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Predicting and Interpreting Students Performance using Supervised Learning and Shapley Additive Explanations

abstract: Due to large data resources generated by online educational applications, Educational Data Mining (EDM) has improved learning effects in different ways: Students Visualization, Recommendations for students, Students Modeling, Grouping Students, etc. A lot of programming assignments have the features like automating submissions, examining the test cases to verify the correctness, but limited studies compared different statistical techniques with latest frameworks, and interpreted models in a unified approach.

In this thesis, several data mining algorithms have been applied to analyze students’ code assignment submission data from a real classroom study. The goal of this work is to explore

and predict students’ performances. Multiple machine learning models and the model accuracy were evaluated based on the Shapley Additive Explanation.

The Cross-Validation shows the Gradient Boosting Decision Tree has the best precision 85.93% with average 82.90%. Features like Component grade, Due Date, Submission Times have higher impact than others. Baseline model received lower precision due to lack of non-linear fitting. / Dissertation/Thesis / Masters Thesis Computer Science 2019

Identiferoai:union.ndltd.org:asu.edu/item:53452
Date January 2019
ContributorsTian, Wenbo (Author), Hsiao, Ihan (Advisor), Bazzi, Rida (Committee member), Davulcu, Hasan (Committee member), Arizona State University (Publisher)
Source SetsArizona State University
LanguageEnglish
Detected LanguageEnglish
TypeMasters Thesis
Format43 pages
Rightshttp://rightsstatements.org/vocab/InC/1.0/

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