Computer science and computational thinking (CT) education in K-12 schools have been escalating in recent years. A couple of CT instructional models have been proposed to depict the roles of CT in K-16 education. Yet, neither of them discussed CT infusion into a subject course. In this article, we proposed a CT-integration model called TPC2T. In this model, we suggested considering CT as a second subject and using an appropriate technological pedagogical approach to make students’ learning of two subjects meaningful and engaging. We implemented this model in a CT-integrated lesson in two sections of a high-school Spanish course. Students worked in small groups and coded three small and one comprehensive digital Spanish-culture stories in Scratch. Results showed that students taking the CT-integrated lesson had the same degree of improvement in their Spanish culture knowledge as their peers who did not take the CT-integrated lesson. Besides, students taking the CT-integrated lesson had a significant improvement in their CT knowledge. At the same time, their CT self-efficacy outperformed those who did not take the CT-integrated lesson. We discussed the results and offered suggestions for researchers and educators at the end of the article.
AngeliC.VoogtJ.FluckA.WebbM.CoxM.Malyn-SmithJ.ZagamiJ. (2016).
A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Education Technology and Society,
19(3), 47–57.
2.
BellS. (2010).
Project-based learning for the 21st century: Skills for the future. The Clearing House: A Journal of Educational Strategies, Issues and Ideas,
83(2), 39–43. https://doi.org/10.1080/00098650903505415
3.
BraunV.ClarkeV. (2006).
Using thematic analysis in psychology. Qualitative Research in Psychology,
3(2), 77–101.
4.
BurkeQ.KafaiY. B. (2012, February). The writers’ workshop for youth programmers: Digital storytelling with scratch in middle school classrooms. In KingL. S.MusicantD. R.CampT.TymannP. T. (Eds.), 43rd ACM technical symposium on computer science education (pp. 433–438.)
ACM. https://doi.org/10.1145/2157136.2157264
5.
CetinI. (2016).
Pre-service teachers’ introduction to computing: Exploring utilization of scratch. Journal of Educational Computing Research,
54(7), 997–1021. https://doi.org/10.1177/0735633116642774
6.
ChangC.-K. (2014).
Effects of using Alice and Scratch in an introductory programming course for corrective instruction. Journal of Educational Computing Research,
51(2), 185–204. https://doi.org/10.2190/EC.51.2.c
CreswellJ. W.ClarkV. L. P. (2010). Designing and conducting mixed methods research (2nded.).
SAGE Publications
9.
DenningP. J. (2017).
Remaining trouble spots with computational thinking. Communications of the ACM,
60(6), 33–39. https://doi.org/10.1145/2998438
10.
DurakH. Y.SaritepeciM. (2018).
Analysis of the relation between computational thinking skills and various variables with the structural equation model. Computers & Education,
116, 191–202. https://doi.org/10.1016/j.compedu.2017.09.004
11.
FeldhausenR.WeeseJ. L.BeanN. H. (2018). February). Increasing student self-efficacy in computational thinking via stem outreach programs. In BarnesT.GarciaD.HawthorneE. K.Pérez-QuiñonesM. A. (Eds.), Annual conference of special interest group on computer science education (SIGCSE) (pp. 302–307).
ACM. https://doi.org/10.1145/3159450.3159593
12.
GautamA.BortzW.TatarD. (2020, February). Abstraction through multiple representations in an integrated computational thinking environment. In ZhangJ.SherriffM.HeckmanS.CutterP.MongeA. (Eds.), 51st ACM technical symposium on computing science education (pp. 392–399).
ACM. https://doi.org/10.1145/3328778.3366892
13.
HungW. (2011).
Theory to reality: A few issues in implementing problem-based learning. Educational Technology Research and Development,
59(4), 529–552. https://doi.org/10.1007/s11423-011-9198-1
KoehlerM. J.MishraP.CainW. (2013).
What is technological pedagogical content knowledge (TPACK)?Journal of Education,
193(3), 13–19
16.
KokotsakiD.MenziesV.WigginsA. (2016).
Project-based learning: a review of the literature. Improving Schools,
19(3), 267–277. https://doi.org/10.1177/1365480216659733
17.
LapawiN.HusninH. (2020).
The effect of computational thinking module on achievement in science. Science Education International,
31(2), 164–171. https://doi.org/10.33828/sei.v31.i2.5
18.
LeeY.-J. (2010).
Developing computer programming concepts and skills via technology-enriched language-art projects: A case study. Journal of Educational Multimedia and Hypermedia,
19(3), 307–326
19.
LeeI.GroverS.MartinF.PillaiS.Malyn-SmithJ. (2020).
Computational thinking from a disciplinary perspective: Integrating computational thinking in k-12 science, technology, engineering, and mathematics education. Journal of Science Education and Technology,
29(1), 1–8. https://doi.org/10.1007/s10956-019-09803-w
20.
LentR. W.LopezA. M.LopezF. G.SheuH.-B. (2008).
Social cognitive career theory and the prediction of interests and choice goals in the computing disciplines. Journal of Vocational Behavior,
73(1), 52–62. https://doi.org/10.1016/j.jvb.2008.01.002
21.
LichtenbergerE.George-JacksonC. (2013).
Predicting high school students’ interest in majoring in a stem field: Insight into high school students’ postsecondary plans. Journal of Career and Technical Education,
28(1), 19–38
22.
LockwoodJ.MooneyA. (2018).
Computational thinking in secondary education: Where does it fit? A systematic literary review. International Journal of Computer Science Education in Schools,
2(1), 41–60. https://doi.org/10.21585/ijcses.v2i1.26
23.
Malyn-SmithJ.LeeI. A.MartinF.GroverS.EvansM. A.PillaiS. (2018, June). Developing a framework for computational thinking from a disciplinary perspective. In KongS. C.AndoneD.BiswasG.CrickT.HoppeH. U.HsuT. C.HuangR. H.LiK. Y.LooiC. K.MilradM.SheldonJ.ShihJ. L.SinK. F.TissenbaumM.VahrenholdJ. (Eds.), International conference on computational thinking education (pp. 182–186).
Education University of Hong Kong
24.
MasterA.MeltzoffA. N. (2016).
Building bridges between psychological science and education: Cultural stereotypes, stem, and equity. Prospects,
46(2), 215–234. https://doi.org/10.1007/s11125-017-9391-z
25.
MishraP.KoehlerM. J. (2006).
Technological pedagogical content knowledge: A framework for integrating technology in teacher knowledge. Teachers College Record,
108(6), 1017–1054. https://doi.org/10.1111/j.14679620.2006.00684.x
26.
ModerK. (2010).
Alternatives to f-test in one way ANOVA in case of heterogeneity of variances (a simulation study). Psychological Test and Assessment Modeling,
52(4), 343–353
27.
Moreno-LeónJ.RoblesG. (2015, March). Computer programming as an educational tool in the English classroom: A preliminary study. In 2015 IEEE global engineering education conference (EDUCON) (pp. 961–966). https://doi.org/10.1109/EDUCON.2015.7096089
28.
Moreno-LeónJ.RoblesG. (2016, April). Code to learn with Scratch? A systematic literature review 2016. In 2016 IEEE global engineering education conference (EDUCON) (pp. 150–156). IEEE. https://doi.org/10.1109/EDUCON.2016.7474546
29.
MouzaC.YangH.PanY.-C.Yilmaz OzdenS.PollockL. (2017).
Resetting educational technology coursework for pre-service teachers: A computational thinking approach to the development of technological pedagogical content knowledge (TPACK). Australasian Journal of Educational Technology,
33(3), 61–76. https://doi.org/10.14742/ajet.3521
30.
National Science Board. (2019). Elementary and secondary mathematics and science education (NSB-2019-6).
National Science Foundation. https://ncses.nsf.gov/pubs/nsb20196/
31.
OhlerJ. (2006).
The world of digital story-telling. Educational Leadership,
63(4), 44–47
32.
PapertS. (1980). Mindstorms: Children, computers, and powerful ideas.
Basic Books
PsycharisS.KalliaM. (2017).
The effects of computer programming on high school students’ reasoning skills and mathematical self-efficacy and problem solving. Instructional Science,
45(5), 583–602. https://doi.org/10.1007/s11251-017-9421-5
35.
PuttickG.Tucker-RaymondE. (2018).
Building systems from scratch: an exploratory study of students learning about climate change. Journal of Science Education and Technology,
27(4), 306–321. https://doi.org/10.1007/s10956-017-9725-x
36.
RichP. J.BrowningS. F.PerkinsM.ShoopT.YoshikawaE.BelikovO. M. (2019).
Coding in k-8: International trends in teaching elementary/primary computing. TechTrends,
63(3), 311–319. https://doi.org/10.1007/s11528-018-0295-4
SeiterL.ForemanB. (2013). August). Modeling the learning progressions of computational thinking of primary grade students. In SimonB.ClearA. L.CuttsQ. (Eds.), Annual international ACM conference on international computing education research (pp. 59–66).
ACM. https://doi.org/10.1145/2493394.2493403
StraussA.CorbinJ. (1998). Basics of qualitative research (2nd ed.).
SAGE Publications
41.
SwellerJ. (1994).
Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction,
4(4), 295–312. https://doi.org/10.1016/0959-4752(94)90003-5
42.
TomarkenA. J.SerlinR. C. (1986).
Comparison of ANOVA alternatives under variance heterogeneity and specific noncentrality structures. Psychological Bulletin,
99(1), 90–99. https://doi.org/10.1037/0033-2909.99.1.90
43.
VoogtJ.FisserP.Pareja RoblinN.TondeurJ.van BraakJ. (2013).
Technological pedagogical content knowledge – A review of the literature. Journal of Computer Assisted Learning,
29(2), 109–121. https://doi.org/10.1111/j.1365-2729.2012.00487.x
44.
WatermanK. P.GoldsmithL.PasqualeM. (2020).
Integrating computational thinking into elementary science curriculum: An examination of activities that support students’ computational thinking in the service of disciplinary learning. Journal of Science Education and Technology,
29(1), 53–64. https://doi.org/10.1007/s10956-019-09801-y
45.
WeeseJ. L.FeldhausenR. (2017, June). STEM outreach: Assessing computational thinking and problem solving [Paper presentation]. In 2017 ASEE annual conference & exposition, Columbus, OH. https://peer.asee.org/28845
46.
WeintropD.WilenskyU. (2017).
Comparing block-based and text-based programming in high school computer science classrooms. ACM Transactions on Computing Education,
18(1), 1–25. https://doi.org/10.1145/3089799
47.
WengX.WongG. K. W. (2017, December). Integrating computational thinking into English dialogue learning through graphical programming tool. In IEEE 6th international conference on teaching, assessment, and learning for engineering (TALE) (pp. 320–325. IEEE. https://doi.org/10.1109/TALE.2017.8252356
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
50.
WolzU.StoneM.PearsonK.PulimoodS. M.SwitzerM. (2011).
Computational thinking and expository writing in the Middle school. ACM Transactions on Computing Education,
11(2), 1–22. https://doi.org/10.1145/1993069.1993073
51.
YigitM. (2014).
A review of the literature: How pre-service mathematics teachers develop their technological, pedagogical, and content knowledge. International Journal of Education in Mathematics, Science and Technology,
2(1), 26–35
52.
ZhangL.NouriJ. (2019).
A systematic review of learning computational thinking through scratch in K-9. Computers & Education,
141, 103607. https://doi.org/10.1016/j.compedu.2019.103607
