Gender Roles in Understanding and Implementing Green Energy Technology in Indonesian Schools: Rasch Analysis

Authors

  • Ismail Ismail Department of Science Education, Faculty of Mathematics and Natural Sciences, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudhi No. 229, Bandung 40154, West Java, Indonesia;
  • Riandi Riandi Department of Science Education, Faculty of Mathematics and Natural Sciences, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudhi No. 229, Bandung 40154, West Java, Indonesia;
  • Ida Kaniawati Department of Science Education, Faculty of Mathematics and Natural Sciences, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudhi No. 229, Bandung 40154, West Java, Indonesia;
  • Wahyu Sopandi Department of Chemistry Education, Faculty of Mathematics and Natural Sciences, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudhi No. 229, Bandung 40154, West Java, Indonesia;
  • Supriyadi Supriyadi Department of Physics Education, Faculty of Science and Technology, Universitas Musamus, Jl. Raya Musamus No. 1, Merauke 99600, Papua, Indonesia;
  • Suhendar Suhendar Department of Biology Education, Faculty of Mathematics and Natural Sciences, Universitas Muhammadiyah Sukabumi, Jl. R Syamsudin SH No. 30, Sukabumi 43115, West Java, Indonesia;
  • Febrian Andi Hidayat Department of Chemistry Education, Faculty of Science and Technology, Universitas Muhammadiyah Sorong, Jl. Mgr. Gabriel Manek No. 10, Sorong 98415, Papua, Indonesia;

DOI:

https://doi.org/10.48161/qaj.v4n3a752

Abstract

This study aims to investigate gender differences in the understanding and application of green energy technology in schools in Indonesia. The method used is a survey with a questionnaire that covers aspects of knowledge attitudes readiness and obstacles related to green energy technology from a gender perspective. The sample of this study consisted of 829 teachers in various schools in Indonesia with a balanced distribution between male and female teachers. The data was analyzed using the Rasch measurement model with WINSTEPS 5.7.1 software to ensure the validity and reliability of the instrument. The results show that the instrument developed has good reliability and validity without significant item bias based on gender. The analysis shows that female teachers tend to have a higher understanding and application of green energy technology than male teachers. The ability distribution shows that most respondents are at a moderate to high level of ability in understanding and applying green energy technologies. These findings indicate the need for more inclusive and gender-sensitive education strategies to ensure all groups can contribute effectively to the implementation of green energy technologies in schools. The results of this study can be used as a basis for developing policy recommendations aimed at increasing equal involvement and understanding of green energy technologies among teachers both men and women. It is hoped that the gender gap in the understanding and application of green energy technology can be minimized and the application of this technology in schools can be improved.

Downloads

Download data is not yet available.

References

Perlaviciute, G., Steg, L., & Sovacool, B. K. (2021). A perspective on the human dimensions of a transition to net-zero energy systems. Energy Climate Change, 2, 10004.

Baruah, B., & Gaudet, C. (2022). Creating and optimizing employment opportunities for women in the clean energy sector in Canada. Journal of Canadian Studies, 56(2), 240–270.

Bray, R., Montero, A. M., & Ford, R. (2022). Skills deployment for a ‘just’ net zero energy transition. Environmental Innovation and Societal Transitions, 42, 395–410.

Mininni, G., & Hiteva, R. (2023). Place-based solutions for net zero: Gender considerations on ‘green’ skills. Proceedings of the International Conference on Gender Research, 2023-April, 185–191.

Kooijman, A., Clancy, J., & Cloke, J. (2023). Extending energy access assessment: The added value of taking a gender perspective. Energy Research & Social Science, 96, 102923.

Das, I., et al. (2023). Frameworks, methods and evidence connecting modern domestic energy services and gender empowerment. Nature Energy, 8(5), 435–449.

Pearl-Martinez, R. (2020). Global trends impacting gender equality in energy access. IDS Bulletin, 51(1).

Zhang, F., & Zhu, L. (2019). Enhancing corporate sustainable development: Stakeholder pressures, organizational learning, and green innovation. Business Strategy and the Environment, 28(6), 1012–1026.

Song, W., Wang, G., & Ma, X. (2020). Environmental innovation practices and green product innovation performance: A perspective from organizational climate. Sustainable Development, 28(1), 224–234.

Cui, R., & Wang, J. (2022). Shaping sustainable development: External environmental pressure, exploratory green learning, and radical green innovation. Corporate Social Responsibility and Environmental Management, 29(3), 481–495.

Tu, Y., & Wu, W. (2021). How does green innovation improve enterprises’ competitive advantage? The role of organizational learning. Sustainable Production and Consumption, 26, 504–516.

Wang, M., Li, Y., & Liao, G. (2021). Research on the impact of green technology innovation on energy total factor productivity, based on provincial data of China. Frontiers in Environmental Science, 9.

Su, T., Chen, Y., & Lin, B. (2023). Uncovering the role of renewable energy innovation in China’s low carbon transition: Evidence from total-factor carbon productivity. Environmental Impact Assessment Review, 101, 107128.

Wang, J., Dong, X., & Dong, K. (2023). Does renewable energy technological innovation matter for green total factor productivity? Empirical evidence from Chinese provinces. Sustainable Energy Technologies and Assessments, 55, 102966.

Li, G., Gao, D., & Li, Y. (2022). Dynamic environmental regulation threshold effect of technical progress on green total factor energy efficiency: Evidence from China. Environmental Science and Pollution Research, 29(6), 8804–8815.

Yeolekar-Kadam, B., & S., S. J. (2022). Feasibility study on integration of green technologies in prospective construction projects: A case of Vishakhapatnam. International Journal of Management Technology and Social Sciences, 7(1), 210–223.

Wang, F. (2021). The application of green energy-saving technology in building design—Take Zhejiang Water Control Museum architectural design as an example. IOP Conference Series: Earth and Environmental Science, 787(1), 12075.

Shi, X., Li, G., Dong, C., & Yang, Y. (2020). Value co-creation behavior in green supply chains: An empirical study. Energies, 13(15).

Xie, M., Zhao, S., & Lv, K. (2024). The impact of green finance and financial technology on regional green energy technological innovation based on the dual machine learning and spatial econometric models. Energies, 17(11).

Zhu, W., & Zou, J. (2022). The impact of green technology innovation of new energy companies on earnings sustainability in China—Based on the regulatory effect of green finance development. American Journal of Industrial and Business Management, 12(08), 1348–1362.

Li, H., Chen, C., & Umair, M. (2023). Green finance, enterprise energy efficiency, and green total factor productivity: Evidence from China. Sustainability, 15(14), 11065.

Jiang, S., Liu, X., Liu, Z., Shi, H., & Xu, H. (2022). Does green finance promote enterprises’ green technology innovation in China? Frontiers in Environmental Science, 10, 981013.

Maurer, M., Koulouris, P., & Bogner, F. X. (2020). Green awareness in action—How energy conservation action forces on environmental knowledge, values, and behaviour in adolescents’ school life. Sustainability, 12(3).

Zelenika, I., Moreau, T., Lane, O., & Zhao, J. (2018). Sustainability education in a botanical garden promotes environmental knowledge, attitudes, and willingness to act. Environmental Education Research, 24(11), 1581–1596.

O’Neill, C., & Buckley, J. (2019). ‘Mum, did you just leave that tap running?!’ The role of positive pester power in prompting sustainable consumption. International Journal of Consumer Studies, 43(3), 253–262.

Herlina, W., Hidayat, T., & Rahman, T. (2022). The effect of green school-based inquiry learning model on students’ ability of scientific literacy. Jurnal Penelitian Pendidikan IPA, 8(5 SE-Research Articles), 2513–2517.

Hoque, F., Yasin, R. M., & Sopian, K. (2023). Mobile learning to promote renewable energy education at the secondary education level in developing countries. IOP Conference Series: Materials Science and Engineering, 1278(1), 12017.

Qiu, Y., Chen, Q., & Ng, P. S. J. (2023). Research on the spillover effects of digital transformation on the sustainable growth of green schools. Proceedings of Business and Economics Studies, 6(6), 16–23.

Handayani, M. N., Ali, M., Wahyudin, D., & Mukhidin. (2020). Green skills understanding of agricultural vocational school teachers around West Java, Indonesia. Indonesian Journal of Science and Technology, 5(1), 21–30.

Cole, L. B., & Hamilton, E. M. (2019). Can a green school building teach? A pre- and post-occupancy evaluation of a teaching green school building. Environmental Behavior, 52(10), 1047–1078.

Sagala, A. F., & Pane, I. F. (2021). The design of boarding school in Simanindo, Samosir (green architecture). International Journal of Architecture and Urbanism, 5(3), 375–386.

Issa, A. (2023). Shaping a sustainable future: The impact of board gender diversity on clean energy use and the moderating role of environmental, social, and governance controversies. Corporate Social Responsibility and Environmental Management, 30(6), 2731–2746.

Lazoroska, D., Palm, J., & Bergek, A. (2021). Perceptions of participation and the role of gender for the engagement in solar energy communities in Sweden. Energy for Sustainable Societies, 11(1), 35.

Oluoch, S., Lal, P., Susaeta, A., Mugabo, R., Masozera, M., & Aridi, J. (2022). Public preferences for renewable energy options: A choice experiment in Rwanda. Frontiers in Climate, 4(May), 1–12.

Khaemba, W., & Kingiri, A. (2020). Access to renewable energy resources: A gender and inclusivity perspective. In W. Leal Filho, A. M. Azul, L. Brandli, A. Lange Salvia, & T. Wall (Eds.), Affordable and Clean Energy (pp. 1–10). Springer International Publishing.

Arias, K., et al. (2023). Green transition and gender bias: An analysis of renewable energy generation companies in Latin America. Energy Research & Social Science, 101(July).

Baruah, B. (2017). Renewable inequity? Women’s employment in clean energy in industrialized, emerging and developing economies. Natural Resources Forum, 41(1), 18–29.

Bond, T. G., & Fox, C. M. (2015). Applying the Rasch Model: Fundamental Measurement in the Human Sciences (3rd ed.). Routledge.

Boone, W. J. (2020). Rasch basics for the novice. Rasch Measurement Applications, Quantitative Educational Research, 9–30.

DiStefano, C., & Jiang, N. (2020). Applying the Rasch rating scale method to questionnaire data. Rasch Measurement Applications, Quantitative Educational Research, 31–46.

Soeharto, S., et al. (2024). The metacognitive awareness of reading strategy among pre-service primary teachers and the possibility of rating improvement using Rasch analysis. Studies in Educational Evaluation, 80(December).

Boone, W., Staver, J., & Yale, M. (2014). Rasch Analysis in the Human Sciences. Springer.

Merbitz, C., Morris, J., & Grip, J. C. (1989). Ordinal scales and foundations of misinference. Archives of Physical Medicine and Rehabilitation, 70(4), 308–312.

Wright, B. D., & Linacre, J. M. (1989). Observations are always ordinal; measurements, however, must be interval. Archives of Physical Medicine and Rehabilitation, 70(12), 857–860.

S. E. Mokshein, H. Ishak, & H. Ahmad. (2003). Optimizing rating scales for self-efficacy (and other) research. Journal, 63(3).

Mokshein, S. E., Ishak, H., & Ahmad, H. (2019). The use of Rasch measurement model in English testing. Jurnal Cakrawala Pendidik, 38(1), 16–32.

Wright, B. D., & Masters, G. N. (1982). Rating Scale Analysis. MESA Press.

Linacre, J. M. (2000). Comparing ‘partial credit’ and ‘rating scale’ models. Rasch Measurement Transactions, 14(3), 768.

Dimitrov, D. M. (2014). Statistical Methods for Validation of Assessment Scale Data in Counseling and Related Fields. John Wiley & Sons.

Engelhard Jr, G., & Wind, S. (2017). Invariant Measurement with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments. Routledge.

Abd-El-Fattah, S. M. (2015). Rasch rating scale analysis of the Arabic version of the physical activity self-efficacy scale for adolescents: A social cognitive perspective. Psychology, 6(16), 2161.

Andrich, D. (2022). Rating scales and Rasch measurement. October, 1–15.

Linacre, J. M. (2002). Investigating [Title]. Journal, 85–106.

Linacre, J. M. (2022). R Statistics: Survey and review of packages for the estimation of Rasch models. International Journal of Medical Education, 13, 171.

Linacre, J. M. (2021). FACETS Rasch Measurement Computer Program (Version 3.83.6). Winsteps.com.

Christensen, R., & Knezek, G. (2017). Readiness for integrating mobile learning in the classroom: Challenges, preferences, and possibilities. Computers in Human Behavior, 76, 112–121.

Bond, T. G., Yan, Z., & Heene, M. (2020). Applying the Rasch Model: Fundamental Measurement in the Human Sciences. Routledge. doi:10.4324/9780429030499

Fisher, W. P. (2007). Rating scale instrument quality criteria. Rasch Measurement Transactions, 21(1), 1095.

Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48, 1273–1296.

Park, M., & Liu, X. (2021). An investigation of item difficulties in energy aspects across biology, chemistry, environmental science, and physics. Research in Science Education, 51, 43–60.

Azizan, N. H., Mahmud, Z., & Rambli, A. (2020). Rasch rating scale item estimates using maximum likelihood approach: Effects of sample size on the accuracy and bias of the estimates. International Journal of Advanced Science and Technology, 29(4), 2526–2531.

Testa, I., et al. (2020). Validation of university entrance tests through Rasch analysis. Rasch Measurement Applications, Quantitative Educational Research, 99–124.

Zwick, R., Thayer, D. T., & Lewis, C. (1999). An empirical Bayes approach to Mantel-Haenszel DIF analysis. Journal of Educational Measurement, 36(1), 1–28.

Published

2024-08-30

How to Cite

Ismail, I., Riandi, R., Kaniawati, I., Sopandi, W., Supriyadi, S., Suhendar, S., & Hidayat, F. A. (2024). Gender Roles in Understanding and Implementing Green Energy Technology in Indonesian Schools: Rasch Analysis . Qubahan Academic Journal, 4(3), 298–314. https://doi.org/10.48161/qaj.v4n3a752

Issue

Section

Articles