Students’ model-based explanations about natural selection and antibiotic resistance through socio-scientific issues-based learning

Peel, Amanda; Zangori, Laura; Friedrichsen, Patricia; Hayes, Eric; Sadler, Troy D.

doi:10.1080/09500693.2018.1564084

Cited by 43 publications

(36 citation statements)

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“…When students use population, selective pressure, favorable trait, and mutation as variables in their algorithmic explanations, this indicates they understand their algorithms can be used to explain other natural selection scenarios. This shows students understand the process of natural selection does not change based on organism or context, which is in direct contrast to our prior findings in which students struggled to explain the process in different contexts (Peel et al, ). The persistent issue of students failing to explain natural selection across organismal contexts has been well documented (Heredia et al, ; Nehm & Ha, ; Nehm & Schonfeld, ; Opfer et al, ).…”

Section: Discussioncontrasting

confidence: 94%

“…This unit used modeling as a focal practice as students made sense of natural selection within the ABR issue context. Results suggest students struggled to apply their natural selection knowledge from their ABR experiences in the unit to the process of natural selection in other contexts (Peel et al, ). For example, students explained natural selection in bacterial resistance differently than they explained natural selection in peppered moths.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The literature about student learning of natural selection is well developed (reviewed in Beardsley, Bloom, & Wise, 2012), and natural selection misconceptions are well categorized (e.g., Ferrari & Chi, 1998;Gregory, 2009). Students' conceptions of natural selection can be positively impacted with natural selection instruction, but some students continue to have inadequate natural selection knowledge and hold misconceptions, or alternate conceptions, after targeted interventions (Bishop & Anderson, 1990;Jensen & Finley, 1996;Nehm & Reilly, 2007;Peel et al, 2019).…”

Section: Natural Selectionmentioning

confidence: 99%

“…Evolution is a fundamental biological theory, and natural selection is the primary mechanism of evolution, making it essential to evolution education (Dobzhansky, ). Although natural selection is central, many students hold misconceptions about natural selection (Gregory, ), even after targeted natural selection instruction (Bishop & Anderson, ; Nehm & Reilly, ; Peel, Zangori, Friedrichsen, Hayes, & Sadler, ). Additionally, studies show students' understandings of natural selection vary across contexts (Heredia, Furtak, & Morrison, ; Nehm & Ha, ; Nehm & Schonfeld, ; Opfer, Nehm, & Ha, ).…”

Section: Introductionmentioning

confidence: 99%

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Learning natural selection through computational thinking: Unplugged design of algorithmic explanations

2019

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Computational thinking (CT) is a way of making sense of the natural world and problem solving with computer science concepts and skills. Although CT and science integrations have been called for in the literature, empirical investigations of such integrations are lacking. Prior work in natural selection education indicates students struggle to explain natural selection in different contexts and natural selection misconceptions are common. In this mixed methods study, secondary honors biology students learn natural selection through CT by engaging in the design of unplugged algorithmic explanations. Students learned CT principles and practices and applied them to learn and explain the natural selection process. Algorithmic explanations were used to scaffold transfer of natural selection knowledge across contexts through investigation of three organisms and the creation of generalized natural selection algorithms. Students' pre‐ and post‐unit algorithmic explanations of natural selection were analyzed to answer the following research questions: (a) How do students' conceptions of natural selection change over the course of a CT focused unit? (b) What is the relationship between CT and natural selection in students' algorithmic explanations? (c) What are students' perspectives of learning natural selection with CT? Results indicate students' conceptions of natural selection increased and natural selection misconceptions decreased over the course of the unit. Within their post‐unit algorithmic explanations, students used specific CT principles in conjunction with natural selection concepts to explain natural selection, which helped them to learn the details of the natural selection process and correct their natural selection misconceptions. Students indicated the use of CT in unplugged algorithmic explanations in different contexts helped them learn natural selection. This study shows unplugged CT can be used to teach students science content, and it provides an example for further CT and science integrations. Implications for the field are discussed.

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Section: Discussioncontrasting

confidence: 94%

Section: Literature Reviewmentioning

confidence: 99%

Section: Natural Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning natural selection through computational thinking: Unplugged design of algorithmic explanations

2019

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“…Connecting aspects of observations with theory is an endeavor for both scientists and science learners [13][14][15]. Especially science learners struggle with this [16][17][18][19][20]. To guide students towards better explanations, it would be useful to have more tools that give insight into the structure and quality of explanations.…”

Section: Introductionmentioning

confidence: 99%

Measuring Characteristics of Explanations with Element Maps

Wagner¹,

Kok²,

Priemer³

2020

Education Sciences

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What are the structural characteristics of written scientific explanations that make them good? This is often difficult to measure. One approach to describing and analyzing structures is to employ network theory. With this research, we aim to describe the elementary structure of written explanations, their qualities, and the differences between those made by experts and students. We do this by converting written explanations into networks called element maps and measure their characteristics: size, the ratio of diameter to size, and betweenness centrality. Our results indicate that experts give longer explanations with more intertwinement, organized around a few central key elements. Students’ explanations vary widely in size, are less intertwined, and often lack a focus around key elements. We have successfully identified and quantified the characteristics that can be a starting point for guiding students towards generating expert-like written explanations.

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About birds and bees, snails and trees: Children's ideas on animal and plant evolution

Adler,

Fiedler,

Harms

2024

Science Education

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Evolution is the integrative framework of the life sciences. Even though the topic is often not formally introduced before high school, young children already have various ideas about evolutionary principles (variation, inheritance, and selection) and their underlying key concepts (e.g., differential fitness, reproduction, and speciation). Describing and refining those ideas has increasingly received attention over the last two decades. However, we see two scopes of improvement in the field: (1) There is a need to examine children's ideas about evolutionary concepts holistically rather than focusing at specific aspects. (2) Although research has shown that older students have different ideas about animal and plant evolution, there is little data on children's ideas about plant evolution to compare with their ideas about animal evolution. All of this results in an incomplete record of children's pre‐existing ideas that would help to design assessments or interventions. Consequently, we developed a set of questions, about the evolutionary principles and interviewed 24 kindergarten children. Most children had basic ideas about individual variation in animals and plants but experienced a lack of knowledge about the origin of variation. Most children seemed to acknowledge plants as living beings and reasoned equally about animals and plants for most concepts. However, many children failed to reason about reproduction and inheritance in plants because they believed plants would not reproduce sexually. Confronted with a selection scenario, most children struggled applying concepts previously shown on an individual level to a population level. Considering our findings, we propose ideas about how to measure and foster children's pre‐existing ideas about evolution.

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Students’ model-based explanations about natural selection and antibiotic resistance through socio-scientific issues-based learning

Cited by 43 publications

References 39 publications

Learning natural selection through computational thinking: Unplugged design of algorithmic explanations

Learning natural selection through computational thinking: Unplugged design of algorithmic explanations

Measuring Characteristics of Explanations with Element Maps

About birds and bees, snails and trees: Children's ideas on animal and plant evolution

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