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Student Exam Performance

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Student Exam Performance

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Llrsavoie@wpi.edu
Last edited Jan 24, 2024
Created on Jan 24, 2024

This visualization examines how study hours and previous exam scores affect pass/fail outcomes in a student performance dataset from Kaggle. The view displays the first record of the CSV as formatted JSON, showing the three variables: Study_Hours, Previous_Exam_Score, and Pass_Fail. The code uses a custom `main` function that writes the parsed data into an HTML `<pre>` element, with the dataset loaded and converted to numeric values using the `+` operator. The analysis questions focus on the independent and combined effects of these two predictors on the result.

AI-generated description

https://www.kaggle.com/datasets/mrsimple07/student-exam-performance-prediction

https://github.com/lukesavoie/dataviz/blob/main/student_exam_data.csv?plain=1 loaded and parsed as CSV.

Questions: The tests I would run on this dataset are how study hours and previous exam score independently affect the result. And then see together if there are any trends resulting in the result.