Assessing Students Understanding of Chemical Bonds Material by Rasch Modeling

Almubarak Almubarak(1*), Parham Saadi(2), Restu Prayogi(3), Pamela Paula Maldini(4)

(1) Faculty of Teacher Training and Education, Universitas Lambung Mangkurat
(2) Faculty of Teacher Training and Education, Universitas Lambung Mangkurat
(3) Faculty of Teacher Training and Education, Universitas Lambung Mangkurat
(4) Faculty of Management and Business, Tampere University of Applied Sciences
(*) Corresponding Author

Abstract

This study aimed to assess high school student’s understanding of Banjarmasin by Rasch modeling, preci-sely the cognitive aspect. The research method was descriptive with a quantitative approach to assess the pattern of reactions and symptoms of Rasch data. According to the research findings, the person reliability (students) was +0.66 based on the Rasch modeling analysis, with the criterion satisfactory, indicating that the students knowledge was adequate. At the same time, Cronbachs’ alpha score had a value of +0.71 and met the criteria of excellent. In addition, the students with the highest level of understanding were coded 127P12B with a logit value (person measure) of 2.52 and average students with logit value of -0.77 or <0. In contrast, the students with low abilities were coded 030P10B and 059L11B (same logit value, -3.27 or <0). Other data were INFIT MNSQ and OUTFIT MNSQ (person) with average values of +0.99 and +1.14 (closer to 1, the better), while the INFIT and OUTFIT ZSTD values were -0.1 and 0.0 respectively (closer to 0, better). The most difficult question was Q16, which had a logit score of 1.96; students’ logit values carried this question. Students were regarded to have appropriate knowledge even though their ability excee-ded the problem ability. In conclusion, the Rasch model-based pre-learning evaluation was found to be useful in measuring students’ cognitive grasp of chemical bonding material. This study could serve as the primary reference for teachers in assessing students’ level of knowledge before they begin learning. In addi-tion to interpreting student knowledge through various Rasch data presentations, a study of the structure of questions with varying difficulty levels could be used to assess the full group of students’ understanding of chemistry.

Keywords

cognitive aspect; learning progress; pre-learning evaluation; rasch modeling

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References

Adimayuda, R., Aminudin, A. H., Kaniawati, I., Suhendi, E., & Samsudin, A. (2020). A Multitier Open-Ended Momentum and Impulse (MOMI) Instrument: Developing and Assessing Quality of Conception of 11th Grade Sundanese Students with Rasch Analysis. International Journal of Scientific and Technology Research, 9(2), 4799–4804.

Andrich, D. (2010). Educational Measurement: Rasch Models. International Encyclopedia of Education, 111–122.

Andrich, D., & Pedler, P. (2019). A Law of Ordinal Random Error: The Rasch Measurement Model and Random Error Distributions of Ordinal Assessments. Measurement: Journal of the International Measurement Confederation, 131, 771–781. https://doi.org/10.1016/j.measurement.2018.08.062

Barke, H.-D., Harsch, G., & Schmid, S. (2012). Essentials of Chemical Education. In Angewandte Chemie International Edition, 6(11), 951–952. Springer.

Barke, H.-D., Hazari, A., & Yitbarek, S. (2009). Misconceptions in Chemistry (Addresing Perceptions in Chemical Education). Sense Publisher. https://doi.org/10.1007/978-3-540-70989-3_2

Boone, W. J. (2016). Rasch Analysis For Instrument Development: Why,When,And How? CBE Life Sciences Education, 15(4). https://doi.org/10.1187/cbe.16-04-0148

Bouw, E., Zitter, I., & de Bruijn, E. (2021). Designable Elements Of Integrative Learning Environments At The Boundary Of School And Work: A Multiple Case Study. In Learning Environments Research (Vol. 24, Issue 3). Springer Netherlands. https://doi.org/10.1007/s10984-020-09338-7

Campbell, M. L. (2015). Multiple-Choice Exams and Guessing: Results from a One-Year Study of General Chemistry Tests Designed to Discourage Guessing. Journal of Chemical Education, 92(7), 1194–1200. https://doi.org/10.1021/ed500465q

Chan, S., Looi, C., & Sumintono, B. (2020). A Rasch Model Measurement Analysis. Journal of Computers in Education, 8(2), 213–236.

Cheng, M., & Gilbert, J. K. (2009). Introduction: Macro, Submicro and Symbolic Representations and the Relationship Between Them: Key Models in Chemical Education. In J. K. Gilbert & D. Treagust (Eds.), Multiple Representations in Chemical Education: Models and Modeling in Science Education (p. 369). Springer.

Chetty, N. D. S., Handayani, L., Sahabudin, N. A., Ali, Z., Hamzah, N., Rahman, N. S. A., & Kasim, S. (2019). Learning Styles And Teaching Styles Determine Students’ Academic Performances. International Journal of Evaluation and Research in Education, 8(4), 610–615. https://doi.org/10.11591/ijere.v8i3. 20345

Chiang, W.-W. (2015). Ninth Grade Student’ Self-assessment in Science: A Rasch Analysis Approach. Procedia - Social and Behavioral Sciences, 176, 200–210. https://doi.org/10.1016/j.sbspro.2015.01.462

Chow, J., Tse, A., & Armatas, C. (2018). Comparing Trained and Untrained Teachers on Their Use of LMS Tools Using The Rasch Analysis. Computers and Education, 123, 124–137. https://doi.org/10.1016/j.compedu.2018.04.009

Creswell, J. (2009). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (3rd ed.). SAGE Publications, Inc.

Darmiyanti, W., Rahmawati, Y., Kurniadewi, F., & Ridwan, A. (2017). Analisis Model Mental Siswa Dalam Penerapan Model Pembelajaran Learning Cycle 8E Pada Materi Hidrolisis Garam. JRPK: Jurnal Riset Pendidikan Kimia, 7(1), 38–51. https://doi.org/10.21009/jrpk.071.06

Dewi, R. K., Wardani, S., Wijayati, N., & Sumarni, W. (2019). Demand of ICT-Based Chemistry Learning Media In The Disruptive Era. International Journal of Evaluation and Research in Education, 8(2), 265–270. https://doi.org/10.11591/ijere.v8i2.17107

Goldstein, H. (1979). Consequences of Using the Rasch Model for Educational Assessment. British Educational Research Journal, 5(2), 211–220. https://doi.org/10.1080/0141192790050207

Grove, N., & Bretz, S. L. (2007). CHEMX: An Instrument To Assess Students’ Cognitive Expectations For Learning Chemistry. Journal of Chemical Education, 84(9), 1524–1529. https://doi.org/10.1021/ed084p1524

Gumartifa, A., Syahri, I., Siroj, R. A., Nurrahmi, M., & Yusof, N. (2023). Perception of Teachers Regarding Problem-Based Learning and Traditional Method in the Classroom Learning Innovation Process. Indonesian Journal on Learning and Advanced Education (IJOLAE), 5(2), 151–166.

Justi, R., Gilbert, J. K., & Ferreira, P. F. . (2009). The Application of a ‘Model of Modelling’ to Illustrate the Importance of Metavisualisation in Respect of the Three Types of Representation. In D. Treagust & J. K. Gilbert (Eds.), Multiple Representations in Chemical Education, Models and Modeling in Science Education. Springer.

Mamat, M. N., Maidin, P., & Mokhtar, F. (2014). Simplified Reliable Procedure for Producing Accurate Student’s Ability Grade Using Rasch Model. Procedia - Social and Behavioral Sciences, 112(Iceepsy 2013), 1077–1082. https://doi.org/10.1016/j.sbspro.2014.01.1272

Masito, F., Oka, I. G. A. A. M., Cahyadi, C. I., Komalasari, Y., Anes, A. M., & Risdianto, E. (2022). Scientific Argumentation Skills Through The Rasch Model on Analysis of Survey Data on The Importance of Aviation Vocational Education in Indonesia. Journal of Innovation in Educational and Cultural Research, 3(3), 487–498. https://doi.org/10.46843/jiecr.v3i3.178

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), 1–20. https://doi.org/10.1103/PhysRevPhysEducRes.15.010115

Mezirow, J. (1997). Transformative Learning: Theory to Practice. New Directions for Adult and Continuing Education, 1997(74), 5–12. https://doi.org/10.1002/ace.7401

Mezirow, J., Cottafava, D., Cavaglià, G., Corazza, L., Raikou, N., Chu, S. Y., Garcia, S., Schnitzler, T., Pappamihiel, N. E., Moreno, M., Mensah, F. M., Vatalaro, A., Szente, J., Levin, J., Buechner, B., Dirkx, J., Konvisser, Z. D., Myers, D., Peleg-Baker, T., … Wikan, G. (2019). Culturally Responsive Teaching Efficacy Beliefs Of In-Service Special Education Teachers. Journal of Hispanic Higher Education, 10(2), 993–1013. https://doi.org/10.1108/IJSHE-05-2019-0168

Noben, I., Maulana, R., Deinum, J. F., & Hofman, W. H. A. (2021). Measuring University Teachers’ Teaching Quality: A Rasch Modelling Approach. Learning Environments Research, 24(1), 87–107. https://doi.org/10.1007/s10984-020-09319-w

Quinlan, D., Vella-Brodrick, D. A., Gray, A., & Swain, N. (2019). Teachers Matter: Student Outcomes Following a Strengths Intervention are Mediated by Teacher Strengths Spotting. Journal of Happiness Studies, 20(8), 2507–2523. https://doi.org/10.1007/s10902-018-0051-7

Rabbitt, M. P. (2018). Causal Inference With Latent Variables From The Rasch Model As Outcomes. Measurement: Journal of the International Measurement Confederation, 120(January), 193–205. https://doi.org/10.1016/j.measurement.2018.01.044

Rauch, D. P., & Hartig, J. (2010). Multiple-Choice Versus Open-Ended Response Formats of Reading Test Items: A Two-Dimensional IRT Analysis. Psychological Test and Assessment Modeling, 52(4), 354–379.

Rizbudiani, A. D., Jaedun, A., Rahim, A., & Nurrahman, A. (2021). Rasch Model Item Response Theory (IRT) To Analyze The Quality of Mathematics Final Semester Exam Test on System of Linear Equations In Two Variables (SLETV). Al-Jabar : Jurnal Pendidikan Matematika, 12(2), 399–412. https://doi.org/10.24042/ajpm.v12i2.9939

Runnels, J. (2012). Using The Rash Model to Validate A Multiple Choice English Achievement Test. International Journal of Language Studies, 6(4), 141–155.

Rusmansyah, Almubarak, Hamid, A., & Analita, R. N. (2021). Analyze Mental Model of Prospective Chemistry Teachers With Chemical Representation Teaching Material Based on 8E Cycle Learning Model. AIP Conference Proceedings, 2331, 0–7. https://doi.org/10.1063/5.0041732

Ryan, S., & Herrington, D. G. (2014). Sticky Ions : A Student-Centered Activity Using Magnetic Models to Explore the Dissolving of Ionic Compounds. Journal of Chemical Education, 91, 860–863.

Setyaningsih, E., Agustina, P., Anif, S., Ahmad, C. N. C., Sofyan, I., Saputra, A., Salleh, W. N. W. M., Shodiq, D. E., Rahayu, S., & Hidayat, M. L. (2022). PBL-STEM Modul Feasibility Test for Preservice Biology Teacher. Indonesian Journal on Learning and Advanced Education (IJOLAE), 4(2), 118–127. https://doi.org/10.23917/ijolae.v4i2.15980

Sihombing, R. U., Naga, D. S., & Rahayu, W. (2018). a Rasch Model Measurement Analysis on Science Literacy Test of Indonesian Students: Smart Way to Improve the Learning Assessment. Indonesian Journal of Educational Review, 6(1), 44–55.

Sulistyanto, H., Anif, S., Sutama, S., Narimo, S., Sutopo, A., Haq, M. I., & Nasir, G. A. (2022). Education Application Testing Perspective to Empower Students’ Higher Order Thinking Skills Related to The Concept of Adaptive Learning Media. Indonesian Journal on Learning and Advanced Education (IJOLAE), 4(3), 257–271.

Sumintono, B. (2018a). Rasch Model Measurements as Tools in Assesment for Learning. 173(Icei 2017), 38–42. https://doi.org/10.2991/icei-17.2018.11

Sumintono, B. (2018b). Rasch Model Measurements as Tools in Assesment for Learning. October 2017. https://doi.org/10.2991/icei-17.2018.11

Sumintono, B., & Widhiarso, W. (2015). Aplikasi Pemodelan Rasch Pada Assessment Pendidikan. Penerbit Trim Komunikata.

Supriyanto, S., Munadi, S., Daryono, R. W., Tuah, Y. A. E., Nurtanto, M., & Arifah, S. (2022). The Influence of Internship Experience and Work Motivation on Work Readiness in Vocational Students: PLS-SEM Analysis. Indonesian Journal on Learning and Advanced Education (IJOLAE), 5(1), 32–44. https://doi.org/10.23917/ijolae.v5i1.20033

Talib, A. M., Alomary, F. O., & Alwadi, H. F. (2018). Assessment of Student Performance for Course Examination Using Rasch Measurement Model: A Case Study of Information Technology Fundamentals Course. Education Research International, 2018. https://doi.org/10.1155/2018/8719012

Valtonen, T., Leppänen, U., Hyypiä, M., Kokko, A., Manninen, J., Vartiainen, H., Sointu, E., & Hirsto, L. (2021). Learning Environments Preferred By University Students: A Shift Toward Informal and Flexible Learning Environments. Learning Environments Research, 24(3), 371–388. https://doi.org/10.1007/s10984-020-09339-6

van de Grift, W. J. C. M., Houtveen, T. A. M., van den Hurk, H. T. G., & Terpstra, O. (2019). Measuring Teaching Skills In Elementary Education Using The Rasch Model. School Effectiveness and School Improvement, 30(4), 455–486. https://doi.org/10.1080/09243453.2019.1577743

van der Lans, R. M., van de Grift, W. J. C. M., & van Veen, K. (2018). Developing an Instrument for Teacher Feedback: Using the Rasch Model to Explore Teachers’ Development of Effective Teaching Strategies and Behaviors. Journal of Experimental Education, 86(2), 247–264. https://doi.org/10.1080/00220973.2016.1268086

Wikipedia. (2023). Nitrogen. Wikimedia.

Winarti, A., & Almubarak. (2019). Rasch Modeling: A Multiple Choice Chemistry Test. Indonesian Journal on Learning and Advanced Education (IJOLAE), 2(1), 1–9. https://doi.org/10.23917/ijolae.v2i1.8985

Winarti, A., Almubarak, & Annurc, S. (2019). How Does The Rasch Model Justify Multiple Choice Question Items As A Measure of Student Understanding Of Acid-Base Material At The Sub-Microscopic Level? International Journal of Innovation, Creativity and Change, 7(11), 344–360.

Yessi, M. (2021). Analisis Literasi Digital Peserta Didik Melalui Pemanfaatan Media Pembelajaran Berbasis Android Smart Apps Creator (SAC) Dan Instagram Dalam Pembelajaran Koloid. Jurnal Riset Pendidikan Kimia, 7(1), 38–51.

Yudha, R. P. (2023). Higher Order Thinking Skills (HOTS) Test Instrument: Validity and Reliability Analysis With The Rasch Model. Eduma : Mathematics Education Learning and Teaching, 12(1), 21. https://doi.org/10.24235/eduma.v12i1.9468

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