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Found 5 papers, 2 papers with code
Causal Inference in Educational Systems: A Graphical Modeling Approach
We discuss the applications and limitations of each method in education.
tsBNgen: A Python Library to Generate Time Series Data from an Arbitrary Dynamic Bayesian Network Structure
Bayesian networks are a type of probabilistic graphical model widely used to model the uncertainties in real-world processes.
Comparative Analysis of the Hidden Markov Model and LSTM: A Simulative Approach
Our results indicate that even an unsupervised hidden Markov model can outperform LSTM when a massive amount of labeled data is not available.
A clustering approach to time series forecasting using neural networks: A comparative study on distance-based vs. feature-based clustering methods
Time series forecasting has gained lots of attention recently; this is because many real-world phenomena can be modeled as time series.
Predicting Student Performance in an Educational Game Using a Hidden Markov Model
This paper shows that students' knowledge throughout the intervention can be estimated by time series analysis using a hidden Markov model.
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