This is the way the authors encode feature to the vector x:

1. Users - they create one-hot encoders for each students.
2. Items - the questions/tasks/items are represented as one-hot encoders.
3. Skills - they can be allocated extra s features for s skills. The skill that is included in the observation of a student over a question j are the ones of KC(j).
4. Attempts - can be encoded of counters of the attempts to learn a skill.
5. Wins & Fails - they can be allocated as s extra features for successfully answering/solving an item and s extra features for the unsuccessful answers.
6. Extra side information - the information like school id, teacher id, low stake or high stake practice can be concatenated to the other features.

This is the example of encoding users + items + skills + wins + fails: