PREDICTING STUDENTS' PERFORMANCE IN DISTANCE LEARNING USING MACHINE LEARNING TECHNIQUES

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Web usage mining for predicting final marks of students that use Moodle courses (2013, methodology): “K nearest neighbors [32] is a method in which a data set is used as a reference to classify new instances with the help of a suitable distance measure.”
Applying Data Mining Techniques to e-Learning Problems (2007, methodology): “[59] Prediction Decision tree Teacher C [3] Prediction Bayesian Network Teacher B [44] Classification and Prediction Naïve Bayes, kNN, MLPANN, C4.”
Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review (2020, methodology): “Several ML algorithms were compared in [2] to predict the performance of new students, where NB showed the best behaviour in a web tool.”
Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores (2018, methodology): “ML algorithms are trained using sample data as inputs and then tested with new data [28].”
Using data mining to predict secondary school student performance (2008, methodology): “This confirms the conclusion found in (Kotsiantis et al. 2004): student achievement is highly affected by previous performances.”