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REID (Research and Evaluation in Education)

Keywords

attitudes; elementary school; instrument development; online learning; self-assessment

Document Type

Article

Abstract

Online learning during the COVID-19 pandemic makes it difficult for teachers to assess student learning attitudes. Limited availability of instruments to measure attitudes of students when they are engaged in online learning leads to difficulty of teachers to conduct appropriate assessments on that measure. The current study, therefore, mainly was intended to produce a self-assessment-based instrument that is feasible to use to measure students' attitudes in online learning. In order to produce such instrument, we used developmental research method by following steps in the instrument design that is proposed by McCoach, Gable, and Madura. Furthermore, in order to provide feasibility of our instrument, we provided evidence of content validity through experts' judgment data as well as evidence of construct validity with confirmatory factor analysis (CFA) and reliability estimation with Cronbach's α through a limited trial and an expanded trial using response data of sixth graders of elementary school engaged in online learning. Our study has produced a self-assessment-based instrument that uses a summated rating scale and is composed of six components (i.e., honest, disciplined, responsible, polite, caring, and self-confident) and 24 items that have demonstrated evidence of content validity, stable factor structure, and high reliability estimates.

Page Range

184-197

Issue

2

Volume

9

Digital Object Identifier (DOI)

10.21831/reid.v9i2.52083

Source

https://journal.uny.ac.id/index.php/reid/article/view/52083

References

Abdurrahmansyah, A., Sugilar, H., Ismail, I., & Warna, D. (2022). Online learning phenomenon: From the perspective of learning facilities, curriculum, and character of elementary school students. Education Sciences, 12(8), 1-18. https://doi.org/10.3390/educsci12080508

Aiken, L. R. (1985). Three coefficients for analyzing the reliability and validity of ratings. Educational and Psychological Measurement, 45(1), 131-142. https://doi.org/10.1177/0013164485451012

Anderson, L. W., & Bourke, S. F. (2000). Assessing affective characteristics in the schools (2nd ed.). Routledge.

Andrade, H. L. (2019). A critical review of research on student self-assessment. Frontiers in Education, 4, 1-13. https://doi.org/10.3389/feduc.2019.00087

Apriyanti, N., & Burhendi, F. C. A. (2020). Analisis evaluasi pembelajaran daring berorientasi pada karakter siswa [Analysis of evaluation of online learning that is oriented to students' characters]. Prosiding Seminar dan Diskusi Nasional Pendidikan Dasar 2020, 1-9.

Bali, M. M. E. I., & Musrifah, M. (2020). The problems of application of online learning in the affective and psychomotor domains during the COVID-19 pandemic. Jurnal Pendidikan Agama Islam, 17(2), 137-154. https://doi.org/10.14421/jpai.2020.172-03

Black, P., & Wiliam, D. (2010). Inside the black box: Raising standards through classroom assessment. Phi Delta Kappan, 92(1), 81-90. https://doi.org/10.1177/003172171009200119

Borožová, H., & Rydval, J. (2014). Analysis of exam results of the subject 'Applied mathematics for IT'. Journal on Efficiency and Responsibility in Education and Science, 7(3-4), 59-65. https://doi.org/10.7160/eriesj.2014.070303

Boud, D., & Falchikov, N. (1989). Quantitative studies of student self-assessment in higher education: A critical analysis of findings. Higher Education, 18(5), 529-549. https://doi.org/10.1007/BF00138746

Calderón, J. L., Morales, L. S., Liu, H., & Hays, R. D. (2006). Variation in the readability of items within surveys. American Journal of Medical Quality, 21(1), 49-56. https://doi.org/10.1177/1062860605283572

Damanhuri, D., & Lestari, R. Y. (2021). The cultivation of social attitudes through civic education learning in the pandemic era as an effort to build students' social skills. Gagasan Pendidikan Indonesia, 2(2), 93-98. https://doi.org/10.30870/gpi.v2i2.12721

Drake, S. M., & Reid, J. L. (2020). 21st century competencies in light of the history of integrated curriculum. Frontiers in Education, 5, 122. https://doi.org/10.3389/feduc.2020.00122

Eagly, A. H., & Chaiken, S. (2007). The advantages of an inclusive definition of attitude. Social Cognition, 25(5), 582-602. https://doi.org/10.1521/soco.2007.25.5.582

Erawati, G. A. P. S. A., Widiana, I. W., & Japa, I. G. N. (2021). Elementary school teachers' problems in online learning during the pandemic. International Journal of Elementary Education, 5(4), 562-573. https://doi.org/10.23887/ijee.v5i4.39233

Fauzani, R. A., Senen, A., & Retnawati, H. (2021). Challenges for elementary school teachers in attitude assessment during online learning. Journal of Evaluation and Research in Education, 5(3), 362-372. https://doi.org/10.23887/jere.v5i3.33226

Ghunu, N. M. S. (2022). The challenges of remote area elementary schools in thematic curriculum implementation. International Journal of Instruction, 15(2), 19-36.

Hair, J. R., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis (7th ed.). Pearson.

Hidayat, M. T., & Listya, T. D. (2021). The implementation of social attitude assessment in elementary schools: A study of Indonesia. Elementary Education Online, 20(1), 575-581. https://doi.org/10.17051/ilkonline.2021.01.48

Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60.

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118

Jöreskog, K. G., & Sörbom, D. (2006). Lisrel 8.80 [Computer software]. Scientific Software International.

Mardapi, D. (2004). Pedoman khusus pengembangan instrumen dan penilaian ranah afektif [Specific guidelines for instrument development and assessment in affective domain]. Lintas Media.

Masry-Herzallah, A., & Stavissky, Y. (2021). The attitudes of elementary and middle school students and teachers towards online learning during the corona pandemic outbreak. SN Social Sciences, 1(3), 1-23. https://doi.org/10.1007/s43545-021-00083-z

McCoach, D. B., Gable, R. K., & Madura, J. P. (2013). Instrument development in the affective domain: School and corporate applications (3rd ed.). Springer.

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

Prihatni, Y., Supriyoko, S., & Rahmatang, R. (2019). Development of attitude competency assessment test based on teaching of Ki Hadjar Dewantara in elementary and secondary school. Jurnal Prima Edukasia, 7(1), 1-8. https://doi.org/10.21831/jpe.v7i1.21517

Reeves, M. F. (1990). An application of Bloom's taxonomy to the teaching of business ethics. Journal of Business Ethics, 9(7), 609-616. https://doi.org/10.1007/BF00383217

Retnawati, H. (2016). Analisis kuantitatif instrumen penelitian [Quantitative analysis of research instrument]. Parama Publishing.

Setiawan, A., Fajaruddin, S., & Andini, D. W. (2019). Development an honesty and discipline assessment instrument in the integrated thematic learning at elementary school. Jurnal Prima Edukasia, 7(1), 9-19. https://doi.org/10.21831/jpe.v7i1.23117

Tavakol, M., & Wetzel, A. (2020). Factor analysis: A means for theory and instrument development in support of construct validity. International Journal of Medical Education, 11, 245-247. https://doi.org/10.5116/ijme.5f96.0f4a

Wu, W.-H., Kao, H.-Y., Wu, S.-H., & Wei, C.-W. (2019). Development and evaluation of affective domain using student's feedback in entrepreneurial massive open online courses. Frontiers in Psychology, 10, 1-9. https://doi.org/10.3389/fpsyg.2019.01109

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