Students' Cognitive Analysis using Rasch Modeling as an Assessment for Planning of Strategies in Chemistry Learning

Rusmansyah Rusmansyah, Almubarak Almubarak


Rasch modelling based on assessment can help teachers analyze the students' cognitive knowledge level and development. However, teachers are considered unsuccessful in teaching where the achievement of indicators in learning science, such as chemistry, is not holistically actualized. This study aimed to analyze High School students' knowledge in Banjarmasin City, especially on students' knowledge (cognitive aspect), using the Rasch modelling data analysis technique and exploring how chemical learning strategies are planned based on the symptoms of the data obtained. The data collection technique used a dichotomous format test technique (multiple choices). The research method used was descriptive with a quantitative approach to examine Rasch's various data, which was then interpreted qualitatively to describe the issues raised. The study results show that person reliability (students) based on Rasch modelling anal­ysis is +0.79, and item reliability is +0.98, where the value indicates that the consistency of the participant response pattern is "sufficient." Then, the mean person measure is -0.07, while the mean item is 0.00. It means that the participants' "mean value" is below the "mean value" of the item that the students' ability is below the item's ability. The Rasch data's recapitulation value showed that the response patterns of various data symptoms and those data were interpreted. It showed students' knowledge of atomic structure material was still considered low based on the Rasch model criteria. This is a reference for making appropriate chemical learning strategy plans to improve their knowledge. In conclusion, Rasch modelling-based assessment is effectively used in analyzing students' (cognitive) ability on atomic structure material. These results produce a strategic plan like what in chemistry learning such as the importance of conducting further diag­noses using misconception tests, identifying students' learning styles, constructing stu­dents' knowledge through the concept of chemical representation, and developing appropriate learning media according to their needs (students).


students' cognitive; rasch modeling; learning assessment; chemical learning

Full Text:



P. Johnson, “Una progresión de aprendizaje para la comprensión del cambio químico,” Educ. Quim., vol. 24, no. 4, pp. 365–372, 2013.

Google Scholar

M. P. Rabbitt, "Causal inference with latent variables from the Rasch model as outcomes," Meas. J. Int. Meas. Confed., vol. 120, no. February, pp. 193–205, 2018.

DOI: 10.1016/j.measurement.2018.01.044

J. Runnels, "Using the Rash model to validate a multiple-choice English achievement test," Int. J. Lang. Stud., vol. 6, no. 4, pp. 141–155, 2012.

Google Scholar

A. M. Talib, F. O. Alomary, and H. F. Alwadi, "Assessment of Student Performance for Course Examination Using Rasch Measurement Model: A Case Study of Information Technology Fundamentals Course," Educ. Res. Int., vol. 2018, pp. 1–8, 2018.


B. Sumintono and W. Widhiarso, Aplikasi Pemodelan Rasch Pada Assessment Pendidikan. Cimahi: Penerbit Trim Komunikata, 2015.

Google Scholar

J. de la Torre, "A Cognitive Diagnosis Model for Cognitively Based Multiple-Choice Options," Appl. Psychol. Meas., vol. 33, no. 3, pp. 163–183, 2009.


C. F. Herrmann-Abell and G. E. DeBoer, "Using distractor-driven standards-based multiple-choice assessments and Rasch modelling to investigate hierarchies of chemistry misconceptions and detect structural problems with individual items," Chem. Educ. Res. Pract., vol. 12, no. 2, pp. 184–192, 2011.

DOI: 10.1039/C1RP90023D

J. Creswell, Research Design: Qualita-tive, Quantitative, and Mixed Methods Approaches, 3rd ed. California, USA: SAGE Publications, Inc, 2009.

ISBN: 9781506386706

S. W. Chan, Z. Ismail, and B. Sumintono, "A Rasch Model Analysis on Secondary Students' Statistical Reasoning Ability in Descriptive Statistics," Procedia - Soc. Behav. Sci., vol. 129, pp. 133–139, 2014.

DOI: 10.1016/j.sbspro.2014.03.658

B. Sumintono, "Rasch Model Measure-ments as Tools in Assessment for Learning," no. October 2017, 2018.

Google Scholar

R. R. Melati and D. Kurniawati, Bank Soal Kimia SMA/MA. Surakarta: PT Aksarra Sinergi Media, 2012.

Google Scholar

J. K. Gilbert and D. F. Treagust, "Introduction: Macro, Submicro, and Symbolic Representations and the Relationship Between Them: Key Models in Chemical Education," in Multiple Representations in Chemical Education, Models and Modeling in Science Education, Berlin, Heidelberg: Springer Science + Business, 2009, pp. 1–8.

DOI: 10.1007/978-1-4020-8872-8_1

M. Walpuski, M. Ropohl, and E. Sumfleth, "Students' knowledge about chemical reactions - development and analysis of standard-based test items," Chem. Educ. Res. Pract., vol. 12, no. 2, pp. 174–183, 2011.

DOI: 10.1039/C1RP90022F

X. Liu, "Difficulties of items related to energy and matter: Implications for learning progression in high school chemistry," Educ. Química, vol. 24, no. 4, pp. 416–422, 2015.

DOI: 10.1016/S0187-893X(13)72495-9

M. Park, X. Liu, and N. Waight, "Development of the Connected Chemistry as Formative Assessment Pedagogy for High School Chemistry Teaching," J. Chem. Educ., vol. 94, no. 3, pp. 273–281, 2017.

DOI: 10.1021/acs.jchemed.6b00299

T. P. Yamato, C. G. Maher, B. T. Saragiotto, M. J. Catley, and A. M. Moseley, "Rasch analysis suggested that items from the template for intervention description and replication (TIDieR) checklist can be summed to create a score," J. Clin. Epidemiol., vol. 101, pp. 28–34, 2018.

DOI: 10.1016/j.jclinepi.2018.05.014

H. S. Lee, O. L. Liu, and M. C. Linn, "Validating measurement of knowledge integration in science using multiple-choice and explanation items," Appl. Meas. Educ., vol. 24, no. 2, pp. 115–136, 2011.

DOI: 10.1080/08957347.2011.554604

A. G. Murray and B. F. Mills, "An application of dichotomous and polytomous Rasch models for scoring energy insecurity," Energy Policy, vol. 51, pp. 946–956, 2012.

DOI: 10.1016/j.enpol.2012.09.070

R. M. Yasin, R. C. Rus, A. Ahmad, M. B. Rahim, and F. A. N. Yunus, "Validity and Reliability Learning Transfer Item Using Rasch Measurement Model," Procedia - Soc. Behav. Sci., vol. 204, no. November 2014, pp. 212–217, 2015.

DOI: 10.1016/j.sbspro.2015.08.143

W. J. Boone, "Rasch analysis for instrument development: Why, when, and how?," CBE Life Sci. Educ., vol. 15, no. 4, 2016.

DOI: 10.1187/cbe.16-04-0148

S. Wei, X. Liu, Z. Wang, and X. Wang, "Using Rasch measurement to develop a computer modelling-based instrument to assess students' conceptual understanding of matter," J. Chem. Educ., vol. 89, no. 3, pp. 335–345, 2012.

DOI: 10.1021/ed100852t

H. D. Barke, A. Hazari, and S. Yitbarek, Misconceptions in Chemistry (Addressing Perceptions in Chemical Education). Berlin, Heidelberg: Sense Publisher, 2009.

DOI: 10.1007/978-3-540-70989-3

Ö. Ö. Oskay, E. Erdem, B. Akkoyunlu, and A. Yolmaz, "Prospective chemistry teachers' learning styles and learning preferences," in WCES-2010, 2010, vol. 2, no. 2, pp. 1362–1367.


J. Mezirow, Transformative Dimen-sions of Adult Learning. San Francisco: Jossey-Bass, 1991.

ISBN: 978-1-555-42339-1

S. Şener and A. Çokçalışkan, "An Investigation between Multiple Intelligences and Learning Styles," J. Educ. Train. Stud., vol. 6, no. 2, p. 125, 2018.

DOI: 10.11114/jets.v6i2.2643

D. F. Donnelly and A. Hume, "Using collaborative technology to enhance pre-service teachers' pedagogical content knowledge in Science," Res. Sci. Technol. Educ., vol. 33, no. 1, pp. 61–87, 2015.


B. Ekiz, A. Tarkin, O. Bektas, M. Tuysuz, E. S. Kutucu, and E. Uzuntiryaki, "Pre-service chemistry teachers' understanding of phase changes and dissolution at macros-copic, symbolic, and microscopic levels," Procedia - Soc. Behav. Sci., vol. 15, pp. 452–455, 2011.


P. Nilsson and G. Karlsson, "Cap-turing student teachers' pedagogical content knowledge (PCK) using CoRes and digital technology," Int. J. Sci. Educ., vol. 41, no. 4, pp. 419–447, 2019.


B. Yakmaci-Guzel and E. Adadan, "Use of multiple representations in developing preservice chemistry teachers' under-standing of the structure of matter," Int. J. Environ. Sci. Educ., vol. 8, no. 1, pp. 109–130, 2013.

Google Scholar

V. C. Santos and A. Arroio, "The representational levels: Influences and contributions to research in chemical education," J. Turkish Sci. Educ., vol. 13, no. 1, pp. 3–18, 2016.


D. Milenkovic, D, M. Segedinac, D, and T. Hrin, N, "Increasing High School Students' Chemistry Perfor-mance and Reducing Cognitive Load through an Instructional Strategy Based on the Interaction of Multiple Levels of Knowledge Represen-tation," J. Chem. Educ., vol. 91, no. 9, pp. 1409–1416, 2014.


D. F. Treagust, G. Chittleborough, and T. L. Mamiala, "The role of submicroscopic and symbolic repre-sentations in chemical explanations," Int. J. Sci. Educ., vol. 25, no. 11, pp. 1353–1368, 2003.


H. Barke, G. Harsch, and S. Schmid, Essentials of Chemical Education. Verlag Berlin Heidelberg: springer, 2012.


D. Insyasiska, S. Zubaidah, and H. Susilo, “Pengaruh Project Based Learning Terhadap Motivasi Belajar, Kreatifitas, Kemampuan Berpikir Kritis, dan Kemamppuan Kognitif Siswa Pada Pembelajaran Biologi,” J. Pendidik. Biol., vol. 7, no. 1, pp. 9–21, 2015.


I. R. Lubis and J. Ikhsan, “Pengem-bangan Media Pembelajaran Kimia Berbasis Android Untuk Meningkat-kan Motivasi Belajar Dan Prestasi Kognitif Peserta Didik Sma,” J. Inov. Pendidik. IPA, vol. 1, no. 2, p. 191, 2015.


N. Supriono and F. Rozi, “Pengem-bangan Media Pembelajaran Bentuk Molekul Kimia Menggunakan Augmen-ted Reality Berbasis Android,” JIPI (Jurnal Ilm. Penelit. dan Pembela-jaran Inform., vol. 3, no. 1, pp. 53–61, 2018.


D. Lattisma, W. Kurniawan, S. Seprima, E. Nirbayani, E. Ellizar, and H. Hardeli, "Effect of Chemistry Triangle Oriented Learning Media on Cooperative, Individual and Conventional Method on Chemistry Learning Result Effect of Chemistry Triangle Oriented Learning Media on Cooperative, Individual and Conventional Method on Chemistry Lea," in IOP Conf. Series: Materials Science and Engineering (ICOMSET), 2018, pp. 1–7.



  • There are currently no refbacks.