Electromotive Forces on Multitier Instruments (EFoMI): Development to measure student misconception with Rasch
Abstract
This research aims to develop Electromotive Forces on Multitier Instruments (EFoMI) in measuring student misconceptions. Method: This development research uses 4D design. Participants in this research were 19 students (10 Females and 9 Males) in one of the Bandung junior high schools. The instruments used are those developed in this research. The Rasch analysis was used to identify the feasibility of EFoMI using WINSTEPS 4.4.5 software which had previously been included in the conceptual categories of Sound Understanding (SU), Partial Positive (PP), Partial Negative (PN), No Understanding (NU), Misconception (MC), and No Coding (NC). Meanwhile, VOSviewer analysis was carried out to look for research trends related to misconceptions. Results: The results show the construct validity and fit statistics which meet all criteria. the reliability of EFoMI, namely 0.82, is in the Very Good category. The difficulty level of EFoMI is evenly distributed, the discrimination in the Very Good category, and no gender bias was detected from EFoMI. The distribution of students' conceptions includes SU= 12%, PP= 4%, PN= 45%, NU= 7%, MC= 27%, and NC=4%. These results have several implications in the field of education. An example is EFoMI's success in identifying student misconceptions, thus it can be an alternative for teachers in developing or identifying student misconceptions. Then, by knowing students' initial conceptions, teachers can develop learning tools that are more focused on student misconceptions and based on data. Likewise, to develop or use teaching materials more effectively by knowing the areas where students often experience misconceptions.
References
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Chan, S. W., Ismail, Z., & Sumintono, B. (2014). A Rasch Model Analysis on Secondary Students’ Statistical Reasoning Ability in Descriptive Statistics. Procedia - Social and Behavioral Sciences, 129, 133–139. https://doi.org/10.1016/j.sbspro.2014.03.658
Deti, R., & Mandasari, V. (2021). A Bibliometric Analysis of E-Learning Research Trends. International Journal of Theory and Application in Elementary and Secondary School Education, 3(1), 74–81. https://doi.org/10.31098/ijtaese.v3i1.518
Garzón, I., De Cock, M., Zuza, K., van Kampen, P., & Guisasola, J. (2014). Probing university students’ understanding of electromotive force in electricity. American Journal of Physics, 82(1), 72–79. https://doi.org/10.1119/1.4833637
Hatami, A. M., Sabour, M. R., Haj Babaei, M. R., & Nematollahi, H. (2021). Global Trends of VOSviewer Research, Emphasizing Environment and Energy Areas: A Bibliometric Analysis During 2000-2020. Environmental Energy and Economic Research, 6(1), 1–11. https://doi.org/10.22097/EEER.2021.301784.1216
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Idulfilastri, R. M., Sari, M. P., & Sutanto, C. (2021). Validation of Cognitive Dimension of Managerial Aptitude Test: Rasch Model Analysis. Proceedings of the International Conference on Economics, Business, Social, and Humanities (ICEBSH 2021), 570(Icebsh), 45–52. https://doi.org/10.2991/assehr.k.210805.007
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Kaniawati, I., Fratiwi, N. J., Danawan, A., Suyana, I., Samsudin, A., & Suhendi, E. (2019). Analyzing students’ misconceptions about Newton’s laws through four-tier Newtonian test ( FTNT ). Journal of Turkish Science Education, 16(1), 110–122. https://doi.org/10.12973/tused.10269a
Lee, W. L., Chinna, K., & Sumintono, B. (2021). Psychometrics assessment of HeartQoL questionnaire: A Rasch analysis. European Journal of Preventive Cardiology, 28(12), E1–E5. https://doi.org/10.1177/2047487320902322
Mokshein, S. E., Ishak, H., & Ahmad, H. (2019). The use of rasch measurement model in English testing. Cakrawala Pendidikan, 38(1), 16–32. https://doi.org/10.21831/cp.v38i1.22750
Muhammad, I., Angraini, L. M., Darmayanti, R., Sugianto, R., & Usmiyatun. (2023). Students ’ Interest in Learning Mathematics Using Augmented Reality : Rasch Model Analysis. Edutechnium, 1(1), 89–99.
Noftiana, N., Nasir, M., & Islami, N. (2019). Developmental Scratch-Based Online Learning Media in Dynamic Electric Dynamic Topic to Increase Students Concept Understanding in Students Junior High School. Journal of Physics: Conference Series, 1351(1), 0–5. https://doi.org/10.1088/1742-6596/1351/1/012014
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). https://doi.org/10.1103/PhysRevPhysEducRes.15.020111
Rodrigues, L. D. S., Andrade, J. De, & Gasparotto, L. H. S. (2018). Electromotive Force versus Electrical Potential Difference: Approaching (but Not Yet at) Equilibrium [Research-article]. Journal of Chemical Education, 95(10), 1811–1815. https://doi.org/10.1021/acs.jchemed.8b00249
Royal, K. D., Gilliland, K. O., & Kernick, E. T. (2014). Using rasch measurement to score, evaluate, and improve examinations in an anatomy course. Anatomical Sciences Education, 7(6), 450–460. https://doi.org/10.1002/ase.1436
Samsudin, A., Aminudin, A. H., Novia, H., & Suhandi, A. (2023). Identifying Javanese Students ’ Conceptions on Fluid Pressure with Wright Map Analysis of Rasch. Journal of Natural Science and Integration, 6(2), 173–185. https://doi.org/10.24014/jnsi.v6i2.21822
Samsudin, A., Cahyani, P. B., Purwanto, Rusdiana, D., Efendi, R., Aminudin, A. H., & Coştu, B. (2021). Development of a multitier open-ended work and energy instrument (MOWEI) using Rasch analysis to identify students’ misconceptions. Cypriot Journal of Educational Sciences, 16(1), 16–31. https://doi.org/10.18844/cjes.v16i1.5504
Samsudin, A., Novia, H., Suhandi, A., Aminudin, A. H., Yusup, M., Supriyatman, S., Masrifah, M., Permana, N. D., & Costu, B. (2023). Cybergogy Trends in Cognitive Psychology of Physics Learning : A Systematic Literature Review from 2019-2023 with NVivo. Jurnal Pendidikan Fisika Dan Keilmuan (JPFK), 9(2). https://doi.org/10.25273/jpfk.v9i2.17257
Sari, Y., Irawati, R. K., & Rahmawati, H. (2023). Practicality of Web-Based Four-Tier Test to Identify Student’s Misconceptions in Chemical Bonding Materials. JPPS (Jurnal Penelitian Pendidikan Sains), 12(2), 108–121. https://doi.org/10.26740/jpps.v12n2.p108-121
Sarwono, Suhandi, A., Wiendartun, & Samsudin, A. (2022). Remediate Senior High School Students’ Misconception Regarding the Runs Out Battery Concept Using AS-CBRText. AIP Conference Proceedings, 2468(December). https://doi.org/10.1063/5.0131699
Soeharto, S., & Csapó, B. (2021). Evaluating item difficulty patterns for assessing student misconceptions in science across physics, chemistry, and biology concepts. Heliyon, 7(11). https://doi.org/10.1016/j.heliyon.2021.e08352
Suhandi, A., Hermita, N., Samsudin, A., Maftuh, B., & Coştu, B. (2017). Effectiveness of visual multimedia supported conceptual change texts on overcoming students’ misconception about boiling concept. Turkish Online Journal of Educational Technology, 2017(October Special Issue INTE), 1012–1022.
Waqar, I., Azizi, Z., Nikmal, P., Rafi, R., Ulfat, W., Abid, O., Niazi, M. J., & Khan, Z. (2023). Comparison between Electromotive Force and Electric Potential Difference. Turkish Journal of Computer and Mathematics Education, 14(01), 236–242.
Yulianto, A., & Widodo, A. (2020). Disclosure of Difficulty Distribution of HOTS-Based Test Questions through Rasch Modeling. Indonesian Journal of Primary Education, 4(2), 197–203. https://doi.org/10.17509/ijpe.v4i2.29318
Zuza, K., De Cock, M., Van Kampen, P., Bollen, L., & Guisasola, J. (2016). University Students’ Understanding of the Electromotive Force Concept in the Context of Electromagnetic Induction. European Journal of Physics, 37(6). https://doi.org/10.1088/0143-0807/37/6/065709
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Aminudin, A. H., Kaniawati, I., Suhendi, E., Samsudin, A., Coştu, B., & Adimayuda, R. (2019). Rasch Analysis of Multitier Open-ended Light-Wave Instrument (MOLWI): Developing and Assessing Second-Years Sundanese-Scholars Alternative Conceptions. Journal for the Education of Gifted Young Scientists, 7(3), 607–629. https://doi.org/10.17478/jegys.574524
Aripiani, S. K., Samsudin, A., Kaniawati, I., Novia, H., Aminudin, A. H., Sutrisno, A. D., & Coştu, B. (2023). Diagnostic Instruments of Four-Tier Test Work and Energy (FORTUNE) to Identify The Level of Students’ Conceptions. Tadris: Jurnal Keguruan Dan Ilmu Tarbiyah, 8(1), 19–32. https://doi.org/10.24042/tadris.v8i1.13524
Bakri, F., Sumardani, D., & Muliyati, D. (2019). The augmented reality application for simulating electromotive force concept. Journal of Physics: Conference Series, 1402(6). https://doi.org/10.1088/1742-6596/1402/6/066039
Chan, S. W., Ismail, Z., & Sumintono, B. (2014). A Rasch Model Analysis on Secondary Students’ Statistical Reasoning Ability in Descriptive Statistics. Procedia - Social and Behavioral Sciences, 129, 133–139. https://doi.org/10.1016/j.sbspro.2014.03.658
Deti, R., & Mandasari, V. (2021). A Bibliometric Analysis of E-Learning Research Trends. International Journal of Theory and Application in Elementary and Secondary School Education, 3(1), 74–81. https://doi.org/10.31098/ijtaese.v3i1.518
Garzón, I., De Cock, M., Zuza, K., van Kampen, P., & Guisasola, J. (2014). Probing university students’ understanding of electromotive force in electricity. American Journal of Physics, 82(1), 72–79. https://doi.org/10.1119/1.4833637
Hatami, A. M., Sabour, M. R., Haj Babaei, M. R., & Nematollahi, H. (2021). Global Trends of VOSviewer Research, Emphasizing Environment and Energy Areas: A Bibliometric Analysis During 2000-2020. Environmental Energy and Economic Research, 6(1), 1–11. https://doi.org/10.22097/EEER.2021.301784.1216
Ho, T., Toh, D., & Ricardo, B. (2022). Electromotive force (emf) for the confused. European Journal of Physics, 43(1), 15202. https://doi.org/10.1088/1361-6404/ac3474
Idulfilastri, R. M., Sari, M. P., & Sutanto, C. (2021). Validation of Cognitive Dimension of Managerial Aptitude Test: Rasch Model Analysis. Proceedings of the International Conference on Economics, Business, Social, and Humanities (ICEBSH 2021), 570(Icebsh), 45–52. https://doi.org/10.2991/assehr.k.210805.007
Istiyono, E., Dwandaru, W. S. B., Setiawan, R., & Megawati, I. (2020). Developing of computerized adaptive testing to measure physics higher order thinking skills of senior high school students and its feasibility of use. European Journal of Educational Research, 9(1), 91–101. https://doi.org/10.12973/eu-jer.9.1.91
Jayanti, P., & Rahayu, Y. S. (2019). Comparative study: Misconceptions on photosynthesis and respiration concepts from past to the present. JPPS (Jurnal Penelitian Pendidikan Sains), 9(1), 1750–1755.
Kaltakci-Gurel, D., Eryilmaz, A., & McDermott, L. C. (2017). Development and application of a four-tier test to assess pre-service physics teachers’ misconceptions about geometrical optics. Research in Science and Technological Education, 35(2), 238–260. https://doi.org/10.1080/02635143.2017.1310094
Kaniawati, I., Fratiwi, N. J., Danawan, A., Suyana, I., Samsudin, A., & Suhendi, E. (2019). Analyzing students’ misconceptions about Newton’s laws through four-tier Newtonian test ( FTNT ). Journal of Turkish Science Education, 16(1), 110–122. https://doi.org/10.12973/tused.10269a
Lee, W. L., Chinna, K., & Sumintono, B. (2021). Psychometrics assessment of HeartQoL questionnaire: A Rasch analysis. European Journal of Preventive Cardiology, 28(12), E1–E5. https://doi.org/10.1177/2047487320902322
Mokshein, S. E., Ishak, H., & Ahmad, H. (2019). The use of rasch measurement model in English testing. Cakrawala Pendidikan, 38(1), 16–32. https://doi.org/10.21831/cp.v38i1.22750
Muhammad, I., Angraini, L. M., Darmayanti, R., Sugianto, R., & Usmiyatun. (2023). Students ’ Interest in Learning Mathematics Using Augmented Reality : Rasch Model Analysis. Edutechnium, 1(1), 89–99.
Noftiana, N., Nasir, M., & Islami, N. (2019). Developmental Scratch-Based Online Learning Media in Dynamic Electric Dynamic Topic to Increase Students Concept Understanding in Students Junior High School. Journal of Physics: Conference Series, 1351(1), 0–5. https://doi.org/10.1088/1742-6596/1351/1/012014
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). https://doi.org/10.1103/PhysRevPhysEducRes.15.020111
Rodrigues, L. D. S., Andrade, J. De, & Gasparotto, L. H. S. (2018). Electromotive Force versus Electrical Potential Difference: Approaching (but Not Yet at) Equilibrium [Research-article]. Journal of Chemical Education, 95(10), 1811–1815. https://doi.org/10.1021/acs.jchemed.8b00249
Royal, K. D., Gilliland, K. O., & Kernick, E. T. (2014). Using rasch measurement to score, evaluate, and improve examinations in an anatomy course. Anatomical Sciences Education, 7(6), 450–460. https://doi.org/10.1002/ase.1436
Samsudin, A., Aminudin, A. H., Novia, H., & Suhandi, A. (2023). Identifying Javanese Students ’ Conceptions on Fluid Pressure with Wright Map Analysis of Rasch. Journal of Natural Science and Integration, 6(2), 173–185. https://doi.org/10.24014/jnsi.v6i2.21822
Samsudin, A., Cahyani, P. B., Purwanto, Rusdiana, D., Efendi, R., Aminudin, A. H., & Coştu, B. (2021). Development of a multitier open-ended work and energy instrument (MOWEI) using Rasch analysis to identify students’ misconceptions. Cypriot Journal of Educational Sciences, 16(1), 16–31. https://doi.org/10.18844/cjes.v16i1.5504
Samsudin, A., Novia, H., Suhandi, A., Aminudin, A. H., Yusup, M., Supriyatman, S., Masrifah, M., Permana, N. D., & Costu, B. (2023). Cybergogy Trends in Cognitive Psychology of Physics Learning : A Systematic Literature Review from 2019-2023 with NVivo. Jurnal Pendidikan Fisika Dan Keilmuan (JPFK), 9(2). https://doi.org/10.25273/jpfk.v9i2.17257
Sari, Y., Irawati, R. K., & Rahmawati, H. (2023). Practicality of Web-Based Four-Tier Test to Identify Student’s Misconceptions in Chemical Bonding Materials. JPPS (Jurnal Penelitian Pendidikan Sains), 12(2), 108–121. https://doi.org/10.26740/jpps.v12n2.p108-121
Sarwono, Suhandi, A., Wiendartun, & Samsudin, A. (2022). Remediate Senior High School Students’ Misconception Regarding the Runs Out Battery Concept Using AS-CBRText. AIP Conference Proceedings, 2468(December). https://doi.org/10.1063/5.0131699
Soeharto, S., & Csapó, B. (2021). Evaluating item difficulty patterns for assessing student misconceptions in science across physics, chemistry, and biology concepts. Heliyon, 7(11). https://doi.org/10.1016/j.heliyon.2021.e08352
Suhandi, A., Hermita, N., Samsudin, A., Maftuh, B., & Coştu, B. (2017). Effectiveness of visual multimedia supported conceptual change texts on overcoming students’ misconception about boiling concept. Turkish Online Journal of Educational Technology, 2017(October Special Issue INTE), 1012–1022.
Waqar, I., Azizi, Z., Nikmal, P., Rafi, R., Ulfat, W., Abid, O., Niazi, M. J., & Khan, Z. (2023). Comparison between Electromotive Force and Electric Potential Difference. Turkish Journal of Computer and Mathematics Education, 14(01), 236–242.
Yulianto, A., & Widodo, A. (2020). Disclosure of Difficulty Distribution of HOTS-Based Test Questions through Rasch Modeling. Indonesian Journal of Primary Education, 4(2), 197–203. https://doi.org/10.17509/ijpe.v4i2.29318
Zuza, K., De Cock, M., Van Kampen, P., Bollen, L., & Guisasola, J. (2016). University Students’ Understanding of the Electromotive Force Concept in the Context of Electromagnetic Induction. European Journal of Physics, 37(6). https://doi.org/10.1088/0143-0807/37/6/065709
Authors
Eliya, M., Purwianingsih, W., Supriatna, A., Samsudin, A., Aminudin, A. H., & Mardiana, M. (2024). Electromotive Forces on Multitier Instruments (EFoMI): Development to measure student misconception with Rasch. Journal of Environment and Sustainability Education, 2(1), 40–52. https://doi.org/10.62672/joease.v2i1.32
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