Experience in a Climate Microworld: Influence of Surface and Structure Learning, Problem Difficulty, and Decision Aids in Reducing Stock-Flow Misconceptions
Research shows that people’s wait-and-see preferences for actions against climate change are a result of several factors, including cognitive misconceptions. The use of simulation tools could help reduce these misconceptions concerning Earth’s climate. However, it is still unclear whether the learni...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-03-01
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| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fpsyg.2018.00299/full |
| _version_ | 1811227759128608768 |
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| author | Medha Kumar Varun Dutt Varun Dutt |
| author_facet | Medha Kumar Varun Dutt Varun Dutt |
| author_sort | Medha Kumar |
| collection | DOAJ |
| description | Research shows that people’s wait-and-see preferences for actions against climate change are a result of several factors, including cognitive misconceptions. The use of simulation tools could help reduce these misconceptions concerning Earth’s climate. However, it is still unclear whether the learning in these tools is of the problem’s surface features (dimensions of emissions and absorptions and cover-story used) or of the problem’s structural features (how emissions and absorptions cause a change in CO2 concentration under different CO2 concentration scenarios). Also, little is known on how problem’s difficulty in these tools (the shape of CO2 concentration trajectory), as well as the use of these tools as a decision aid influences performance. The primary objective of this paper was to investigate how learning about Earth’s climate via simulation tools is influenced by problem’s surface and structural features, problem’s difficulty, and decision aids. In experiment 1, we tested the influence of problem’s surface and structural features in a simulation called Dynamic Climate Change Simulator (DCCS) on subsequent performance in a paper-and-pencil Climate Stabilization (CS) task (N = 100 across four between-subject conditions). In experiment 2, we tested the effects of problem’s difficulty in DCCS on subsequent performance in the CS task (N = 90 across three between-subject conditions). In experiment 3, we tested the influence of DCCS as a decision aid on subsequent performance in the CS task (N = 60 across two between-subject conditions). Results revealed a significant reduction in people’s misconceptions in the CS task after performing in DCCS compared to when performing in CS task in the absence of DCCS. The decrease in misconceptions in the CS task was similar for both problems’ surface and structural features, showing both structure and surface learning in DCCS. However, the proportion of misconceptions was similar across both simple and difficult problems, indicating the role of cognitive load to hamper learning. Finally, misconceptions were reduced when DCCS was used as a decision aid. Overall, these results highlight the role of simulation tools in alleviating climate misconceptions. We discuss the implication of using simulation tools for climate education and policymaking. |
| first_indexed | 2024-04-12T09:47:10Z |
| format | Article |
| id | doaj.art-003f8180bf8d46909854f6e6354112a7 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-12T09:47:10Z |
| publishDate | 2018-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-003f8180bf8d46909854f6e6354112a72022-12-22T03:37:54ZengFrontiers Media S.A.Frontiers in Psychology1664-10782018-03-01910.3389/fpsyg.2018.00299299397Experience in a Climate Microworld: Influence of Surface and Structure Learning, Problem Difficulty, and Decision Aids in Reducing Stock-Flow MisconceptionsMedha Kumar0Varun Dutt1Varun Dutt2Applied Cognitive Science Laboratory, School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Kamand, IndiaApplied Cognitive Science Laboratory, School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Kamand, IndiaSchool of Humanities and Social Sciences, Indian Institute of Technology Mandi, Kamand, IndiaResearch shows that people’s wait-and-see preferences for actions against climate change are a result of several factors, including cognitive misconceptions. The use of simulation tools could help reduce these misconceptions concerning Earth’s climate. However, it is still unclear whether the learning in these tools is of the problem’s surface features (dimensions of emissions and absorptions and cover-story used) or of the problem’s structural features (how emissions and absorptions cause a change in CO2 concentration under different CO2 concentration scenarios). Also, little is known on how problem’s difficulty in these tools (the shape of CO2 concentration trajectory), as well as the use of these tools as a decision aid influences performance. The primary objective of this paper was to investigate how learning about Earth’s climate via simulation tools is influenced by problem’s surface and structural features, problem’s difficulty, and decision aids. In experiment 1, we tested the influence of problem’s surface and structural features in a simulation called Dynamic Climate Change Simulator (DCCS) on subsequent performance in a paper-and-pencil Climate Stabilization (CS) task (N = 100 across four between-subject conditions). In experiment 2, we tested the effects of problem’s difficulty in DCCS on subsequent performance in the CS task (N = 90 across three between-subject conditions). In experiment 3, we tested the influence of DCCS as a decision aid on subsequent performance in the CS task (N = 60 across two between-subject conditions). Results revealed a significant reduction in people’s misconceptions in the CS task after performing in DCCS compared to when performing in CS task in the absence of DCCS. The decrease in misconceptions in the CS task was similar for both problems’ surface and structural features, showing both structure and surface learning in DCCS. However, the proportion of misconceptions was similar across both simple and difficult problems, indicating the role of cognitive load to hamper learning. Finally, misconceptions were reduced when DCCS was used as a decision aid. Overall, these results highlight the role of simulation tools in alleviating climate misconceptions. We discuss the implication of using simulation tools for climate education and policymaking.http://journal.frontiersin.org/article/10.3389/fpsyg.2018.00299/fullstock-and-flow simulationscorrelation heuristicviolation of mass balanceexperienceproblem structuredecision aids |
| spellingShingle | Medha Kumar Varun Dutt Varun Dutt Experience in a Climate Microworld: Influence of Surface and Structure Learning, Problem Difficulty, and Decision Aids in Reducing Stock-Flow Misconceptions Frontiers in Psychology stock-and-flow simulations correlation heuristic violation of mass balance experience problem structure decision aids |
| title | Experience in a Climate Microworld: Influence of Surface and Structure Learning, Problem Difficulty, and Decision Aids in Reducing Stock-Flow Misconceptions |
| title_full | Experience in a Climate Microworld: Influence of Surface and Structure Learning, Problem Difficulty, and Decision Aids in Reducing Stock-Flow Misconceptions |
| title_fullStr | Experience in a Climate Microworld: Influence of Surface and Structure Learning, Problem Difficulty, and Decision Aids in Reducing Stock-Flow Misconceptions |
| title_full_unstemmed | Experience in a Climate Microworld: Influence of Surface and Structure Learning, Problem Difficulty, and Decision Aids in Reducing Stock-Flow Misconceptions |
| title_short | Experience in a Climate Microworld: Influence of Surface and Structure Learning, Problem Difficulty, and Decision Aids in Reducing Stock-Flow Misconceptions |
| title_sort | experience in a climate microworld influence of surface and structure learning problem difficulty and decision aids in reducing stock flow misconceptions |
| topic | stock-and-flow simulations correlation heuristic violation of mass balance experience problem structure decision aids |
| url | http://journal.frontiersin.org/article/10.3389/fpsyg.2018.00299/full |
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