# LANA: Towards Personalized Deep Knowledge Tracing Through Distinguishable Interactive Sequences

arXiv: https://arxiv.org/abs/2105.06266 · HTML: https://mlsanity.com/paper/2105.06266/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 21 Apr 2021.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Rethinking Positional Encoding in Language Pre-training](https://mlsanity.com/paper/2006.15595/) (2020)
- [Context-Aware Attentive Knowledge Tracing](https://mlsanity.com/paper/2007.12324/) (2020)
- [A Self-Attentive model for Knowledge Tracing](https://mlsanity.com/paper/1907.06837/) (2019)
- [Dynamic Key-Value Memory Networks for Knowledge Tracing](https://mlsanity.com/paper/1611.08108/) (2016)
- [EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction](https://mlsanity.com/paper/1906.05658/) (2019)
- [EdNet: A Large-Scale Hierarchical Dataset in Education](https://mlsanity.com/paper/1912.03072/) (2019)
- [Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing](https://mlsanity.com/paper/2002.07033/) (2020)
- [RKT : Relation-Aware Self-Attention for Knowledge Tracing](https://mlsanity.com/paper/2008.12736/) (2020)
- [SAINT+: Integrating Temporal Features for EdNet Correctness Prediction](https://mlsanity.com/paper/2010.12042/) (2020)
- [Engaging with Massive Online Courses](https://mlsanity.com/paper/1403.3100/) (2014)
- [Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation](https://mlsanity.com/paper/1604.02336/) (2016)
- [Deep Knowledge Tracing with Side Information](https://mlsanity.com/paper/1909.00372/) (2019)

## Built on by (most cited first, 4 of 4)

- [A Survey of Explainable Knowledge Tracing](https://mlsanity.com/paper/2403.07279/) (2024)
- [Assessing the Performance of Online Students -- New Data, New Approaches, Improved Accuracy](https://mlsanity.com/paper/2109.01753/) (2021)
- [Exploring Student Representation For Neural Cognitive Diagnosis](https://mlsanity.com/paper/2111.08951/) (2021)
- [Diagnostic Assessment Generation via Combinatorial Search](https://mlsanity.com/paper/2112.11188/) (2021)

## Abstract (arXiv)

In educational applications, Knowledge Tracing (KT), the problem of accurately predicting students' responses to future questions by summarizing their knowledge states, has been widely studied for decades as it is considered a fundamental task towards adaptive online learning. Among all the proposed KT methods, Deep Knowledge Tracing (DKT) and its variants are by far the most effective ones due to the high flexibility of the neural network. However, DKT often ignores the inherent differences between students (e.g. memory skills, reasoning skills, ...), averaging the performances of all students, leading to the lack of personalization, and therefore was considered insufficient for adaptive learning. To alleviate this problem, in this paper, we proposed Leveled Attentive KNowledge TrAcing (LANA), which firstly uses a novel student-related features extractor (SRFE) to distill students' unique inherent properties from their respective interactive sequences. Secondly, the pivot module was utilized to dynamically reconstruct the decoder of the neural network on attention of the extracted features, successfully distinguishing the performance between students over time. Moreover, inspired by Item Response Theory (IRT), the interpretable Rasch model was used to cluster students by their ability levels, and thereby utilizing leveled learning to assign different encoders to different groups of students. With pivot module reconstructed the decoder for individual students and leveled learning specialized encoders for groups, personalized DKT was achieved. Extensive experiments conducted on two real-world large-scale datasets demonstrated that our proposed LANA improves the AUC score by at least 1.00% (i.e. EdNet 1.46% and RAIEd2020 1.00%), substantially surpassing the other State-Of-The-Art KT methods.