Predicting and Interpreting Students Performance using Supervised Learning and Shapley Additive Explanations
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.]]>autTian, WenbothsHsiao, IhandgcBazzi, RidadgcDavulcu, HasanpblArizona State UniversityengMasters Thesis Computer Science 2019https://hdl.handle.net/2286/R.I.5345200Masters ThesisAcademic theses43 pages115579410301630032421157028systemIn Copyright2019TextComputer ScienceStatisticsComputer EngineeringdataEducational Data MiningMachine LearningShapley Additive ExplanationsStudentssupervised learning