The investigation of exploratory learning strategies using computer-based modeling tools when solving veterinarian clinical case study problems
Michael Patrick Law, University of Georgia, United States
University of Georgia . Awarded
The purpose of this study was to investigate the effectiveness of using two types of exploratory, model-based learning activities to help participants solve clinical case study problems in the content domain of veterinarian pharmacokinetics. A total of 75 veterinary medicine students used a computer modeling program called STELLA to either construct a model of a pharmacokinetic system to help solve a clinical case study problem, or to experiment with a model already constructed. This study investigated two model-based learning approaches: (1) Model-Provided Learning and (2) Model-Assembling Learning. It was hypothesized that Model-Assembling learning would allow participants to use more inductive reasoning strategies by creating their own model and promote self-reflection on a specific mental model. Furthermore, it was believed that this approach would allow participants to make their mental models explicit and test them against the mathematical and scientific models (i.e., conceptual models) presented to them during the instruction.
Results indicated that metacognitive awareness as measured by the Metacognitive Awareness Inventory (MAI) was not a good predictor for learning outcomes as measured by an instructor-developed exam. The Model-Assembling group outperformed the Model-Provided group on an Instructor-Developed test designed to measure both basic-level and inference-level comprehension of the concepts of pharmacokinetics presented in the case study problem. Descriptive data collected provided an understanding of the nature of the strategic knowledge (cognitive and metacognitive) that students brought to bear on the problem-solving tasks using the software and materials provided. Mental model analyses of concept maps drawn by a select number of participants indicated that they had to actively test and revise their mental via a trial-and-error approach using STELLA.
Recommendations are provided for educators interested in using model-based learning activities and modeling tools such as STELLA to teach complex science concepts. These recommendations are based on a comparison of the results of this study along with an analysis of relevant research literature. Recommendations include the use of support measures, model progression techniques, adequate training, collaborative learning, and other learning environment issues.
Law, M.P. The investigation of exploratory learning strategies using computer-based modeling tools when solving veterinarian clinical case study problems. Ph.D. thesis, University of Georgia.
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