مؤتمر
Back to the Basics: Bayesian Extensions of IRT Outperform Neural Networks for Proficiency Estimation
| العنوان: | Back to the Basics: Bayesian Extensions of IRT Outperform Neural Networks for Proficiency Estimation |
|---|---|
| اللغة: | English |
| المؤلفون: | Wilson, Kevin H., Karklin, Yan, Han, Bojian, Ekanadham, Chaitanya |
| المصدر: | International Educational Data Mining Society. 2016. |
| الإتاحة: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Peer Reviewed: | Y |
| Page Count: | 6 |
| تاريخ النشر: | 2016 |
| نوع الوثيقة: | Speeches/Meeting Papers Reports - Research |
| Descriptors: | Item Response Theory, Bayesian Statistics, Computation, Artificial Intelligence, Computer Assisted Instruction, Prediction, Student Reaction, Accuracy |
| مستخلص: | Estimating student proficiency is an important task for computer based learning systems. We compare a family of IRT-based proficiency estimation methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural network model with promising initial results. We evaluate how well each model predicts a student's future response given previous responses using two publicly available and one proprietary data set. We find that IRT-based methods consistently matched or outperformed DKT across all data sets at the finest level of content granularity that was tractable for them to be trained on. A hierarchical extension of IRT that captured item grouping structure performed best overall. When data sets included non-trivial autocorrelations in student response patterns, a temporal extension of IRT improved performance over standard IRT while the RNN-based method did not. We conclude that IRT-based models provide a simpler, better-performing alternative to existing RNN-based models of student interaction data while also affording more interpretability and guarantees due to their formulation as Bayesian probabilistic models. [For the full proceedings, see ED592609.] |
| Abstractor: | As Provided |
| Entry Date: | 2019 |
| رقم الأكسشن: | ED592649 |
| قاعدة البيانات: | ERIC |
| الوصف غير متاح. |