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This lesson introduces students to the various design approaches for decision trees used in classifying datasets. It has been changed to also incorporate the "Is it for dress code?" lesson. My modification addition includes multiple discussion questions exploring the diverse types of winter experienced across different regions of the US. Furthermore, the modified lesson delves into the potential biases that may infiltrate decision trees.
I adapted this lesson to aid my students in comprehending how biases can influence decision tree outcomes. For instance, given that my students live in southern New Mexico, their perception of winters differs significantly from those living on the upper east coast. This adjustment allows students to recognize and understand the impact of their unique perspectives on decision-making processes.
In this context, these modifications are especially important for my students as they foster a deeper understanding of decision tree algorithms by contextualizing them within regional variations and potential biases. By incorporating discussions on different winter experiences across the US, students can relate the concepts to their own environment, enhancing engagement and relevance. This not only strengthens their grasp of decision tree design but also cultivates critical thinking skills as they consider the implications of biases within their own datasets and decision-making processes.
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