Integrating CT into Biology

Using Decision Tree Models to Classify Cell Types

Abstract

The integration of computational thinking (CT) into subject learning has the potential not only to foster digital literacy, but also to deepen STEM learning because the use of computational models and development of computational solutions advances students' understanding of subject area content. However, designing and implementing a curriculum that effectively integrates STEM and CT is challenging for educators because they have little experience in computing terminology, key concepts, and approaches to learning. We therefore aimed to develop CT integrated K-12 lessons in collaboration with subject teachers to determine suitable CT learning objectives as well as teaching and learning strategies. In this study, we focus on a 9th-grade biology lesson where students were asked to construct decision trees for determining cell types in as few steps as possible. Decision trees form a computational model that fits a wide range of classification systems in biology. We investigated the effect of using decision trees on students' biology and CT learning outcomes by analyzing their end products in the assignment. Additionally, we analyzed students' and teachers' views about the CT integrated lesson using questionnaires and semi-structured interviews. We found that students valued the way a decision tree helps them to structure the information. The teacher expressed that drawing a decision tree enabled the students to reason about the cell types, fostering a different way of thinking. Regarding CT, decision trees may help to improve decision analysis and classification, which are related to abstraction and algorithmic thinking skills.