Abstract
This study presents the systematic development, validation, and use of a new instrument for measuring student interest in science and technology. The Student Interest in Technology and Science (SITS) survey is composed of 5 sub-sections assessing the following dimensions: interest in learning science, using technology to learn science, science careers, technology careers, and attitudes toward biotechnology. Our development process included review of existing instrumentation, pilot testing, and expert panel review. The resulting instrument was administered before and after implementation of a biotechnology intervention which used a computer-based game to engage learners in the use of biotechnology to address a societal issue. We employed item response theory (IRT) to explore instrument validity and precision. Results of the psychometric analyses suggest that the SITS survey has a well-defined structure and meets IRT assumptions. Difficulty and discrimination parameters as well as reliability analyses indicate that SITS items provide useful measures of student interest. Finally, we use the SITS to explore the extent to which the intervention used in this study supports changes in student interest and association between students’ interest and related content knowledge. Implications for the future use of this instrument are discussed.
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Alexander, P. A. & Jetton, T. L. (1996). The role of importance and interest in the processing of text. Educational Psychology Review, 8, 89–121.
Altmann, T. K. (2008). Attitude: A concept analysis. Nursing Forum, 43, 144–150.
Annetta, L. A., Cheng, M.-T. & Holmes, S. (2010). Assessing twenty-first century skills through a teacher created video game for high school biology teachers. Research in Science and Technological Education, 28, 101–114.
Barab, S. A., Sadler, T. D., Heiselt, C., Hickey, D. & Zuiker, S. (2007). Relating narrative, inquiry, and inscriptions: Supporting consequential play. Journal of Science Education and Technology, 16, 59–82.
Bransford, J. D., Brown, A. L. & Cocking, R. R. (1999). How people learn: Brain, mind, and school. Washington, DC: National Academy Press.
Chen, S. & Raffan, J. (1999). Biotechnology students’ knowledge and attitudes in the UK and Taiwan. Journal of Biological Education, 34, 17–23.
De Vries, M., Bame, A. & Dugger, W. Jr. (1988). Pupils’ Attitudes Toward Technology (PATT-USA). Developed by Virginia Tech—Technology Education and Eindhoven University. Retrieved June 26, 2012 from http://www.iteea.org/Conference/PATT/PATTSI/PATTSurveyInstrument.pdf.
Dewey, J. (1913). Interest and effort in education. Boston: Riverside.
Eastwood, J. L., & Sadler, T. D. (2013). Teachers’ implementation of a game-based biotechnology curriculum. Computers and Education. doi:10.1016/j.compedu.2013.02.003.
Fraser, B. L. (1978). Development of a test of science-related attitudes. Science Education, 62, 509–515.
Garner, R., Brown, R., Sanders, S. & Menke, D. J. (1992). “Seductive details” and learning from text. In K. A. Renninger, S. Hidi & A. Krapp (Eds.), The role of interest in learning and development. Hillsdale, NJ: Erlbaum.
Gee, J. (2007). What video games have to teach us about learning and literacy. New York: Palgrave.
Germann, P. J. (1988). Development of the attitude toward science in school assessment and its use to investigate the relationship between science achievement and attitude toward science in school. Journal of Research in Science Teaching, 25, 689–703.
Hassan, G. (2008). Attitudes toward science among Australian tertiary and secondary school students. Research in Science and Technological Education, 26, 129–147.
Hidi, S. & Renninger, K. A. (2006). The four phase model of interest development. Education psychologist, 41(2), 111–127.
Hidi, S., Renninger, K. A. & Krapp, A. (2004). Interest, a motivational variable that combines affecting and cognitive functioning. In D. Y. Dai & R. J. Sternberg (Eds.), Motivation, emotion, and cognition: Integrative perspectives on intellectual functioning and development (pp. 89–115). Mahwah, NJ: Erlbaum.
Honey, M. A. & Hilton, M. H. (2011). Learning science: Computer games and simulations. Washington, DC: National Academies Press.
Hu, L. T. & Bentler, P. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling. Concepts, issues, and applications (pp. 76–99). London: Sage.
Human Genetics Commission (2000). Public attitudes to human genetic information. People’s panel quantitative study conducted for the Human Genetics Commission. London: Human Genetics Commission.
Joreskog, K.G. (2004). Structural modeling of ordinal variables using LISREL. Lincolnwood, IL: Scientific Software International, Inc.
Junker, B. & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with non-parametric item response theory. Applied Psychological Measurement, 25, 258–272.
Kay, R. (1993). An exploration of theoretical and practical foundations for assessing attitudes towards computers: The computer attitude measure (CAM). Computers in Human Behavior, 9, 371–386.
Ketelhut, D. J., Nelson, B. C., Clarke, J. & Dede, C. (2010). A multi-user virtual environment for building and assessing higher order inquiry skills in science. British Journal of Educational Technology, 41, 56–68.
Kind, P. K., Jones, K. & Barmby, P. (2007). Developing attitudes towards science measures. International Journal of Science Education, 29, 871–893.
Klop, T. (2007). An exploration of attitudes towards modern biotechnology: A study among Dutch secondary school students. International Journal of Science Education, 29, 663–679.
Koballa, T. R. & Glynn, S. M. (2007). Attitudinal and motivational constructs in science learning. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research on science education (pp. 75–102). Mahwah, NJ: Lawrence Erlbaum Associates.
Lamb, R. L., Annetta, L., Meldrum, J. & Vallett, D. (2012). Measuring science interest: Rasch validation of the science interest survey. International Journal of Science and Mathematics Education, 10, 643–668.
Lenhart, A., Kahne, J., Middaugh, E., Macgill, A., Evans, C., & Vitek, J. (2008). Teens, video games, and civics. Retrieved from Pew Internet & American Life Project website: http://www.pewinternet.org/Reports/2008/Teens-Video-Games-and-Civics.aspx.
Lord, F. M. & Novick, M. R. (1968). Statistical theories of mental test scores. Reading, MA: Addison-Wesley.
Lukow, J. (2002). Learning styles as predictors of student attitudes toward the use of technology in recreation courses. Unpublished doctoral dissertation, Indiana University, Bloomington (UMI No. 3054366).
Macer, D. (1994). International bioethics survey: World view. In Macer, D. (Ed.), Bioethics for the people by the people (pp 125–138). Christchurch, New Zealand: Eubios Ethics Institute.
McDaniel, M. A., Waddill, P. J., Finstad, K. & Bourg, T. (2000). The effects of text-based interest on attention and recall. Journal of Educational Psychology, 92, 492–502.
McLeod, D. B. & Adams, V. M. (1989). Affect in mathematical problem solving: a new perspective. New York: Springer-Berlag.
Moore, R. W. & Foy, R. L. (1997). The scientific attitude inventory: A revision (SAI II). Journal of Research in Science Teaching, 34, 327–336.
Nieswandt, M. (2007). Student affective and conceptual understanding in learning chemistry. Journal of Research in Science Teaching, 44, 908–937.
Pintrich, P. R. & Schrauben, B. (1992). Students’ motivational beliefs and their cognitive engagement in academic tasks. In D. Schunk & J. Meece (Eds.), Students’ perception in the classroom: Causes and consequences. Hillsdale, NJ: Erlbaum.
President’s Council of Advisors of Science and Technology (2010). Prepare and inspire: K–12 education in Science, Technology, Engineering, and Math (STEM) for America’s future. Washington, DC: President’s Council of Advisors of Science and Technology.
Ormond, J. E. (2008). Human learning. Upper Saddle River, NJ: Pearson.
Sadler, T. D., Romine, W., Stuart, P. E., & Merle-Johnson, D. (2013). Game-based curricula in biology classes: Differential effects among varying academic levels. Journal of Research in Science Teaching. doi:10.1002/tea.21085.
Samejima, F. (1972). A general model for free response data. Psychometrika Monograph, 18.
Schiefele, U. (1991). Interest, learning, and motivation. Educational Psychologist, 26, 299–323.
Schiefele, U., Krapp, A. & Winteler, A. (1992). Interest as a predictor of academic achievement: A meta-analysis of research. In K. A. Renninger, S. Hidi & A. Krapp (Eds.), The role of interest in learning and development (pp. 183–212). Hillsdale, NJ: Lawrence Erlbaum Associates.
Schraw, G. & Lehman, S. (2001). Situational interest: A review of the literature and directions for future research. Educational Psychology Review, 13, 23–52.
Selwyn, N. (1997). Students’ attitudes towards computers: Validation of a computer attitude scale for 16–19 education. Computers in Education, 28, 35–41.
Shaffer, D. W. (2006). How computer games help children learn. New York: Palgrave Macmillan.
Shrigley, R. L., Koballa, T. R. & Simpson, R. D. (1988). Defining attitude for science educators. Journal of Research in Science Teaching, 25(8), 659–678.
Siegel, M. A. & Ranney, M. A. (2003). Developing the Changes in Attitude about the Relevance of Science (CARS) questionnaire and assessing two high school science classes. Journal of Research in Science Teaching, 40, 757–775.
Silvia, P. (2006). Exploring the psychology of interest. Oxford, UK: Oxford University Press.
Silvia, P. J. (2001). Interest and interests: The psychology of constructive capriciousness. Review of General Psychology, 5, 270–290.
Sjøberg, S. (2005). How do learners in different cultures related to science and technology? Results and perspectives from the project ROSE (Relevance of Science Education). Asia-Pacific Forum on Science Learning and Teaching, 6, 2. Forward.
Squire, K. (2011). Video games and learning: Teaching and participatory culture in the digital age. New York: Teachers College Press.
Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25, 173–180.
Swarate, S., Ortony, A. & Revelle, W. (2012). Activity matters: Understanding student interest in school science. Journal of Research in Science Teaching, 49, 515–537.
Tobias, S. (1994). Interest, prior knowledge, and learning. Review of Educational Research, 64, 37–54.
Tyler-Wood, T., Knezek, G. & Christensen, R. (2010). Instruments for assessing interest in STEM content and careers. Journal of Technology and Teacher Education, 18, 341–363.
Weinburgh, M. E. & Steele, D. (2000). The modified attitudes toward science inventory: Developing an instrument to be used with fifth grade urban students. Journal of Women and Minorities in Science and Engineering, 6, 87–94.
Zeidler, D. L. (1984). Thirty studies involving the “Scientific Attitude Inventory”: What confidence can we have in this instrument? Journal of Research in Science Teaching, 21, 341–342.
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Romine, W., Sadler, T.D., Presley, M. et al. STUDENT INTEREST IN TECHNOLOGY AND SCIENCE (SITS) SURVEY: DEVELOPMENT, VALIDATION, AND USE OF A NEW INSTRUMENT. Int J of Sci and Math Educ 12, 261–283 (2014). https://doi.org/10.1007/s10763-013-9410-3
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DOI: https://doi.org/10.1007/s10763-013-9410-3