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The aim of this paper is to analyze students' chemical literacy through implementing an Ethical Dilemma-STEAM teaching model in chemistry acid and base lessons. The model is designed to engage students in values-based learning through dilemma stories that pose environmental problems to be solved through the development of an interdisciplinary project. The study involved 40 grade 11 students from a high school in Jakarta, Indonesia, during the 2021/2022 academic year. A qualitative research method was employed to investigate students' learning experiences through classroom observations, semi-structured interviews, reflective journals, and a chemical literacy test. The model engaged students through five steps of learning: value reflection, problem-solving and decision-making, content integration, STEAM integration, and transformation. In this study students were challenged to develop a jet-powered boat as a solution to an inter-island mobility dilemma. The study found that the Ethical Dilemma-STEAM teaching model supported students' chemical literacy development. The study demonstrated that students were encouraged to think critically by applying their conceptual understanding to practice. They were motivated to look closely at chemistry issues in everyday life, an indication that this model could be used as an alternative way to engage students to see the relevance of chemistry concepts and their application to the local environment. The paper also addresses the difficulties researchers faced in implementing an innovative approach to chemistry education during multiple COVID-19 outbreaks. [ABSTRACT FROM AUTHOR]

On data that are not normally distributed, classical linear regression is developed into the Generalized Linear Model (GLM). One of them is logistic regression which is used for binary discrete data. A different strategy is to consider all variables (response and predictors) to be random observations from the joint probability distribution. A model with dependencies should be used because some factors are dependent. The copula model, one of which is the Gaussian copula, is a popular technique for creating multivariate models with dependencies. The Gaussian copula regression model (GCMR) is a general structure that can be used to model any form of dependent response. The Gaussian copula blends the ease of interpretation of marginal modeling with the flexibility of the dependency structure specification. In this case, the estimated parameter is the regression coefficient and the error is expressed in the dependency structure. The model estimation uses the maximum likelihood method. This paper aims to apply the GCMR model to time series data where the dependent variable is discrete with a binomial distribution. A case study was conducted to see the effect of weather factors on Covid cases in DKI Jakarta. The regression model used is binomial data logistic regression with the dependency structure expressed in the correlation matrix with the assumption that the error is obtained from the ARMA(p,q) process. The dependent variable is the number of Covid-19 cases and the independent variable is the maximum temperature, average temperature and humidity. The data are a daily time series with a range of March 1, 2020 to April 30, 2022. Based on the GCMR model, Maximum temperature, average temperature and Humidity have a significant influence on the number of Covid-19 cases in Jakarta. For this case study, the results show that the GCMR model is better than logistic regression analysis. [ABSTRACT FROM AUTHOR]