The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A validity and reliability study of the computational thinking scales (CTS)
Open accessAbstract
It is possible to define Computational Thinking briefly as having the knowledge, skill and attitudes necessary to be able to use the computers in the solution of the life problems for production purposes. In this study, a scale has been developed for the purpose of determining the levels of computational thinking skills (CTS) of the students. CTS is a five-point likert type scale and consists of 29 items that could be collected under five factors. The study group of this work consists of 726 students educated at the levels of associate degree and undergraduate degree with formal education in Amasya University for the first application. For the second application 580 students who were educated in pedagogical formation education via distance education in Amasya University. The validity and reliability of the scale have been studied by conducting exploratory factor analysis, confirmatory factor analysis, item distinctiveness analyses, internal consistency coefficients and constancy analyses. As a result of the conducted analyses, it has been concluded that the scale is a valid and reliable measurement tool that could measure the computational thinking skills of the students. In addition; the digital age individuals are expected to have the computational thinking skill, and at what degree they have these skills, the revelation of whether the levels they have are sufficient or not are a requirement. Within this frame, it could be said that the scale could make significant contributions to the literature. We aimed to develop computational thinking scale.A 29 item computational thinking scale was composed.Five factor were emerged for computational thinking scale.Factors are termed Creativity, Cooperativity, Algoritmic- Critical Thinking and Problem Solving.Reliability and validity of the scales are psychometrically good.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
SERVQUAL: A multiple-Item Scale for measuring consumer perceptions of service quality
Parasuraman, A., Berry, L. L., & Zeithaml, V. A. (1988) · Zenodo · doi:10.5281/zenodo.14579729 · Cited by 21,118
Wing, J. M. (2006) · Communications of the ACM · doi:10.1145/1118178.1118215 · Cited by 8,102
Reliability and Validity Assessment
Carmines, E., & Zeller, R. (1979) · doi:10.4135/9781412985642 · Cited by 5,884
Computational thinking and thinking about computing
Wing, J. M. (2008) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2008.0118 · Cited by 1,784
Barr, V., & Stephenson, C. (2011) · ACM Inroads · doi:10.1145/1929887.1929905 · Cited by 1,692
Russell, D. W. (2002) · Personality and Social Psychology Bulletin · doi:10.1177/014616702237645 · Cited by 954
Cited by
Demystifying computational thinking
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017) · Educational Research Review · doi:10.1016/j.edurev.2017.09.003 · Cited by 1,466
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100147 · Cited by 745
Assessing computational thinking: A systematic review of empirical studies
Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020) · Computers & Education · doi:10.1016/j.compedu.2019.103798 · Cited by 647
Tikva, C., & Tambouris, E. (2021) · Computers & Education · doi:10.1016/j.compedu.2020.104083 · Cited by 296
Sun, L., & Zhou, L. (2024) · Journal of Educational Computing Research · doi:10.1177/07356331241277937 · Cited by 129
Huang, X., & Qiao, C. (2024) · Science & Education · doi:10.1007/s11191-022-00392-6 · Cited by 116
Effects of augmented reality application integration with computational thinking in geometry topics
Abdul Hanid, M. F., Mohamad Said, M. N. H., Yahaya, N., & Abdullah, Z. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10994-w · Cited by 76
Wu, S.-Y., & Yang, K.-K. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.868623 · Cited by 37
Seker, O., Kwon, K., & Kocak, O. (2025) · Information Development · doi:10.1177/02666669251336368 · Cited by 21
Peng, H.-H., Murti, A. T., Silitonga, L. M., & Wu, T.-T. (2023) · Sustainability · doi:10.3390/su15075855 · Cited by 15
Computing Education Research Today
Fincher, S. A., Tenenberg, J., Dorn, B., Hundhausen, C., McCartney, R., & Murphy, L. (2019) · The Cambridge Handbook of Computing Education Research · doi:10.1017/9781108654555.003 · Cited by 7
Sun, J., & Rui, J. (2026) · Journal of Educational Computing Research · doi:10.1177/07356331251399452 · Cited by 6
Chapter 1: Problem-based learning: A 21st-century teaching and learning strategy
Research Unit Self-Directed Learning, F. O. E., Golightly, A., Sebatana, M. J., & School of Mathematics, S. A. T. E. (2023) · NWU Self-Directed Learning Series · doi:10.4102/aosis.2023.bk409.01 · Cited by 1
Metadata
- Authors
- Korkmaz, Ö., Çakir, R., & Özden, M. Y.
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 72
- Pages
- 558-569
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W2569623901
- Cited by
- 665
- References
- 6
- FWCI
- 43.73
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.