Dynamic Key-Value Memory Networks for Knowledge Tracing
- MXNet - The framework used
- Both Python2 and Python3 are supported
- progress - Dependency package
The first line the number of exercises a student attempted. The second line is the exercise tag sequence. The third line is the response sequence.
15
1,1,1,1,7,7,9,10,10,10,10,11,11,45,54
0,1,1,1,1,1,0,0,1,1,1,1,1,0,0
--gpus: the gpus will be used, e.g "0,1,2,3"
--max_iter: the number of iterations
--test: enable testing
--train_test: enable testing after training
--show: print progress
--init_std: weight initialization std
--init_lr: initial learning rate
--final_lr: learning rate will not decrease after hitting this threshold
--momentum: momentum rate
--maxgradnorm: maximum gradient norm
--final_fc_dim: hidden state dim for final fc layer
--n_question: the number of unique questions in the dataset
--seqlen: the allowed maximum length of a sequence
--data_dir: data directory
--data_name: data set name
--load: model file to load
--save: path to save model
python main.py --gpus 0
python main.py --gpus 0 --test True
Jiani Zhang, Xingjian Shi, Irwin King, Dit-Yan Yeung. Dynamic Key-Value Memory Networks for Knowledge Tracing. In Proceedings of the 26th International Conference on World Wide Web, 2017: 765-774.