# Transferable Student Performance Modeling for Intelligent Tutoring Systems

arXiv: https://arxiv.org/abs/2202.03980 · HTML: https://mlsanity.com/paper/2202.03980/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 56 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Learned in Translation: Contextualized Word Vectors](https://mlsanity.com/paper/1708.00107/) (2017)
- [SAINT+: Integrating Temporal Features for EdNet Correctness Prediction](https://mlsanity.com/paper/2010.12042/) (2020)
- [DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills](https://mlsanity.com/paper/1905.06873/) (2019)
- [R2DE: a NLP approach to estimating IRT parameters of newly generated questions](https://mlsanity.com/paper/2001.07569/) (2020)
- [pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models](https://mlsanity.com/paper/2105.00385/) (2021)
- [Introducing a framework to assess newly created questions with Natural Language Processing](https://mlsanity.com/paper/2004.13530/) (2020)
- [Transfer Learning using Representation Learning in Massive Open Online Courses](https://mlsanity.com/paper/1812.05043/) (2018)
- [Assessment Modeling: Fundamental Pre-training Tasks for Interactive Educational Systems](https://mlsanity.com/paper/2002.05505/) (2020)
- [Knowledge Transfer by Discriminative Pre-training for Academic Performance Prediction](https://mlsanity.com/paper/2107.04009/) (2021)

## Abstract (arXiv)

Millions of learners worldwide are now using intelligent tutoring systems (ITSs). At their core, ITSs rely on machine learning algorithms to track each user's changing performance level over time to provide personalized instruction. Crucially, student performance models are trained using interaction sequence data of previous learners to analyse data generated by future learners. This induces a cold-start problem when a new course is introduced for which no training data is available. Here, we consider transfer learning techniques as a way to provide accurate performance predictions for new courses by leveraging log data from existing courses. We study two settings: (i) In the naive transfer setting, we propose course-agnostic performance models that can be applied to any course. (ii) In the inductive transfer setting, we tune pre-trained course-agnostic performance models to new courses using small-scale target course data (e.g., collected during a pilot study). We evaluate the proposed techniques using student interaction sequence data from 5 different mathematics courses containing data from over 47,000 students in a real world large-scale ITS. The course-agnostic models that use additional features provided by human domain experts (e.g, difficulty ratings for questions in the new course) but no student interaction training data for the new course, achieve prediction accuracy on par with standard BKT and PFA models that use training data from thousands of students in the new course. In the inductive setting our transfer learning approach yields more accurate predictions than conventional performance models when only limited student interaction training data (<100 students) is available to both.