Abstract
In this chapter, we use machine learning techniques to investigate how the effects of climate change and certain risk factors for mental health affect students' cognitive skills in the classroom. The mental health of at-risk populations, especially students, must be considered in light of the fact that the world's environment is changing significantly. Using state-of-the-art machine learning algorithms, we analyze large datasets that include environmental variables, socio-economic characteristics, and markers of mental health among school-aged persons. We are primarily interested in identifying key relationships and trends that might help us understand the complex relationship between climate change and cognitive health in this population. In order to uncover complex insights, the chapter takes a holistic approach by combining feature selection, model training, and interpretability analysis. The cognitive capacities of school-aged children may be significantly impacted by some climate-related stresses, according to preliminary results. The findings add to our knowledge of the interconnected webs of environmental shifts, psychological susceptibilities, and cognitive consequences. Educators, legislators, and healthcare providers can benefit from this study's use of machine learning insights into the possible effects of climate change on students' mental health. It also paves the way for the creation of tailored treatments and adaptive techniques to deal with the highlighted dangers, fostering resilience and prosperity in the face of a changing environment.
TopReview Of Literature
More and more, people are starting to notice that climate change is having an effect on people's mental health, and they're paying close attention to how this may affect vulnerable groups, including children. In order to set the stage for comprehending how climate change, mental health, and students' cognitive capacities interact with one another, this study compiles important results from previous research, with an emphasis on insights obtained using machine learning approaches. Climate change impacts mental health outcomes in a variety of ways, according to research. Anxiety, despair, and post-traumatic stress disorder (PTSD) are more common among those who experience severe weather, according to research by Berry et al. (2018). Loss of livelihoods, relocation, and other forms of climate-related environmental degradation can cause psychological distress, as highlighted by Watts et al. (2015). This is especially true for low-income groups. Given their developmental stage and need on stable conditions for learning and emotional well-being, schoolchildren are an especially susceptible demographic in the context of climate change. Children are particularly vulnerable to the psychological and emotional effects of climate change, according to Haines et al. (2019), which can lead to difficulties in school and even mental illness.
New opportunities for unravelling the knotty interplay of context, emotion, and thought have opened up with the help of machine learning. One example is the work of Smith et al. (2021), who show how machine learning algorithms may help find subtle trends in massive datasets on the effects of climate change on people's mental health. This can lead to more specific therapies. Public health officials are starting to take notice of the negative effects of climate change on people's mental health (Clayton et al., 2017; Fritze et al., 2008). Extreme weather and environmental degradation are examples of climate-related stresses that might worsen anxiety, sadness, and PTSD in certain populations (Clayton et al., 2017). There has to be focused treatments to enhance mental well-being in the face of climate change because of the psychological distress that comes from climate-related disturbances to livelihoods and communities (Fritze et al., 2008).