We present a study that examines the effects of guidance
on learning about addressing ill-defined problems in undergraduate bi-
ology education. Two groups of college students used an online labo-
ratory named VERA to learn about ill-defined ecological phenomena.
While one group received guidance, such as giving the learners a specific
problem and instruction on problem-solving methods, the other group re-
ceived minimal guidance. The results indicate that, while performance in
a problem-solving task was not different between groups receiving more
vs. minimal guidance, the group that received minimal guidance adopted
a more exploratory strategy and generated more interesting models of
the given phenomena in a problem-solving task.
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Virtual laboratories that enable novice scientists to construct, evaluate and revise models of complex systems heavily involve parameter estimation tasks. We seek to understand novice strategies for parameter estimation in model exploration to design better cognitive supports for them. We conducted a study of 50 college students for a parameter estimation task in exploring an ecological model. We identified three types of behavioral patterns and their underlying cognitive strategies. Specifically, the students used systematic search, problem decomposition and reduction, and global search followed by local search as their cognitive strategiesmore » « less
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Modeling is an important aspect of scientific problem-solving. How- ever, modeling is a difficult cognitive process for novice learners in part due to the high dimensionality of the parameter search space. This work investigates 50 college students’ parameter search behaviors in the context of ecological modeling. The study revealed important differences in behaviors of successful and unsuccessful students in navigating the parameter space. These differences suggest opportunities for future development of adaptive cognitive scaffolds to support different classes of learnersmore » « less
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Parameter estimation is a common challenge that arises in the domain of computational scientific modeling. Agent-based models offer particular challenges in this regard, and many solutions are too computationally intense and scale with the number of parameters. In this paper, we propose knowledge-based function approximation methods to deal with this problem in agent-based modeling. Our method is implemented within the VERA modeling system, and we show the validity of our methods using an internal model as well as an external model.more » « less
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null (Ed.)The intelligent research assistant, VERA, supports inquiry- based modeling by supplying contextualized large-scale domain knowl- edge in the Encyclopedia of Life. Learners can use VERA to construct conceptual models of ecological phenomena, run them as simulations, and review their predictions. A study on the use of VERA by college-level students indicates that providing access to large scale but contextual- ized knowledge helped students build more complex models and generate more hypotheses in problem-solving.more » « less
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Citizen scientists have the potential to expand scientific research. The virtual research assistant called VERA empowers citizen scientists to engage in environmental science in two ways. First, it automatically generates simulations based on the conceptual models of ecological phenomena for repeated testing and feedback. Second, it leverages the Encyclopedia of Life biodiversity knowledgebase to support the process of model construction and revision.more » « less
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