A Framework for Modeling-Based Learning, Teaching, and Assessment

Constantinou, Constantinos; Nicolaou, Christiana Th.; Papaevripidou, Marios

doi:10.1007/978-3-030-30255-9_3

Cited by 19 publications

(19 citation statements)

References 58 publications

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“…Second, a model is never an exact copy but is, by default, a limited, abstracted representation or theoretical reconstruction of the phenomenon (Mahr, 2011; Upmeier zu Belzen et al, 2019). Third, due to their evidence‐based nature, scientific models are always preliminary and thus constantly evolving (Constantinou et al, 2019; Pluta et al, 2011). Accordingly, they can be adapted and/or iteratively modified to optimize their utility and usability when engaging with and making sense of the natural world (Constantinou et al, 2019; Odden & Russ, 2018; Upmeier zu Belzen et al, 2019).…”

Section: Theoretical Framework For Scientific Modelingmentioning

confidence: 99%

“…Third, due to their evidence‐based nature, scientific models are always preliminary and thus constantly evolving (Constantinou et al, 2019; Pluta et al, 2011). Accordingly, they can be adapted and/or iteratively modified to optimize their utility and usability when engaging with and making sense of the natural world (Constantinou et al, 2019; Odden & Russ, 2018; Upmeier zu Belzen et al, 2019). Consistent with these key features, scientific models can be defined as abstracted, multimodal representations or theoretical reconstructions of systems, not exact recreations, which are used within communities to illustrate, explain, and/or predict system‐specific phenomena of the natural world.…”

Section: Theoretical Framework For Scientific Modelingmentioning

confidence: 99%

“…Model construction emphasizes and draws on students’ already existing ideas and preconceptions about the phenomenon in question (Chen, 2021; Forbes et al, 2014), as the literature suggests that encouraging/prompting students to employ their own knowledge during the construction steps leads to models of higher quality and serves as an anchor for subsequent modeling steps (Sins et al, 2005). The initial models developed in this step are explications of learners’ mental models (Upmeier zu Belzen et al, 2019) and available information and/or data regarding the stimulus (Baumfalk et al, 2019; Constantinou et al, 2019; Vo et al, 2015). They build the foundation and provide leverage points for advancing students’ thinking and developing subject‐matter expertize (Gaytán, 2017; Lehrer & Schauble, 2010; Schwarz & White, 2005; Schwarz, Reiser, et al, 2009).…”

Section: Theoretical Framework For Scientific Modelingmentioning

confidence: 99%

“…It entails or crosscuts a plethora of other important scientific practices, such as planning and conducting investigations or experiments, collecting data, predicting possible outcomes, generating scientific explanations, documenting, and communicating ideas and findings (Manz et al, 2020; NGSS, 2013; Vo et al, 2015). Accordingly, as Constantinou et al (2019) emphasize, being or becoming competent in the use of models is also key to gaining a “purposeful, meaningful, and fruitful understanding of scientific knowledge” (p. 45) as well as in‐depth insights into the more epistemological aspects of science (Constantinou et al, 2019; Schwartz, 2019). Model evaluation involves the use of new data and/or new evidence, with which students should engage in model evaluation to determine if the model(s) used adequately explain the phenomenon under investigation (Pluta et al, 2011; Vo et al, 2015).…”

Section: Theoretical Framework For Scientific Modelingmentioning

confidence: 99%

“…To effectively do so, students should adhere to and utilize topic‐ and/or community‐specific criteria and standards of model goodness/appropriateness, such as “conceptual coherence, evidential fit (e.g., the scope of evidence covered, degree of fit with that evidence), and “parsimony” (Pluta et al, 2011; p. 488). Additionally, students should also be encouraged to critique, compare (Constantinou et al, 2019; Lehrer & Schauble, 2010; Pluta et al, 2011), and reconcile their (mental) model(s) used with alternate models and ideas related to the same (aspects of the) phenomenon, such as models from peers, experts, and textbooks (Vo et al, 2015). In elementary classrooms, observable aspects of model evaluation include discussions about appropriate criteria for judging model quality and their application.…”

Section: Theoretical Framework For Scientific Modelingmentioning

confidence: 99%

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Investigating scientific modeling practices in U.S. and German elementary science classrooms: A comparative, cross‐national video study

2022

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Science education reform efforts have emphasized the incorporation of more authentic and meaningful scientific practices into formal learning environments. With this focus on practices, such as scientific modeling, a powerful tool for sense‐making and reasoning, comes a need to observe and understand if and how it is actually occurring, particularly in elementary science learning environments. To address this need, and moving forward an international, collaborative research effort centered around scientific modeling, we undertook a cross‐national observational study in which we used an observation protocol to characterize scientific modeling practices in elementary classrooms in both the United States and Germany. Drawing on existing video‐based datasets from multiple projects, this study's findings provide empirical evidence for model‐based teaching and learning and explore the relative frequencies of relevant modeling subpractices (i.e., construction, use, evaluation, and revision of scientific models) in regular, day‐to‐day elementary science classrooms. This study also illustrates the differences and similarities between German and American science instruction. In general, students in both countries experience model construction more often than other modeling practices. German teachers more consistently foreground anchoring phenomena and students’ questions/preconceptions as part of the modeling process, thus leading to slightly higher overall modeling scores—despite a complete lack of model evaluation and revision in any of the observed German classrooms. Ultimately, however, young learners in both nations seem to experience alarmingly little opportunities to engage in any of the modeling subpractices. Particularly in terms of modeling as a means to make sense of phenomena, constrained by teachers most commonly using finished representations (i.e., models) of phenomena and processes to teach science/deliver content. Our results indicate the need for a greater focus on all scientific modeling subprocesses, particularly the integration of scientific modeling with already established, more general (e.g., inquiry‐based) strategies for effective science teaching and an emphasis on actualizing modeling as an authentic sense‐making endeavor.

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Section: Theoretical Framework For Scientific Modelingmentioning

confidence: 99%

Section: Theoretical Framework For Scientific Modelingmentioning

confidence: 99%

Section: Theoretical Framework For Scientific Modelingmentioning

confidence: 99%

Section: Theoretical Framework For Scientific Modelingmentioning

confidence: 99%

Section: Theoretical Framework For Scientific Modelingmentioning

confidence: 99%

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Investigating scientific modeling practices in U.S. and German elementary science classrooms: A comparative, cross‐national video study

2022

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Developing an instrument to examine students' analogical modeling competence: An example of electricity

Lin,

Chao

2023

Science Education

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Science education reforms advocate modeling as a core practice in which analogy is a significant form and analogical modeling is a creative process for scientific explanation and discovery. This study adopts the self‐generated analogical modeling approach involving electricity, which considers all the modeling subprocesses and includes two subtopics (circuits and energy) with incompatible ontological presuppositions as a conceptual space to develop an analogical modeling competence (AMC) instrument. Two constructs, phenomenon‐related abductive premises (Phenomenon‐AbPr) and analogical modeling processes (AMP), that describe the increasing sophistication of students' AMC (i.e., Level 0 experience to Level 4 high‐order relation) are considered. The procedures utilize a construct‐based approach of developing an assessment to validate the underlying constructs and subtopics to develop the mapping between the target and the analogical models, design items, and determine outcome spaces. Data were collected from 294 Grade 6 students to examine their AMC. The item response theory technique based on the Rasch partial credit model was used to validate the two‐phase model. Student results revealed that the infit and outfit mean square values were between 0.75 and 1.33, indicating acceptable item fit. Results revealed that students scored highest on the open series circuit and lowest on electric energy in the Phenomenon‐AbPr phase. Most students demonstrated relational mapping in the analogical model validation (Level 3) stage, which performed best in the AMP phase. However, they struggled to transfer factors during the analogical model application and deployment (Level 0). Implications for science assessment and teaching are discussed.

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Research on Modeling Competence in Science Education from 1991 to 2020 with Cultural and Global Implications

Chiu

Lin

2021

Springer International Handbooks of Education

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A Framework for Modeling-Based Learning, Teaching, and Assessment

Cited by 19 publications

References 58 publications

Investigating scientific modeling practices in U.S. and German elementary science classrooms: A comparative, cross‐national video study

Investigating scientific modeling practices in U.S. and German elementary science classrooms: A comparative, cross‐national video study

Developing an instrument to examine students' analogical modeling competence: An example of electricity

Research on Modeling Competence in Science Education from 1991 to 2020 with Cultural and Global Implications

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