Progressive Prediction of Student Performance in College Programs
Authors: Jie Xu, Yuli Han, Daniel Marcu, Mihaela van der Schaar
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove its prediction performance guarantee and show its performance improvement against benchmark algorithms on a real-world student dataset from UCLA . |
| Researcher Affiliation | Academia | University of Miami, Coral Gables, FL 33124 University of California Los Angeles, Los Angeles, CA 90095 Information Sciences Institute, University of Southern California, Marina del Ray, CA 90292 |
| Pseudocode | Yes | Algorithm 1 Ensemble-based Progressive Prediction (EPP) |
| Open Source Code | No | The paper does not provide any statement about making its source code available or provide a link to a code repository. |
| Open Datasets | No | Student data used to test our algorithm is collected from UCLA Mechanical and Aerospace Engineering Department. The dataset has 367 anonymized students enrolled in the same program. |
| Dataset Splits | No | Half of the students were used as the training data and the remaining students were used as the testing data. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions implementing SVM and KNN but does not provide specific version numbers for any software or libraries used. |
| Experiment Setup | No | The paper describes feature construction and the general algorithm, but lacks specific details such as hyperparameter values (e.g., learning rates, batch sizes) or explicit training configurations. |