AI/ML in Next Generation E-learning Solutions

As the Next Gen E-learning solutions will cover some or all objectives to bring effective utilization of e-learning solutions using AI/ML, some of them are already present in some solutions.


Moodle is shipped with the following core models,

  • Students at risk of dropping out
  • Upcoming activities due
  • No teaching

The following table contains, AI/ ML, Data-mining Techniques in E-learning solutions for different objectives with prediction accuracy.

E-learning FeatureML Models and TechniquePrediction AccuracyCriteria Evaluated using ML
User OpinionHMM [Hidden Markov Model],SVM [Support Vector Machine]
Data Mining Technique :
MI [Mutual Information],IG[Infor-
mation Gain],CHI
F-Measure 0.803The accuracy rate of user opinion predicted
Course Recommendation to StudentsADTree classification Algorithm, Apriori Association Rule algorithm, Simple K-Means AlgorithmApriori Association gives the best cluster of coursesCourse mapping is obtained
Timely system response to studentsGenetic Algorithm, ML techniqueAutomated web bot gives timely reply
Student performance knowledgeSVMF-Measures-0.986Predicts the rate of student’s knowledge
Student Emotionsk-NN, SVMSVM accuracy ration- 97.15%Accurately predicts students emotions
Online session assessmentEnsemble Classifier Baggins embedded with ML78.04% accuracyPredict the beneficial session
Student ranking creditsECOC [Error Correcting Output Code] combines ClassifierF statistics is 3.05Predicts college opportunity
Learning style and learning objectsBayesian EstimationBayesian infer the increase in the visual categoryEstimate learning style
Learner behaviors sequenceFuzzy cluster technique78% matched with real-world dataPredicts learner behavior
Learner sequence, Learning patternFCM [Fuzzy Clustering Methodd], K-means clusteringFCM shows 96.89% accuracy
K-Means shows 80.12 % accuracy
Classified learner Sequence
Student graduation resultsperception ANN [Artificial Neural Network]Predicts successful 77% unsuccessful 68%Predicts graduation successfulness
AUI features coursesFelder Silverman modelClassifies learning ModelsLearning Models predicted
Course informationANN, LMA algorithmR-value 9.08%Evaluate future GPA
learning processing dataConv-GRU-avgP in P-xNNaccuracy 80.04%Predicted Learning Performance
Student assessmentDeep learning TensorFlow Engine80%-91% of accuracyPredicts student pathway
Student test resultsRadom Forest26.7% error ratepredicts student performance
Student engagement in the courseK-means clusteringSilhouette coefficient for two-level cluster is .7003Classify student groups

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