Using Imagery Support Strategies to Develop Powerful Imagistic Models

Price, Norman; Stephens, A. Lynn; Clement, John G.; Núñez-Oviedo, Maria Cecilia

doi:10.2505/4/ss17_041_04_40

Cited by 4 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We believe it would need to be learned slowly, ideally with a support group in an in-service course. Scaffolding strategies would need to be simplified and introduced one major piece or level at a time (see Price et al, 2017;Krajcik and Merrit (2012, 11-12); Williams and Clement, 2015;and Stephens and Clement, 2010).…”

Section: General Instructional Implications For Teacher Educationmentioning

confidence: 99%

Large Scale Scientific Modeling Practices That Can Organize Science Instruction at the Unit and Lesson Levels

Núñez-Oviedo

Clement

2019

Front. Educ.

View full text Add to dashboard Cite

Science educators today still struggle with finding better ways to help students develop strong conceptual understandings as opposed to memorizing isolated facts. Recently there has been increased attention on learning explanatory models as a key to conceptual understanding. Science educators also struggle with how to teach students scientific thinking practices, and sometimes this goal is seen as being in competition with content goals for conceptual understanding. In this study we ask whether whole class discussion can contribute to both of these goals at the same time and whether there are ways that a teacher can support this. We describe the results of a case study of an experienced teacher leading modeling discussions in a series of three middle school life sciences classes. A qualitative microanalysis of the videotaped whole class discussions led to the identification of a variety of modeling processes operating across the lessons at two different time scale levels. These include model competition, in which students compare and evaluate their models, and model evolution, in which the models go through stepwise evaluation and improvement. The latter process involves a smaller time scale pattern of model generation, evaluation, and modification cycles. All of these processes are similar to those found in recent studies of practices of expert scientists. Implications from the case study suggest that: (1) A teacher need not be limited to the two opposing interaction styles of Open Discussion vs. Authoritative lecture. Rather, there are there intermediate discussion styles between these that involve co-construction and cognitive scaffolding;(2) It is possible to start from student-generated models that conflict with the target model in a number of ways, and still arrive at the target model for the lesson through discussion. Processes of model competition and disconfirmation, as well as model evolution, both supported by the teacher's cognitive scaffolding, were central in this accomplishment; (3) In doing so, it is possible for a teacher to foster student modeling practices, as a type of scientific thinking, at the same time that they are teaching science content, by scaffolding the two levels of model construction processes identified.

show abstract

Section: General Instructional Implications For Teacher Educationmentioning

confidence: 99%

Large Scale Scientific Modeling Practices That Can Organize Science Instruction at the Unit and Lesson Levels

Núñez-Oviedo

Clement

2019

Front. Educ.

View full text Add to dashboard Cite

show abstract

“…En el ejemplo 2 la cantidad de ciclos GEM fue de uno (declaraciones: 1 -5, Tabla 2) y el Forms © se utilizó dos veces como una evaluación previa y como una posterior. Es importante señalar que, para lograr el objetivo de esta actividad, la elaboración de dibujos (Price et al 2007) fue crucial dentro del estudiantado.…”

Section: Resultados De Las Evaluaciones Sobre El Gráfico De Concentra...unclassified

“…Para este ejemplo, el docente le pidió al estudiantado dibujar cómo sería un gráfico de concentración versus tiempo para una reacción del tipo A � B. Los dibujos son evidencia indirecta importante de los niveles de representación del estudiantado (López-Cortés et al, 2021) y de lo que están pensando (Price et al, 2007), lo que es de relevancia en esta sección. A diferencia de los ejemplos anteriores, la actividad no comenzó con un Forms © , sino que se hizo a partir de los dibujos (ver Figura 7) que el estudiantado hizo y luego se aplicó la votación sobre estos como alternativas.…”

Section: Aplicación De Forms © Y Detección De Ciclos Gemunclassified

Sistemas de Respuesta Inmediata combinada con ciclos de Generación, Evaluación y Modificación en Química General

Flores-Morales¹

2023

Educación Química

View full text Add to dashboard Cite

Los Sistemas de Respuesta Inmediata (SRI) son aplicaciones o programas que permiten la medición del aprendizaje con retroalimentación inmediata para quienes las usan. Gracias a la pandemia, se convirtieron en instrumentos de evaluación masiva. Sin embargo, su uso no es muy distinto al de las cliqueras o tarjetas en aula. Por lo mismo, una pedagogía que pudiera incorporar esta TIC dentro de la sala de clases como un elemento potenciador del aprendizaje es un desafío actual. La Teoría de la Enseñanza y Aprendizaje de la Ciencia basada en la Revisión y Construcción de Modelos Mentales proporciona un andamiaje para lograr este objetivo al hacer reflexionar al estudiantado partiendo de un SRI.En esta investigación se muestra cómo gracias a la combinación de una parte de la teoría mencionada con los SRI, se logra un aprendizaje profundo en algunos tópicos de Química General. Los resultados muestran que gracias a esta unión el estudiantado participa, cambia sus preconcepciones por ideas de rigurosidad científica superior y obtiene resultados positivos comparando evaluaciones previas y posteriores.

show abstract

“…An implication is that work on frameworks may inform socio-cognitive approaches that are badly needed to teach scientific thinking skills (e.g. Osborne et al, 2004 ; Schwarz et al, 2009 ; Wiser and Smith, 2009 ; Windschitl et al, 2012 ; Lattery, 2016 ; Bielik et al, 2021 ), including grounded ( Stephens and Clement, 2010 ; Newcombe, 2013 ; Price et al, 2017 ; Alibali and Nathan, 2018 ; Ramey et al, 2020 ; Mathayas et al, 2021 ) and hierarchical approaches ( Schoenfeld, 1998 ; Williams and Clement, 2015 ; Nunez-Oviedo and Clement, 2019 ).…”

Section: Limitationsmentioning

confidence: 99%

Multiple Levels of Heuristic Reasoning Processes in Scientific Model Construction

Clement

2022

Front. Psychol.

View full text Add to dashboard Cite

Science historians have recognized the importance of heuristic reasoning strategies for constructing theories, but their extent and degree of organization are still poorly understood. This paper first consolidates a set of important heuristic strategies for constructing scientific models from three books, including studies in the history of genetics and electromagnetism, and an expert think-aloud study in the field of mechanics. The books focus on qualitative reasoning strategies (processes) involved in creative model construction, scientific breakthroughs, and conceptual change. Twenty four processes are examined, most of which are field-general, but all are heuristic in not being guaranteed to work. An organizing framework is then proposed as a four-level hierarchy of nested reasoning processes and subprocesses at different size and time scales, including: Level (L4) Several longer-time-scale Major Modeling Modes, such as Model Evolution and Model Competition; the former mode utilizes: (L3) Modeling Cycle Phases of Model Generation, Evaluation, and Modification under Constraints; which can utilize: (L2) Thirteen Tactical Heuristic Processes, e.g., Analogy, Infer new model feature (e.g., by running the model), etc.; many of which selectively utilize: (L1) Grounded Imagistic Processes, namely Mental Simulations and Structural Transformations. Incomplete serial ordering in the framework gives it an intermediate degree of organization that is neither anarchistic nor fully algorithmic. Its organizational structure is hypothesized to promote a difficult balance between divergent and convergent processes as it alternates between them in modeling cycles with increasingly constrained modifications. Videotaped think-aloud protocols that include depictive gestures and other imagery indicators indicate that the processes in L1 above can be imagistic. From neurological evidence that imagery uses many of the same brain regions as actual perception and action, it is argued that these expert reasoning processes are grounded in the sense of utilizing the perceptual and motor systems, and interconnections to and possible benefits for reasoning processes at higher levels are examined. The discussion examines whether this grounding and the various forms of organization in the framework may begin to explain how processes that are only sometimes useful and not guaranteed to work can combine successfully to achieve innovative scientific model construction.

show abstract

Using Imagery Support Strategies to Develop Powerful Imagistic Models

Cited by 4 publications

References 2 publications

Large Scale Scientific Modeling Practices That Can Organize Science Instruction at the Unit and Lesson Levels

Large Scale Scientific Modeling Practices That Can Organize Science Instruction at the Unit and Lesson Levels

Sistemas de Respuesta Inmediata combinada con ciclos de Generación, Evaluación y Modificación en Química General

Multiple Levels of Heuristic Reasoning Processes in Scientific Model Construction

Contact Info

Product

Resources

About