A Study of Educational Data Mining: Evidence from a Thai University
Authors: Ruangsak Trakunphutthirak, Yen Cheung, Vincent C. S. Lee734-741
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To contribute to this area of research, we employed two datasets such as web-browsing categories and Internet access activity types to select the best outcomes, and compared different weights in the time and frequency domains. We found that the random forest technique provides the best outcome in these datasets to identify those students who are at-risk of failure. |
| Researcher Affiliation | Academia | Ruangsak Trakunphutthirak, Yen Cheung, Vincent C. S. Lee, SMIEEE Faculty of IT, Clayton Campus, Monash University Melbourne, Australia {ruangsak.trakunphutthirak, yen.cheung, vincent.cs.lee} @monash.edu |
| Pseudocode | No | The paper describes methods and includes flowcharts (e.g., Figure 1) but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available, nor does it provide a link to a code repository for its methodology. |
| Open Datasets | No | The university's log file was gathered by recording all internet access activities of students. ... The permission to use the dataset has been approved by the research ethics committee of the university. Due to privacy and security concerns, students identification was also encrypted and de-identified in the dataset used for this study. This indicates a private, internal dataset, not a publicly available one. |
| Dataset Splits | Yes | This study used 10 folds cross-validation to reduce the bias of a test dataset. |
| Hardware Specification | No | The paper does not mention any specific hardware used for running the experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions using various machine learning techniques like Decision Tree (J48), Logistic Regression, Naive Bayes, Neural Network, and Random Forest, but it does not specify any software platforms, libraries, or their version numbers used for implementation. |
| Experiment Setup | No | The paper discusses data preprocessing, attribute selection, and the use of different datasets (APP and CAT) and their combinations. However, it does not specify concrete hyperparameters or system-level training settings for the machine learning models (e.g., learning rates, batch sizes, epochs, optimizer details). |