Personalized Automated Formative Feedback Can Support Students in Generating Causal Explanations in Biology

Abstract

The ability to formulate scientific explanations is considered a major goal in science education. However, explaining scientific phenomena is a challenging task, and research has shown that many students are unable to construct proper explanations. And while personalized formative feedback can greatly assist students in closing the gaps between their current and the expected level of reasoning, teachers rarely find the time that is required to provide it. We present a novel framework for designing and providing formative feedback on causal explanations in biology, which can be generated automatically using NLP-based algorithms. Results from a controlled experiment showed that applying this framework led to significant improvement in student explanations. Based on these findings, we believe that our framework provides an effective approach for aligning between automated NLP-based analysis and formative assessment of biological explanations in the science classroom.