Data-Driven Understanding of Computational Thinking Assessment: A Systematic Literature Review

A movement to include problem-solving and computer science in k-12 education has sparked significant interest in introducing computational thinking (CT). CT education is mainly defined as teaching and learning problem-solving skills. CT is considered a 21-century skill, and like other essential skills aiming to educate students as efficient members of the technology-dependent society, CT learning and assessment are associated with the use of technology-enhanced learning methods and environments. Although most researchers categorize CT skills into three groups, including CT concepts, practices, and perspectives, there is no consensus view regarding CT assessment methods to evaluate these three CT skill categories. Addressing this gap, we explored key topics in the computational thinking assessment (CTA) literature using a data-driven approach for topic modeling. We analyzed 395 articles in CTA literature and identified 11 research topics of CTA. We also performed a network analysis to explore the key links between CTA's identified topics. Based on the results from topic modeling, we presented CTA methods and categorized the assessment tools based on their assessment strategy and the types of CT skills they aim to evaluate. Also, the paper analyzes the identified assessment methods based on the purpose of assessment and the different types of insights they provide for the evaluation of CT skills. The paper discusses the advantages of new forms of CTA through technology compared to traditional assessment methods and provides recommendations for further studies.