The Knowledge Query Network for Knowledge Tracing (KQN) model was evaluated using four different datasets:
1. **ASSISTments 2009-2010**: Collected by the ASSISTments online tutoring system.
2. **ASSISTments 2015**: A larger dataset than the ASSISTments 2009-2010 dataset.
3. **OLI Engineering Statics 2011**: A college-level statistics course dataset.
4. **Synthetic-5**: A dataset synthesized by the authors of the Deep Knowledge Tracing (DKT) paper.

San Diego State University

Google

Experiments (Knowledge Query Network for Knowledge Tracing)

1. **Correctness Prediction**: Every dataset was split into train-valid-test sets except Synthetic-5. The number of epochs varied between 50-200 and they used grid search for the hyper-parameters. Hidden state size for RNN and MLP layers and size of embedding dimensionality: 32, 64, 128. Adam optimization was used for the error minimization. Apart from that, the authors fed KQN learned skill vectors to the DKT model to evaluate their quality in predicting. They denote this model as DKT + KQN and the authors searched for the parameters in the same manner as in the KQN model.

2. **Skill Domain Analysis**: They clustered skills based on skill distances. After clustering they evaluated its performance by comparing them to ground true labels.

3. **Sensitivity Analysis of Vector Space Dimensionality**: $$d$$ denotes dimensionality and $$d_{opt}$$ optimal values of $$d$$ which we get from the previous correctness prediction task. In order to measure the effect of $$d$$ on the performance, the authors kept all the hyperparameters set to optimal values.

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