Araştırma Makalesi
BibTex RIS Kaynak Göster

A RASCH MODEL ANALYSIS OF PRIMARY SCHOOL STUDENTS’ CONCEPTUAL UNDERSTANDING LEVELS OF THE CONCEPT OF LIGHT

Yıl 2021, Cilt: 10 Sayı: 1, 160 - 179, 30.06.2021

Öz

The study determines the conceptual understanding levels of primary school students on the concept of light according to the Rasch Model with a Four-tier Light Conceptual Understanding Test (LCUT). The participants were 355 (164 girls and 191 boys) primary school students studying at a public school in Izmir city center. In the study, the Rasch Model, which is included in the Latent Trait Theory, was used. Also, the data regarding the answers given and the level of confidence in the responses were associated with the Rasch analysis of LCUT. The results of Rasch analysis showed that LCUT was in full harmony in the context of infit, outfit, and point measurement correlation statistics, and is a valid and reliable measurement tool for conceptual understanding. Moreover, these results explained that the students' average conceptual understanding ability regarding the Light unit was above the average item difficulty.

Kaynakça

  • Akın, Ö. & Baştürk, R. (2012). The evaluation of the basic skills in violin training by many facet rasch model. Pamukkale University Journal of Education, 31(31), 175-187.
  • Altun, D. G. (2006). Effect of sound and light unit prepared using multiple intelligence theory to the student success, level of retaining, attitudes towards science and multiple intelligence theory. (Unpublished master’s thesis). Institute of Science, Gazi University, Ankara, Turkey
  • Aminudin, A. H., Kaniawati, I., Suhendi, E., Samsudin, A., Coştu, B., & Adimayuda, R. (2019). Rasch analysis of multitier open-ended light-wave ınstrument (MOLWI): Developing and assessing second-years sundanese-scholars alternative conceptions. Journal for the Education of Gifted Young Scientists, 7(3), 557-579. doi:10.17478/jegys.574524
  • Andersson, B., & Bach F. (2005). On designing and evaluating teaching sequences taking geometrical optics as an example. Science Education, 89, 196-218. doi:10.1002/sce.20044
  • Anshel, M. H., Weatherby, N. L., Kang, M., & Watson, T. (2009). Rasch calibration of a unidimensional perfectionism inventory for sport. Psychology of Sport and Exercise, 10(1), 210-216. doi:10.1016/j.psychsport.2008.07.006
  • Apaydın, Z., Akman, E., Taş, E., & Peker, E. A. (2014). Analysis of knowledge structures about light concept of first level elementary students according to conceptual change theories. Journal of Computer and Education Research, 2(3), 44-68. Retrieved from https://dergipark.org.tr/tr/pub/jcer/issue/18615/196501
  • Aydoslu, M. (2018). Determination of cognitive structures and misconceptions of middle school students about the concepts of light and reflection by using alternative measurement and evaluation techniques. (Unpublished master’s thesis). Institute of Science, Kırıkkale University, Kırıkkale, Turkey
  • Ayvacı, H. S., & Candas, B. (2018). Students’ understandings on light reflection from different educational level. Journal of Computer and Education Research, 6(11), 1-32. doi:10.18009/jcer.309748
  • Baharun, N., Razi, N. F. M., Abidin, R. Z., Musa, N. A. C., & Mahmud, Z. (2017). Measuring students’ understanding in counting rules and its probability via e-learning mode: a Rasch measurement approach. Journal of Fundamental and Applied Sciences, 9(6S), 429-441. doi:10.4314/jfas.v9i6s.33
  • Benek, İ., & Kocakaya, S. (2012). Students’ opinion on learning in stations technique. Journal of Research in Education and Teaching, 1(3), 2146-9199. Retrieved from http://www.jret.org/FileUpload/ks281142/File/02z.benek.pdf
  • Blizak, D., Chafiqi, F., & Kendil, D. (2009). Students misconceptions about light in Algeria. In Education and Training in Optics and Photonics (p. EMA5). Optical Society of America. doi:10.1364/etop.2009.ema5
  • Bond, T. G & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences (2nd ed.). New Jersey: Lawrence Erlbaum Inc. Publishers
  • Boone, W. J. (2016). Rasch analysis for instrument development: why, when, and how?. CBE-Life Sciences Education, 15(4), rm4.1-7. doi:10.1187/cbe.16-04-0148
  • Boone, W. J., & Noltemeyer, A. (2017). Rasch analysis: A primer for school psychology researchers and practitioners. Cogent Education, 4(1), 1416898. doi:10.1080/2331186x.2017.1416898
  • Boone, W. J., & Scantlebury, K. (2006). The role of Rasch analysis when conducting science education research, utilizing multiple choice tests. Science Education, 90(2), 253-269. doi:10.1002/sce.20106
  • Boone, W. J., Townsend, J. S., & Staver, J. (2011). Using Rasch theory to guide the practice of survey development and survey data analysis in science education and to inform science reform efforts: An exemplar utilizing STEBI self efficacy data. Science Education, 95(2), 258-280. doi:10.1002/sce.20413
  • Bulut, G. (2018). Açık ve uzaktan öğrenmede şans başarısı: Klasik test kuramı (KTK) ve madde tepki kurama (MTK) temelinde karşılaştırmalı bir analiz. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 4(1), 78-93. Retrieved from https://dergipark.org.tr/tr/pub/auad/issue/35189/390471
  • Büyüköztürk, Ş., Çakmak, E. K., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2014). Scientific research methods. Ankara: Pegem Publications.
  • Clements, D. H., Sarama, J. H., & Liu, X. H. (2008). Development of a measure of early mathematics achievement using the Rasch model: The research based early maths assessment. Educational Psychology, 28(4), 457-482. doi:10.1080/01443410701777272
  • Çetin, Ş. (2019, November). Analysis of open-ended questions with many facet Rasch model. In 4th International Symposium on Innovative Approaches in Health and Sports Sciences. Samsun, Turkey
  • Demirci, N., & Ahçı, M. (2016). University students’ conceptual understanding on the subjects of light and optics. Necatibey Faculty of Education Electronic Journal of Science and Mathematics Education 10(1), 142-181. doi:10.17522/nefefmed.39726
  • Demirtaşlı, N. Ç. (1996). New approaches in test development: The latent trait theory-basic features, assumptions, models and limitations. Ankara University Journal of Faculty of Educational Sciences, 28(2), 161-173. doi:10.1501/Egifak_0000000298
  • Djanette, B., Fouad, C., & Djamel, K. (2013). What thinks the university's students about propagation of light in the vacuum? European Scientific Journal, 9(24), 197-213.
  • Doğan, N. & Tezbaşaran, A. (2003). Klasik test kuramı ve örtük özellikler kuramının örneklemler bağlamında karşılaştırılması [Comparison of classical test theory and latent traits theory by Samples]. Hacettepe University Journal of Ecucation, 25(25), 58-67.
  • Doğru, Ş. C. (2019). The comparison of classical test theory and Rasch model in determining the psychometric properties of mixed tests. (Unpublished master’s thesis). Institute of Science, Hacettepe University, Ankara, Turkey
  • Duncan, P. W., Bode, R. K., Lai, S. M., Perera, S., & Glycine Antagonist in Neuroprotection Americas Investigators (2003). Rasch analysis of a new stroke-specific outcome scale: the stroke impact scale. Archives of Physical Medicine and Rehabilitation, 84(7), 950-963. doi:10.1016/s0003-9993(03)00035-2
  • Eggert, S., & Bögeholz, S. (2010). Students' use of decision making strategies with regard to socioscientific issues: An application of the Rasch partial credit model. Science Education, 94(2), 230-258. doi:10.1002/sce.20358
  • Epik, Ö., Kalem, R., Kavcar, N., & Çallıca, H. (2002). Investigation of student views on the concepts of light, image formation and image observation. Buca Faculty of Education Journal, 14, 64-73
  • Fariyani, Q., Rusilowati, A., & Sugianto, S. (2017). Four-tier diagnostic test to identify misconceptions in geometrical optics. Unnes Science Education Journal, 6(3), 1724-1729. doi:10.15294/USEJ.V6I3.20396
  • Fisher, W.P. Jr (2007). Rasch measurement transaction. Rasch Measurement Transactions 21(1), 1087-1096
  • Galili, I., & Hazan, A. (2000). Learners' knowledge in optics: interpretation, structure and analysis. International Journal of Science Education, 22(1), 57-88. doi:10.1080/095006900290000
  • Gülkaya, D. (2018). Rasch analysis and its application. (Unpublished master’s thesis). Institute of Science, Süleyman Demirel University, Isparta, Turkey
  • Hambleton, R. K., & Jones, R. W. (1993). An NCME instructional module on: Comparison of classical test theory and item response theory and their applications to test development. Educational Measurement: Issues and Practice, 12(3), 38-47. doi:10.1111/j.1745-3992.1993.tb00543.x
  • Irawan, A. G., Nyoman Padmadewi, N., & Artini, L. P. (2018). Instructional materials development through 4D model. In SHS Web of Conferences, 42. doi:10.1051/shsconf/20184200086
  • İlhan, M., & Güler, N. (2017). Testing the agreement between ability estimations made through classical test theory and the ones made through Rasch analysis in likert type scales. Ege Journal of Education, 18(1), 244-265. doi:10.12984/egeefd.289576
  • Kan, A. (2006). An empirical study on the comparison of predicted item parameters with respect to classical and item response test theories. Mersin University Journal of the Faculty of Education, 2(2), 227-235. Retrieved from https://dergipark.org.tr/tr/pub/mersinefd/issue/17389/181743
  • Kaplan, E. (2017). Determination of sixth grade students misconceptions on, the light and sound unit with concept test, concept cartoons, and semi-structured interviews. (Unpublished master’s thesis). Graduate School of Educational Sciences, Erciyes University, Kayseri, Turkey
  • Kara, İ. Avcı, D., & Çekbaş, Y. (2008). Investigation of the science teacher candidates’ knowledge level about the concept of light. Mehmet Akif Ersoy University Journal of Education Faculty, 8 (16), 46-57
  • Kaskatı, O. (2011). Development of computer adaptive testing method using with Rasch models for assessment of disability in rheumatoid arthritis patients. (Unpublished doctorate thesis). Institute of Health Science, Ankara University, Ankara, Turkey
  • Kauertz, A., & Fischer, H. E. (2006). Assessing students’ level of knowledge and analysing the reasons for learning difficulties in physics by Rasch analysis. In X.Liu &W.J.Boone (Eds.) Applications of Rasch measurement in science education, (pp. 212-246). Maple Grove.MN: Jam Press
  • Keeves, J. P. (1998). Educational research, methodology and measurement: An international Handbook. Oxford: Pergamon Pres
  • Kelecioğlu, H. (2001). Relationship between b and a parameters in latent trait theory and p and r statistics in classical test theory. Hacettepe University Journal of Education, 20, 104-110
  • Linacre, J.M. (2002). What do infit and outfit, mean-square and standardized mean? Rasch Measurement Transactions, 16(2), 878. Retrieved from https://www.rasch.org/rmt/rmt162f.htm
  • Linacre, J. M. (2006). Data variance explained by Rasch measures Rasch Measurement Transactions, 20(1), 1045. Retrieved from https://www.rasch.org/rmt/rmt201a.htm
  • Linacre, J. M. (2014). Reliability and separation of measures. A user’s guide to Winsteps Ministep Rasch-model computer programs (version 3.81.0). Retrieved from http://www.winsteps.com/winman/reliability.htm
  • Linacre, J. M. (2019). A user guide to winsteps ministep Rasch model computer programs: Program manual 4.4.7. Retrieved from https://www.winsteps.com/a/Winsteps-Manual.pdf
  • Liu, X. (2010). Using and developing measurement instruments in science education: A Rasch modeling approach. IAP Information Age Publishing
  • Maat, S. M. (2015). Psychometric evaluation on mathematics beliefs instrument using Rasch model. Creative Education, 6(16), 1797-1801. doi:10.4236/ce.2015.616183
  • Mazlum, E. & Yiğit, N. (2017). Examining indicators of knowledge of light concept through peer tutoring applications. Hacettepe University Journal of Education, 32(2), 295-311. doi:10.16986/HUJE.2016019933
  • Mešić, V., Neumann, K., Aviani, I., Hasović, E., Boone, W. J., Erceg, N., Grubelnik, V., Sušac, A., Glamočić, D.S., Karuza, M., Vidak, A., Alihodžić, A., & Repnik, R. (2019). Measuring students’ conceptual understanding of wave optics: A Rasch modeling approach. Physical Review Physics Education Research, 15(1), 010115(20). doi:10.1103/physrevphyseducres.15.010115
  • Ministry of Education (MNE) (2018). Primary education institutions (primary and secondary schools) science course (3,4,5,6,7,8. Grades) curriculum. Ankara: Ministry of Education, Board of Education and Discipline
  • Othman, N. B., Salleh, S. M., Hussein, H., & Wahid, H. B. A. (2014). Assessing construct validity and reliability of competitiveness scale using Rasch model approach. In Proceedings of the 2014 WEI International Academic Conference (pp. 113-120)
  • Özcan, Ö., & Tavukçuoğlu, E. (2018). Investigating the high school students’ cognitive structures about the light concept through word association test. Journal of Education and Future, (13), 121-132. doi:10.1063/1.5025996
  • Planinic, M., Ivanjek, L., & Susac, A. (2010). Rasch model based analysis of the force concept inventory. Physical Review Special Topics-Physics Education Research, 6(1), 010103(20). doi:10.1103/physrevstper.6.010103
  • Planinic, M., Boone, W. J., Susac, A., & Ivanjek, L. (2019). Rasch analysis in physics education research: Why measurement matters. Physical Review Physics Education Research, 15(2), 1-14. doi:10.1103/physrevphyseducres.15.020111
  • Preece, P. F. W. (1979). Objective measurement in education: The Rasch model. School Science Review, 60(213), 770-73. ERIC ID. EJ207029
  • Rasch, G. (1961, July). On general laws and the meaning of measurement in psychology. In Proceedings of the fourth Berkeley symposium on mathematical statistics and probability. Berkeley, USA.
  • Siang, L. K. (2011). Developing a profile of conceptual understanding and misconceptions in Newtonian mechanics: Rasch modelling approach. In International Conference on Measurement and Evaluation in Education, University of Malaya. Kuala Lumpur, Malaysia.
  • Sondergeld, T. A., & Johnson, C. C. (2014). Using Rasch measurement for the development and use of affective assessments in science education research. Science Education, 98(4), 581-613. doi:10.1002/sce.21118
  • Şahin, Ç., İpek, H., & Ayas, A. (2008, June). Students' understanding of light concepts primary school: A cross-age study. Asia-Pacific Forum on Science learning and teaching, 9(1), 1-119. Retrieved from https://www.eduhk.hk/apfslt/
  • Şenel, S.Ö. (2016). Activities based on multiple intelligence theory related on learning and effects about the 7th grade subject light. (Unpublished master’s thesis). Gazi University, Ankara, Turkey
  • Taşlıdere, E. & Eryılmaz, A. (2015). Assessment of pre-service teachers’ misconceptions in geometrical optics via a three-tier misconception test. Bartın University Journal of Faculty of Education, 4 (1), 269-289. doi:10.14686/buefad.2015111057
  • Uzun, E. & Karaman İ. (2015). An analysis of prospective science teachers’ mental models about light and sound. Ekev Akademi Dergisi. 20(65), 141-154. doi:10.17753/Ekev549
  • Uzunsakal, E. & Yıldız, D. (2018). A comparison of reliability tests in field researches and an applicatıon on agricultural data. Applied Social Sciences Journal of Istanbul University-Cerrahpasa, 2(1), 14-28. Retrieved from https://dergipark.org.tr/en/pub/iuusbd/issue/38311/399621
  • Xiao, Y., Han, J., Koenig, K., Xiong, J., & Bao, L. (2018). Multilevel Rasch modeling of two-tier multiple choice test: A case study using Lawson’s classroom test of scientific reasoning. Physical Review Physics Education Research, 14(2), 020104(14). doi:10.1103/physrevphyseducres.14.020104
  • Wahyuningsih, S., Rusilowati, A., & Hindarto, N. (2017). Analysis of misconception to science literacy using three tier multiple choice test in the materials of characteristic of light. Unnes Science Education Journal, 6(3), 1736-1743. doi:10.15294/USEJ.V6I3.20426
  • Wei, S., Liu, X., & Jia, Y. (2014). Using Rasch measurement to validate the instrument of students’ understanding of models in science (sums). International Journal of Science and Mathematics Education, 12(5), 1067-1082. doi:10.1007/s10763-013-9459-z
  • Wei, S., Liu, X., Wang, Z., & Wang, X. (2012). Using Rasch measurement to develop a computer modeling-based instrument to assess students’ conceptual understanding of matter. Journal of Chemical Education, 89(3), 335-345. doi:10.1021/ed100852t
  • Yüksel, S. (2012). Analyzing differential item functioning by mixed rasch models which stated in scales. (Unpublished doctorate thesis). Institute of Health Education, Ankara University, Ankara, Turkey
  • Zain, J. M., Mohd, W. M. W., & El-Qawasmeh, E. (Eds.). (2011). Software engineering and computer systems. In Proceedings of Second International Conference, ICSECS 2011, Kuantan, Pahang, Malaysia
Yıl 2021, Cilt: 10 Sayı: 1, 160 - 179, 30.06.2021

Öz

Kaynakça

  • Akın, Ö. & Baştürk, R. (2012). The evaluation of the basic skills in violin training by many facet rasch model. Pamukkale University Journal of Education, 31(31), 175-187.
  • Altun, D. G. (2006). Effect of sound and light unit prepared using multiple intelligence theory to the student success, level of retaining, attitudes towards science and multiple intelligence theory. (Unpublished master’s thesis). Institute of Science, Gazi University, Ankara, Turkey
  • Aminudin, A. H., Kaniawati, I., Suhendi, E., Samsudin, A., Coştu, B., & Adimayuda, R. (2019). Rasch analysis of multitier open-ended light-wave ınstrument (MOLWI): Developing and assessing second-years sundanese-scholars alternative conceptions. Journal for the Education of Gifted Young Scientists, 7(3), 557-579. doi:10.17478/jegys.574524
  • Andersson, B., & Bach F. (2005). On designing and evaluating teaching sequences taking geometrical optics as an example. Science Education, 89, 196-218. doi:10.1002/sce.20044
  • Anshel, M. H., Weatherby, N. L., Kang, M., & Watson, T. (2009). Rasch calibration of a unidimensional perfectionism inventory for sport. Psychology of Sport and Exercise, 10(1), 210-216. doi:10.1016/j.psychsport.2008.07.006
  • Apaydın, Z., Akman, E., Taş, E., & Peker, E. A. (2014). Analysis of knowledge structures about light concept of first level elementary students according to conceptual change theories. Journal of Computer and Education Research, 2(3), 44-68. Retrieved from https://dergipark.org.tr/tr/pub/jcer/issue/18615/196501
  • Aydoslu, M. (2018). Determination of cognitive structures and misconceptions of middle school students about the concepts of light and reflection by using alternative measurement and evaluation techniques. (Unpublished master’s thesis). Institute of Science, Kırıkkale University, Kırıkkale, Turkey
  • Ayvacı, H. S., & Candas, B. (2018). Students’ understandings on light reflection from different educational level. Journal of Computer and Education Research, 6(11), 1-32. doi:10.18009/jcer.309748
  • Baharun, N., Razi, N. F. M., Abidin, R. Z., Musa, N. A. C., & Mahmud, Z. (2017). Measuring students’ understanding in counting rules and its probability via e-learning mode: a Rasch measurement approach. Journal of Fundamental and Applied Sciences, 9(6S), 429-441. doi:10.4314/jfas.v9i6s.33
  • Benek, İ., & Kocakaya, S. (2012). Students’ opinion on learning in stations technique. Journal of Research in Education and Teaching, 1(3), 2146-9199. Retrieved from http://www.jret.org/FileUpload/ks281142/File/02z.benek.pdf
  • Blizak, D., Chafiqi, F., & Kendil, D. (2009). Students misconceptions about light in Algeria. In Education and Training in Optics and Photonics (p. EMA5). Optical Society of America. doi:10.1364/etop.2009.ema5
  • Bond, T. G & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences (2nd ed.). New Jersey: Lawrence Erlbaum Inc. Publishers
  • Boone, W. J. (2016). Rasch analysis for instrument development: why, when, and how?. CBE-Life Sciences Education, 15(4), rm4.1-7. doi:10.1187/cbe.16-04-0148
  • Boone, W. J., & Noltemeyer, A. (2017). Rasch analysis: A primer for school psychology researchers and practitioners. Cogent Education, 4(1), 1416898. doi:10.1080/2331186x.2017.1416898
  • Boone, W. J., & Scantlebury, K. (2006). The role of Rasch analysis when conducting science education research, utilizing multiple choice tests. Science Education, 90(2), 253-269. doi:10.1002/sce.20106
  • Boone, W. J., Townsend, J. S., & Staver, J. (2011). Using Rasch theory to guide the practice of survey development and survey data analysis in science education and to inform science reform efforts: An exemplar utilizing STEBI self efficacy data. Science Education, 95(2), 258-280. doi:10.1002/sce.20413
  • Bulut, G. (2018). Açık ve uzaktan öğrenmede şans başarısı: Klasik test kuramı (KTK) ve madde tepki kurama (MTK) temelinde karşılaştırmalı bir analiz. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 4(1), 78-93. Retrieved from https://dergipark.org.tr/tr/pub/auad/issue/35189/390471
  • Büyüköztürk, Ş., Çakmak, E. K., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2014). Scientific research methods. Ankara: Pegem Publications.
  • Clements, D. H., Sarama, J. H., & Liu, X. H. (2008). Development of a measure of early mathematics achievement using the Rasch model: The research based early maths assessment. Educational Psychology, 28(4), 457-482. doi:10.1080/01443410701777272
  • Çetin, Ş. (2019, November). Analysis of open-ended questions with many facet Rasch model. In 4th International Symposium on Innovative Approaches in Health and Sports Sciences. Samsun, Turkey
  • Demirci, N., & Ahçı, M. (2016). University students’ conceptual understanding on the subjects of light and optics. Necatibey Faculty of Education Electronic Journal of Science and Mathematics Education 10(1), 142-181. doi:10.17522/nefefmed.39726
  • Demirtaşlı, N. Ç. (1996). New approaches in test development: The latent trait theory-basic features, assumptions, models and limitations. Ankara University Journal of Faculty of Educational Sciences, 28(2), 161-173. doi:10.1501/Egifak_0000000298
  • Djanette, B., Fouad, C., & Djamel, K. (2013). What thinks the university's students about propagation of light in the vacuum? European Scientific Journal, 9(24), 197-213.
  • Doğan, N. & Tezbaşaran, A. (2003). Klasik test kuramı ve örtük özellikler kuramının örneklemler bağlamında karşılaştırılması [Comparison of classical test theory and latent traits theory by Samples]. Hacettepe University Journal of Ecucation, 25(25), 58-67.
  • Doğru, Ş. C. (2019). The comparison of classical test theory and Rasch model in determining the psychometric properties of mixed tests. (Unpublished master’s thesis). Institute of Science, Hacettepe University, Ankara, Turkey
  • Duncan, P. W., Bode, R. K., Lai, S. M., Perera, S., & Glycine Antagonist in Neuroprotection Americas Investigators (2003). Rasch analysis of a new stroke-specific outcome scale: the stroke impact scale. Archives of Physical Medicine and Rehabilitation, 84(7), 950-963. doi:10.1016/s0003-9993(03)00035-2
  • Eggert, S., & Bögeholz, S. (2010). Students' use of decision making strategies with regard to socioscientific issues: An application of the Rasch partial credit model. Science Education, 94(2), 230-258. doi:10.1002/sce.20358
  • Epik, Ö., Kalem, R., Kavcar, N., & Çallıca, H. (2002). Investigation of student views on the concepts of light, image formation and image observation. Buca Faculty of Education Journal, 14, 64-73
  • Fariyani, Q., Rusilowati, A., & Sugianto, S. (2017). Four-tier diagnostic test to identify misconceptions in geometrical optics. Unnes Science Education Journal, 6(3), 1724-1729. doi:10.15294/USEJ.V6I3.20396
  • Fisher, W.P. Jr (2007). Rasch measurement transaction. Rasch Measurement Transactions 21(1), 1087-1096
  • Galili, I., & Hazan, A. (2000). Learners' knowledge in optics: interpretation, structure and analysis. International Journal of Science Education, 22(1), 57-88. doi:10.1080/095006900290000
  • Gülkaya, D. (2018). Rasch analysis and its application. (Unpublished master’s thesis). Institute of Science, Süleyman Demirel University, Isparta, Turkey
  • Hambleton, R. K., & Jones, R. W. (1993). An NCME instructional module on: Comparison of classical test theory and item response theory and their applications to test development. Educational Measurement: Issues and Practice, 12(3), 38-47. doi:10.1111/j.1745-3992.1993.tb00543.x
  • Irawan, A. G., Nyoman Padmadewi, N., & Artini, L. P. (2018). Instructional materials development through 4D model. In SHS Web of Conferences, 42. doi:10.1051/shsconf/20184200086
  • İlhan, M., & Güler, N. (2017). Testing the agreement between ability estimations made through classical test theory and the ones made through Rasch analysis in likert type scales. Ege Journal of Education, 18(1), 244-265. doi:10.12984/egeefd.289576
  • Kan, A. (2006). An empirical study on the comparison of predicted item parameters with respect to classical and item response test theories. Mersin University Journal of the Faculty of Education, 2(2), 227-235. Retrieved from https://dergipark.org.tr/tr/pub/mersinefd/issue/17389/181743
  • Kaplan, E. (2017). Determination of sixth grade students misconceptions on, the light and sound unit with concept test, concept cartoons, and semi-structured interviews. (Unpublished master’s thesis). Graduate School of Educational Sciences, Erciyes University, Kayseri, Turkey
  • Kara, İ. Avcı, D., & Çekbaş, Y. (2008). Investigation of the science teacher candidates’ knowledge level about the concept of light. Mehmet Akif Ersoy University Journal of Education Faculty, 8 (16), 46-57
  • Kaskatı, O. (2011). Development of computer adaptive testing method using with Rasch models for assessment of disability in rheumatoid arthritis patients. (Unpublished doctorate thesis). Institute of Health Science, Ankara University, Ankara, Turkey
  • Kauertz, A., & Fischer, H. E. (2006). Assessing students’ level of knowledge and analysing the reasons for learning difficulties in physics by Rasch analysis. In X.Liu &W.J.Boone (Eds.) Applications of Rasch measurement in science education, (pp. 212-246). Maple Grove.MN: Jam Press
  • Keeves, J. P. (1998). Educational research, methodology and measurement: An international Handbook. Oxford: Pergamon Pres
  • Kelecioğlu, H. (2001). Relationship between b and a parameters in latent trait theory and p and r statistics in classical test theory. Hacettepe University Journal of Education, 20, 104-110
  • Linacre, J.M. (2002). What do infit and outfit, mean-square and standardized mean? Rasch Measurement Transactions, 16(2), 878. Retrieved from https://www.rasch.org/rmt/rmt162f.htm
  • Linacre, J. M. (2006). Data variance explained by Rasch measures Rasch Measurement Transactions, 20(1), 1045. Retrieved from https://www.rasch.org/rmt/rmt201a.htm
  • Linacre, J. M. (2014). Reliability and separation of measures. A user’s guide to Winsteps Ministep Rasch-model computer programs (version 3.81.0). Retrieved from http://www.winsteps.com/winman/reliability.htm
  • Linacre, J. M. (2019). A user guide to winsteps ministep Rasch model computer programs: Program manual 4.4.7. Retrieved from https://www.winsteps.com/a/Winsteps-Manual.pdf
  • Liu, X. (2010). Using and developing measurement instruments in science education: A Rasch modeling approach. IAP Information Age Publishing
  • Maat, S. M. (2015). Psychometric evaluation on mathematics beliefs instrument using Rasch model. Creative Education, 6(16), 1797-1801. doi:10.4236/ce.2015.616183
  • Mazlum, E. & Yiğit, N. (2017). Examining indicators of knowledge of light concept through peer tutoring applications. Hacettepe University Journal of Education, 32(2), 295-311. doi:10.16986/HUJE.2016019933
  • Mešić, V., Neumann, K., Aviani, I., Hasović, E., Boone, W. J., Erceg, N., Grubelnik, V., Sušac, A., Glamočić, D.S., Karuza, M., Vidak, A., Alihodžić, A., & Repnik, R. (2019). Measuring students’ conceptual understanding of wave optics: A Rasch modeling approach. Physical Review Physics Education Research, 15(1), 010115(20). doi:10.1103/physrevphyseducres.15.010115
  • Ministry of Education (MNE) (2018). Primary education institutions (primary and secondary schools) science course (3,4,5,6,7,8. Grades) curriculum. Ankara: Ministry of Education, Board of Education and Discipline
  • Othman, N. B., Salleh, S. M., Hussein, H., & Wahid, H. B. A. (2014). Assessing construct validity and reliability of competitiveness scale using Rasch model approach. In Proceedings of the 2014 WEI International Academic Conference (pp. 113-120)
  • Özcan, Ö., & Tavukçuoğlu, E. (2018). Investigating the high school students’ cognitive structures about the light concept through word association test. Journal of Education and Future, (13), 121-132. doi:10.1063/1.5025996
  • Planinic, M., Ivanjek, L., & Susac, A. (2010). Rasch model based analysis of the force concept inventory. Physical Review Special Topics-Physics Education Research, 6(1), 010103(20). doi:10.1103/physrevstper.6.010103
  • Planinic, M., Boone, W. J., Susac, A., & Ivanjek, L. (2019). Rasch analysis in physics education research: Why measurement matters. Physical Review Physics Education Research, 15(2), 1-14. doi:10.1103/physrevphyseducres.15.020111
  • Preece, P. F. W. (1979). Objective measurement in education: The Rasch model. School Science Review, 60(213), 770-73. ERIC ID. EJ207029
  • Rasch, G. (1961, July). On general laws and the meaning of measurement in psychology. In Proceedings of the fourth Berkeley symposium on mathematical statistics and probability. Berkeley, USA.
  • Siang, L. K. (2011). Developing a profile of conceptual understanding and misconceptions in Newtonian mechanics: Rasch modelling approach. In International Conference on Measurement and Evaluation in Education, University of Malaya. Kuala Lumpur, Malaysia.
  • Sondergeld, T. A., & Johnson, C. C. (2014). Using Rasch measurement for the development and use of affective assessments in science education research. Science Education, 98(4), 581-613. doi:10.1002/sce.21118
  • Şahin, Ç., İpek, H., & Ayas, A. (2008, June). Students' understanding of light concepts primary school: A cross-age study. Asia-Pacific Forum on Science learning and teaching, 9(1), 1-119. Retrieved from https://www.eduhk.hk/apfslt/
  • Şenel, S.Ö. (2016). Activities based on multiple intelligence theory related on learning and effects about the 7th grade subject light. (Unpublished master’s thesis). Gazi University, Ankara, Turkey
  • Taşlıdere, E. & Eryılmaz, A. (2015). Assessment of pre-service teachers’ misconceptions in geometrical optics via a three-tier misconception test. Bartın University Journal of Faculty of Education, 4 (1), 269-289. doi:10.14686/buefad.2015111057
  • Uzun, E. & Karaman İ. (2015). An analysis of prospective science teachers’ mental models about light and sound. Ekev Akademi Dergisi. 20(65), 141-154. doi:10.17753/Ekev549
  • Uzunsakal, E. & Yıldız, D. (2018). A comparison of reliability tests in field researches and an applicatıon on agricultural data. Applied Social Sciences Journal of Istanbul University-Cerrahpasa, 2(1), 14-28. Retrieved from https://dergipark.org.tr/en/pub/iuusbd/issue/38311/399621
  • Xiao, Y., Han, J., Koenig, K., Xiong, J., & Bao, L. (2018). Multilevel Rasch modeling of two-tier multiple choice test: A case study using Lawson’s classroom test of scientific reasoning. Physical Review Physics Education Research, 14(2), 020104(14). doi:10.1103/physrevphyseducres.14.020104
  • Wahyuningsih, S., Rusilowati, A., & Hindarto, N. (2017). Analysis of misconception to science literacy using three tier multiple choice test in the materials of characteristic of light. Unnes Science Education Journal, 6(3), 1736-1743. doi:10.15294/USEJ.V6I3.20426
  • Wei, S., Liu, X., & Jia, Y. (2014). Using Rasch measurement to validate the instrument of students’ understanding of models in science (sums). International Journal of Science and Mathematics Education, 12(5), 1067-1082. doi:10.1007/s10763-013-9459-z
  • Wei, S., Liu, X., Wang, Z., & Wang, X. (2012). Using Rasch measurement to develop a computer modeling-based instrument to assess students’ conceptual understanding of matter. Journal of Chemical Education, 89(3), 335-345. doi:10.1021/ed100852t
  • Yüksel, S. (2012). Analyzing differential item functioning by mixed rasch models which stated in scales. (Unpublished doctorate thesis). Institute of Health Education, Ankara University, Ankara, Turkey
  • Zain, J. M., Mohd, W. M. W., & El-Qawasmeh, E. (Eds.). (2011). Software engineering and computer systems. In Proceedings of Second International Conference, ICSECS 2011, Kuantan, Pahang, Malaysia
Toplam 70 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Alan Eğitimleri
Bölüm Research Articles
Yazarlar

Hüseyin Cihan Bozdağ Bu kişi benim 0000-0001-6735-7096

Suat Türkoğuz Bu kişi benim 0000-0002-7850-2305

Yayımlanma Tarihi 30 Haziran 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 10 Sayı: 1

Kaynak Göster

APA Bozdağ, H. C., & Türkoğuz, S. (2021). A RASCH MODEL ANALYSIS OF PRIMARY SCHOOL STUDENTS’ CONCEPTUAL UNDERSTANDING LEVELS OF THE CONCEPT OF LIGHT. International Online Journal of Primary Education, 10(1), 160-179.

 Creative Commons Licenses

mceclip0-43bf150298f9613a4c817c567db8d92d.png


All articles published in International Online Journal of Primary Education's content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


mceclip1.png          mceclip2.png        mceclip3.png


Free counters!


(Counter start: February 28, 2021)