Pengembangan Student’s Metacognitive Awareness Scale (SMAS) pada Siswa Di Indonesia

Abiyyu Arib Mahyiyuddin

doi:10.21009/JPPP.142.09

Authors

Abiyyu Arib Mahyiyuddin Magister Psikologi, Program Pascasarjana, Universitas Negeri Makassar, Makassar, Indonesia

DOI:

https://doi.org/10.21009/JPPP.142.09

Keywords:

Metacognitive, Students, Validity

Abstract

This study aims to develop a scale that measures students' metacognitive awareness, called the Student's Metacognitive Awareness Scale (SMAS). Data collection was carried out for 13 days, starting from March 24, 2025 to April 6, 2025, in Senior High Schools (SMA) and Vocational High Schools (SMK). The respondent criteria were high school/vocational high school students aged 15-18 years. A total of 212 students filled out the measuring instrument, with 208 data that could be processed. Of the initial 52 items, 24 items survived until the end. Content validation using the Aiken's V formula obtained a value of 0.92 - 1. The item discrimination power moved from 0.76 - 0.86. The McDonald's Omega reliability coefficient obtained was 0.980. Confirmatory Factor Analysis (CFA) showed that the model was fit, with a loading factor range of 0.89 - 1.14. The results of this study indicate that SMAS is a valid and reliable measuring tool for assessing students' level of metacognitive awareness.

References

Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460- 475. https://doi.org/10.1006/ceps.1994.1033

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive- developmental inquiry. American Psychologist, 34(10), 906- 911. https://doi.org/10.1037/0003-066X.34.10.906

Brown, A. L. (1987). Metacognition, executive control, self-regulation, and other more mysterious mechanisms. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, motivation, and understanding (pp. 65-116). Lawrence Erlbaum Associates.

Pintrich, P. R. (2002). The role of metacognitive knowledge in learning, teaching, and assessing. Theory Into Practice, 41(4), 219- 225. https://doi.org/10.1207/s15430421tip4104_3

Penfield, R. D., & Giacobbi, P. R. (2004). Applying a score confidence interval to Aiken's item content-relevance index. Measurement in Physical Education and Exercise Science, 8(4), 213-225. https://doi.org/10.1207/s15327841mpee0804_3

Cicchetti, D. V. (1994). Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychological Assessment, 6(4), 284-290. https://doi.org/10.1037/1040-3590.6.4.284

Polit, D. F., Beck, C. T., & Owen, S. V. (2007). Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Research in Nursing & Health, 30(4), 459-467. https://doi.org/10.1002/nur.20199

Wynd, C. A., Schmidt, B., & Schaefer, M. A. (2003). Two quantitative approaches for estimating content validity. Western Journal of Nursing Research, 25(5), 508- 518. https://doi.org/10.1177/0193945903252998

Clark, L. A., & Watson, D. (2019). Constructing validity: New developments in creating objective measuring instruments. Psychological Assessment, 31(12), 1412-1427. https://doi.org/10.1037/pas0000626

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw- Hill.

Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS (7th ed.). Open University Press.

Smith, G. T., McCarthy, D. M., & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102-111. https://doi.org/10.1037/1040-3590.12.1.102

Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

Teo, T., & Fan, X. (2013). Coefficient alpha and beyond: Issues and alternatives for educational research.The Asia-Pacific Education Researcher, 22(2), 209- 213. https://doi.org/10.1007/s40299-013-0075-z

Marsh, H. W., Guo, J., Parker, P. D., Nagengast, B., Asparouhov, T., Muthén, B., & Dicke, T. (2020). Confirmatory factor analysis (CFA), exploratory structural equation modeling (ESEM), and set-ESEM: Optimal balance between goodness of fit and parsimony.Multivariate Behavioral Research, 55(1), 42- 72. https://doi.org/10.1080/00273171.2019.1602503

Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399-412. https://doi.org/10.1111/bjop.12046

Peters, G. J. Y. (2014). The alpha and the omega of scale reliability and validity: Why and how to abandon Cronbach's alpha and the route towards more comprehensive assessment of scale quality. European Health Psychologist, 16(2), 56-69.

Trizano-Hermosilla, I., & Alvarado, J. M. (2016). Best alternatives to Cronbach's alpha reliability in realistic conditions: Congeneric and asymmetrical measurements. Frontiers in Psychology, 7, 769. https://doi.org/10.3389/fpsyg.2016.00769

Mcneish, D. (2018). Thanks coefficient alpha, we'll take it from here. Psychological Methods, 23(3), 412-433. https://doi.org/10.1037/met0000144