Team Members: Dr.M.Krishnaveni, Assistant Professor (SG), Department of Computer Science
Dr.S.Meenakshi, Associate Professor & Head , Department of Computer Science , Gobi Arts and Science College
Ms.Jayashree Ganeshkumar, Research Scholar, Department of Computer Science
Ms.T.Bharathi , II MCA , Gobi Arts and Science College
Project Summary:The Project entitled “STUDENT’S PERFORMANCE PREDICTION USING STACKED ENSEMBLE TECHNIQUE ON ONLINE PROGRAMMING COURSE” for identify the students who are at risk of facing challenges in online programming courses and eventually dropout of the course. Once at-risk students are identified, the system aims to facilitate timely intervention strategies, such as additional support, counseling, or targeted learning resources, to help these students improve their programming performance.
To predict student’s final scores based on their programming submission data and elucidate the predictive model's decisions, a structured approach involving four key steps is followed. i) data preprocessing is executed to refine the raw data, ensuring its cleanliness and suitability for analysis. This involves tasks such as handling missing values and encoding categorical variables. ii) feature engineering is conducted to extract data-driven features from the programming submission data. This step aims to enhance the representation of patterns and relationships within the dataset, facilitating more accurate predictions. iii) regression models are developed and utilized to forecast the final scores using the engineered features. Regression techniques are chosen due to their suitability for predicting continuous outcomes like numerical scores. iv) The model's decisions are elucidated using interpretability techniques such as SHAP(SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). These methods provide valuable insights into the influential features driving the model's predictions, thereby enhancing transparency and understanding of the predictive process.
This project has been developed to predict the student performance using the Stacked Ensemble model with a R2 of 0.72. This project has been developed using python.
