Renewal of the Concept of Diverse Education: Possibility of Further Education Based on a Novel AI-Based RF–ISSA Model
Abstract
:1. Introduction
(1) Admission criteria and academic performance | |||||
Author | Analysis Target | Data Set Size | Machine Learning Model | Maximum Accuracy | R2 |
Adekitan, AI [21] | Admission criteria and the academic performance of the student after the first academic session | 100 | KNIME model | 50.23% | - |
Orange model | 51.9% | - | |||
Linear regression model | - | 0.207 | |||
Quadratic regression model | - | 0.232 | |||
(2) Students learning ability | |||||
Author | Analysis Target | Data Set Size | Machine Learning Model | Accuracy | |
Kukkar [22] | Predicting students’ pass or fail outcome in certain courses | 32,593 | Recurrent neural network + long short term memory network + support vector machine (RNNs + LSTM + SVM) | 90.67% | |
Recurrent neural network + long short term memory network + naive Bayes (RNNs + LSTM + NB) | 86.45% | ||||
Recurrent neural network + long short term memory network + decision tree (RNNs + LSTM + DT) | 84.42% | ||||
Raheela Asif [23] | Study the performance of undergraduate students | 210 | Decision tree with Gini index | 68.27% | |
Decision tree with information gain | 69.23% | ||||
Decision tree with accuracy | 60.58% | ||||
Rule induction with information gain | 55.77% | ||||
1-Nearest neighbor | 74.04% | ||||
Neural networks | 62.50% | ||||
Random forest trees with accuracy | 62.50% | ||||
Jui-Long Hung [24] | Evaluates the accuracy of student learning ability | 509 | DNN | 84.79% | |
RF | 85.37% | ||||
DNN | 95.89% | ||||
RF | 95.53% | ||||
Ashima Kukkar [25] | Performance of students in higher education | 32593 | Three-layer stacked long short term memory + random forest + gradient boosting (LSTM + RF + GB) | 91.66% | |
Two-layer stacked LSTM + RF + GB | 86.54% | ||||
One-layer LSTM + RF + GB | 75.47% | ||||
(3) Students’ comprehensive psychological quality and achievement | |||||
Author | Analysis Target | Data Set Size | Machine Learning Model | Mean AUC | |
Radwan, AM [26] | Response of students with autism and students’ failure | 3739 | Support vector machine | 0.74 | |
AdaBoost | 0.73 | ||||
Logistic regression | 0.72 | ||||
Liana Maria Crivei [27] | Students’ course achievement | 2401 | Students’ performance prediction using relational association rules (SPRAR) | 0.74 | |
DT | 0.61 | ||||
ANN | 0.67 | ||||
SVM | 0.65 | ||||
Original SPRAR | 0.7 |
2. Materials
3. Proposed Methodology
3.1. ISSA
3.2. RF
3.3. Ten-Fold Cross-Validation and Judgment Index
3.3.1. Ten-Fold Cross-Validation
- The entire data set is randomly divided into 10 subsets of similar size.
- One subset is selected as the verification set and the other nine subsets are selected as the training set. The training set is used as the data for model construction and training. The rest of the verification set is used to check the accuracy of the model built at this time.
- The above steps are repeated 10 times, each time selecting a different subset as the validation set to ensure that the validation set used in each experiment is unique.
- The performance indicators after each iteration were recorded and the values of ten different experiments were compared in order to evaluate the accuracy and generalizability of the model.
3.3.2. Judgment Index
4. Results and Discussion
4.1. Data Analysis
4.2. Correlation Analysis
4.3. Hyperparameter Tuning
4.4. Model Construction
4.5. Ten-Fold Cross-Validation Result
4.6. Importance of Input Variables
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Li, E.; Wang, Z.; Liu, J.; Huang, J. Renewal of the Concept of Diverse Education: Possibility of Further Education Based on a Novel AI-Based RF–ISSA Model. Appl. Sci. 2025, 15, 250. https://doi.org/10.3390/app15010250
Li E, Wang Z, Liu J, Huang J. Renewal of the Concept of Diverse Education: Possibility of Further Education Based on a Novel AI-Based RF–ISSA Model. Applied Sciences. 2025; 15(1):250. https://doi.org/10.3390/app15010250
Chicago/Turabian StyleLi, Enhui, Zixi Wang, Jin Liu, and Jiandong Huang. 2025. "Renewal of the Concept of Diverse Education: Possibility of Further Education Based on a Novel AI-Based RF–ISSA Model" Applied Sciences 15, no. 1: 250. https://doi.org/10.3390/app15010250
APA StyleLi, E., Wang, Z., Liu, J., & Huang, J. (2025). Renewal of the Concept of Diverse Education: Possibility of Further Education Based on a Novel AI-Based RF–ISSA Model. Applied Sciences, 15(1), 250. https://doi.org/10.3390/app15010250