Classifying Emergent Student Learning in a Computational High School Chemistry Unit

• In Event: 45.095 - Sheraton Roundtable Seven - Osgoode Ballroom
  In Roundtable Session: 45.095-5 - Division C Section 1e Roundtable 2

Abstract

STEM education communities recognize the importance of integrating computational thinking (CT) into high school curricula. However, in many K-12 schools computation still remains separate from STEM. Moreover, it is not well understood how students develop scientific knowledge when engaging in computational practices. In this study, we designed a CT-STEM unit focusing on the relationship between micro-level gas particles and macro-level phenomena. We used Epistemic Network Analysis to create student discourse network models (N = 384) and a k-means clustering to group students with similar discourse networks. Our results revealed four different student groups. Of those four, the group containing the highest number of students made connections across multiple scientific concepts, indicating an understanding of micro-macro level relationships of chemistry concepts.

Authors

• Golnaz Arastoopour Irgens, Northwestern University

• Sumit Chandra

• Sugat Dabholkar, Northwestern University

• Michael S. Horn, Northwestern University

• Uri J. Wilensky, Northwestern University