What are your thoughts on training a model with pre-existing data? For instance, DKT/logistic models do not require any student/item specific parameters, hence, the model could be trained with data collected elsewhere, and then implemented in an application to make immediate, accurate predictions.
For a model that does not require item or student parameters, would this be appropriate? What are the benefits of using data from the same students/items to train general weights? A model trained on dataset A could then be tested on datasets B and C, just like a real flashcard scenario; what are your thoughts on this?
When trying to evaluate an IRT model through online prediction accuracy, after determining all item parameters, is the ability parameter updated through retraining the model with ALL the data collected thus far (all the students + training data), or just the students’ INDIVIDUAL data? In other words, what data is used to train the student-level parameters?
Thanks again, looking forward to your response.
What are your thoughts on training a model with pre-existing data? For instance, DKT/logistic models do not require any student/item specific parameters, hence, the model could be trained with data collected elsewhere, and then implemented in an application to make immediate, accurate predictions. For a model that does not require item or student parameters, would this be appropriate? What are the benefits of using data from the same students/items to train general weights? A model trained on dataset A could then be tested on datasets B and C, just like a real flashcard scenario; what are your thoughts on this?
When trying to evaluate an IRT model through online prediction accuracy, after determining all item parameters, is the ability parameter updated through retraining the model with ALL the data collected thus far (all the students + training data), or just the students’ INDIVIDUAL data? In other words, what data is used to train the student-level parameters? Thanks again, looking forward to your response.