Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering

Abstract

This study aims to solve the problem of limited learning efficiency caused by information overload and resource diversity in online course learning. We adopt a recommendation algorithm that combines knowledge graph and collaborative filtering, aiming to provide an application that can meet users’ personalized learning needs and consider the semantic information of learning resources. In addition, this article collects and models implicit data in online courses and compares the impact of video and text learning resources on user learning needs under different weights in order to deeply understand the different contributions of video and text learning resources to meeting learning needs. The experimental results show that the video high-weight experimental group performs better than the text high-weight experimental group; students tend to prefer video resources. This experiment can help students cope with the challenges brought by numerous types of learning resources and provide personalized and high-quality learning experiences for learners. At the same time, adjusting and innovating teaching models for teachers has great reference value.

The Impact of AI-Based Course-Recommender System on Students’ Course-Selection Decision-Making Process

“…Recent literature [7] highlights a multitude of recommendation techniques-ranging from collaborative filtering to knowledge representation, fuzzy logic, and various machine learning algorithms-along with input parameters such as learning style, objectives, preferences, and academic information, all tailored to cater to the diverse educational needs of students. For example, collaborative filtering, widely employed in AI-based CRSs, suggests courses based on community records of peers with similar characteristics [33,34]. Similarly, content-based filtering techniques aid in recommending courses aligning with students' academic interests [35,36].…”

Evaluating Embeddings from Pre-Trained Language Models and Knowledge Graphs for Educational Content Recommendation

“…Content similarity-based recommenders focus on the content itself, projecting the content into numeric representations within a feature space and evaluating the semantic distance between texts. The semantics of learning materials can be captured through ontology-based approaches, representing educational concepts [1,3,18], or through contextual semantic meanings of entire sentences [19,20] in an approach based on contextual semantics. Learning materials can then be linked using methods like exact concept phrase matching or using word embeddings for STS.…”

“…Content recommendation systems often leverage information retrieval and semantic search technologies for content processing, integrating rulebased and behavior-based perspectives for more relevant recommendations. Niu et al [18] combined an ontology-based content similarity approach with students' behaviors, using extracted concepts from the learning contents to construct a knowledge graph and aggregating concept similarities and students' behavior (student-concept interactions) into the knowledge graph for collaborative content recommendation. Rahdari et al [3] used a graph database, Neo4j, to store the constructed knowledge graph that contains Wikipedia articles, textbook content, and the student model, and used Neo4j's internal full-text search engine, Lucene, to calculate the relevance scores for the recommendations.…”