1. Synthetic
2. ASSIST2009
3. ASSIST2015
4. ASSISTChall
5. STATICS2011

San Diego State University

Experimental Setting (A Self-Attentive model for Knowledge Tracing)

Dataset (A Self-Attentive model for Knowledge Tracing)

**Metrics** - prediction task is binary classification task and the authors use AUC (area under curve) to measure the performance.
**Approaches** - their created model is compared to previous models, namely, DKT (deep knowledge tracing), DKT+, DKVMN(dynamic key value memory network).
**Model training and parameter selection** the dataset was divided into train/test sets in the following way: 80% goes to train and 20% to test. Different size of hidden state have been tried: {50, 100, 150, 200}. The model was implemented in tensorflow and adam optimizer with 0.001 was utilized.  

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