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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2312.06871 |
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| _version_ | 1866909012039041024 |
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| author | Kos, John Eaton, Kenneth Zhang, Sareen Dass, Rahul Buckley, Stephen An, Sungeun Goel, Ashok |
| author_facet | Kos, John Eaton, Kenneth Zhang, Sareen Dass, Rahul Buckley, Stephen An, Sungeun Goel, Ashok |
| contents | Conceptual and simulation models can function as useful pedagogical tools, however it is important to categorize different outcomes when evaluating them in order to more meaningfully interpret results. VERA is a ecology-based conceptual modeling software that enables users to simulate interactions between biotics and abiotics in an ecosystem, allowing users to form and then verify hypothesis through observing a time series of the species populations. In this paper, we classify this time series into common patterns found in the domain of ecological modeling through two methods, hierarchical clustering and curve fitting, illustrating a general methodology for showing content validity when combining different pedagogical tools. When applied to a diverse sample of 263 models containing 971 time series collected from three different VERA user categories: a Georgia Tech (GATECH), North Georgia Technical College (NGTC), and ``Self Directed Learners'', results showed agreement between both classification methods on 89.38\% of the sample curves in the test set. This serves as a good indication that our methodology for determining content validity was successful. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_06871 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Using Analytics on Student Created Data to Content Validate Pedagogical Tools Kos, John Eaton, Kenneth Zhang, Sareen Dass, Rahul Buckley, Stephen An, Sungeun Goel, Ashok Artificial Intelligence Machine Learning Multiagent Systems Conceptual and simulation models can function as useful pedagogical tools, however it is important to categorize different outcomes when evaluating them in order to more meaningfully interpret results. VERA is a ecology-based conceptual modeling software that enables users to simulate interactions between biotics and abiotics in an ecosystem, allowing users to form and then verify hypothesis through observing a time series of the species populations. In this paper, we classify this time series into common patterns found in the domain of ecological modeling through two methods, hierarchical clustering and curve fitting, illustrating a general methodology for showing content validity when combining different pedagogical tools. When applied to a diverse sample of 263 models containing 971 time series collected from three different VERA user categories: a Georgia Tech (GATECH), North Georgia Technical College (NGTC), and ``Self Directed Learners'', results showed agreement between both classification methods on 89.38\% of the sample curves in the test set. This serves as a good indication that our methodology for determining content validity was successful. |
| title | Using Analytics on Student Created Data to Content Validate Pedagogical Tools |
| topic | Artificial Intelligence Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2312.06871 |