Rasch Analysis for Disposition Levels of Computational Thinking Instrument Among Secondary School Students

Saralah Sovey; Kamisah Osman; Mohd Effendi Ewan Mohd Matore

doi:10.29333/ejmste/11794

Full Text (PDF)

Saralah Sovey ¹ , Kamisah Osman ¹ ^* , Mohd Effendi Ewan Mohd Matore ¹

More Detail

¹ Faculty of Education, Universiti Kebangsaan Malaysia, Bangi Selangor, MALAYSIA^* Corresponding Author

Abstract

Computational thinking is a strategy of thinking to tackle complex problems. There is a paucity of conceptualization and instruments that cogitate on computational thinking disposition and attitudes. This study reacts to these constraints by establishing an instrument to test computational thinking related dispositions and attitudes. The computational thinking disposition Instrument is an indicator of student’s disposition towards computational thinking in daily life. The objective of this study is to investigate the psychometric features using Rasch model. Data of 535 form four computer science students in Malaysia were obtained. Instrument consists of 55 core measures in three domains: cognitive, affective and conative. The Rasch analysis indicated good psychometric features of the instrument. In these three domains no items showed disordered thresholds and the reliability was good. As a result, the Rasch analysis provides basis for cautious optimism permitting more detailed and finer level investigation of the instrument.

Keywords

attitudes
computational thinking
disposition
secondary school students

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, Volume 18, Issue 3, March 2022, Article No: em2088

https://doi.org/10.29333/ejmste/11794

Publication date: 23 Feb 2022

Article Views: 2191

Article Downloads: 1761

Open Access Disclosures References How to cite this article

Disclosure

Declaration of Conflict of Interest: No conflict of interest is declared by author(s).

Data sharing statement: Data supporting the findings and conclusions are available upon request from the corresponding author(s).

References

Abdullah, N., Zakaria, E., & Halim, L. (2012). The effect of a thinking strategy approach through visual representation on achievement and conceptual understanding in solving mathematical word problems. Asian Social Science, 8(16), 30-37. https://doi.org/10.5539/ass.v8n16p30
Andrich, D., & Styles, I. (2004). Final report on the psychometric analysis of the early development instrument (EDI) using the Rasch model: A technical paper commissioned for the development of the Australian early development instrument (AEDI). http://ww2.rch.org.au/emplibrary/australianedi/Final_Rasch_report.pdf
Ariffin, S. R. (2008). Inovasi dalam pengukuran dan penilaian [Inovation in measurement and evaluation]. UKM Press.
Aziz, A. A., Masodi, M. S., & Zaharim, A. (2015). Rasch measurement model basis: Scale development and measurement structure. UKM Press.
Baghaei, P. (2008). Local dependency and Rasch measures. Rasch Measurement Transactions, 21(3), 1105-1106.
Balsamo, M., Giampaglia, G., & Saggino, A. (2014). Building a new Rasch-based self-report inventory of depression. Neuropsychiatric Disease and Treatment, 10, 153-165. https://doi.org/10.2147/ndt.s53425
Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning and Leading with Technology, 38(6), 20-23.
Barr, V., & Stephenson, C. (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
Belanger, C., Christenson, H., & Lopac, K. (2018). Confidence and common challenges: The effects of teaching computational thinking to students ages 10-16 [Masters’s thesis, St. Catherine University]. St. Catherine University Repository.
Beyer, B. K. (1988). Developing a thinking skills programme. Allyn and Bacon Inc.
Beyer, B. K. (1995). Critical thinking. Phi Delta Kappa Educational Foundations.
Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., Engelhardt, K., Kampylis, P., & Punie, Y. (2016). Developing computational thinking in compulsory education. Implications for policy and practice. EUR-Scientific and Technical Research Reports. https://doi.org/10.2791/792158
Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences (2nded.). Routledge
Bond, T. G., & Fox, C. M. (2015). Applying the Rasch model: Fundamental measurement in the human sciences (3rd ed.). Routledge.
Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the development of computational thinking [Paper presentation]. Annual Meeting of the American Educational Research Association (AERA), Vancouver, BC, Canada. http://scratched.gse.harvard.edu/ct/files/AERA2012.pdf
Chongo, S., Osman, K., & Nayan, N. A. (2020). Level of computational thinking level of computational thinking skills among secondary science student: Variation across gender and mathematics achievement skills among secondary science student. Science Education International, 31(2), 159-163. https://doi.org/10.33828/sei.v31.i2.4
College Board. (2014). Advanced placement computer science principles: Curriculum framework. http://securemedia.collegeboard.org/digitalServices/pdf/ap/ap-computerscience-principles-curriculum-framework.pdf
Computer Science Teachers Association. (2017). CSTA K-12 Computer Science Standards. http://www.csteachers.org/standards
Conrad, K. M., Conrad, K. J., Passetti, L. L., Funk, R. R., & Dennis, M. L. (2015). Validation of the full and short-form self-help involvement scale against the Rasch measurement model. Evaluation Review, 39(4), 395-427. https://doi.org/10.1177/0193841x15599645
Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Pearson.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches. SAGE.
Denning, P. J. (2009). The profession of IT beyond computational thinking. Communications of the ACM, 52(6), 28-30. https://doi.org/10.1145/1516046.1516054
DeVon, H. A., Block, M. E., Moyle-Wright, P., Ernst, D. M., Hayden, S. J., Lazzara, D. J., Savoy, S. M., & Kostas-Polston, E. (2007). A psychometric toolbox for testing validity and reliability. Journal of Nursing Scholarship, 39(2), 155-164. https://doi.org/10.1111/j.1547-5069.2007.00161.x
Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Lawrence Erlbaum Associates. https://doi.org/10.1037/10519-153
Facione, N. C., Facione, P. A., & Sanchez, C. A. (1994). Critical thinking disposition as a measure of competent clinical judgment: The development of the California critical thinking disposition inventory. Journal of Nursing Education, 33(8), 345-350. https://doi.org/10.3928/0148-4834-19941001-05
Facione, P. A. (2000). The disposition toward critical thinking: Its character, measurement, and relationship to critical thinking skill. Informal Logic, 20(1), 61-84. https://doi.org/10.22329/il.v20i1.2254
Fisher, J. W. P. (2006). Survey design recommendations. Rasch Measurements Transactions, 20(3), 1072-1074.
Fisher, J. W. P. (2007). Rating scale instrument quality criteria. Rasch Measurement Transactions, 21(1), 1095.
Fisher, W. (1992). Reliability, separation, strata statistics. Rasch Measurement Transactions, 6(3), 238.
Fox, C. M., & Jones, J. A. (1998). Uses of Rasch modeling in counseling psychology research. Journal of Counseling Psychology, 45(1), 30-45. https://doi.org/10.1037/0022-0167.45.1.30
Gay, L. R., & Mills, G. E. (2018). Educational research: Competencies for analysis and applications. Merrill Prentice Hall.
Grover, S., & Pea, R. (2013). Computational thinking in K-12. Educational Researcher, 42(1), 38-43. https://doi.org/10.3102/0013189x12463051
Hair, J. F., Celsi, M. W., Oritinau, D. J., & Bush, R. P. (2017). Essentials of marketing research. McGraw Hill.
Haseski, H. I., Ilic, U., & Tugtekin, U. (2018). Defining a new 21st century skill-computational thinking: Concepts and trends. International Education Studies, 11(4), 29. https://doi.org/10.5539/ies.v11n4p29
Hilgard, E. R. (1980). The trilogy of mind: Cognition, affection, and conation. Journal of the History of the Behavioral Sciences, 16(2), 107-117. https://doi.org/10.1002/1520-6696(198004)16:2<107::AID-JHBS2300160202>3.0.CO;2-Y
Hill, C., & Koekemoer, E. (2013). The development of the MACE work-family enrichment instrument. SA Journal of Industrial Psychology, 39, 1-16. https://doi.org/10.4102/sajip.v39i2.1147
Jong, M. S. -Y., Geng, J., Chai, C. S., & Lin, P. Y. (2020). Development and predictive validity of the computational thinking disposition questionnaire. Sustainability, 12(11), 4459. https://doi.org/10.3390/su12114459
Kim, B., Kim, T., & Kim, J. (2013). Paper-and-pencil programming strategy toward computational thinking for non-majors: design your solution. Journal of Educational Computing Research, 49(4), 437-459. https://doi.org/10.2190/ec.49.4.b
Lai, J. S., & Eton, D. T. (2002). Clinically meaningful gaps. Rasch Measurement Transactions, 15(4), 850.
Linacre, J. M. (1994). Sample size and item calibration (or person measure) stability. Rasch Measurement Transactions, 7(4), 328.
Linacre, J. M. (2002). What do infit and outfit, mean-square and standardized mean? Rasch Measurement Transactions, 16(2), 878.
Linacre, J. M. (2003). Winsteps computer program version 3.48. Chicago. http://www.winsteps.com/a/winsteps-tutorial
Linacre, J. M. (2005). Winstep Rasch measurement computer program. MESA Press.
Linacre, J. M. (2007). A user’s guide to WINSTEPS Rasch-model computer program. MESA Press.
Linacre, J. M. (2010). When to stop removing items and persons in Rasch misfit analysis? Rasch Measurement Transactions, 23(4), 1241.
Linacre, J. M. (2012). A user’s guide to WINSTEPS: Rasch model computer programs. MESA Press.
Linacre, J. M. (2017). Winsteps Rasch measurement computer program user’s guide. Winsteps.com.
Marret, M. J., & Choo, W. Y. (2017). Factors associated with online victimization among Malaysian adolescents who use social networking sites: A cross-sectional study. BMJ Open, 7(6), e014959. https://doi.org/10.1136/bmjopen-2016-014959
Masek, A., & Nasaruddin, N. (2016). Students’ perception and readiness on school-based assessment. Mediterranean Journal of Social Sciences, 7(6), 189. https://doi.org/10.5901/mjss.2016.v7n6p189
Matore, M. E. E. M, Zainal, M. A., Noh, M. F. M., Khairani, A. Z., Rahman, N. A., & Idris, H. (2020). Pengujian psikometrik item kecerdasan menghadapi cabaran untuk pelajar lelaki kejuruteraan mekanikal menggunakan model pengukuran Rasch [Psychometric properties of an adversity quotient items for mechanical engineering male students using Rasch measurement model]. Malaysia Education Journal, 45(1), 87-100. https://doi.org/10.17576/jpen-2020-45.01si-11
Miller, L. A., & Lovler, R. L. (2019). Foundations of psychological testing: A practical approach. SAGE.
National Research Council. (2011). Report of a workshop on the pedagogical aspects of computational thinking. National Academies Press.
Norris, S. P., & Ennis, R. H. (1989). Evaluating critical thinking. Midwest Publications.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. McGraw Hill.
Olabe, J. C., Basogain, X., Olabe, M. Á., Maíz, I., & Castaño, C. (2014). Solving math and science problem in the real world with a computational mind. New Approaches in Education Research, 3(2), 75-82. https://doi.org/10.7821/naer.3.2.75-82
Othman, N., Salleh, S., Hussein, H., & Wahid, H. A. (2014). Assessing construct validity and reliability of competitiveness scale using Rasch model approach. In WEI International Academic Conference Proceedings (pp. 113-120). Indonesia.
Pallant, J. (2001). SPSS survival manual. Open University Press.
Pallant, J. F., & Tennant, A. (2007). An introduction to the Rasch measurement model: An example using the hospital anxiety and depression scale (HADS). British Journal of Clinical Psychology, 46(1), 1-18. https://doi.org/10.1348/014466506x96931
Perera, C. J., Sumintono, B., Jiang, N. (2018). The psychometric validation of the principal practices questionnaire based on item response theory. Educational Leadership, 2(1), 21-38. https://doi.org/10.22452/iojel.vol2no1.3
Qiu, R. G. (2009). Computational thinking of service systems: dynamics and adaptiveness modeling. Service Science, 1(1), 42-55. https://doi.org/10.1287/serv.1.1.42
Salihuddin, M. S., Hasnah, M., Zaleha, A., Norasykin, M. Z., Baharuddin, A., & Mageswaran, S. (2016). Enhancing student’s higher order thinking skills (HOTS) through the Socratic method approach with technology. In 1St ICRIL-International Conference on Innovation in Science and Technology. Technology University of Malaysia.
Sands, P., Yadav, A., Good, J. (2018). Computational thinking in K-12: In-service teacher perceptions of computational thinking. In M. S. Khine (ed.), Computational Thinking in the STEM Disciplines (pp. 151-164). Springer. http://dx.doi.org/10.1007/978-3-319-93566-9_8
Schiffman, L. G., Lazar, L., & Håvard, K. (2012). Consumer behavior: A European outlook. Pearson.
Sekaran, U. (2003). Research methods for business: A skill-building approach. John Wiley & Sons.
Settle, A., Franke, B., Hansen, R., Spaltro, F., Jurisson, C., Rennert-May, C., & Wildeman, B. (2012). Infusing computational thinking into the middle and high-school curriculum. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education - ITiCSE ‘12 (pp. 22-27). Association for Computing Machinery. https://doi.org/10.1145/2325296.2325306
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158. https://doi.org/10.1016/j.edurev.2017.09.003
Sondakh, D. E., Osman, K., & Zainudin, S. (2020a). A proposal for holistic assessment of computational thinking for undergraduate: Content validity. (2020). European Journal of Educational Research, 9(1), 33-50. https://doi.org/10.12973/eu-jer.9.1.33
Sondakh, D. E., Osman, K., & Zainudin, S. (2020b). A pilot study of an instrument to assess undergraduates’ computational thinking proficiency. International Journal of Advanced Computer Science and Applications, 11(11), 263-273. https://doi.org/10.14569/ijacsa.2020.0111134
Sumintono, B., & Widhiarso, W. (2014). Aplikasi model Rasch untuk penelitian ilmu-ilmu sosial [Rasch model application for social disciplines]. Trim Komunikata Publishing House.
Sysło, M. M., & Kwiatkowska, A. B. (2015). Introducing a new computer science curriculum for all school levels in Poland. In A. Brodnik, & J. Vahrenhold (Eds.), ISEEP (pp. 141-154). Springer. https://doi.org/10.1007/978-3-319-25396-1_13
Tang, K. Y., Chou, T. L., & Tsai, C. C. (2019). A content analysis of computational thinking research: An international publication trends and research typology. The Asia-Pacific Education Researcher, 29(1), 9-19. https://doi.org/10.1007/s40299-019-00442-8
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53-55. https://doi.org/10.5116/ijme.4dfb.8dfd
Weese, J. L. (2016). Mixed methods for the assessment and incorporation of computational thinking in K-12 and higher education. In Proceedings of the 2016 ACM Conference on International Computing Education Research-ICER ‘16 (pp. 279-280). Association for Computing Machinery. https://doi.org/10.1145/2960310.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society: A Mathematical, Physical and Engineering Sciences, 366(1881), 3717-3725. https://doi.org/10.1098/rsta.2008.0118
Wright, B. D., & Master, G. N. (1982). Rating scale analysis. Cesa Press.
Wright, B. D., & Mok, M. M. (2004). An overview of the family of Rasch measurement models. In E. V. Smith, & R. M. Smith (Eds.), Introduction to Rasch measurement: Theory, models, and applications. JAM Press.
Wu, M., & Adams, R. (2007). Applying the Rasch model to psycho-social measurement: A practical approach. Educational Measurement Solutions.
Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education, 14(1), 1-16. https://doi.org/10.1145/2576872

How to cite this article

APA

Sovey, S., Osman, K., & Mohd Matore, M. E. E. (2022). Rasch Analysis for Disposition Levels of Computational Thinking Instrument Among Secondary School Students. Eurasia Journal of Mathematics, Science and Technology Education, 18(3), em2088. https://doi.org/10.29333/ejmste/11794

Vancouver

Sovey S, Osman K, Mohd Matore MEE. Rasch Analysis for Disposition Levels of Computational Thinking Instrument Among Secondary School Students. EURASIA J Math Sci Tech Ed. 2022;18(3):em2088. https://doi.org/10.29333/ejmste/11794

AMA

Sovey S, Osman K, Mohd Matore MEE. Rasch Analysis for Disposition Levels of Computational Thinking Instrument Among Secondary School Students. EURASIA J Math Sci Tech Ed. 2022;18(3), em2088. https://doi.org/10.29333/ejmste/11794

Chicago

Sovey, Saralah, Kamisah Osman, and Mohd Effendi Ewan Mohd Matore. "Rasch Analysis for Disposition Levels of Computational Thinking Instrument Among Secondary School Students". Eurasia Journal of Mathematics, Science and Technology Education 2022 18 no. 3 (2022): em2088. https://doi.org/10.29333/ejmste/11794

Harvard

Sovey, S., Osman, K., and Mohd Matore, M. E. E. (2022). Rasch Analysis for Disposition Levels of Computational Thinking Instrument Among Secondary School Students. Eurasia Journal of Mathematics, Science and Technology Education, 18(3), em2088. https://doi.org/10.29333/ejmste/11794

MLA

Sovey, Saralah et al. "Rasch Analysis for Disposition Levels of Computational Thinking Instrument Among Secondary School Students". Eurasia Journal of Mathematics, Science and Technology Education, vol. 18, no. 3, 2022, em2088. https://doi.org/10.29333/ejmste/11794