Jonathan Bowers
Dados Biográficos
| ID | 9505586 |
|---|---|
| NOME | Jonathan Bowers |
| PRENOMES | Jonathan |
| SOBRENOME | Bowers |
| ASSINATURA | BOWERS J |
| AFILIAÇÕES | Michigan State University |
| ORCID | 0000-0001-8993-6988 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAÇÕES | 0 |
| OBRAS COMO AUTOR | 2 |
| OBRAS COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2022 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2023 |
| ÍNDICE H | 0 |
The effect of using different computational system modeling approaches on applying systems thinking
This paper discusses the potential of two computational modeling approaches in moving students from simple linear causal reasoning to applying more complex aspects of systems thinking (ST) in explanations of scientific phenomena. While linear causal reasoning can help students understand some natural phenomena, it may not be sufficient for understanding more complex issues such as global warming and pandemics, which involve feedback, cyclic patte…
A framework for supporting systems thinking and computational thinking through constructing models
We face complex global issues such as climate change that challenge our ability as humans to manage them. Models have been used as a pivotal science and engineering tool to investigate, represent, explain, and predict phenomena or solve problems that involve multi-faceted systems across many fields. To fully explain complex phenomena or solve problems using models requires both systems thinking (ST) and computational thinking (CT). This study pro…
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A framework for supporting systems thinking and computational thinking through constructing models
We face complex global issues such as climate change that challenge our ability as humans to manage them. Models have been used as a pivotal science and engineering tool to investigate, represent, explain, and predict phenomena or solve problems that involve multi-faceted systems across many fields. To fully explain complex phenomena or solve problems using models requires both systems thinking (ST) and computational thinking (CT). This study pro…
The effect of using different computational system modeling approaches on applying systems thinking
This paper discusses the potential of two computational modeling approaches in moving students from simple linear causal reasoning to applying more complex aspects of systems thinking (ST) in explanations of scientific phenomena. While linear causal reasoning can help students understand some natural phenomena, it may not be sufficient for understanding more complex issues such as global warming and pandemics, which involve feedback, cyclic patte…
Artificial Intelligence (2 obras) · Complex system (2 obras) · Computational model (2 obras) · Computer Science (2 obras) · Engineering (2 obras) · Innovative Teaching and Learning Methods (2 obras) · Management science (2 obras) · Psychology (2 obras) · Systems thinking (2 obras) · Artificial Intelligence (1 obra)