The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Thinking Like a Wolf, a Sheep, or a Firefly: Learning Biology Through Constructing and Testing Computational Theories—An Embodied Modeling Approach
Abstract
Biological phenomena can be investigated at multiple levels, from the molecular to the cellular to the organismic to the ecological. In typical biology instruction, these levels have been segregated. Yet, it is by examining the connections between such levels that many phenomena in biology, and complex systems in general, are best explained. We describe a computation-based approach that enables students to investigate the connections between different biological levels. Using agent-based, embodied modeling tools, students model the microrules underlying a biological phenomenon and observe the resultant aggregate dynamics. We describe 2 cases in which this approach was used. In both cases, students framed hypotheses, constructed multiagent models that incorporate these hypotheses, and tested these by running their models and observing the outcomes. Contrasting these cases against traditionally used, classical equation-based approaches, we argue that the embodied modeling approach connects more directly to students' experience, enables extended investigations as well as deeper understanding, and enables "advanced" topics to be productively introduced into the high school curriculum.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fluctuations in the Abundance of a Species considered Mathematically1
VOLTERRA, V. (1926) · Nature · doi:10.1038/118558a0 · Cited by 2,054
Cited by
(2014) · Medical Entomology and Zoology · Cited by 1,154
Commonsense Conceptions of Emergent Processes: Why Some Misconceptions Are Robust
Chi, M. T. H. (2005) · Journal of the Learning Sciences · doi:10.1207/s15327809jls1402_1 · Cited by 894
Jacobson, M. J., & Wilensky, U. (2006) · Journal of the Learning Sciences · doi:10.1207/s15327809jls1501_4 · Cited by 679
Self-citationEvagorou, M., Korfiatis, K., Nicolaou, C., & Constantinou, C. (2009) · International Journal of Science Education · doi:10.1080/09500690701749313 · Cited by 216
Weintrop, D., & Wilensky, U. (2015) · Proceedings of the eleventh annual International Conference on International Computing Education Research · doi:10.1145/2787622.2787721 · Cited by 198
Self-citationSystems learning with a conceptual representation: a quasi-experimental study
Hmelo-Silver, C. E., Jordan, R., Eberbach, C., & Sinha, S. (2017) · Instructional Science · doi:10.1007/s11251-016-9392-y · Cited by 138
Defining a New 21st Century Skill-Computational Thinking: Concepts and Trends
Haseski, H. I., Ilic, U., & Tugtekin, U. (2018) · International Education Studies · doi:10.5539/ies.v11n4p29 · Cited by 105
How is the body's systemic nature manifested amongst high school biology students?
Tripto, J., Assaraf, O. B. Z., Snapir, Z., & Amit, M. (2017) · Instructional Science · doi:10.1007/s11251-016-9390-0 · Cited by 42
Luminous Science: Teachers Designing For and Developing Transdisciplinary Thinking and Learning
Finch, L., Moreno, C., & Shapiro, R. B. (2021) · Cognition and Instruction · doi:10.1080/07370008.2021.1945064 · Cited by 28
Cycles of Exploration, Reflection, and Consolidation in Model-Based Learning of Genetics
Kim, B., Pathak, S. A., Jacobson, M. J., Zhang, B., & Gobert, J. D. (2015) · Journal of Science Education and Technology · doi:10.1007/s10956-015-9564-6 · Cited by 19
Faculty of Education, U. O. H., Billie, E., Dorit, R., & Faculty of Education, U. O. H. (2017) · Journal of Advances in Education Research · doi:10.22606/jaer.2017.22001 · Cited by 19
Distributed practice in classroom inquiry science learning
Svihla, V., Wester, M. J., & Linn, M. C. (2018) · Learning: Research and Practice · doi:10.1080/23735082.2017.1371321 · Cited by 8
Metadata
- Authors
- Wilensky, U., & Reisman, K.
- Venue
- Cognition and Instruction
- Publisher
- Informa UK Limited
- Published
- 2006-06
- Volume
- 24
- Issue
- 2
- Pages
- 171-209
- ISSN
- 0737-0008 (ISSN-L)
- 1532-690X
- OpenAlex
- W2119649150
- Cited by
- 557
- References
- 1
- FWCI
- 62.58
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
