Feature Correlation with Student Education Performance

Student Feature Correlation with Student Academic Performance in the Student Performance Dataset

Authors

  • Ritvik Gupta Student - High School
  • Claire Gueneau Mentor - High School AP Research Teacher

DOI:

https://doi.org/10.47611/jsrhs.v10i2.1680

Keywords:

Machine Learning, Artificial Intelligence, AI, ML, EDM, Educational Data Mining, UCI, Student Performance Dataset

Abstract

The 21st century has seen the advent of the internet as well as the spread of increasingly powerful computer technologies. One of these new technologies is Artificial Intelligence and Machine Learning. These computer models assist in pattern recognition, task performance as well as prediction. One place where this technology can be used is Educational Data Mining. This study used these ML technologies on the Student Performance Dataset to see what features are correlated with high student academic performance. This study also utilized Feature Engineering to derive features that represent the interactions of different features from the original dataset in order to conduct further analysis.This study found that multiple different features such as parent relationship status, travel time between home and school, among others, had a positive correlation with student academic performance. Features such as past failures and increasing frequency of hanging out with friends after school was correlated with negative student academic performance. However, results with the ML models as well as Feature Engineering were inconclusive due to the results not having a high enough accuracy to merit analysis.

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Author Biography

Claire Gueneau, Mentor - High School AP Research Teacher

Ms. Gueneau is my AP Research teacher as well as my mentor for this study.

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Published

08-16-2021

How to Cite

Gupta, R., & Gueneau, C. (2021). Feature Correlation with Student Education Performance: Student Feature Correlation with Student Academic Performance in the Student Performance Dataset. Journal of Student Research, 10(2). https://doi.org/10.47611/jsrhs.v10i2.1680

Issue

Section

AP Capstone™ Research