A Two-Phase Ensemble-Based Method for Predicting Learners’ Grade in MOOCs
MOOCs are online learning environments which many students use, but the success rate of online learning is low. Machine learning can be used to predict learning success based on how people learn in MOOCs. Predicting the learning performance can promote learning through various methods, such as ident...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/3/1492 |
| _version_ | 1827760424546729984 |
|---|---|
| author | Warunya Wunnasri Pakarat Musikawan Chakchai So-In |
| author_facet | Warunya Wunnasri Pakarat Musikawan Chakchai So-In |
| author_sort | Warunya Wunnasri |
| collection | DOAJ |
| description | MOOCs are online learning environments which many students use, but the success rate of online learning is low. Machine learning can be used to predict learning success based on how people learn in MOOCs. Predicting the learning performance can promote learning through various methods, such as identifying low-performance students or by grouping students together. Recent machine learning has enabled the development of predictive models, and the ensemble method can assist in reducing the variance and bias errors associated with single-machine learning. This study uses a two-phase classification model with an ensemble technique to predict the learners’ grades. In the first phase, binary classification is used, and the non-majority class is then sent to the second phase, which is multi-class classification. The new features are computed based on the distance from the class’s center. The distance between the data and the center of an overlapping cluster is calculated using silhouette score-based feature selection. Lastly, Bayesian optimization boosts the performance by fine tuning the optimal parameter set. Using data from the HMPC- and the CNPC datasets, the experiment results demonstrate that the proposed design, the two-phase ensemble-based method, outperforms the state-of-the-art machine learning algorithms. |
| first_indexed | 2024-03-11T09:53:01Z |
| format | Article |
| id | doaj.art-73c7632ff2a440bcac4765b0b1239358 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:53:01Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-73c7632ff2a440bcac4765b0b12393582023-11-16T16:06:14ZengMDPI AGApplied Sciences2076-34172023-01-01133149210.3390/app13031492A Two-Phase Ensemble-Based Method for Predicting Learners’ Grade in MOOCsWarunya Wunnasri0Pakarat Musikawan1Chakchai So-In2Department of Computer Science, College of Computing, Khon Kaen University, Khon Kaen 40002, ThailandDepartment of Computer Science, College of Computing, Khon Kaen University, Khon Kaen 40002, ThailandDepartment of Computer Science, College of Computing, Khon Kaen University, Khon Kaen 40002, ThailandMOOCs are online learning environments which many students use, but the success rate of online learning is low. Machine learning can be used to predict learning success based on how people learn in MOOCs. Predicting the learning performance can promote learning through various methods, such as identifying low-performance students or by grouping students together. Recent machine learning has enabled the development of predictive models, and the ensemble method can assist in reducing the variance and bias errors associated with single-machine learning. This study uses a two-phase classification model with an ensemble technique to predict the learners’ grades. In the first phase, binary classification is used, and the non-majority class is then sent to the second phase, which is multi-class classification. The new features are computed based on the distance from the class’s center. The distance between the data and the center of an overlapping cluster is calculated using silhouette score-based feature selection. Lastly, Bayesian optimization boosts the performance by fine tuning the optimal parameter set. Using data from the HMPC- and the CNPC datasets, the experiment results demonstrate that the proposed design, the two-phase ensemble-based method, outperforms the state-of-the-art machine learning algorithms.https://www.mdpi.com/2076-3417/13/3/1492learning performancegrade predictionMOOCimbalance dataensemble method |
| spellingShingle | Warunya Wunnasri Pakarat Musikawan Chakchai So-In A Two-Phase Ensemble-Based Method for Predicting Learners’ Grade in MOOCs Applied Sciences learning performance grade prediction MOOC imbalance data ensemble method |
| title | A Two-Phase Ensemble-Based Method for Predicting Learners’ Grade in MOOCs |
| title_full | A Two-Phase Ensemble-Based Method for Predicting Learners’ Grade in MOOCs |
| title_fullStr | A Two-Phase Ensemble-Based Method for Predicting Learners’ Grade in MOOCs |
| title_full_unstemmed | A Two-Phase Ensemble-Based Method for Predicting Learners’ Grade in MOOCs |
| title_short | A Two-Phase Ensemble-Based Method for Predicting Learners’ Grade in MOOCs |
| title_sort | two phase ensemble based method for predicting learners grade in moocs |
| topic | learning performance grade prediction MOOC imbalance data ensemble method |
| url | https://www.mdpi.com/2076-3417/13/3/1492 |
| work_keys_str_mv | AT warunyawunnasri atwophaseensemblebasedmethodforpredictinglearnersgradeinmoocs AT pakaratmusikawan atwophaseensemblebasedmethodforpredictinglearnersgradeinmoocs AT chakchaisoin atwophaseensemblebasedmethodforpredictinglearnersgradeinmoocs AT warunyawunnasri twophaseensemblebasedmethodforpredictinglearnersgradeinmoocs AT pakaratmusikawan twophaseensemblebasedmethodforpredictinglearnersgradeinmoocs AT chakchaisoin twophaseensemblebasedmethodforpredictinglearnersgradeinmoocs |