Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3341525.3387415acmconferencesArticle/Chapter ViewAbstractPublication PagesiticseConference Proceedingsconference-collections
research-article

SCAPA: Development of a Questionnaire Assessing Self-Concept and Attitudes Toward Programming

Published: 15 June 2020 Publication History

Abstract

There is a constantly growing number of initiatives asserting the relevance of programming already in primary education and offering respective interventions with the goal to foster interest in and positive attitudes toward programming. To evaluate to what extent this goal is achieved, assessing students' attitudes toward programming reliably is indispensable. However, there still is a need for validated instruments for assessing this in elementary school students. This seems particularly relevant as self-concept and attitudes toward a school subject were repeatedly observed to be significant predictors of learning motivation and achievement. The newly developed Self-Concept and Attitude toward Programming Assessment (SCAPA) is based on existing instruments for assessing students' self-concept and attitude toward mathematics. SCAPA measures aspects of students' self-concept and attitudes toward programming on seven scales: i) self-reported previous programming experience and understanding, ii) self-concept, iii) intrinsic value belief, iv) attainment value belief, v) utility value belief, vi) cost belief, and vii) compliance and persistence. We administered SCAPA to 197 elementary school students between seven and ten years of age in the context of an evaluation of a computational thinking intervention. Data were analyzed for reliability (i.e., internal consistency on item and scale level) and construct validity (by means of confirmatory factor analysis). Results indicated good reliability for all scales except for the self-reported previous programming experience and understanding scale. Overall, these results reflect SCAPA's suitability for assessing different aspects of elementary school students' self-concept and attitudes toward programming.

References

[1]
[n. d.]. Pearson's Correlation Coefficient. https://www.statisticssolutions.com/pearsons-correlation-coefficient/
[2]
Jane M. Armstrong and Richard A. Price. 1982. Correlates and Predictors of Women's Mathematics Participation. Journal for Research in Mathematics Education, Vol. 13, 2 (mar 1982), 99. https://doi.org/10.2307/748357
[3]
Anja Balanskat and Katja Engelhardt. 2015. Computing our future: Computer programming and coding - Priorities, school curricula and initiatives across Europe.
[4]
Brigitte Maria Brisson, Anna-Lena Dicke, Hanna Gaspard, Isabelle H"a fner, Barbara Flunger, Benjamin Nagengast, and Ulrich Trautwein. 2017. Short Intervention, Sustained Effects: Promoting Students' Math Competence Beliefs, Effort, and Achievement. American Educational Research Journal, Vol. 54, 6 (2017), 1048--1078. https://doi.org/10.3102/0002831217716084
[5]
Code.org. [n. d.] a. CS Fundamentals for grades K-5. https://code.org/educate/curriculum/elementary-school
[6]
Code.org. [n. d.] b. Hour of Code: Join the Movement. https://hourofcode.com/
[7]
Brian Dorn and Allison Elliott Tew. 2015. Empirical validation and application of the computing attitudes survey. Computer Science Education, Vol. 25, 1 (2015), 1--36.
[8]
Amanda M. Durik, Mina Vida, and Jacquelynne S. Eccles. 2006. Task values and ability beliefs as predictors of high school literacy choices: A developmental analysis. Journal of Educational Psychology, Vol. 98, 2 (2006), 382--393. https://doi.org/10.1037/0022-0663.98.2.382
[9]
Jacquelynne S. Eccles, T. F. Alder, R. Futterman, S. B. Goff, C. M. Kaczala, J. L. Meece, and C. Midgley. 1983. Expectancies, values, and academic behaviors. In Achievement and achievement motivation. Freeman, 75--146. https://ci.nii.ac.jp/naid/10020820462/
[10]
Hanna Gaspard. 2017. Promoting Value Beliefs in Mathematics : A Multidimensional Perspective and the Role of Gender. January 2015 (2017). https://doi.org/10.15496/publikation-5241
[11]
Hanna Gaspard, Isabelle H"afner, Cora Parrisius, Ulrich Trautwein, and Benjamin Nagengast. 2017. Assessing task values in five subjects during secondary school: Measurement structure and mean level differences across grade level, gender, and academic subject. Contemporary Educational Psychology, Vol. 48 (2017), 67--84.
[12]
Katharina Geldreich, Alexandra Simon, and Elena Starke. 2019. Which Perceptions Do Primary School Children Have about Programming?. In Proceedings of the 14th Workshop in Primary and Secondary Computing Education. 1--7.
[13]
Daniel Heersink and Barbara M. Moskal. 2010. Measuring High School Students' Attitudes Toward Computing. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education (SIGCSE '10). ACM, New York, NY, USA, 446--450. https://doi.org/10.1145/1734263.1734413
[14]
Andrew Hoegh and Barbara M Moskal. 2009. Examining science and engineering students' attitudes toward computer science. In 2009 39th IEEE Frontiers in Education Conference. IEEE, 1--6.
[15]
Krisztiá n Jó zsa and George A. Morgan. 2017. Reversed Items in Likert Scales: Filtering out Invalid Responders. Technical Report 1. 7--25 pages. https://fac.ksu.edu.sa/sites/default/files/likert2_0.pdf
[16]
Kodable. [n. d.]. Programming for Kids. https://www.kodable.com/
[17]
Alma E. Lantz and Gregory P. Smith. 1981. Factors influencing the choice of nonrequired mathematics courses. Journal of Educational Psychology, Vol. 73, 6 (1981), 825--837. https://doi.org/10.1037/0022-0663.73.6.825
[18]
Herbert W Marsh, Olaf Kö ller, Ulrich Trautwein, Oliver Lü dtke, and Jü rgen Baumert. 2005. Academic self-concept, interest, grades, and standardized test scores: Reciprocal effects models of causal ordering. Child Development, Vol. 76, 2 (2005), 397--416. https://doi.org/10.1111/j.1467--8624.2005.00853.x
[19]
Benjamin Nagengast, Ulrich Trautwein, L. Francesca Scalas, Kit-Tai Hau, Herbert W. Marsh, and Man K. Xu. 2011. Who Took the "×" out of Expectancy-Value Theory? Psychological Science, Vol. 22, 8 (2011), 1058--1066. https://doi.org/10.1177/0956797611415540
[20]
J. Steve Oliver and Ronald D. Simpson. 1988. Influences of attitude toward science, achievement motivation, and science self concept on achievement in science: A longitudinal study. Science Education, Vol. 72, 2 (1988), 143--155. https://doi.org/10.1002/sce.3730720204
[21]
Tony Perez, Jennifer G. Cromley, and Avi Kaplan. 2014. The role of identity development, values, and costs in college STEM retention. Journal of Educational Psychology, Vol. 106, 1 (2014), 315--329. https://doi.org/10.1037/a0034027
[22]
Laurie Hart Reyes. 2005. Affective Variables and Mathematics Education. The Elementary School Journal, Vol. 84, 5 (2005), 558--581. https://doi.org/10.1086/461384
[23]
Iris M Riggs and Larry G Enochs. 1993. A microcomputer beliefs inventory for middle school students: Scale development and validation. Journal of Research on Computing in Education, Vol. 25, 3 (1993), 383--390.
[24]
Neil Selwyn. 1997. Students' attitudes toward computers: Validation of a computer attitude scale for 16--19 education. Computers & Education, Vol. 28, 1 (1997), 35--41.
[25]
Gita Taasoobshirazi and Shanshan Wang. 2016. The performance of the SRMR, RMSEA, CFI, and TLI: An examination of sample size, path size, and degrees of freedom. Journal of Applied Quantitative Methods, Vol. 11, 3 (2016), 31--39.
[26]
Keith S. Taber. 2018. The Use of Cronbach's Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education, Vol. 48, 6 (01 Dec 2018), 1273--1296. https://doi.org/10.1007/s11165-016--9602--2
[27]
Ulrich Trautwein and Olaf Kö ller. 2005. Was lange w"a hrt, wird nicht immer gut. Zeitschrift für Padagogische Psychologie, Vol. 17, 3/4 (2005), 199--209. https://doi.org/10.1024//1010-0652.17.34.199
[28]
Ulrich Trautwein, Herbert W Marsh, Benjamin Nagengast, Oliver Lüdtke, Gabriel Nagy, and Kathrin Jonkmann. 2012. Probing for the multiplicative term in modern expectancy--value theory: A latent interaction modeling study. Journal of educational psychology, Vol. 104, 3 (2012), 763.
[29]
Allan Wigfield and Jenna Cambria. 2010. Students' achievement values, goal orientations, and interest: Definitions, development, and relations to achievement outcomes. In Developmental Review. Vol. 30. Academic Press, 1--35. https://doi.org/10.1016/j.dr.2009.12.001
[30]
Allan Wigfield and Jacquelynne S. Eccles. 1992. The development of achievement task values: A theoretical analysis. Developmental Review, Vol. 12, 3 (sep 1992), 265--310. https://doi.org/10.1016/0273--2297(92)90011-P
[31]
Jesse L.M. Wilkins. 2004. Mathematics and science self-concept: An international investigation. Journal of Experimental Education, Vol. 72, 4 (2004), 331--346. https://doi.org/10.3200/JEXE.72.4.331--346

Cited By

View all
  • (2024)The End is the Beginning is the End: The closed-loop learning analytics frameworkComputers in Human Behavior10.1016/j.chb.2024.108305(108305)Online publication date: May-2024
  • (2024)EL Greco Platform: A novel Python programming learning platform that uses a real robotComputer Applications in Engineering Education10.1002/cae.2274232:4Online publication date: 11-Apr-2024
  • (2023)Identity in Higher Computer Education Research: A Systematic Literature ReviewACM Transactions on Computing Education10.1145/360670723:3(1-35)Online publication date: 12-Sep-2023
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
ITiCSE '20: Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education
June 2020
615 pages
ISBN:9781450368742
DOI:10.1145/3341525
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 June 2020

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. K-12 education
  2. assessment
  3. computer science education
  4. computing education
  5. instrument development
  6. primary education

Qualifiers

  • Research-article

Funding Sources

  • Hector Stiftung II
  • LEAD Graduate School and Research Network

Conference

ITiCSE '20
Sponsor:

Acceptance Rates

Overall Acceptance Rate 552 of 1,613 submissions, 34%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)125
  • Downloads (Last 6 weeks)13
Reflects downloads up to 27 Nov 2024

Other Metrics

Citations

Cited By

View all
  • (2024)The End is the Beginning is the End: The closed-loop learning analytics frameworkComputers in Human Behavior10.1016/j.chb.2024.108305(108305)Online publication date: May-2024
  • (2024)EL Greco Platform: A novel Python programming learning platform that uses a real robotComputer Applications in Engineering Education10.1002/cae.2274232:4Online publication date: 11-Apr-2024
  • (2023)Identity in Higher Computer Education Research: A Systematic Literature ReviewACM Transactions on Computing Education10.1145/360670723:3(1-35)Online publication date: 12-Sep-2023
  • (2023)Making the Transition to Text-Based Programming: The Pilot Evaluation of a Computational Thinking Intervention for Primary School StudentsProceedings of the 18th WiPSCE Conference on Primary and Secondary Computing Education Research10.1145/3605468.3609770(1-6)Online publication date: 27-Sep-2023
  • (2023)Employing an underwater vehicle in education as a learning tool for Python programmingComputer Applications in Engineering Education10.1002/cae.2269332:1Online publication date: 23-Oct-2023
  • (2022)Investigating Effectiveness of Various Pair Programming Modes for Female High School StudentsProceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 210.1145/3502717.3532116(654-655)Online publication date: 7-Jul-2022
  • (2021)Identity in K-12 Computer Education Research: A Systematic Literature ReviewProceedings of the 17th ACM Conference on International Computing Education Research10.1145/3446871.3469757(169-183)Online publication date: 16-Aug-2021

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media