Four-Tier Test Model to Measure Prospective Physics Teacher Students’ Multiple Representation Ability on Electricity Topic

Rahmawati Rahmawati; Widiasih Widiasih; Nasrah Nasrah; A. Muafiah Nur; Dewi Hikmah Marisda

doi:10.26737/jipf.v7i3.3066

Four-Tier Test Model to Measure Prospective Physics Teacher Students’ Multiple Representation Ability on Electricity Topic

Rahmawati Rahmawati, Widiasih Widiasih, Nasrah Nasrah, A. Muafiah Nur, Dewi Hikmah Marisda

Abstract

The advantage of four-tier model test is that it has four levels of questions that require complex reasoning abilities from students to express the reasons for their answers to the problems given. This study aimed to develop a multiple representation test with a four-tier model to measure students’ multiple representation in electricity topics. The stages of developing this test instrument used a stage model of The Design Based Research which consists of five stages, namely developing an assessment framework, designing items, developing rubrics, conducting trials, and applying the Racsh Model analysis with the Item Response approach. Theory (IRT) program assisted Winsteps version 3.68.2. The research method used was descriptive-exploratory method to describe the results of the development and validation of four-tier model test. This test consisted to 20 items were developed based on four types of representation, namely verbal representations, diagrammatic representations (pictures), mathematical representations, and graphic representations with each indicator. The research subjects involved were 30 prospective physics students at the pilot test stage and 79 prospective physics students at the field test stage from different universities in Makassar city. The results of the development of the four-tier test model test overall items are valid with a high level of reliability (Cronbach's Alpha value = 0.80). Based on the results of expert judgment validation and testing, it can be concluded that the multiple representation test with four-tier model on electricity topic is feasible to use.

Keywords

Multiple Representation; Item Response Theory, Rasch Model; Electricity Topic; Winstep Version 3.68.2

Full Text:

PDF (English)

References

Hubber, P., & Tytler, R. (2017). Enacting a representation construction approach to teaching and learning astronomy. In Multiple representations in physics education (pp. 139-161). Springer, Cham.

Waldrip, B., Prain, V., & Carolan, J. (2010). Using multi-modal representations to improve learning in junior secondary science. Research in science education, 40(1): 65-80.

Nieminen, P., Savinainen, A., & Viiri, J. (2017). Learning about forces using multiple representations. In Multiple Representations in Physics Education (pp. 163-182). Springer, Cham.

Ornek, F., Robinson, W. R., & Haugan, M. P. (2008). What Makes Physics Difficult?. International Journal of Environmental and Science Education, 3(1): 30-34.

Rahmawati, R., Rustaman, N. Y., Hamidah, I., & Rusdiana, D. (2018). The Development and Validation of Conceptual Knowledge Test to Evaluate Conceptual Knowledge of Physics Prospective Teachers on Electricity and Magnetism Topic. Jurnal Pendidikan IPA Indonesia, 7(4): 283-490.

Gurcay, D., & Gulbas, E. (2015). Development of three-tier heat, temperature and internal energy diagnostic test. Research in Science & Technological Education, 33(2): 197-217.

Beichner, R. J. (1994). Testing student interpretation of kinematics graphs. American journal of Physics, 62(8): 750-762.

Wiser, M. (1986). The Differentiation of Heat and Temperature: An Evaluation of the Effect of Microcomputer Teaching on Students' Misconceptions. Technical Report 87-5.

Baser, M., & Geban, Ö. (2007). Effectiveness of conceptual change instruction on understanding of heat and temperature concepts. Research in science & technological education, 25(1): 115-133.

Başer, M., & Geban, Ö. (2007). Effect of instruction based on conceptual change activities on students’ understanding of static electricity concepts. Research in Science & Technological Education, 25(2): 243-267.

Tan, K. C. D., Goh, N. K., Chia, L. S., & Treagust, D. F. (2002). Development and application of a two‐tier multiple choice diagnostic instrument to assess high school students' understanding of inorganic chemistry qualitative analysis. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 39(4): 283-301.

Tan, K. C. D., Taber, K. S., Goh, N. K., & Chia, L. S. (2005). The ionisation energy diagnostic instrument: a two-tier multiple-choice instrument to determine high school students’ understanding of ionisation energy. Chemistry Education Research and Practice, 6(4): 180-197.

Arslan, H. O., Cigdemoglu, C., & Moseley, C. (2012). A three-tier diagnostic test to assess pre-service teachers’ misconceptions about global warming, greenhouse effect, ozone layer depletion, and acid rain. International journal of science education, 34(11): 1667-1686.

Caleon, I., & Subramaniam, R. (2010). Development and application of a three‐tier diagnostic test to assess secondary students’ understanding of waves. International journal of science education, 32(7): 939-961.

Romine, W. L., Schaffer, D. L., & Barrow, L. (2015). Development and application of a novel Rasch-based methodology for evaluating multi-tiered assessment instruments: Validation and utilization of an undergraduate diagnostic test of the water cycle. International Journal of Science Education, 37(16): 2740-2768.

Peşman, H., & Eryılmaz, A. (2010). Development of a three-tier test to assess misconceptions about simple electric circuits. The Journal of educational research, 103(3): 208-222.

Shipstone, D. (1988). Pupils' understanding of simple electrical circuits. Some implications for instruction. Physics education, 23(2): 92.

Shipstone, D. M. (1984). A study of children's understanding of electricity in simple DC circuits. European journal of science education, 6(2): 185-198.

Arnold, M., & Millar, R. (1987). Being constructive: An alternative approach to the teaching of introductory ideas in electricity. International Journal of Science Education, 9(5): 553-563.

Osborne, R. J., & Cosgrove, M. M. (1983). Children's conceptions of the changes of state of water. Journal of research in Science Teaching, 20(9): 825-838.

Osborne, R. (1983). Towards modifying children's ideas about electric current. Research in Science & Technological Education, 1(1): 73-82.

Hekkenberg, A., Lemmer, M., & Dekkers, P. (2015). An analysis of teachers' concept confusion concerning electric and magnetic fields. African Journal of Research in Mathematics, Science and Technology Education, 19(1): 34-44.

Tarciso Borges, A., & Gilbert, J. K. (1999). Mental models of electricity. International journal of science education, 21(1): 95-117.

Cosgrove, M. (1995). A study of science‐in‐the‐making as students generate an analogy for electricity. International journal of science education, 17(3): 295-310.

Cohen, R., Eylon, B., & Ganiel, U. (1983). Potential difference and current in simple electric circuits: A study of students’ concepts. American Journal of Physics, 51(5): 407-412.

Paatz, R., Ryder, J., Schwedes, H., & Scott, P. (2004). A case study analysing the process of analogy‐based learning in a teaching unit about simple electric circuits. International Journal of Science Education, 26(9): 1065-1081.

Psillos, D., Koumaras, P., & Valassiades, O. (1987). Pupils’ representations of electric current before, during and after instruction on DC circuits. Research in Science & Technological Education, 5(2): 185-199.

Engelhardt, P. V., & Beichner, R. J. (2004). Students’ understanding of direct current resistive electrical circuits. American journal of physics, 72(1): 98-115.

Finkelstein, N. (2005). Learning physics in context: A study of student learning about electricity and magnetism. International Journal of Science Education, 27(10): 1187-1209.

Zacharia, Z. C., & De Jong, T. (2014). The effects on students’ conceptual understanding of electric circuits of introducing virtual manipulatives within a physical manipulatives-oriented curriculum. Cognition and instruction, 32(2): 101-158.

Stocklmayer, S. M., & Treagust, D. F. (1996). Images of electricity: How do novices and experts model electric current?. International Journal of Science Education, 18(2): 163-178.

Heller, P. M., & Finley, F. N. (1992). Variable uses of alternative conceptions: A case study in current electricity. Journal of Research in Science Teaching, 29(3): 259-275.

Heywood, D., & Parker, J. (1997). Confronting the analogy: primary teachers exploring the usefulness of analogies in the teaching and learning of electricity. International Journal of Science Education, 19(8): 869-885.

Retnawati, H. (2016). Validitas, Reliabilitas, & Karakteristik Butir (Panduan untuk Peneliti, Mahasiswa, dan Psikometrian). Yogyakarta: Parama Publishing.

Hambleton, R. K., Swaminatan, H. & Rogers, H. J. (1991). Fundamentals of Item Respons Theory. United States of America: Sage Publication, Inc.

Demars, C. (2010). Item Respons Theory: Understanding Statistics Measurement. United States of America: Oxford University Press.

Hambleton, R. K., & Swaminatan, H. (1985). Item Response Theory: Principles and Applications. New York: Springer Science+Business Media, LLC.

Kuo, C. Y., Wu, H. K., Jen, T. H., & Hsu, Y. S. (2015). Development and validation of a multimedia-based assessment of scientific inquiry abilities. International Journal of Science Education, 37(14): 2326-2357.

Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology, 28(4): 563-575.

Wilson, F. R., Pan, W., & Schumsky, D. A. (2012). Recalculation of the critical values for Lawshe’s content validity ratio. Measurement and evaluation in counseling and development, 45(3): 197-210.

Sumintono, B., & Widhiarso, W. (2015). Aplikasi Pemodelan Rasch pada Assessment Pendidikan. Cimahi: Penerbit Trim Komunikasi.

Boone, W. J., Staver, J. R., & Yale, M. S. (2014). Rasch Analysis in the Human Sciences. New York, London: Springer.

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.

Davidowitz, B., & Potgieter, M. (2016). Use of the Rasch measurement model to explore the relationship between content knowledge and topic-specific pedagogical content knowledge for organic chemistry. International Journal of Science Education, 38(9): 1483-1503.

Anderson, L. W., & Krathwohl, D. R. (2001) A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. New York: David McKay Company, Inc.

Marzano, R. J. (2006). Classroom Assessment & Grading that Work. Alexandria, Virginia: Association for Supervision and Curriculum Development.

Wilson, M. (2005). Constructing Measures: An Item Response Modeling Approach. Mahwah, New Jersey, London: Lawrence Erlbaum Associates, Publishers.

Bansilal, S. (2015). A Rasch analysis of a Grade 12 test written by mathematics teachers. South African Journal of Science, 111(5-6): 1-9.

Gronlund, N. E. (1985). Measurement and evaluation in teaching. In Measurement and evaluation in teaching (pp. xv-540).

Thorndike, R. L. (1971). Educational Measurement, Second Edi. United State of America: American Council on Education.

Linacre, J. M. (2002). Optimizing rating scale category effectiveness. Journal of applied measurement, 3(1): 85-106.

Linacre, J. M. (2010). Predicting responses from Rasch measures. Journal of Applied Measurement, 11(1): 1.

Bond, T. G., & Fox, C. M. (2-15). Applying the Rasch Model: Fundamental Measurement in the Human Science, Third Edit. New York and London: Routledge Taylor & Francis Group.

Krishnan, S., & Idris, N. (2014). Investigating reliability and validity for the construct of inferential statistics. International Journal of Learning, Teaching and Educational Research, 4(1): 51-60.

Sumintono, B., &Widhiarso, W. (2013). Aplikasi Model Rasch untuk Penelitian Ilmu-ilmu Sosial. Cimahi: Trim Komunikasi Publishing House.

Sumintono, B. (2018). Rasch Model Measurement as Tools in Assessment for, ResearchGate, Kuala Lumpur.

DOI: http://dx.doi.org/10.26737/jipf.v7i3.3066