# Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View

arXiv: https://arxiv.org/abs/2006.13257 · HTML: https://mlsanity.com/paper/2006.13257/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 30 of 30)

- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks](https://mlsanity.com/paper/2104.07886/) (2021)
- [Sequential Recommendation via Stochastic Self-Attention](https://mlsanity.com/paper/2201.06035/) (2022)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [Geometry Interaction Knowledge Graph Embeddings](https://mlsanity.com/paper/2206.12418/) (2022)
- [Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges](https://mlsanity.com/paper/2401.08664/) (2023)
- [Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation](https://mlsanity.com/paper/2405.12442/) (2024)
- [Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2202.09177/) (2022)
- [Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2210.00538/) (2022)
- [Mamba4KT:An Efficient and Effective Mamba-based Knowledge Tracing Model](https://mlsanity.com/paper/2405.16542/) (2024)
- [A Review of Data Mining in Personalized Education: Current Trends and Future Prospects](https://mlsanity.com/paper/2402.17236/) (2024)
- [Towards a General Pre-training Framework for Adaptive Learning in MOOCs](https://mlsanity.com/paper/2208.04708/) (2022)
- [Learning Student Interest Trajectory for MOOCThread Recommendation](https://mlsanity.com/paper/2101.05625/) (2021)
- [Modeling Balanced Explicit and Implicit Relations with Contrastive Learning for Knowledge Concept Recommendation in MOOCs](https://mlsanity.com/paper/2402.08256/) (2024)
- [MOOCRep: A Unified Pre-trained Embedding of MOOC Entities](https://mlsanity.com/paper/2107.05154/) (2021)
- [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021)
- [VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search](https://mlsanity.com/paper/2108.02725/) (2021)
- [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021)
- [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022)
- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)
- [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022)
- [Tribe or Not? Critical Inspection of Group Differences Using TribalGram](https://mlsanity.com/paper/2303.09664/) (2023)
- [Personalized Programming Guidance based on Deep Programming Learning Style Capturing](https://mlsanity.com/paper/2403.14638/) (2024)
- [AdvKT: An Adversarial Multi-Step Training Framework for Knowledge Tracing](https://mlsanity.com/paper/2504.04706/) (2025)
- [How Good Are Large Language Models for Course Recommendation in MOOCs?](https://mlsanity.com/paper/2504.08208/) (2025)
- [Advancing Knowledge Tracing by Exploring Follow-up Performance Trends](https://mlsanity.com/paper/2508.08019/) (2025)
- [CLLMRec: LLM-powered Cognitive-Aware Concept Recommendation via Semantic Alignment and Prerequisite Knowledge Distillation](https://mlsanity.com/paper/2511.17041/) (2025)
- [Aspect-Aware MOOC Recommendation in a Heterogeneous Network](https://mlsanity.com/paper/2602.05297/) (2026)
- [AgentGR: Semantic-aware Agentic Group Decision-Making Simulator for Group Recommendation](https://mlsanity.com/paper/2605.10367/) (2026)

## Abstract (arXiv)

Massive open online courses are becoming a modish way for education, which provides a large-scale and open-access learning opportunity for students to grasp the knowledge. To attract students' interest, the recommendation system is applied by MOOCs providers to recommend courses to students. However, as a course usually consists of a number of video lectures, with each one covering some specific knowledge concepts, directly recommending courses overlook students'interest to some specific knowledge concepts. To fill this gap, in this paper, we study the problem of knowledge concept recommendation. We propose an end-to-end graph neural network-based approach calledAttentionalHeterogeneous Graph Convolutional Deep Knowledge Recommender(ACKRec) for knowledge concept recommendation in MOOCs. Like other recommendation problems, it suffers from sparsity issues. To address this issue, we leverage both content information and context information to learn the representation of entities via graph convolution network. In addition to students and knowledge concepts, we consider other types of entities (e.g., courses, videos, teachers) and construct a heterogeneous information network to capture the corresponding fruitful semantic relationships among different types of entities and incorporate them into the representation learning process. Specifically, we use meta-path on the HIN to guide the propagation of students' preferences. With the help of these meta-paths, the students' preference distribution with respect to a candidate knowledge concept can be captured. Furthermore, we propose an attention mechanism to adaptively fuse the context information from different meta-paths, in order to capture the different interests of different students. The promising experiment results show that the proposedACKRecis able to effectively recommend knowledge concepts to students pursuing online learning in MOOCs.