@inproceedings{2f833225789049e286d6ff0cc7d6fc45,
title = "Studying the relationship between BKT fitting error and the skill difficulty index",
abstract = "Bayesian Knowledge Tracing (BKT) is one of the most popular knowledge inference models due to its interpretability and ability to infer student knowledge. A proper student modeling can help guide the behavior of a cognitive tutor system and provide insight to researchers on understanding how students learn. Using four different datasets we study the relationship between the error coming from fitting the parameters and the difficulty index of the skills and the effect of the size of the dataset in this relationship. The relationship between the fitting error and the difficulty index can be very easy modeled and might be indicating some problems with BKTs performance. However, large datasets are required to clearly see this connection as there is an important sample size effect.",
keywords = "BKT, BKT-BF, Difficulty index, Educational data mining, RMSE modeling",
author = "Francesc Martori and Jordi Cuadros and Lucinio, {Gonz{\'a}lez Sabat{\'e}}",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 6th International Conference on Learning Analytics and Knowledge, LAK 2016 ; Conference date: 25-04-2016 Through 29-04-2016",
year = "2016",
month = apr,
day = "25",
doi = "10.1145/2883851.2883901",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "364--368",
booktitle = "LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact",
}