Constructing a Computational Thinking Evaluation Framework for Pupils

Bibliographic Data

ID20295415
AuthorsYu-Sheng Su (0000-0002-1531-3363, National Chung Cheng University), Xiao Wang (0000-0001-8231-6778, Nanjing Normal University), Li Zhao (0009-0004-7836-6878, Nanjing Normal University)
Year2024
Volume67
Issue6
Pages878-888
Publication date2024-12-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueIEEE Transactions on Education (JOURNAL)
Journal identifiersISSN: 0018-9359 • E-ISSN: 1557-9638
PublisherInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/te.2024.3424423
OpenAlexW4402451524
LanguageEN
Citations received1
References cited49

Research Purpose and Contribution: The study aimed to construct an evaluation framework for assessing pupils’ computational thinking (CT) during classroom learning problem solving. As a self-report evaluation scale for pupils, this evaluation framework further enriched the CT assessment instruments for pupils and provided a specialized instrument for experts to evaluate pupils’ CT in problem-solving situations during classroom learning. Background: CT cultivation and assessment methods are hot topics in the field of education. CT assessment can effectively test the effect of CT cultivation. There are many CT assessment methods, of which evaluation frameworks are an effective self-reporting assessment method. Existing studies on self-reported CT evaluation frameworks are commonly applicable to students at different stages. However, few studies have focused on the specific context from the perspective of practice for pupils. Thus, the evaluation framework of pupils’ CT is worth exploring. Intended Outcomes: A CT evaluation framework for evaluating pupils’ CT in problem-solving situations in classroom learning was constructed to facilitate researchers’ understanding of pupils’ CT levels and problem-solving skills. Application Design: In this study, data from 897 pupils in the fifth and sixth grades were collected using an online questionnaire that included 27 items about CT. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and item analysis were conducted to analyze the data in this study, with 17 items remaining in the final evaluation framework. Findings: The fitting validity, convergence validity, and discriminant validity all met the recommended criteria, which showed that the evaluation framework was effective. The total reliability of CT was 0.911, indicating that the consistency and reliability of the evaluation framework constructed in this study were satisfied

Computational Thinking · Management science · Mathematics education · Artificial Intelligence · Computer Science · Engineering · Mathematics · Teaching and Learning Programming

Tools

Open DOI