ANALYSIS OF “MODEL-DRIVEN” VS. “DATA-DRIVEN” APPROACHES TO ENGAGING STUDENT LEARNING IN INTRODUCTORY GEOSCIENCE LABORATORIES

This study seeks to constrain the definition of an effective data-rich experience in reference to Earth science education, provide formal evidence, both qualitative and quantitative, of the benefits of engaging students with data, and suggest effective ways to use data in established curricula. To achieve these goals, we created, evaluated, and compared two different approaches to using data in an introductory geoscience laboratory exercise for calculating plate motion. One module engaged students with a “model-driven” approach in which students are taught a descriptive model and work with a set of data that has been streamlined to support the model. The other module used a “data-driven” approach in which students are asked to make sense of real data by creating a descriptive model based on it. Using a pretest-posttest experimental design, students were assessed for their knowledge of both content and the nature of science using a survey with a Likert-like scale. Mean survey results indicate that students significantly gained content knowledge and increased their understanding of the nature of science with both approaches. However, when the nature of science results are graphically analyzed by Likert-like scale responses, a notable shift in the direction of greater knowledge gains is observed in the “model-driven” group. Lab observations and written interviews suggest that the “model-driven” group were more likely to perceive that the exercise had “right” answers whereas the “data-driven” group more readily accepted uncertainty in their model based on the variability of the data. This study suggests that care must be taken in the interpretation of quantitative instruments which may indicate that students have made greater gains than in actuality.