Exploring Opportunities for Argumentation in Modelling Classrooms

Passmore, Cynthia; Svoboda, Julia

doi:10.1080/09500693.2011.577842

Cited by 117 publications

(104 citation statements)

References 20 publications

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“…Critical monitoring also involves the goals of argumentative discourse, such as persuading and sense-making, and the students showed active participation in the epistemic practice of science learning. Several researchers have pointed out that modeling practice is based on argumentation (Böttcher and Meisert 2011;Mendonça and Justi 2013;Passmore and Svoboda 2012). Our results supported their assertions.…”

Section: Model Evaluation and Modification Phasessupporting

confidence: 82%

“…This study supported the findings of previous studies (e.g., Böttcher and Meisert 2011;Mendonça and Justi 2013;Passmore and Svoboda 2012), which revealed that the modeling process is based on argumentation since the participants' statements associated with DLA affected the argumentative interaction in the group modeling process in this study. In addition, our study showed that the statements associated with DLA might function as the critical factor enhancing argumentative interactions.…”

Section: Experience Caused By the Statements Associated With Dla In Gsupporting

confidence: 82%

“…Cognitive collaboration in modeling has been explored only between teachers and students-that is, the teacher provides question prompts and clues containing scientific knowledge and critically evaluates students' models (Mendonça and Justi 2013;Núñez-Oveido et al 2008;Passmore and Svoboda 2012). However, while students do sometimes need help from teachers when in difficulty, some groups may try to construct their models through interaction between group members, with little help from the teacher.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…In this regard, Next Generation Science Standards (NGSS) in the USA introduced a major practice that helps students develop and use models to learn core ideas (Achieve, Inc. 2013). To elaborate on the earlier definitions, modeling is the process of explaining the relationships between systems, or elements of a system, based on empirical and conceptual evidence (Böttcher and Meisert 2011;Mendonça and Justi 2013;Passmore and Svoboda 2012;Svoboda and Passmore 2013). Hence, when students engage in modeling in science lessons, they do not just describe empirical experiences, as they would with experiments and observations; they can also reason, explain, and communicate phenomena or systems using empirical experiences as evidence.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…In argumentative discussion during modeling, students can generate evidence-based models through sense-making, while articulation and persuasion allow for argumentative interaction around an evaluation of the strengths and weaknesses of the models or ideas generated by the group (Passmore and Svoboda 2012). As they can experience small-group argumentation in which they connect claims and evidence through that reasoning (Driver et al 2000;Jiménez-Aleixandre and Erduran 2008), small-group modeling can be viewed as a type of reasoning in which students justify their models and criticize others' ideas based on appropriate reasons.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

See 4 more Smart Citations

Exploring the Impact of Students’ Learning Approach on Collaborative Group Modeling of Blood Circulation

2014

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This study aimed to explore the effect on group dynamics of statements associated with deep learning approaches (DLA) and their contribution to cognitive collaboration and model development during group modeling of blood circulation. A group was selected for an in-depth analysis of collaborative group modeling. This group constructed a model in a similar fashion to a target model and demonstrated within-group dialogic interaction patterns. It was found that statements associated with DLA contributed to the collaborative group dynamics by providing cognitive scaffolding and enabling critical monitoring, which together facilitated model development and students' participation and understanding. In the model generation phase, the skills demonstrated indicated the use of statements associated with DLA as one student focused on the principles of blood circulation, thereby providing scaffolding for the other students. These students then generated another sub-model. In the model elaboration phase, statements associated with DLA elements such as request information of mechanism (AQ-a) and resolve discrepancies in knowledge (AQ-b) provided students with metacognitive scaffolding and enabled them to show their deep cognitive participation. Moreover, statements associated with DLA elements such as asking questions or metacognitive activity enabled the students to monitor others' models or ideas critically, showing that active cognitive interaction was taking place within the group. These findings reveal that individual learning approaches will bring a synergistic effect to a group modeling process and can lead to practical educational insights for educators seeking to use lessons based on group modeling.

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Section: Model Evaluation and Modification Phasessupporting

confidence: 82%

Section: Experience Caused By the Statements Associated With Dla In Gsupporting

confidence: 82%

Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

See 3 more Smart Citations

Exploring the Impact of Students’ Learning Approach on Collaborative Group Modeling of Blood Circulation

2014

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Undergraduate and graduate students' conceptual understanding of model classification outcomes under the lens of scientific argumentation

Wiese,

Will Pinto,

Magana

2024

Comp Applic In Engineering

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Recent advancements in artificial intelligence (AI) and machine learning (ML) have driven research and development across multiple industries to meet national economic and technological demands. Consequently, companies are investing in AI, ML, and data analytics workforce development efforts to digitalize operations and enhance global competitiveness. As such, evidence‐based educational research around ML is essential to provide a foundation for the future workforce as they face complex AI challenges. This study explored students' conceptual ML understanding through a scientific argumentation framework, where we examined how they used evidence and reasoning to support claims about their ML models. This framework lets us gain insight into students' conceptualizations and helped scaffold student learning via a cognitive apprenticeship model. Thirty students in a mechanical engineering classroom at Purdue University experimented with neural network ML models within a computational notebook to create visual claims (ML models) with textual explanations of their evidence and reasoning. Accordingly, we qualitatively analyzed their learning artifacts to examine their underfit, fit, and overfit models and explanations. It was found that some students tended toward technical explanations while others used visual explanations. Students with technically dominant explanations had higher proficiency in generating correctly fit models but lacked explanatory evidence. Conversely, students with visually dominant explanations provided evidence but lacked technical reasoning and were less accurate in identifying fit models. We discuss implications for both groups of students and offer future research directions to examine how positive pedagogical elements of learning design can optimize ML educational material and AI workforce development.

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For whom is argument and explanation a necessary distinction? A response to Osborne and Patterson

Berland

McNeill

2012

Science Education

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Exploring Opportunities for Argumentation in Modelling Classrooms

Cited by 117 publications

References 20 publications

Exploring the Impact of Students’ Learning Approach on Collaborative Group Modeling of Blood Circulation

Exploring the Impact of Students’ Learning Approach on Collaborative Group Modeling of Blood Circulation

Undergraduate and graduate students' conceptual understanding of model classification outcomes under the lens of scientific argumentation

For whom is argument and explanation a necessary distinction? A response to Osborne and Patterson

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