I have few students'(38) sequential data. For example, one student sequential data is like:
A B B A C D E . .
Each student has different length of sequences and their used letter was within this 5 letter. So, to predict the next letter/letters I used feed-forward neural network and recurrent-neural network(LSTM). I got reasonable accuracy with these models.
Now, I want to add a new attribute of the students to the input. Suppose, the students are from four different departments. I added the one hot vector of a particular department to the corresponding student.
I used Leave one out as a cross-validation. But for this new input, the testing accuracy is always 100% which is not possible.
My question is, Are there any methods for adding a new attribute to the sequential data?