Research Article
[Retracted] Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern
Algorithm 2
Prediction of students’ course grade according to their model.
| Input: U | | Output: prediction of students’ grade (A to F) | (1) | classification algorithms C = {Nearest Neighbors, Linear SVM, RBF SVM, Gaussian Process, Decision Tree, Random Forest, Neural Net, AdaBoost, Naive Bayes} | (2) | Evaluation measure = {accuracy, precision, f1, recall, Jaccard, fbeta} | (3) | | (4) | for i in C | (5) | Learning classification algorithm i with U | (6) | for j in | (7) | Evaluating the results of i with j | (8) | end | (9) | end | (10) | Choose the best classification algorithm by using TOPSIS | (11) | Select classification algorithm with highest performance | (12) | Use the algorithm to predict course grade | (13) | return prediction of students’ course grade |
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