Development of Students’ Perception Instrument of New and Renew able Energy (PINRE)

In line with the alteration from fossil toward new and renewable energy sources, students’ perception about new and renewable energy become critical and an instrument to measure their perception is needed. This article reports the development process of Students Perception Instrument of New and Renewable Energy (PINRE) through three development phases. After scales, subscales and items were designed, the review by experts and practitioners was done to fulfil and validate the content. A trial process was conducted with 229 students from 8 schools (grades 9 and 12) in three cities involved. Statistical and additional qualitative data suggest that the PINRE is a valid and reliable instrument. Therefore, PINRE provides an alternative of a useful instrument for educators and researchers who will measure students’ perceptions about new and renewable energy.

REFERENCES:

• Acikgoz, C. (2011). Renewable energy education in Turkey. Renewable Energy, 36(2), 608-611.
• Blockstein, D.E., Middlecamp, C.H., & Perkins, J.H. (2015). Energy Education: Easy, Difficult, or Both? Journal of Sustainability Education, 8.
• Brown, T.A. (2006). Confirmatory Factor Analysis for Applied Research (1st ed.). New York: Guilford Press.
• Cortina, J.M. (1993). What is coefficient alpha? An examination of theory and application. J. Appl. Psycol, 78, 98-104.
• Cronbach, L.J., & Meehl, P.E. (1995). Construct Validity In Psychological Tests. Psychological Bulletin, 52, 281-302.
• Drost, E.A. (2011). Validity and Reliability in Social Science Research. International Perspectives on Higher Education Research, 38(1), 105-123.
• Guterres, A. (2017). The Sustainable Development Goals Report. New York.
• Hamdan Said, Badru, B.B., & Shahid, M. (2011). Confirmatory Factor Analysis (Cfa) for Testing Validity And Reliability Instrument in the Study of Education. Australian Journal of Basic and Applied Sciences, 5(12), 1098-1103.
• Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
• Jan DeWaters, & Susan Powers. (2013). Establishing Measurement Criteria for an Energy Literacy Questionnaire. The Journal of Environmental Education, 44(1), 38-55. http://dx.doi.org/10.1080/00958964.2012.711378
• Kishore, P., & Kisiel, J. (2013). Exploring high school students’ perceptions of solar energy and solar cells. International Journal of Environmental & Science Education, 8, 521-534.
• Klakayan, J., & Singseewo, A. (2016). The Development of a Curriculum for Renewable Energy: A Case Study of Charcoal Briquettes from Agricultural Residues for Environmental Literacy of Secondary School Students at Samaki Wittaya Municipality School. Journal of Education and Learning, 5(2), 121-128.
• Kline, R.B. (2011). Principles and Practice of Structural Equation Modeling (3rd ed.). New York: The Guilford Press.
• Lamb, S., Maire, Q., & Doecke, E. (2017). Key Skills for the 21st Century: an evidence-based review (pp. 1-65). New South Wales: Victoria University.
• Ministry of Education and Cultre Indonesia. (2017). Silabus Mata Pelajaran Sekolah Menengah Pertama/Madrasah Tsanawiah (Indonesia Middle School Silaby). Kemendikbud.
• Mulliner, E., & Tucker, M. (2017). Feedback on feedback practice: perceptions of students and academics. Assessment & Evaluation in Higher Education, 42(2).
• Murphy, K.R., & Davidshofer, C.O. (2005). Psycological Testing: Principle and Testing (6t ed.). New Jersey: Upper Sadler River.
• National Academy of Engineering & National Research Council. (2009). Engineering in K-12 education: Understanding the status and improving the prospects. Washington DC: National Academies Press.
• National Research Council. (2013). Next Generation Science Standards For States, By States. Washington, DC: The National Academies Press.
• NGSS Lead States. (2013). Next Generation Science Standards: For States, By States. Washington, D.C.: The National Academies Press.
• Shrish Bajpai, & Naimur Rahman Kidwai. (2017). Renewable Energy Education In India. Comparative Professional Pedagogy, 7(4), 103-113.
• Slawomir Pasikowski. (2018). The Problem of Matching Rating Scales in Educational Measurement of Variables Modelled as Sets of Oppositional Pairs. The New Educational Review, 54. https://doi.org/10.15804/tner.2018.54.4.22
• Taherdoost, H. (2016). Validity and Reliability of the Research Instrument; How to Test the Validation of a Questionnaire/Survey in a Research. International Journal of Academic Research in Management, 5(3), 28-36.
• Trilling, B., & Fadel, C. (2009). 21st Century Skills: Learning for Life in Our Times. San Francisco, CA: Jossey-Bass.
• Ugulu, I. (2013). Confirmatory factor analysis for testing validity and reliability of traditional knowledge scale to measure university students’ attitudes. Educational Research and Reviews, 8(16), 1399-1408.
• Walz, K.A., Slowinski, M., & Alfano, K. (2016). International Approaches To Renewable Energy Education - A Faculty Professional Development Case Study With Recommended Practices For STEM Educators. American Journal of Engineering Education, 7(2), 98-116.
• Westen, D., & Rosenthal, R. (2003). Quantifying Construct Validity: Two Simple Measures. Journal of Personality and Social Psychology, 84(3), 608-618.
• Yoichi Kiyohara. (2017). How we learn for better science education Improvement of science education in Japan. Presented at the OECD/JAPAN Seminar, Elementary and Secondary Education Bureau.