Right but Wrong: How Students' Mechanistic Reasoning and Conceptual Understandings Shift When Designing Agent-Based Models Using Data
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| Title: | Right but Wrong: How Students' Mechanistic Reasoning and Conceptual Understandings Shift When Designing Agent-Based Models Using Data |
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| Language: | English |
| Authors: | Tamar Fuhrmann (ORCID |
| Source: | Science Education. 2025 109(1):3-26. |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 24 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Elementary Education Grade 6 Intermediate Grades Middle Schools |
| Descriptors: | Mechanics (Physics), Thinking Skills, Scientific Concepts, Concept Formation, Computer Software, Models, Accuracy, Teaching Methods, Grade 6, Units of Study, Science Instruction, Data Analysis |
| DOI: | 10.1002/sce.21890 |
| ISSN: | 0036-8326 1098-237X |
| Abstract: | When learning about scientific phenomena, students are expected to "mechanistically" explain how underlying interactions produce the observable phenomenon and "conceptually" connect the observed phenomenon to canonical scientific knowledge. This paper investigates how the integration of the complementary processes of designing and refining computational models using real-world data can support students in developing mechanistic and canonically accurate explanations of diffusion. Specifically, we examine two types of shifts in how students explain diffusion as they create and refine computational models using real-world data: a shift towards mechanistic reasoning and a shift from noncanonical to canonical explanations. We present descriptive statistics for the whole class as well as three student work examples to illustrate these two shifts as 6th grade students engage in an 8-day unit on the diffusion of ink in hot and cold water. Our findings show that (1) students develop mechanistic explanations as they build agent-based models, (2) students' mechanistic reasoning can co-exist with noncanonical explanations, and (3) students shift their thinking toward canonical explanations after comparing their models against data. These findings could inform the design of modeling tools that support learners in both expressing a diverse range of mechanistic explanations of scientific phenomena and aligning those explanations with canonical science. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1455641 |
| Database: | ERIC |
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| Abstract: | When learning about scientific phenomena, students are expected to "mechanistically" explain how underlying interactions produce the observable phenomenon and "conceptually" connect the observed phenomenon to canonical scientific knowledge. This paper investigates how the integration of the complementary processes of designing and refining computational models using real-world data can support students in developing mechanistic and canonically accurate explanations of diffusion. Specifically, we examine two types of shifts in how students explain diffusion as they create and refine computational models using real-world data: a shift towards mechanistic reasoning and a shift from noncanonical to canonical explanations. We present descriptive statistics for the whole class as well as three student work examples to illustrate these two shifts as 6th grade students engage in an 8-day unit on the diffusion of ink in hot and cold water. Our findings show that (1) students develop mechanistic explanations as they build agent-based models, (2) students' mechanistic reasoning can co-exist with noncanonical explanations, and (3) students shift their thinking toward canonical explanations after comparing their models against data. These findings could inform the design of modeling tools that support learners in both expressing a diverse range of mechanistic explanations of scientific phenomena and aligning those explanations with canonical science. |
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| ISSN: | 0036-8326 1098-237X |
| DOI: | 10.1002/sce.21890 |
