The experiment for predicting study session dropout in a mobile learning environment involves specific training configurations and baseline comparisons:

1. Training Details

• The model with the best Area Under the ROC Curve (AUC) score was selected for the testing phase.
• The optimal model configuration included 4 stacked encoder/decoder layers, a latent dimension of 512, and 8 heads in the multi-head attention layer.
• Xavier initialization and the Adam optimizer were used during training.

2. Baseline Comparison and Input Representation

• Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks were evaluated as baselines alongside the Deep Attentive Study (DAS) model.
• The input representations for these baseline models were: $in_{i-1} + e_i$ and $in_{i-1} = l_{i-1} + e_{i-1}$
• Different sequence sizes of 5 and 25 were tested across all models.