From an early age, students in formal schools have been taught how to interact with nature through natural science lessons. However, have we made sure the lesson is appropriate to teach interaction with nature? Climate change is happening and has already been confirmed by scientists; teachers need to take action in class to spread awareness. Education is a way to educate people and can be a good way to prevent climate change before the tipping point (+1.5° C) is exceeded. Even worse, today, humans face many crises in many fields along with the development of human civilization; this thesis will discuss three crises around the field: the climate crisis, the 21st-century learning crisis, and the replication crisis. Three of them are demonstrated in a study of Scientific Inquiry Assessment in this thesis which included two studies. The First Study discusses Revolutionizing Scientific Learning using Innovating Interactive Web-based Assessment for Scientific Inquiry. This study aims to analyze the process of creating an interactive web-based evaluation that provides complete information about students' scientific inquiry abilities. Science education reform is transpiring worldwide, facilitating scientific inquiry (SI) ability which aims for students to understand how scientists do their work. Interest has rapidly expanded in integrating scientific inquiry into computer-based assessments, which can measure complex inquiry skills more effectively. According to Bayesian inference, there are differences between some demographic characteristics among eleventh-grade students from natural science classes in Southwest China’s public schools in terms of students’ scientific inquiry ability, in which “being” male and schools located in the city have evident to have higher probability on understanding climate change issue. The Second Study discusses Bayesian Multilevel Model Analysis on Scientific Inquiry Ability. Understanding the replication crisis is a hot topic that is widely discussed among scientists. This replication crisis is characterized by the difference between the latest results and the previous results. There is a need for alternatives for hypothesis testing in science education. This study aims to demonstrate and apply Multilevel Bayesian statistics to analyze the scientific inquiry ability of students in China. Finally, a truncated Poisson regression-based approach was used, clustered by Student ID, which was a good fit for the data. In summary, results varied widely between districts and schools but less so between classrooms. Moreover, adjusting for gender (which is an important control) is taken into account; students who take longer to take the test score about two points higher on average. In conclusion, this thesis answers three main keywords on the design of this thesis: Technology (Computer-based Assessment) as the answer to the 21st-century learning crisis, Bayesian (Statistical analysis) as the answer to Replication Crisis, and Climate change awareness content as the answer for Climate Change Crisis.

Acton, K. S., & Saxe, D. (2020). A Discussion of Critical Issues in Environmental Education: An Interview with Dianne Saxe. Journal of Philosophy of Education, 54(4), 808–816. https://doi.org/10.1111/1467-9752.12460

Australia Wildlife Protection Council. (2016). The dingo bounty – Victorian labor's environmental policy amnesia – Political opportunism trumps principle. Australia Wildlife Protection Council. https://awpc.org.au/the-dingo-bounty-victorian-labors-environmental-policy-amnesia-political-opportunism-trumps-principle/

Bonnett, M. (2019). Towards an ecologization of education. Journal of Environmental Education, 50(4–6), 251–258. https://doi.org/10.1080/00958964.2019.1687409

Choquenot, D., & Forsyth, D. M. (2013). Exploitation ecosystems and trophic cascades in non-equilibrium systems: Pasture - red kangaroo - dingo interactions in arid Australia. Oikos, 122(9), 1292–1306. https://doi.org/10.1111/j.1600-0706.2012.20976.x

Hern, W. M. (1993). Has the human species become a cancer on the planet? A theoretical view of population growth as a sign of pathology. Current World Leaders, 36(6), 1089–1124.

Jamieson, A. J., Brooks, L. S. R., Reid, W. D. K., Piertney, S. B., Narayanaswamy, B. E., & Linley, T. D. (2019). Microplastics and synthetic particles ingested by deep-sea amphipods in six of the deepest marine ecosystems on Earth. Royal Society Open Science, 6(2). https://doi.org/10.1098/rsos.180667

Law, K. L., & Thompson, R. C. (2014). Microplastics in the seas. Science, 345(6193), 144–145. https://doi.org/10.1126/science.1254065

Letnic, M., Ritchie, E. G., & Dickman, C. R. (2012). Top predators as biodiversity regulators: The dingo Canis lupus dingo as a case study. Biological Reviews, 87(2), 390–413. https://doi.org/10.1111/j.1469-185X.2011.00203.x

Wilson, G. R., & Edwards, M. (2019). Professional kangaroo population control leads to better animal welfare, conservation outcomes and avoids waste. Australian Zoologist, 40(1), 181–202. https://doi.org/10.7882/AZ.2018.043

---
Arnold, J. C., Boone, W. J., Kremer, K., & Mayer, J. (2018). Assessment of Competencies in Scientific Inquiry Through the Application of Rasch Measurement Techniques. In Education Sciences (Vol. 8, Issue 4, pp. 1–20). https://doi.org/10.3390/educsci8040184

Brown, N. J. S., & Wilson, M. (2011). A Model of Cognition: The Missing Cornerstone of Assessment. Educational Psychology Review, 23(2), 221. https://doi.org/10.1007/s10648-011-9161-z

Bush, D., Sieber, R., Seiler, G., & Chandler, M. (2017). University-level teaching of Anthropogenic Global Climate Change (AGCC) via student inquiry. Studies in Science Education, 53(2), 113–136.

Calvetti, D., & Somersalo, E. (2010). Subjective knowledge or objective belief? an oblique look to bayesian methods. In Large‐Scale Inverse Problems and Quantification of Uncertainty (pp. 33–70). Wiley. https://doi.org/10.1002/9780470685853.ch3

Chang, P.-S., Lee, S.-H., & Wen, M. L. (2022). Developing an inquiry-based laboratory curriculum to engage students in planning investigations and argumentation. International Journal of Science Education, 1–26.

Cheng, M., Su, C.-Y., & Kinshuk. (2020). Integrating Smartphone-Controlled Paper Airplane Into Gamified Science Inquiry for Junior High School Students. Journal of Educational Computing Research, 59(1), 71–94. https://doi.org/10.1177/0735633120953598

Cheng, P.-H., Molina, J., Lin, M.-C., Liu, H.-H., & Chang, C.-Y. (2022). A New TPACK Training Model for Tackling the Ongoing Challenges of COVID-19. Applied System Innovation, 5(2), 32. https://doi.org/10.3390/asi5020032

Chi, S., Liu, X., Wang, Z., & Won Han, S. (2018). Moderation of the effects of scientific inquiry activities on low SES students' PISA 2015 science achievement by school teacher support and disciplinary climate in science classroom across gender. International Journal of Science Education, 40(11), 1284–1304.

Chyi, H., & Zhou, B. (2014). The effects of tuition reforms on school enrollment in rural China. Economics of Education Review, 38, 104–123.

Cuevas, P., Lee, O., Hart, J., & Deaktor, R. (2005). Improving science inquiry with elementary students of diverse backgrounds. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 42(3), 337–357.

de Klerk, S., Veldkamp, B. P., & Eggen, T. J. H. M. (2018). A framework for designing and developing multimedia-based performance assessment in vocational education. Educational Technology Research and Development, 66(1), 147–171.

Deboer, G. E. (2006). Historical perspectives on inquiry teaching in schools. In Scientific Inquiry and Nature of Science (pp. 17–35). Springer. https://doi.org/10.1007/978-1-4020-5814-1_2

Deng, A. (2015). Objective Bayesian two sample hypothesis testing for online controlled experiments. Proceedings of the 24th International Conference on World Wide Web, 923–928.

Dilipkumar, D. (2021). Frequentist and Bayesian Inference. Towards Data Science. https://towardsdatascience.com/frequentist-and-bayesian-inference-83af2595f172

Dogan, N. (2017). Blending Problem Based Learning and History of Science Approaches to Enhance Views about Scientific Inquiry: New Wine in an Old Bottle. Journal of Education and Training Studies, 5(10), 99–112.

Dow, P. (1999). Why inquiry? A historical and philosophical commentary. Foundations, 2, 5–8.

Drummond, A., Palmer, M. A., & Sauer, J. D. (2016). Enhancing endorsement of scientific inquiry increases support for pro-environment policies. Royal Society Open Science, 3(9), 160360.

Dulic, A., Angel, J., & Sheppard, S. (2016). Designing futures: Inquiry in climate change communication. Futures, 81, 54–67. https://doi.org/https://doi.org/10.1016/j.futures.2016.01.004

Gelman, A., & Hennig, C. (2017). Beyond subjective and objective in statistics. Journal of the Royal Statistical Society, 180(4), 967–1033.

Gelman, A., & Pardoe, I. (2006). Bayesian measures of explained variance and pooling in multilevel (hierarchical) models. Technometrics, 48(2), 241–251.

Goldstein, M. (2006). Subjective Bayesian analysis: principles and practice. Bayesian Analysis, 1(3). https://doi.org/10.1214/06-BA116

Han, H., Park, J., & Thoma, S. J. (2018). Why do we need to employ Bayesian statistics and how can we employ it in studies of moral education?: With practical guidelines to use JASP for educators and researchers. Journal of Moral Education, 47(4), 519–537.

Hedges, L. V, & Schauer, J. M. (2019). More than one replication study is needed for unambiguous tests of replication. Journal of Educational and Behavioral Statistics, 44(5), 543–570.

İlhan, N., & Tosun, C. (2016). Kindergarten students' levels of understanding some science concepts and scientific inquiry processes according to demographic variables (the sampling of Kilis Province in Turkey). Cogent Education, 3(1), 1144246.

Kaplan, A., Gheen, M., & Midgley, C. (2002). Classroom goal structure and student disruptive behaviour. British Journal of Educational Psychology, 72(2), 191–211.

Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795.

Ketelhut, D. J., Clarke, J., & Nelson, B. C. (2010). The Development of River City, a Multi-User Virtual Environment-Based Scientific Inquiry Curriculum: Historical and Design Evolutions. In M. J. Jacobson & P. Reimann (Eds.), Designs for Learning Environments of the Future (pp. 89–110). Springer US. https://doi.org/10.1007/978-0-387-88279-6_4

Kubsch, M., Stamer, I., Steiner, M., Neumann, K., & Parchmann, I. (2021). Beyond p-values: Using Bayesian Data Analysis in Science Education Research. Practical Assessment, Research and Evaluation, 26, 1–18.

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.

Lau, R. W. H., Yen, N. Y., Li, F., & Wah, B. (2014). Recent development in multimedia e-learning technologies. World Wide Web, 17(2), 189–198.

Lederman, J. S., Lederman, N. G., Bartels, S., Jimenez, J., Akubo, M., Aly, S., Bao, C., Blanquet, E., Blonder, R., & Bologna Soares de Andrade, M. (2019). An international collaborative investigation of beginning seventh grade students' understandings of scientific inquiry: Establishing a baseline. Journal of Research in Science Teaching, 56(4), 486–515.

Lederman, J. S., Lederman, N. G., Bartos, S. A., Bartels, S. L., Meyer, A. A., & Schwartz, R. S. (2014). Meaningful assessment of learners' understandings about scientific inquiry—The views about scientific inquiry (VASI) questionnaire. Journal of Research in Science Teaching, 51(1), 65–83.

Maxwell, S. E., Lau, M. Y., & Howard, G. S. (2015). Is psychology suffering from a replication crisis? What does "failure to replicate" really mean? American Psychologist, 70(6), 487.

McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC press.

Nagel, J. (2012). Intersecting identities and global climate change. Identities, 19(4), 467–476.

National Research Council. (2000). Inquiry and the national science education standards: A guide for teaching and learning. National Academies Press. https://doi.org/10.17226/9596

OECD. (2009). The Rasch Model. In PISA Data Analysis Manual: SPSS, Second Edition (pp. 443–454). PISA, OECD Publishing. https://doi.org/10.1787/9789264056275-en

Perry, T., Morris, R., & Lea, R. (2022). A decade of replication study in education? A mapping review (2011–2020). Educational Research and Evaluation, 27(1–2), 12–34.

Rekker, R. (2021). The nature and origins of political polarization over science. Public Understanding of Science, 30(4), 352–368.

Roberts, D. A. (2007). Handbook of research on science education. In Handbook of research in science education (pp. 729–779). Routledge.

Ruiz‐Primo, M. A., Li, M., Tsai, S., & Schneider, J. (2010). Testing one premise of scientific inquiry in science classrooms: Examining students' scientific explanations and student learning. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 47(5), 583–608.

Schooler, J. W. (2014). Metascience could rescue the 'replication crisis.' Nature, 515(7525), 9.

Sprenger, J. (2010). Statistical inference without frequentist justifications. In EPSA Epistemology and Methodology of Science. Springer. https://doi.org/10.1007/978-90-481-3263-8_25

Teig, N., Scherer, R., & Kjærnsli, M. (2020). Identifying patterns of students' performance on simulated inquiry tasks using PISA 2015 log‐file data. Journal of Research in Science Teaching, 57(9), 1400–1429.

The jamovi project. (2022). jamovi (Version 2.3). [Computer Software]. https://www.jamovi.org

Turney, S. (2022). Central Limit Theorem | Formula, Definition & Examples. Scribbr. https://www.scribbr.com/statistics/central-limit-theorem/

Van Den Bergh, D., Van Doorn, J., Marsman, M., Draws, T., Van Kesteren, E. J., Derks, K., Dablander, F., Gronau, Q. F., Kucharský, Š., Gupta, A. R. K. N., Sarafoglou, A., Voelkel, J. G., Stefan, A., Ly, A., Hinne, M., Matzke, D., & Wagenmakers, E. J. (2020). A tutorial on conducting and interpreting a bayesian ANOVA in JASP. LAnnee Psychologique, 120(1), 73–96. https://doi.org/10.3917/anpsy1.201.0073

Wiliam, D. (2022). How should educational research respond to the replication "crisis" in the social sciences? Reflections on the papers in the Special Issue. Educational Research and Evaluation, 27(1–2), 208–214.

Wright, B. D., & Masters, G. N. (1982). Rating scale analysis. MESA press.

Wu, H.-K., Kuo, C.-Y., Jen, T.-H., & Hsu, Y.-S. (2015). What makes an item more difficult? Effects of modality and type of visual information in a computer-based assessment of scientific inquiry abilities. Computers & Education, 85, 35–48.

Wu, P.-H., Kuo, C.-Y., Wu, H.-K., Jen, T.-H., & Hsu, Y.-S. (2018). Learning benefits of secondary school students' inquiry-related curiosity: A cross-grade comparison of the relationships among learning experiences, curiosity, engagement, and inquiry abilities. Science Education, 102(5), 917–950. https://doi.org/https://doi.org/10.1002/sce.21456

Zhang, D., Li, X., & Xue, J. (2015). Education inequality between rural and urban areas of the People's Republic of China, migrants' children education, and some implications. Asian Development Review, 32(1), 196–224.

---
Albers, C. J., Kiers, H. A. L., & van Ravenzwaaij, D. (2018). Credible confidence: A pragmatic view on the frequentist vs Bayesian debate. Collabra: Psychology, 4(1).

Albert, M. (2005). Should Bayesians bet where frequentists fear to tread? Philosophy of Science, 72(4), 584–593.

Calvetti, D., & Somersalo, E. (2010). Subjective knowledge or objective belief? an oblique look to bayesian methods. In Large‐Scale Inverse Problems and Quantification of Uncertainty (pp. 33–70). Wiley. https://doi.org/10.1002/9780470685853.ch3

Deng, A. (2015). Objective Bayesian two sample hypothesis testing for online controlled experiments. Proceedings of the 24th International Conference on World Wide Web, 923–928.

Dilipkumar, D. (2021). Frequentist and Bayesian Inference. Towards Data Science. https://towardsdatascience.com/frequentist-and-bayesian-inference-83af2595f172

Flores, A. (2007). Examining disparities in mathematics education: Achievement gap or opportunity gap? The High School Journal, 91(1), 29–42.

Gelman, A., & Hennig, C. (2017). Beyond subjective and objective in statistics. Journal of the Royal Statistical Society, 180(4), 967–1033.

Gelman, A., & Pardoe, I. (2006). Bayesian measures of explained variance and pooling in multilevel (hierarchical) models. Technometrics, 48(2), 241–251.

Goldhammer, F., Naumann, J., Stelter, A., Tóth, K., Rölke, H., & Klieme, E. (2014). The time on task effect in reading and problem solving is moderated by task difficulty and skill: Insights from a computer-based large-scale assessment. Journal of Educational Psychology, 106(3), 608.

Goldstein, M. (2006). Subjective Bayesian analysis: principles and practice. Bayesian Analysis, 1(3). https://doi.org/10.1214/06-BA116

Goodrich, B., Gabry, J., Ali, I., & Brilleman, S. (2023). Package 'rstanarm': Bayesian applied regression modeling via Stan.

Han, H., Park, J., & Thoma, S. J. (2018). Why do we need to employ Bayesian statistics and how can we employ it in studies of moral education?: With practical guidelines to use JASP for educators and researchers. Journal of Moral Education, 47(4), 519–537.

Hedges, L. V, & Schauer, J. M. (2019). More than one replication study is needed for unambiguous tests of replication. Journal of Educational and Behavioral Statistics, 44(5), 543–570.

Kubsch, M., Stamer, I., Steiner, M., Neumann, K., & Parchmann, I. (2021). Beyond p-values: Using Bayesian Data Analysis in Science Education Research. Practical Assessment, Research and Evaluation, 26, 1–18.

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.

Logan, J. R., & Burdick-Will, J. (2017). School segregation and disparities in urban, suburban, and rural areas. The ANNALS of the American Academy of Political and Social Science, 674(1), 199–216.

Maxwell, S. E., Lau, M. Y., & Howard, G. S. (2015). Is psychology suffering from a replication crisis? What does "failure to replicate" really mean? American Psychologist, 70(6), 487.

McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC press.

Muth, C., Oravecz, Z., & Gabry, J. (2018). User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. The Quantitative Methods for Psychology, 14(2), 99–119.

Perry, T., Morris, R., & Lea, R. (2022). A decade of replication study in education? A mapping review (2011–2020). Educational Research and Evaluation, 27(1–2), 12–34.

Schooler, J. W. (2014). Metascience could rescue the 'replication crisis.' Nature, 515(7525), 9.

Sprenger, J. (2010). Statistical inference without frequentist justifications. In EPSA Epistemology and Methodology of Science. Springer. https://doi.org/10.1007/978-90-481-3263-8_25

Turney, S. (2022). Central Limit Theorem | Formula, Definition & Examples. Scribbr. https://www.scribbr.com/statistics/central-limit-theorem/

Wiliam, D. (2022). How should educational research respond to the replication "crisis" in the social sciences? Reflections on the papers in the Special Issue. Educational Research and Evaluation, 27(1–2), 208–214.

Wright, W. (2012). The disparities between urban and suburban American education systems: A comparative analysis using social closure theory. National Conference On Undergraduate Research.

Zhang, D., Li, X., & Xue, J. (2015). Education inequality between rural and urban areas of the People's Republic of China, migrants' children education, and some implications. Asian Development Review, 32(1), 196–224.