A data-driven approach to assess computational thinking concepts based on learners’ artifacts

Abstract

Integrating computational thinking into K-12 Education has been a widely explored topic in recent years. Particularly, effective assessment of computational thinking can support the understanding of how learners develop computational concepts and practices. Aiming to help advance research on this topic, we propose a data-driven approach to assess computational thinking concepts, based on the automatic analysis of data from learners’ computational artifacts. As a proof of concept, the approach was applied to a Massive Open Online Course (MOOC) to investigate the course’s effectiveness as well as to identify points for improvement. The data analyzed consists of over 3300 projects from the course participants, using the Scratch programming language. From that sample, we found patterns in how computational thinking manifests in projects, which can be used as evidence to guide opportunities for improving course design, as well as insights to support further research on the assessment of computational thinking.

Keywords

• Automatic assessment tools
• Computational Thinking
• computer science education
• online learning