PENGEMBANGAN INSTRUMEN DIAGNOSIS KESULITAN BELAJAR PADA PEMBELAJARAN KIMIA DI SMA

Authors

Sri Yamtinah, Program Studi Pendidikan Kimia FKIP UNS, Indonesia
Budiyono Budiyono, PPs Universitas Sebelas Maret, Indonesia

Keywords

Attribute Hierarchy Method; Ordered Multiple Choice; Graded Response Model

Document Type

Article

Abstract

Tujuan penelitian ini untuk: (1) mengembangkan instrumen ordered multiple-choice untuk diagnosis kesulitan belajar Stoikiometri kelas X, (2) menetapkan karakteristik dari instrumen, dan (3) membuat profil diagnostik peserta didik sebagai laporan yang informatif. Penelitian pengembangan ini menggunakan model Borg & Gall. Preliminary study menghasilkan pokok bahasan tersulit Stoikiometri. Penetapan atribut dan hierarki atribut dengan focus group discussion dilanjutkan dua putaran teknik Delphi. Indikator dan rumusan butir soal ditelaah expert untuk membuktikan validasi isi dengan rumus Aiken. Ujicoba terbatas dan ujicoba menengah pada sekolah kategori tinggi, sedang dan rendah. Ujicoba kelayakan dilakukan pada sekolah-sekolah di wilayah Surakarta, Karanganyar, Boyolali, dan Sragen. Kesimpulan: (1) penelitian berhasil mengembangkan tiga paket soal Ordered Multiple Choice (OMC) untuk mendeteksi kesulitan belajar peserta didik pada materi Stoikiometri kelas X; (2) butir-butir soal OMC pada paket A, B, dan C memiliki validitas konstruk yang baik dengan Goodness of Fit (GoF) berdasar Smart PLS > 0,36 yaitu 0,437; 0,466 dan 0,433. Instrumen memiliki reliabilitas tinggi berturut-turut 0,79; 0,81 dan 0,75; (3) profil peserta didik berupa laporan diagnostik tentang atribut yang sudah dikuasai dan belum dikuasai peserta didik.

Kata kunci: Attribute Hierarchy Method, Ordered Multiple Choice, Graded Response Model

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DEVELOPING A DIAGNOSTIC INSTRUMENT FOR LEARNING DIFFICULTIES IN CHEMISTRY IN SECONDARY HIGH SCHOOL

Abstract The purposes of this study are to: (1) develop Ordered Multiple Choice (OMC) instruments with a model of Attribute Hierarchy Method (AHM) for the diagnosis of stoichiometry learning difficulties in chemistry learning in secondary high school, (2) determine the characteristic of instruments which have been developed based on the Graded Response Model (GRM), (3) create a diagnostic profile of learners as an informative report. This development research used the Borg & Gall model. The development of the instrument is done using the AHM with the form of OMC. The determination of attributes and attribute hierarchy was done by the focus group discussion (FGD) by three experts, six teachers, and two measurement experts and continued with the Delphi technique by three experts with two rounds. A limited try out was conducted in the high, medium, and low category schools. A feasibility try out was conducted to the high, medium, and low category schools in the regions of Surakarta, Karanganyar, Boyolali, and Sragen. The results are as follows. 1). This research has developed three packages of OMC to detect the learning difficulties of students in the subjects of Chemistry, especially in Stoichiometry of class X. 2). The OMC test items on packages A, B, and C have good construct validity with the Goodness of Fit (GoF) greater than 0.36 namely 0.437, 0.466, and 0.433. 3). The learners' profiles are created in the form of diagnostic report about the attributes which have been mastered and have not been mastered by the learners.

Keywords: Attribute Hierarchy Method, Ordered Multiple Choice, Graded Response Model

First Page

69

Last Page

81

Issue

1

Volume

19

Digital Object Identifier (DOI)

10.21831/pep.v19i1.4557

Recommended Citation

Yamtinah, Sri and Budiyono, Budiyono (2015) "PENGEMBANGAN INSTRUMEN DIAGNOSIS KESULITAN BELAJAR PADA PEMBELAJARAN KIMIA DI SMA," Jurnal Penelitian dan Evaluasi Pendidikan: Vol. 19: Iss. 1, Article 7.
DOI: 10.21831/pep.v19i1.4557
Available at: https://scholarhub.uny.ac.id/jpep/vol19/iss1/7

References

Aiken, L.R. (1985). Three Coefficient for Analyzing the Reliability and Validity of Ratings. Educational and Psychological Measurement, 45.

Barke, H.D., Al Hazari, & Yitbarek, S. (2009). Misconceptions in Chemistry, Addressing Perceptions in Chemical Education. Germany: Springer-Verlag Berlin Heidelberg.

Bennet, J. (2007). Teaching and Learning Science. London: Biddles Ltd., King’s Lyan. Norfolk.

Borg, W.R., & Gall, M.D. (1983). Educational Research: An Introduction, Fourth edition. New York: Longman, Inc.

Briggs, D.C., Alonzo, A.C., Schwab, C., & Wilson, M. (2006). Diagnostik Assessment With Ordered Multiple-Choice Items. Educational Assessment, 11(1), 33-63.

Briggs, D.C., & Alonzo, A.C. (2009). The Psychometric Modeling of Ordered Multiple-Choice Item Responses for Diagnostik Assessment with a Learning Progression. Paper presented at The Learning Progression in Science (LeaPS) Conference, June 2009, Iowa City.

Burton, W.H. (1952). The Guidance of Learning Activities. New York: Appleton-Century-Croffts, Inc.

Chandrasegaran, A.L., Treagust, D.F., & Mocerino, M. (2007). The development of a two-tier multiple-choice diagnostic instrument for evaluating secondary school students’ ability to describe and explain chemical reactions using multiple levels of representation. Chemistry Education Research and Practice, 2007, 8(3), 293-307.

Depdiknas. (2003). Kurikulum Berbasis Kompetensi Mata Pelajaran Kimia SMA. Jakarta: Puskur Depdiknas.

Ghozali, I. (2008). Structural Equation Modeling: Metode Alternatif dengan Partial Least Square (PLS). Badan Penerbit Universitas Diponegoro: Semarang.

Gierl, M.J. (2007). Making Diagnostic Inferences About Cognitive Attributes Using the Rule-Space Model and Attribute Hierarchy Method. Journal of Educational Measurement, Winter 2007, Vol. 44, No. 4, pp. 325–340.

Gierl, M.J., Leighton, J.P., & Hunka, S. (2000). Exploring the logic of Tatsuoka’s rule-space model for test development and analysis. Educational Measurement: Issues and Practice, 19, 34-44.

Gierl, M.J., Wang, C., & Zhou, J. (2008). Using the Attribute Hierarchy Method to Make Diagnostic Inferences about Examinees’ Cognitive Skills in Algebra on the SAT©. Journal of Technology, Learning, and Assessment, 6(6). http://www.jtla.org.

Gierl, M.J., Cui, Y., & Zhou, J. (2009). Reliability and Attribute Based Scoring in Cognitive Diagnostic Assessment. Journal of Educational Measurement, Fall 2009, Vol. 46, No. 3, pp. 293-313.

Hambleton, R.K., Swaminathan, H., & Rogers, H.J. (1991). Fundamentals of Item Response Theory. Sage Publications.

Leighton, J.P., & Gierl, M.J. (2011). The Learning Sciences in Educational Assessment: The Role of Cognitive Models. Cambridge University Press, 32 Avenue of the Americas, New York, NY 10013-2473, USA.

Leighton, J.P., & Gierl, M.J. (2007). Cognitive Diagnostic Assessment: theory and applications. Cambridge University Press.

Leighton, J.P., Gierl, M.J., & Hunka, S. (2004). The Attribute Hierarchy Method for Cognitive Assessment: A Variation on Tatsuoka’s Rule-Space Approach. Journal of Educational Measurement, Fall 2004, Vol. 41, No. 3, pp. 205-237.

Mc Glohen, M.K. (2004). The application of a cognitive diagnosis model via an analysis of a large-scale assessment and a computerized adaptive testing administration. Dissertation. The University of Texas at Austin. Retrieved July 10, 2010, from scholar.google.com.

Nichols, P.D., Chipman, S.F., & Brennan, R.L. (1995). Cognitively Diagnostic Assessment. Lawrence Erlbaum Associate Publishers. Hillsdale, New Jersey.

Okanlawon, A.E. (2010). Constructing a Framework for Teaching Reaction Stoichiometry Using Pedagogical Content Knowledge. Chemistry, 19(2), 27-44.

Pritchard, A., & Woolard, J. (2010). Psychology for the Classroom: Constructivism and Social Learning. Routledge, 2 Park Square, Milton Park, Abingdon, Oxon.

Rahayu, S., & Kita, M. (2009). An Analysis Of Indonesian And Japanese Students’ Understandings Of Macroscopic And Submicroscopic Levels Of Representing Matter And Its Changes. International Journal of Science and Mathematics Education (2010) 8: 667-688.

Tatsuoka, K.K. (2009). Cognitive Assessment: An Introduction to the Rule Space Method. Routledge, Taylor & Francis Group. New York.

Tenenhaus, M., et al. (2005). PLS Path Modelling. Computational Statistics & Data Analysis, 48, 159-205.

Van der Linden, W.J., & Hambleton, R.K. (1997). Item response theory: Brief history, common models, and extensions. In W.J. Van der Linden & R.K. Hambleton (Eds.), Handbook of Modern Item Response Theory (pp. 1-28). New York: Springer-Verlag Inc.

Vinci, V.E., et al. (2010). Handbook of Partial Least Square: Concept, Method, and Applications. Springer-Verlag Berlin Heidenberg: Germany.

Vosniadou, S., & Brewer, W.F. (1992). Mental models of the earth: A study of conceptual change in childhood. Cognitive Psychology, 24, 535-585.