Experimental Setting (DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills)
To evaluate the DAS3H model, researchers split the student population into five disjoint groups for cross-validation. Every feature weight and embedding component was set to follow a normal prior distribution, and the models were implemented in Python. The DAS3H model was compared against the DASH, IRT, PFA, and AFM models using 0, 5, and 20 embedding dimensions. These experiments utilized three datasets: ASSISTments (assist12), Bridge to Algebra (bridge06), and Algebra I (algebra05).
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Experimental Setting (DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills)
Results (DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills)
Discussion (DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills)