- Supervised Learning: In supervised learning, labeled datasets are used to train models to predict specific outcomes. For example, models can predict learner success or identify learners who may need additional support based on historical performance data.
- Unsupervised Learning: Unsupervised learning algorithms, such as clustering, are used to group learners based on shared characteristics or behaviors without pre-existing labels. This helps in segmenting learners for targeted interventions.
- Recurrent Neural Networks (RNNs): RNNs are used to model temporal sequences of learner interactions. This is useful for understanding how learning behaviors change over time and predicting future behaviors based on historical sequences.