Abstract:

One of the goal of sustainable development is education. Education for Sustainable Development (ESD) empowers learners of all ages with the knowledge, skills, values and attitudes to address the interconnected global challenges we are facing, including climate change, environmental degradation, loss of biodiversity, poverty and inequality. Education for Sustainable Development (ESD) empowers learners of all ages with the knowledge, skills, values and attitudes to address the interconnected global challenges we are facing, including climate change, environmental degradation, loss of biodiversity, poverty and inequality. Learning must prepare students and learners of all ages to find solutions for the challenges of today and the future. Education should be transformative and allow us to make informed decisions and take individual and collective action to change our societies and care for the planet. Education for Sustainable Development is recognized as an integral element of Sustainable Development Goal (SDG) 4 on quality education and a key enabler of all other SDGs. In this paper apply Item Response Theory (IRT) to verify the hypothesis that education knowledge is essential for further sustainable development of the country and plays a crucial role in labor market perspectives as well as reduces social exclusion and poverty. Item response theory (IRT) has become a popular methodological framework for modeling response data from assessments in education and health; however, its use is not widespread in economic research. In this paper we apply IRT models for the analysis of economic using author`s economic knowledge scale. We constructed nominal-item test made up 20 one correct answer test items. The test was administered among Polish students at the University of Economics in Katowice. We applied IRT model, we conducted detailed item analysis and item response theory. We presented graphically Item Information Curve (IIC) and Item Characteristic Curve (ICC). Thanks to graphical presentation we can analyze the level of information based on different levels of latent variable and the probability of answering the question correctly for each item for Rasch and Birnbaum two-parameter model. All calculations are conducted in R software.