome and hypothesize that revisions to the explanation prompts to make them more expert-like may have also made them – and the erroneous examples that they targeted – less understandable and less effective. Whether prompted self-explanation options should be modeled after the shorter, less precise language students tend to use or the longer, more precise language of experts is an open question, and an important one both for understanding the mechanisms of self-explanation and for designing self-explanation options deployed in instructional materials. 

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