In this research, a scale was developed to determine the programming-oriented computational thinking skills of university students. The participants were 360 students studying in various departments at different universities in Turkey for computer programming. The scale consists of 33 items under conceptual knowledge, algorithmic thinking, and evaluation subscale. While there was no significant difference between the students’ conceptual knowledge and algorithmic thinking skills, the evaluation skills of male students differed significantly compared to females. Programming experience has a significant effect on conceptual knowledge, algorithmic thinking, and evaluation. The algorithmic thinking skills of the students who have low, middle, and high-level programming experience differed significantly. In terms of the development of conceptual knowledge and evaluation skills, it was observed that students should have at least one year of programming experience, but this experience will not make a significant difference if it is four years or more. It is thought that this scale, which is structured for different applications (e.g., web, game, robot) and learning environments (e.g., text, block) within the framework of its programming capabilities (conceptual, semantic, strategic knowledge), will contribute significantly to the evaluation of computational thinking as programming oriented.
Aharoni, D. (2000). Cogito, Ergo sum! Cognitive processes of students dealing with data structures. ACM SIGCSE Bulletin, 32(1), 26–30.
2.
ArmstrongJ.VirdingR.WikströmC.WilliamsM. (1996). Concurrent programming in Erlang. Prentice Hall.
3.
AskarP.DavenportD. (2009).
An investigation of factors related to self-efficacy for java programming among engineering students. The Turkish Online Journal of Educational Technology,
8(1), 26–32.
4.
AtmatzidouS.DemetriadisS. (2016).
Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems,
75, 661–670. https://doi.org/10.1016/j.robot.2015.10.008
5.
BarrD.HarrisonJ.ConeryL. (2011).
Computational thinking: A digital age skill for everyone. Learning & Leading with Technology,
38(6), 20–23.
6.
BarrV.StephensonC. (2011).
Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community. ACM Inroads,
2(1), 48–54. https://doi.org/10.1145/1929887.1929905
7.
BasawapatnaA.KohK. H.RepenningA.WebbD. C.MarshallK. S. (2011, March 17–21). Recognizing computational thinking patterns [Paper presentation]. The 42nd ACM Technical Symposium on Computer Science Education, Dallas, USA.
8.
BaymanP.MayerR. E. (1988).
Using conceptual models to teach BASIC computer programming. Journal of Educational Psychology,
80(3), 291–298. https://doi.org/10.1037/0022-0663.80.3.291
9.
BerlandM.LeeV. R. (2011).
Collaborative strategic board games as a site for distributed computational thinking. International Journal of Game-Based Learning (IJGBL),
1(2), 65–81. https://doi.org/10.4018/ijgbl.2011040105
10.
BersM. U.FlanneryL.KazakoffE. R.SullivanA. (2014).
Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education,
72, 145–157. https://doi.org/10.1016/j.compedu.2013.10.020
11.
BocconiS.ChioccarielloA.DettoriG.FerrariA.EngelhardtK.KampylisP.PunieY. (2016). Developing computational thinking in compulsory education [Policy Report]. European Commission.
12.
BrennanK.ResnickM. (2012, April 13–17). New frameworks for studying and assessing the development of computational thinking [paper presentation]. 2012 Annual Meeting of the American Educational Research Association, The University of British Columbia, Vancouver.
CortinaJ. M. (1993).
What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology,
78(1), 98–104. https://doi.org/10.1037/0021-9010.78.1.98
DennerJ.WernerL.OrtizE. (2012).
Computer games created by middle school girls: Can they be used to measure understanding of computer science concepts?Computers & Education,
58(1), 240–249. https://doi.org/10.1016/j.compedu.2011.08.006
DenningP. J.StaffU. (2007).
An interview with peter denning on the great principles of computing. Ubiquity, 1–9.
29.
DeVellisR. F. (2003). Scale development: Theory and applications (2nd ed.).
SAGE Publications.
30.
DurakH. Y. (2020).
The effects of using different tools in programming teaching of secondary school students on engagement, computational thinking and reflective thinking skills for problem solving. Technology, Knowledge and Learning,
25(1), 179–195. https://doi.org/10.1007/s10758-018-9391-y
31.
EguchiA. (2014).
Educational robotics for promoting 21st century skills. Journal of Automation, Mobile Robotics & Intelligent Systems,
8(1), 5–11. https://doi.org/10.14313/JAMRIS_1-2014/1
32.
Eguchi, A. (2016). Educational robotics as a learning tool for promoting rich environments for active learning (REALs). In P. Zaphiris & C. S. Ang (Eds.), Human-computer Interaction: Concepts, methodologies, tools, and applications (pp. 740–767). IGI Global.
33.
FieldA. (2017). Discovering statistics using IBM SPSS statistics (5th ed.).
Sage Publications.
García-PeñalvoF. J.MendesJ. A. (2018).
Exploring the computational thinking effects in pre-university education. Computers in Human Behavior,
80, 407–411. https://doi.org/10.1016/j.chb.2017.12.005
36.
GroverS. (2011, April 7–11). Robotics and engineering for middle and high school students to develop computational thinking [paper presentation]. Annual Meeting of the American Educational Research Association. New Orleans, USA.
37.
GroverS.PeaR. (2013).
Computational thinking in K–12: A review of the state of the field. Educational Researcher,
42(1), 38–43. https://doi.org/10.3102/0013189X12463051
38.
GülbaharY.KertS. B.KalelioğluF. (2019).
The self-efficacy perception scale for computational thinking skill: Validity and reliability study. Turkish Journal of Computer and Mathematics Education,
10(1), 1–29.
39.
HoR. (2006). Handbook of univariate and multivariate data analysis and interpretation with SPSS. Chapman & Hall/CRC.
KaleliogluF.GulbaharY.KukulV. (2016).
A framework for computational thinking based on a systematic research review. Baltic Journal of Modern Computing,
4(3), 583–598.
44.
KazakoffE. R.SullivanA.BersM. U. (2013).
The effect of a classroom-based intensive robotics and programming workshop on sequencing ability in early childhood. Early Childhood Education Journal,
41(4), 245–255. https://doi.org/10.1007/s10643-012-0554-5
45.
KlineR. B. (2011). Principles and practice of structural equation modeling (3rd ed.).
The Guilford Press.
46.
KohK. H.BasawapatnaA.BennettV.RepenningA. (2010, September). Towards the automatic recognition of computational thinking for adaptive visual language learning. In 2010 IEEE symposium on visual languages and human-centric computing (pp. 59–66). IEEE.
47.
KorkmazÖ.CakirR.ÖzdenM. Y. (2017).
A validity and reliability study of the computational thinking scales (CTS). Computers in Human Behavior,
72, 558–569. https://doi.org/10.1016/j.chb.2017.01.005
48.
KukulV.KarataS. (2019).
Computational thinking Self-Efficacy scale: Development, validity and reliability. Informatics in Education,
18(1), 151–164.
49.
L’HeureuxJ.BoisvertD.CohenR.SangheraK. (2012, October 7–9). It problem solving. An implementation of computational thinking in information technology. In The 13th annual conference on information technology education (pp.183–188). ACM. doi:10.1145/2380552.2380606
50.
LeeI.MartinF.DennerJ.CoulterB.AllanW.EricksonJ.Malyn-SmithJ.WernerL. (2011).
Computational thinking for youth in practice. ACM Inroads,
2(1), 32–37. https://doi.org/10.1145/1929887.1929902
51.
LeechN. L.BarrettK. C.MorganG. A. (2005). SPSS for intermediate statistics: Use and interpretation (2nd ed.).
Lawrence Erlbaum Associates.
52.
LiaoY.-K.BrightG. (1991).
Effects of computer-assisted instruction and computer programming on cognitive outcomes: A meta-analysis. Journal of Educational Computing Research,
7(3), 251–268.
53.
LinC. C.ChaoP. Y.LinE. T.TzengH. L. (2018, August 7–9). Exploring the role of visual programming activities in computational thinking. In 2018 1st International cognitive cities conference (IC3) (pp. 135–138). IEEE.
54.
LinnM. C.DalbeyJ. (1985).
Cognitive consequences of programming instruction: Instruction, access, and ability. Educational Psychologist,
20(4), 191–206.
55.
MannilaL.DagieneV.DemoB.GrgurinaN.MiroloC.RolandssonL.SettleA. (2014, June 21–24). Computational thinking in K-9 education. In 2014 Innovation & technology in computer science education conference (pp. 1–29). New York.
56.
Moreno-LeónJ.RoblesG.Román-GonzálezM. (2015).
Dr. Scratch: Automatic analysis of scratch projects to assess and foster computational thinking. Revista de Educación a Distancia, (46), 1–23.
NickersonR. S. (1982).
Computer programming as a vehicle for teaching thinking skills. Thinking: The Journal of Philosophy for Children,
4, 42–48. https://doi.org/10.5840/thinking19834310
59.
OliverR. (1993).
Measuring hierarchical levels of programming knowledge. Journal of Educational Computing Research,
9(3), 299–312. https://doi.org/10.2190/0LGX-M45X-2WBK-B7A6
60.
OlukA.KorkmazÖ.OlukH. A. (2018).
Effect of scratch on 5th graders’ algorithm development and computational thinking skills. Turkish Journal of Computer and Mathematics Education,
9(1), 54–71. https://dx.doi.org/10.16949/turkbilmat.399588
61.
ÖzyurtÖ.ÖzyurtH. (2015).
A study for determining computer programming students’ attitudes towards programming and their programming self-efficacy. Journal of Theory and Practice in Education,
11(1), 51–67.
62.
PalaF. K.Mıhçı TürkerP. (2019).
The effects of different programming trainings on the computational thinking skills. Interactive Learning Environments, 1–11. https://doi.org/10.1080/10494820.2019.1635495
63.
PenmetchaM. R. (2012). Exploring the effectiveness of robotics as a vehicle for computational thinking (Unpublished doctoral dissertation). Purdue University, USA.
64.
Pérez-MarínD.Hijón-NeiraR.BaceloA.PizarroC. (2020).
Can computational thinking be improved by using a methodology based on metaphors and scratch to teach computer programming to children?Computers in Human Behavior,
105, 105849. https://doi.org/10.1016/j.chb.2018.12.027
65.
PerrenetJ.GrooteJ. F.KaasenbroodE. (2005).
Exploring students' understanding of the concept of algorithm: Levels of abstraction. ACM SIGCSE Bulletin,
37(3), 64–68.
66.
PerrenetJ.KaasenbroodE. (2006).
Levels of abstraction in students' understanding of the concept of algorithm: The qualitative perspective. ACM SIGCSE Bulletin,
38(3), 270–274.
67.
RileyD. D.HuntK. A. (2014). Computational thinking for the modern problem solver.
CRC Press.
68.
Rodríguez-MartínezJ. A.González-CaleroJ. A.Sáez-LópezJ. M. (2020).
Computational thinking and mathematics using scratch: An experiment with sixth-grade students. Interactive Learning Environments,
28(3), 316–327. https://doi.org/10.1080/10494820.2019.1612448
69.
Román-González, M. (2015, July 6á8). Computational thinking test: Design guidelines and content validation. In Proceedings of the 7th Annual International Conference on Education and New Learning Technologies (EDULEARN15) (pp. 2436–2444). Barcelona, Spain. https://doi.org/10.13140/RG.2.1.4203.4329
70.
SchneiderG. M.GerstingJ. (2018). Invitation to computer science. Cengage Learning.
SeiterL.ForemanB. (2013, September 9–11). Modeling the learning progressions of computational thinking of primary grade students. In Ninth annual international ACM conference on international computing education research (pp. 59–66). Association for Computing Machinery.
StevensJ. P. (2009). Applied multivariate statistics for the social sciences.
Taylor & Francis.
75.
SysłoM. M.KwiatkowskaA. B. (2013, February 26–March 2). Informatics for all high school students. In International conference on informatics in schools: Situation, evolution, and perspectives (pp. 43–56). Springer.
76.
TabachnickB. G.FidellL. S. (2007). Using multivariate statistics (5th ed.).
Boston:
Allyn & Bacon.
77.
TangX.YinY.LinQ.HadadR.ZhaiX. (2020).
Assessing computational thinking: A systematic review of empirical studies. Computers & Education,
148, 103798. https://doi.org/10.1016/j.compedu.2019.103798
78.
TouretzkyD. S.MarghituD.LudiS.BernsteinD.NiL. (2013, March 6–9). Accelerating K-12 computational thinking using scaffolding, staging, and abstraction. In 44th ACM technical symposium on computer science education (pp. 609–614). Association for Computing Machinery.
79.
TsaiM. J.WangC. Y.HsuP. F. (2019).
Developing the computer programming self-efficacy scale for computer literacy education. Journal of Educational Computing Research,
56(8), 1345–1360. https://doi.org/10.1177/0735633117746747
80.
WeinbergA. E. (2013). Computational thinking: An investigation of the existing scholarship and research (Unpublished doctoral dissertation). Colorado State University, USA.
81.
WeintropD.BeheshtiE.HornM.OrtonK.JonaK.TrouilleL.WilenskyU. (2016).
Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology,
25(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5
WingJ. M. (2008).
Computational thinking and thinking about computing. Philosophical Transactions: Series A, Mathematical, Physical, and Engineering Sciences,
366(1881), 3717–3725. https://doi.org/10.1098/rsta.2008.0118
84.
WongG. K. W.CheungH. Y. (2020).
Exploring children’s perceptions of developing twenty-first century skills through computational thinking and programming. Interactive Learning Environments,
28(4), 438–413. https://doi.org/10.1080/10494820.2018.1534245
85.
YadavA.KristC.GoodJ.CaeliE. N. (2018).
Computational thinking in elementary classrooms: Measuring teacher understanding of computational ideas for teaching science. Computer Science Education,
28(4), 371–400. https://doi.org/10.1080/08993408.2018.1560550
86.
YağcıM. (2019).
A valid and reliable tool for examining computational thinking skills. Education and Information Technologies,
24(1), 929–951. https://doi.org/10.1007/s10639-018-9801-8
87.
YeniS. (2018). How is computational thinking skill evaluated?
Ankara:
Pegem Academy.
