Didit Ardianto, Bibin Rubini, Indarini Dwi Pursitasari

Judul	:	Assessing STEM career interest among secondary students: A Rasch model measurement analysis
Abstrak	:	This study aims to validate the STEM career interest survey (STEM-CIS) using Rasch model approach. This study involved 572 junior high school students with 105 seventh-grade students, 124 eighth-grade students, and 343 ninth-grade students. The data were analyzed using Rasch model, which included analysis of item validity and reliability, item fit order, Wright map analysis, and DIF analysis. The results present that the STEM-CIS items show good measurement skills and have logical predictive abilities. STEM-CIS items also have very good reliability, and most items meet the item fit order test criteria. However, there are some items from the STEM-CIS that still detect gender and grade level bias. This study provides evidence that the STEM-CIS items are tested to be valid and reliable to measure students’ interest. In addition, this study also provides evidence that some STEM-CIS items still detect gender and grade level bias.
Tahun	:	2023	Media Publikasi	:	Jurnal Internasional
Kategori	:	Jurnal	No/Vol/Tahun	:	1 / 19 / 2023
ISSN/ISBN	:	-
PTN/S	:	Universitas Pakuan	Program Studi	:	PENDIDIKAN ILMU PENGETAHUAN ALAM
Bibliography	:	Adams, W. K., Perkins, K. K., Podolefsky, N. S., Dubson, M., Finkelstein, N. D., & Wieman, C. E. (2006). New instrument for measuring student beliefs about physics and learning physics: The Colorado learning attitudes about science survey. Physical Review Special Topics–Physics Education Research, 2(1), 010101. https://doi.org/10.1103/PhysRevSTPER.2.010101 Alagumalai, S., Curtis, D. D., & Hungi, N. (Eds.). (2005). Applied Rasch measurement: Book of exemplars. Springer. Ashby Plant, E., Baylor, A. L., Doerr, C. E., & Rosenberg-Kima, R. B. (2009). Changing middle-school students’ attitudes and performance regarding engineering with computer-based social models. Computers & Education, 53(2), 209-215. https://doi.org/10.1016/j.compedu.2009.01.013 Avery, L. M. (2013). Rural science education: Valuing local knowledge. Theory into Practice, 52(1), 28-35. https://doi.org/10.1080/07351690.2013.743769 Baghaei, P. (2008). The Rasch model as a construct validation tool. Rasch Measurements Transactions, 22, 19. Blanchard, M. R., Albert, J. L., Alsbury, T. L., & Williams, B. (2012). ITEST annual project outcomes report: Innovative technology experiences for students and teachers. National Science Foundation. https://www.nsf.gov Bond, T. G., & Fox, C. M. (2015). Applying the Rasch model: Fundamental measurement in the human sciences. Routledge. Drew, C., (2011, November 4). Why science majors change their minds (it’s just so darn hard). The New York Times. https://www.nytimes.com/2011/11/06/education/edlife/why-science-majors-change-their-mind-its-just-so-darn-hard.html Firman, H. (2015). Pendidikan STEM [STEM education]. In Proceedings of the Science Seminar and PKLH. Gorin, J. S., & Embretson, S. E. (2007). Item response theory and Rasch models. In D. McKay (Ed.) Handbook of research methods in abnormal and clinical psychology (pp. 314-334). SAGE. Guzey, S. S., Harwell, M., & Moore, T. (2014). Development of an instrument to assess attitudes toward science, technology, engineering, and mathematics (STEM): Attitudes toward STEM. School Science and Mathematics, 114(6), 271-279. https://doi.org/10.1111/ssm.12077 Kier, M. W., Blanchard, M. R., Osborne, J. W., & Albert, J. L. (2014). The development of the STEM career interest survey (STEM-CIS). Research in Science Education, 44(3), 461-481. https://doi.org/10.1007/s11165-013-9389-3 Kopcha, T. J., McGregor, J., Shin, S., Qian, Y., Choi, J., Hill, R., Mativo, J., & Choi, I. (2017). Developing an integrative STEM curriculum for robotics education through educational design research. Journal of Formative Design in Learning, 1(1), 31-44. https://doi.org/10.1007/s41686-017-0005-1 Koyunlu Unlu, Z., Dokme, I., & Unlu, V. (2016). Adaptation of the science, technology, engineering, and mathematics career interest survey (STEM-CIS) into Turkish. Eurasian Journal of Educational Research, 16(63), 21-36. https://doi.org/10.14689/ejer.2016.63.2 Lent, R. W., & Brown, S. D. (2006). On conceptualizing and assessing social cognitive constructs in career research: A measurement guide. Journal of Career Assessment, 14(1), 12-35. https://doi.org/10.1177/1069072705281364 Luo, W., Wei, H.-R., Ritzhaupt, A. D., Huggins-Manley, A. C., & Gardner-McCune, C. (2019). Using the S-STEM survey to evaluate a middle school robotics learning environment: Validity evidence in a different context. Journal of Science Education and Technology, 28(4), 429-443. https://doi.org/10.1007/s10956-019-09773-z Magno, C. (2009). Demonstrating the difference between classical test theory and item response theory using derived test data. International Journal of Educational and Psychological Assessment, 1(1), 1-11. Oh, Y. J., Jia, Y., Lorentson, M., & LaBanca, F. (2013). Development of the educational and career interest scale in science, technology, and mathematics for high school students. Journal of Science Education and Technology, 22(5), 780-790. https://doi.org/10.1007/s10956-012-9430-8 Osborne, J., Simon, S., & Collins, S. (2003). Attitudes towards science: A review of the literature and its implications. International Journal of Science Education, 25(9), 1049-1079. https://doi.org/10.1080/0950069032000032199 Regisford, K. (2021). Life and work in a global city—The need to improve STEM education. The Recruitment & Employment Confederation. http://www.rec.uk.com/press/news/2253 Sadler, P. M., Sonnert, G., Hazari, Z., & Tai, R. (2012). Stability and volatility of STEM career interest in high school: A gender study. Science Education, 96(3), 411-427. https://doi.org/10.1002/sce.21007 Scott, A., & Martin, A. (2012). Dissecting the data 2012: Examining STEM opportunities and outcomes for underrepresented students in California. http://toped.svefoundation.org/wp-content/uploads/2012/04/Achieve-LPFIstudy032812.pdf Sekaran, U. (2003). Research methods for business: A skill-building approach. John Wiley & Sons. Sjaastad, J. (2013). Measuring the ways significant persons influence attitudes towards science and mathematics. International Journal of Science Education, 35(2), 192-212. https://doi.org/10.1080/09500693.2012.672775 Skamp, K. (2007). Conceptual learning in the primary and middle years: The interplay of heads, hearts, and hands-on science. Teaching Science, 53(3), 18-22. https://search.informit.org/doi/10.3316/aeipt.162057 Stout, J. G., Dasgupta, N., Hunsinger, M., & McManus, M. A. (2011). STEMing the tide: Using ingroup experts to inoculate women’s self-concept in science, technology, engineering, and mathematics (STEM). Journal of Personality and Social Psychology, 100(2), 255-270. https://doi.org/10.1037/a0021385 Sumintono, B., & Widhiarso, W. (2015). Aplikasi pemodelan Rasch pada assessment Pendidikan [Application of Rasch modeling in educational assessment]. Trimkom Publishing House. Tyler-Wood, T., Knezek, G., & Christensen, R. (2010). Instruments for assessing interest in STEM content and careers. Journal of Technology and Teacher Education, 18(2), 341-363. https://www.learntechlib.org/primary/p/32311 VanLeuvan, P. (2004). Young women’s science/mathematics career goals from seventh grade to high school graduation. The Journal of Educational Research, 97(5), 248-268. https://doi.org/10.3200/JOER.97.5.248-268 Wells, B., Sanchez, A., & Attridge, J. (2007). Modeling student interest in science, technology, engineering and mathematics. In Proceedings of the Meeting the Growing Demand for Engineers and Their Educators 2010-2020 International Summit. IEEE. https://doi.org/10.1109/MGDETE.2007.4760362 Whitfield, A., Feller, R., & Wood, C. (2008). A counselor’s guide to career assessment instruments. National Career Development Association. Wright, B. D. (1977). Solving measurement problems with the Rasch model. Journal of Educational Measurement, 14(2), 97-116. https://doi.org/10.1111/j.1745-3984.1977.tb00031.x Zeldin, A., Britner, S., & Pajares, F. (2008). A comparative study of the self-efficacy beliefs of successful men and women in mathematics, science, and technology careers. Journal of Research in Science Teaching, 45(9), 1036-1058. https://doi.org/10.1002/tea.20195
URL	: