In the rapidly changing, 21st century globalized world, with increasing environmental pressures and challenges, education for the environment and sustainability is a priority at all levels; from kindergarten to higher education. It is the education that will create the environmentally aware and socially responsible individuals, capable of addressing existing and future environmental challenges. Courses on the environment and/or sustainability are now an essential element of all Higher Education Institutions with a sustainability vision. But, does teaching about the environment and sustainability lead to a change in student attitudes? What teaching and learning methods seem to have a more significant effect on attitudes and behaviors and what are the challenges for instructors? In this study, instructors reflect on which educational methods seem most effective in promoting change in student attitudes and behaviors towards the environment and sustainability. This reflection is based on instructor experiences from selected courses or course activities (learning objects) and it focuses on the goals, teaching methods and effect on student learning and attitudes; changes in student attitudes in the course of the last years are also discussed. Suggestions are offered and implications for higher education institutions are outlined.
Keywords: education for the environment, education for sustainability, higher education, active learning, student behaviors, emotional engagement
The emerging advances of Bioinformatics have already contributed toward the establishment of better next generation medicine and medical systems by putting emphasis on improvement of prognosis, diagnosis and therapy of diseases including better management of medical systems. The purpose of this chapter is to explore ways by which the use of Bioinformatics and Smart Data Analysis will provide an overview and solutions to challenges in the fields of genomics, medicine and Health Informatics. The focus of this chapter would be on Smart Data Analysis and ways needed to filter out the noise. The chapter addresses challenges researchers and data analysts are facing in terms of the developed computational methods used to extract insights from NGS and high-throughput screening data. In this chapter the concept “Wise Data” is proposed reflecting the distinction between individual health and wellness on the one hand, and social improvement, cohesion and sustainability on the other, leading to more effective medical systems, healthier individuals and more socially cohesive societies.
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