# Knowledge Graph Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness

arXiv: https://arxiv.org/abs/2307.15076 · HTML: https://mlsanity.com/paper/2307.15076/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 70 references that are in the map.
- First posted on arXiv 15 Jul 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Neural Cognitive Diagnosis for Intelligent Education Systems](https://mlsanity.com/paper/1908.08733/) (2019)
- [Sequence-to-Sequence Knowledge Graph Completion and Question Answering](https://mlsanity.com/paper/2203.10321/) (2022)
- [GIKT: A Graph-based Interaction Model for Knowledge Tracing](https://mlsanity.com/paper/2009.05991/) (2020)
- [Variational Item Response Theory: Fast, Accurate, and Expressive](https://mlsanity.com/paper/2002.00276/) (2020)
- [Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing](https://mlsanity.com/paper/2101.05986/) (2021)
- [Question Answering Over Biological Knowledge Graph via Amazon Alexa](https://mlsanity.com/paper/2210.06040/) (2022)

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

- [Is the Lecture Engaging for Learning? Lecture Voice Sentiment Analysis for Knowledge Graph-Supported Intelligent Lecturing Assistant (ILA) System](https://mlsanity.com/paper/2408.10492/) (2024)
- [Multi-Scale Deformable Transformers for Student Learning Behavior Detection in Smart Classroom](https://mlsanity.com/paper/2410.07834/) (2024)
- [A Closed-Loop Personalized Learning Agent Integrating Neural Cognitive Diagnosis, Bounded-Ability Adaptive Testing, and LLM-Driven Feedback](https://mlsanity.com/paper/2510.22559/) (2025)
- [TLCD: A Deep Transfer Learning Framework for Cross-Disciplinary Cognitive Diagnosis](https://mlsanity.com/paper/2510.23062/) (2025)
- [Dynamically Weighted Momentum with Adaptive Step Sizes for Efficient Deep Network Training](https://mlsanity.com/paper/2510.25042/) (2025)
- [Mask-Robust Face Verification for Online Learning via YOLOv5 and Residual Networks](https://mlsanity.com/paper/2510.25184/) (2025)

## Abstract (arXiv)

Presently, knowledge graph-based recommendation algorithms have garnered considerable attention among researchers. However, these algorithms solely consider knowledge graphs with single relationships and do not effectively model exercise-rich features, such as exercise representativeness and informativeness. Consequently, this paper proposes a framework, namely the Knowledge-Graph-Exercise Representativeness and Informativeness Framework, to address these two issues. The framework consists of four intricate components and a novel cognitive diagnosis model called the Neural Attentive cognitive diagnosis model. These components encompass the informativeness component, exercise representation component, knowledge importance component, and exercise representativeness component. The informativeness component evaluates the informational value of each question and identifies the candidate question set that exhibits the highest exercise informativeness. Furthermore, the skill embeddings are employed as input for the knowledge importance component. This component transforms a one-dimensional knowledge graph into a multi-dimensional one through four class relations and calculates skill importance weights based on novelty and popularity. Subsequently, the exercise representativeness component incorporates exercise weight knowledge coverage to select questions from the candidate question set for the tested question set. Lastly, the cognitive diagnosis model leverages exercise representation and skill importance weights to predict student performance on the test set and estimate their knowledge state. To evaluate the effectiveness of our selection strategy, extensive experiments were conducted on two publicly available educational datasets. The experimental results demonstrate that our framework can recommend appropriate exercises to students, leading to improved student performance.