How Can We Help Students Reason About the Mechanisms by Which Genes Affect Traits?

Haskel‐Ittah, Michal

doi:10.1007/978-3-030-86051-6_5

Cited by 3 publications

(1 citation statement)

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“…This may lead to contingencies between different ideas, meaning unpacking the mechanism in the context of one idea is dependent on establishing it as a black box in the context of another. Haskel‐Ittah (2021) used the lens of black boxes to observe a learning progression in genetics and explained empirically tested contingencies between ideas by manifesting different unpacking patterns of the genes‐to‐traits mechanism. Specifically, it noted that the idea that deals with mechanisms of protein function (unpacking of the mechanism between proteins and traits) is only relevant for students who reached the level of understanding that genes affect traits by coding for proteins (genes [black box]–proteins [black box]–traits).…”

Section: How the Lense Of Black Boxes May Benefit Research In Science...mentioning

confidence: 99%

Explanatory black boxes and mechanistic reasoning

Haskel‐Ittah

2022

J Res Sci Teach

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Many studies have characterized students' difficulties in understanding and reasoning about scientific mechanisms. Some of those studies have drawn implications on teaching mechanisms and how to guide students while reasoning mechanistically. In this theoretical article, I claim that one component that has not garnered much attention in the science education literature, unlike other components of mechanistic explanations, is the black box construct, that is, missing mechanistic parts within mechanistic explanations (explanatory black box). By reviewing the literature on mechanisms and mechanistic explanations in the philosophy of science and cognitive psychology, I argue that explanatory black boxes are an inherent part of mechanistic explanations and that their recognition is essential for learning mechanisms, scientific literacy, and understanding the nature of science. Examples from biology education are provided as a case of a complex multileveled scientific field. In the absence of a pedagogical approach for teaching explanatory black boxes, I turn to studies and frameworks from computer science education that may guide educators on how to begin discussing this construct in the science classroom.

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Section: How the Lense Of Black Boxes May Benefit Research In Science...mentioning

confidence: 99%

Explanatory black boxes and mechanistic reasoning

Haskel‐Ittah

2022

J Res Sci Teach

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Causal‐mechanical explanations in biology: Applying automated assessment for personalized learning in the science classroom

Ariely,

Nazaretsky,

Alexandron

2024

J Res Sci Teach

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One of the core practices of science is constructing scientific explanations. However, numerous studies have shown that constructing scientific explanations poses significant challenges to students. Proper assessment of scientific explanations is costly and time‐consuming, and teachers often do not have a clear definition of the educational goals for formulating scientific explanations. Consequently, teachers struggle to support their students in this process. It is hoped that recent advances in machine learning (ML) and its application to educational technologies can assist teachers and learners in analyzing student responses and providing automated formative feedback according to well‐defined pedagogical criteria. In this study, we present a method to automate the entire assessment‐feedback process. First, we developed a causal‐mechanical (CM)‐based grading rubric and applied it to student responses to two open‐ended items. Second, we used unsupervised ML tools to identify patterns in student responses. Those patterns enable the definition of “meta‐categories” of explanation types and the design of personalized feedback adapted to each category. Third, we designed an in‐class intervention with personalized formative feedback that matches the response patterns. We used natural language processing and ML algorithms to assess students' explanations and provide feedback. Findings from a controlled experiment demonstrated that a CM‐based grading scheme can be used to identify meaningful patterns and inform the design of formative feedback that promotes student ability to construct explanations in biology. We discuss possible implications for automated assessment and personalized teaching and learning of scientific writing in K‐12 science education.

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