# Modeling Knowledge Acquisition from Multiple Learning Resource Types

arXiv: https://arxiv.org/abs/2006.13390 · HTML: https://mlsanity.com/paper/2006.13390/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems](https://mlsanity.com/paper/2402.01746/) (2024)
- [Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization](https://mlsanity.com/paper/2210.09013/) (2022)
- [Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI](https://mlsanity.com/paper/2409.15631/) (2024)
- [Assessing the Performance of Online Students -- New Data, New Approaches, Improved Accuracy](https://mlsanity.com/paper/2109.01753/) (2021)
- [Transition-Aware Multi-Activity Knowledge Tracing](https://mlsanity.com/paper/2301.12916/) (2023)
- [Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks](https://mlsanity.com/paper/2503.18982/) (2025)

## Abstract (arXiv)

Students acquire knowledge as they interact with a variety of learning materials, such as video lectures, problems, and discussions. Modeling student knowledge at each point during their learning period and understanding the contribution of each learning material to student knowledge are essential for detecting students' knowledge gaps and recommending learning materials to them. Current student knowledge modeling techniques mostly rely on one type of learning material, mainly problems, to model student knowledge growth. These approaches ignore the fact that students also learn from other types of material. In this paper, we propose a student knowledge model that can capture knowledge growth as a result of learning from a diverse set of learning resource types while unveiling the association between the learning materials of different types. Our multi-view knowledge model (MVKM) incorporates a flexible knowledge increase objective on top of a multi-view tensor factorization to capture occasional forgetting while representing student knowledge and learning material concepts in a lower-dimensional latent space. We evaluate our model in different experiments toshow that it can accurately predict students' future performance, differentiate between knowledge gain in different student groups and concepts, and unveil hidden similarities across learning materials of different types.