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AnalysisofBased Test InstrumentsLocal Wisdomof Terbang Papatto Measure Mathematical Representation Capabilities in Learning Physics

Year 2019, Volume: 4 Issue: 4, 555 - 563, 01.10.2019
https://doi.org/10.24331/ijere.628331

Abstract

This study aims to analyze the mathematical representation ability test instruments based on local wisdomof terbang papatto high school students. The research method used by compiling the instrument grid based on aspects of the ability of mathematical representation based on local wisdomof terbang papat, which is then used to compile indicators and items. The test instrument consists of four indicators of the ability of mathematical representation, each of which has five items and has been validated by expert judgment, physics teacher and peer reviewer. The validated instrument was tested on 338 students from two high schools in Kudus Regency. Data obtained in the form of polytomus data, then analyzed using Partial Credit Model (PCM). The results obtained showed that all test instruments of mathematical representation ability were proven to be fit with PCM, reliability of test instruments was declared reliable based on PCM results, total information function was between -2.00 to +1.8, difficulty indexes were more than -2.0 and less than 2.0 (-2.0 ≤b≤ + 2.0) which means all items are in good category. Thus the ability test instrument based on terbang papat local wisdom can be used to measure the ability of students' mathematical representation according to the partial credit model based on the five categories of polytomus data

References

  • Adams, R. J. & Khoo, S. T. (1996). Quest: The interactive test analysis system version2.1. Victoria: The Australian Council for Educational Research.Asysyifa, D.S., Jumadi, Wilujeng.I. & Kuswanto, H. (2019). Analysis of student critical thingking skills using partial credit models (PCM) in physics learning. International Journal of Educational Research Review, 4(2), 245-253.Cock, M. De. (2012). Representation use and strategy choice in physics problem solving. Physics Education Research8,020117(November), 1–15. https://doi.org/10.1103/PhysRevSTPER.8.020117Depdiknas. (2007). Peraturan Menteri Pendidikan Nasional No 20 Tahun tentang Standar Penilaian Docktor, J. L., & Mestre, J. P. (2014). Synthesis of discipline-based education research in physics.Physics Education Research,10, 1–58. https://doi.org/10.1103/PhysRevSTPER.10.020119Hambleton dan Swaminathan (1991).Fundamentals of item response theory. California: SAGE Publications, Inc Harvey, R. J., & Hammer, A. L. (1999). Item response theory. The Counseling Psychologist, 27 (3), 353-383Hogg R.V. &Craig,A.T. (1978). Introduction to mathematical statistics. New York: Macmillan Publishing Co Inc.Kun, P. Z. (2013). Pembelajaran sains berbasis kearifan lokal. PROSIDING: Seminar Nasional Fisika Dan Pendidikan Fisika, 2(1), 246–256. Retrieved from http://jurnal.fkip.uns.ac.id/index.php/prosfis1/article/view/3316. Masters, G.N. (1999). Partial credit model. Dalam J.P. Keeves & G.N. Masters (Eds.). Advances in Measurement in Educational Research and Assessment. Amsterdam: Pergamon.Matthew, S.J. (2007). Marginal maximum likelihood estimation of ıtem response model in R. Journal of Statistical Software,20(10),1-24. Merriënboer, J. J. G. Van. (2013). Perspectives on problem solving and instruction. Computers & Education, 64, 153–160. Pospiech, G. (2012). Modelling mathematical reasoning in physics education.Science & Education,21(4),485–506. https://doi.org/10.1007/s11191-011-9396-6 Supahar & Prasetyo, Zuhdan Kun. (2015). Pengembangan instrumen penilaian kinerja kemampuan inkuiri peserta didik pada mata pelajaran fisika SMA. Jurnal Penelitian dan Evaluasi Pendidikan Volume 19, No 1, Juni 2015 (96-10)Suryabrata, S. (2002). Pengembangan alat ukur psikologis. Yogyakarta: Andi Offset.Taale, K. D. (2011). Improving physics problem solving skills of students of Somanya Senior High Secondary Technical School in the Yilo Krobo District of Eastern Region of Ghana. Journal of Education and Practice, 2(6), 8–21. Wang, F.-H. (2006). Application of componential IRT model for diagnostic test in a standard conformant elearning system. In Sixth international conference on advanced learning technologies (ICALT’06).5-7 July,Kerkrade, Netherlands.Widhiarso W., (2010). Model politomi dalam teori respon butir. Fakultas Psikologi UGM, Yogyakarta
Year 2019, Volume: 4 Issue: 4, 555 - 563, 01.10.2019
https://doi.org/10.24331/ijere.628331

Abstract

References

  • Adams, R. J. & Khoo, S. T. (1996). Quest: The interactive test analysis system version2.1. Victoria: The Australian Council for Educational Research.Asysyifa, D.S., Jumadi, Wilujeng.I. & Kuswanto, H. (2019). Analysis of student critical thingking skills using partial credit models (PCM) in physics learning. International Journal of Educational Research Review, 4(2), 245-253.Cock, M. De. (2012). Representation use and strategy choice in physics problem solving. Physics Education Research8,020117(November), 1–15. https://doi.org/10.1103/PhysRevSTPER.8.020117Depdiknas. (2007). Peraturan Menteri Pendidikan Nasional No 20 Tahun tentang Standar Penilaian Docktor, J. L., & Mestre, J. P. (2014). Synthesis of discipline-based education research in physics.Physics Education Research,10, 1–58. https://doi.org/10.1103/PhysRevSTPER.10.020119Hambleton dan Swaminathan (1991).Fundamentals of item response theory. California: SAGE Publications, Inc Harvey, R. J., & Hammer, A. L. (1999). Item response theory. The Counseling Psychologist, 27 (3), 353-383Hogg R.V. &Craig,A.T. (1978). Introduction to mathematical statistics. New York: Macmillan Publishing Co Inc.Kun, P. Z. (2013). Pembelajaran sains berbasis kearifan lokal. PROSIDING: Seminar Nasional Fisika Dan Pendidikan Fisika, 2(1), 246–256. Retrieved from http://jurnal.fkip.uns.ac.id/index.php/prosfis1/article/view/3316. Masters, G.N. (1999). Partial credit model. Dalam J.P. Keeves & G.N. Masters (Eds.). Advances in Measurement in Educational Research and Assessment. Amsterdam: Pergamon.Matthew, S.J. (2007). Marginal maximum likelihood estimation of ıtem response model in R. Journal of Statistical Software,20(10),1-24. Merriënboer, J. J. G. Van. (2013). Perspectives on problem solving and instruction. Computers & Education, 64, 153–160. Pospiech, G. (2012). Modelling mathematical reasoning in physics education.Science & Education,21(4),485–506. https://doi.org/10.1007/s11191-011-9396-6 Supahar & Prasetyo, Zuhdan Kun. (2015). Pengembangan instrumen penilaian kinerja kemampuan inkuiri peserta didik pada mata pelajaran fisika SMA. Jurnal Penelitian dan Evaluasi Pendidikan Volume 19, No 1, Juni 2015 (96-10)Suryabrata, S. (2002). Pengembangan alat ukur psikologis. Yogyakarta: Andi Offset.Taale, K. D. (2011). Improving physics problem solving skills of students of Somanya Senior High Secondary Technical School in the Yilo Krobo District of Eastern Region of Ghana. Journal of Education and Practice, 2(6), 8–21. Wang, F.-H. (2006). Application of componential IRT model for diagnostic test in a standard conformant elearning system. In Sixth international conference on advanced learning technologies (ICALT’06).5-7 July,Kerkrade, Netherlands.Widhiarso W., (2010). Model politomi dalam teori respon butir. Fakultas Psikologi UGM, Yogyakarta
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Details

Primary Language English
Journal Section Articles
Authors

Danis Alif Oktavia This is me

Ahdika Setyadi This is me

Warsono . This is me

Supahar . This is me

Publication Date October 1, 2019
Published in Issue Year 2019 Volume: 4 Issue: 4

Cite

APA Oktavia, D. A., Setyadi, A., ., W., ., S. (2019). AnalysisofBased Test InstrumentsLocal Wisdomof Terbang Papatto Measure Mathematical Representation Capabilities in Learning Physics. International Journal of Educational Research Review, 4(4), 555-563. https://doi.org/10.24331/ijere.628331
AMA Oktavia DA, Setyadi A, . W, . S. AnalysisofBased Test InstrumentsLocal Wisdomof Terbang Papatto Measure Mathematical Representation Capabilities in Learning Physics. IJERE. October 2019;4(4):555-563. doi:10.24331/ijere.628331
Chicago Oktavia, Danis Alif, Ahdika Setyadi, Warsono ., and Supahar . “AnalysisofBased Test InstrumentsLocal Wisdomof Terbang Papatto Measure Mathematical Representation Capabilities in Learning Physics”. International Journal of Educational Research Review 4, no. 4 (October 2019): 555-63. https://doi.org/10.24331/ijere.628331.
EndNote Oktavia DA, Setyadi A, . W, . S (October 1, 2019) AnalysisofBased Test InstrumentsLocal Wisdomof Terbang Papatto Measure Mathematical Representation Capabilities in Learning Physics. International Journal of Educational Research Review 4 4 555–563.
IEEE D. A. Oktavia, A. Setyadi, W. ., and S. ., “AnalysisofBased Test InstrumentsLocal Wisdomof Terbang Papatto Measure Mathematical Representation Capabilities in Learning Physics”, IJERE, vol. 4, no. 4, pp. 555–563, 2019, doi: 10.24331/ijere.628331.
ISNAD Oktavia, Danis Alif et al. “AnalysisofBased Test InstrumentsLocal Wisdomof Terbang Papatto Measure Mathematical Representation Capabilities in Learning Physics”. International Journal of Educational Research Review 4/4 (October 2019), 555-563. https://doi.org/10.24331/ijere.628331.
JAMA Oktavia DA, Setyadi A, . W, . S. AnalysisofBased Test InstrumentsLocal Wisdomof Terbang Papatto Measure Mathematical Representation Capabilities in Learning Physics. IJERE. 2019;4:555–563.
MLA Oktavia, Danis Alif et al. “AnalysisofBased Test InstrumentsLocal Wisdomof Terbang Papatto Measure Mathematical Representation Capabilities in Learning Physics”. International Journal of Educational Research Review, vol. 4, no. 4, 2019, pp. 555-63, doi:10.24331/ijere.628331.
Vancouver Oktavia DA, Setyadi A, . W, . S. AnalysisofBased Test InstrumentsLocal Wisdomof Terbang Papatto Measure Mathematical Representation Capabilities in Learning Physics. IJERE. 2019;4(4):555-63.

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