In Quest of productive modeling‐based learning discourse in elementary school science

Louca, Loucas Τ.; Zacharia, Zacharias C.; Constantinou, Constantinos P.

doi:10.1002/tea.20435

Cited by 69 publications

(70 citation statements)

References 61 publications

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“…This is because model generation is not about a simple description of a phenomenon; instead, it is similar to the process of creating a new concept using various representation skills (Wells et al 1995). In this phase, students collect data that can be used as evidence in order to produce the best model to describe the phenomenon (Louca et al 2011). The knowledge to be used as evidence may be obtained through experiments, from a literature review, or it might already exist as prior knowledge (Justi and Gilbert 2002).…”

Section: Model Generation Phasementioning

confidence: 99%

“…In the same manner, Windschitl et al (2008) claimed that modelbased inquiry supported the five epistemic features of scientific knowledge: It is testable, revisable, explanatory, conjectural, and generative. Some previous studies explored secondary students' modeling practice with the help of the teacher (e.g., Louca et al 2011;Mendonça and Justi 2013;Núñez-Oveido et al 2008;Passmore and Svoboda 2012) and investigated the epistemic value of modeling by only focusing on the model evaluation phase (e.g., Lee and Kim 2014;Nelson and Davis 2012;Penner et al 1997). However, our study findings have the possibility of reinforcing the epistemic value of group modeling practice by exploring the students' argumentative interaction in all modeling phases such as model generation, elaboration, evaluation, and modification.…”

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

confidence: 99%

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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 Generation Phasementioning

confidence: 99%

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

confidence: 99%

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

2014

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“…The Consortium for Policy Research in Education report Learning Progressions in Science: An evidence-based approach to reform (Corcoran et al 2009) identified learning progressions as a promising model that can advance effective adaptive instruction teaching techniques and thereby change the norms of practice in schools. A number of learning progressions in science have incorporated components of QR (Louca et al 2011;Pluta et al 2011;Schwarz, et al, 2009;Stefani and Tsaparlis 2009;Taylor and Jones 2009;Lehrer andSchauble 2002, Smith et al 2006), but the one proposed here is the first progression specifically addressing the development of QR in the sciences.…”

Section: Literature Reviewmentioning

confidence: 99%

Quantitative Reasoning in Environmental Science: Rasch Measurement to Support QR Assessment

Mayes¹,

Rittschof²,

Forrester³

et al. 2015

Numeracy

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The ability of middle and high school students to reason quantitatively within the context of environmental science was investigated. A quantitative reasoning (QR) learning progression, with associated QR assessments in the content areas of biodiversity, water, and carbon, was developed based on three QR progress variables: quantification act, quantitative interpretation, and quantitative modeling. Diagnostic instruments were developed specifically for the progress variable quantitative interpretation (QI), each consisting of 96 Likertscale items. Each content version of the instrument focused on three scale levels (macro scale, micro scale, and landscape scale) and four elements of QI identified in prior research (trend, translation, prediction, and revision). The QI assessments were completed by 362, 6 th to 12 th grade students in three U.S. states. Rasch (1960Rasch ( /1980) measurement was used to determine item and person measures for the QI instruments, both to examine validity and reliability characteristics of the instrument administration and inform the evolution of the learning progression. Rasch methods allowed identification of several QI instrument revisions, including modification of specific items, reducing number of items to avoid cognitive fatigue, reconsidering proposed item difficulty levels, and reducing Likert scale to 4 levels. Rasch diagnostics also indicated favorable levels of instrument reliability and appropriate targeting of item abilities to student abilities for the majority of participants. A revised QI instrument is available for STEM researchers and educators. Robert Mayes is a Research Professor in Education at Georgia Southern University, United States, and Director of the Institute for Interdisciplinary STEM Education (i2 STEMe). His research focus is on systems reasoning, computational reasoning, and quantitative reasoning in STEM and development of reasoning learning progressions within the context of science. His work supports interdisciplinary STEM teaching that impacts underrepresented populations and rural areas. Kent Rittschof is a Professor of Educational Psychology at Georgia Southern University, and Chair of the Department of Curriculum, Foundations, & Reading. His expertise is in learning and cognition. His recent research has emphasized contemporary measurement approaches for examining student attitudes and cognitive abilities. He was the lead on conducting Rasch analysis in this study. Jennifer Harris Forrester is an Assistant Professor in Elementary and Early Childhood Education at the University of Wyoming. Her research focuses on the use of quantitative reasoning skills in different science contents and contexts. She is interested in documenting how professional scientists use QR skills in their research (specifically field ecology) and how they teach students QR within the context of fieldwork. Her work supports best practices in STEM teaching at the K-12 and undergraduate level.This article is available in Numeracy: http://scholarcommons.usf.edu/numer...

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“…Ao contrário dos modelos mentais, os modelos científicos resultam da criação específica dos dos estudantes (Louca et al 2011, Palmero 2008. No caso concreto do ensino das Geociências, a modelação 1 apresenta-se como uma ferramenta fulcral pois, a recriação dos fenómenos geológicos através de modelos pode reduzir milhões de anos a algumas horas ou minutos ), e permite recriar fenómenos que não são facilmente observáveis num contexto real, como é o caso dos riscos naturais.…”

Section: Introductionunclassified

A modelação aplicada à temática dos riscos naturais: O caso concreto de instituições de ensino superior em Portugal

2016

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TerraeDid atic a ARTIGO ABSTRACT: The use of modelling as a methodology to aid in building mental models is very important in the process of recreating and simulating natural phenomena, in particular geological and environmental phenomena. In order to check whether the use of modelling is significant as a methodology for the success of student learning of natural hazards in higher education, this study included all Portuguese public university syllabi that address natural hazards. Only graduation syllabi were selected for this study. Eight graduation syllabi were analyzed, through an analysis screen, conducted in seven Portuguese universities. The evidence suggests that none of the syllabi in this analysis entail the use of modeling as a teaching strategy for natural hazards subjects, while an essentially traditional method is predominantly used. The need for intervention regarding teaching methodologies that value the role of models and modeling in higher education is highlighted. Manuscrito IntroduçãoA psicologia cognitiva é uma disciplina fundamental ao nível da educação em ciências, pois tem dado grandes contributos para a compreensão do processo de aprendizagem, não havendo dúvidas quanto à sua importância e o seu papel ao nível dos estudos relacionados com a educação (Greca & Moreira 2000). Atualmente defende-se que o ensino das ciências deve potencializar uma aprendizagem significativa, considerando-se fundamental o recurso a metodologias que promovam o desenvolvimento do conhecimento científico dos estudantes. A escola é vista como um lugar de aprendizagem, ou seja, de mudanças relativamente permanentes na conduta e no conhecimento dos sujeitos, como resultado da experiência e dos processos de associação e condicionamento (Alves 2014). A psicologia cognitiva explica que a forma como os sujeitos compreendem o mundo depende daquilo que nele existe e, daquilo que compõe a mente humana (Johnson-Laird 1983), admitindo--se que esta tem a capacidade de processar informação e interpretar o mundo através da manipulação de símbolos (Palmero 2008). Este processo resulta no desenvolvimento de modelos mentais dos sujeitos, que lhes permitem compreender o mundo em que vivem e dar resposta a situações do cotidiano com que são confrontados.A Aprendizagem Baseada em Modelos (ModelBased Learning) é uma metodologia que promove a aprendizagem dos conteúdos científicos através da construção e utilização de modelos, que recriam fenómenos físicos, permitindo desenvolver o conhecimento científico dos estudantes e a sua literacia científica (Gobert & Buckley 2000), aproximando o processo de aprendizagem dos sujeitos à forma como estes desenvolvem os seus conhecimentos de forma inata, ou seja, os modelos mentais. Esta metodologia promove o contato dos estudantes com modelos, a sua manipulação e comparação com os seus modelos mentais. Desta forma, são geradas as condições necessárias para que se desenvolva uma aprendizagem significativa, através da restruturação das estruturas cognitivas

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In Quest of productive modeling‐based learning discourse in elementary school science

Cited by 69 publications

References 61 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

Quantitative Reasoning in Environmental Science: Rasch Measurement to Support QR Assessment

A modelação aplicada à temática dos riscos naturais: O caso concreto de instituições de ensino superior em Portugal

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