An Exploration of Young Program Learners' Computational Thinking: A Path Modeling Approach Using Log Data

• In Event: 77.056 - Fairmont Royal York Roundtable Session Seven
  In Roundtable Session: 77.056-2 - Data Mining and Unconventional Data Sources

Abstract

This study aims to explore the factors that affect young program learners’ computational thinking (CT), with a focus on examining the effects of remixing practices. It presents findings of log data from more than 100,000 students and 200,000 programs in the Scratch online community. Drawn from social cognitive theory, a path model are proposed with cognitive factors (i.e., remixing projects and remixing frequency), environmental factors (i.e., project view and project like), behavioral factors (i.e., programming concepts), and tested with the log data. The results confirm the hypothesized model. In general, the results show that remixing can lead to CT improvement, but that overexposure to the community and excessive remixing can actually hinder CT development. Results and implications are further discussed.

Authors

• Miaoting Cheng, The University of Hong Kong

• Jaesub Shim, Texas Tech University

• Wanli Xing, Texas Tech University