Speaker
Description
The aim of the presented research is to analyse individual learning trajectories on five factors of evolution in order to anticipate potential challenges to learning about evolution early and enable teachers to give more specific feedback in the future. To achieve this, we employ methods of learning analytics, utilizing large datasets from realistic classroom settings to gain deeper insights into learning processes.
We developed a hybrid teaching unit on five factors of evolution for upper secondary level, which was implemented in schools over the school year 2022/23 (N = ~300 students). Over the course of the unit, students created five concept maps on factors of evolution to elicit their conceptual knowledge on the topic. Students also completed test items on evolution before, after, and during the unit.
Students’ individual learning trajectories are now being recreated through comparison of their concept maps with an expert concept map and based on an analysis of key and threshold concepts. A detailed analysis of the individual learning trajectories from the concept maps and its implications for teaching evolution will be presented and discussed.
Further research will put an emphasis on identifying potential predictors for productive and unproductive learning trajectories on evolution. By breaking with one-size-fits-all approaches to science education, this research particularly benefits traditionally underserved groups in STEM education, such as women.
