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

Abstract

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.

Keywords: Conceptual understanding, primary school, four-tier diagnostic test, light unit, Rasch model.

REFERENCES

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 (Unpublished master’s thesis).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 (2^nd 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 4^th 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