The development of new generation artificial intelligence technologies such as deep learning, knowledge graphs, and reinforcement learning is driving “Internet + Education” into a new era of “smart education”. As a core driving force for the development of artificial intelligence, knowledge graphs provide new empowering capabilities for education in the era of informationization 2.0. From the perspective of research paradigms in artificial intelligence, knowledge graphs represent the evolution and development of the symbolic research paradigm in the era of big data and artificial intelligence; from the perspective of the development stages of artificial intelligence, knowledge graphs are an important foundation for advancing from “perceptual intelligence” to “cognitive intelligence”. Therefore, from the perspective of “AI +”, educational knowledge graphs have broad application prospects in areas such as intelligent processing of educational big data, semantic aggregation of teaching resources, optimization of smart teaching, learner profiling, adaptive learning diagnostics, personalized learning recommendations, and intelligent educational robots.
1. Introduction to Knowledge Graphs
A knowledge graph is a large-scale semantic network, representing a semantic form of the real world. It represents entities as nodes, and the attributes of entities and the relationships between entities as edges, forming a network-like graph structure. This structured form is recognizable by humans and friendly to machines, facilitating machine understanding. The large-scale concepts, attributes, and relationships between entities in the graph provide rich semantic information and associations, naturally possessing various features of graphs, allowing for related operations and applications. Once constructed, it can also serve as background knowledge for downstream applications. Initially proposed by Google, knowledge graphs were primarily used to improve search engine capabilities and enhance search quality. Due to their unique characteristics, knowledge graphs can play an important role in various aspects of artificial intelligence.
2. The Connotation and Classification of Educational Knowledge Graphs
(1) The Connotation of Educational Knowledge Graphs
Currently, there is no unified understanding of the concept of educational knowledge graphs in academia, and different researchers have explained it from various research perspectives. We should recognize educational knowledge graphs from multiple dimensions: from the perspective of knowledge modeling, educational knowledge graphs are a method for modeling the ontology of subject knowledge; from the perspective of resource management, educational knowledge graphs are a way to semantically organize resources and their relationships in the education field in the form of a “graph”; from the perspective of knowledge navigation, educational knowledge graphs can generate personalized learning paths oriented towards learning goals with the support of technologies such as big data and artificial intelligence; from the perspective of learning cognition, based on knowledge graphs combined with learners’ knowledge mastery state information, cognitive schemas of learners can be formed; from the perspective of knowledge bases, educational knowledge graphs are a structured semantic knowledge base that stores knowledge in the education field in a way that can be “understood” by computers.
(2) Classification of Educational Knowledge Graphs
Educational knowledge graphs can be divided into two categories: (1) Static Knowledge Graphs (SKG), which are semantic networks formed by teaching elements as entity nodes and the logical relationships between teaching elements as edges. Here, teaching elements can refer to knowledge points in the subject (concepts, formulas, theorems, principles, etc.), or more generally to textbooks, courses, teaching resources, knowledge themes, knowledge units, teaching objectives, teaching questions, teaching participants, teaching contexts, etc.; (2) Dynamic Reason Graphs (DRG), which are multi-relational graphs characterized by teaching events or activities, with logical relationships (sequential, causal, reversal, conditional, hierarchical, compositional, etc.) as edges. The following images show static knowledge graphs oriented towards knowledge points and dynamic reason graphs oriented towards activities.
3. Applications of Educational Knowledge Graphs Under the AI + Perspective
(1) Supporting Intelligent Processing of Educational Big Data
Educational big data is the foundation for the application of artificial intelligence in education. The analysis and mining of educational big data is an extraction process from “data” to “knowledge”. Through knowledge graphs, we can integrate the vast and complex big data in the education field into a semantic knowledge network, solving the challenges of data aggregation and optimizing the process of data value extraction, as shown in the following image.
From the perspective of data aggregation and integration, the low value density and sparse nature of educational big data necessitate the integration of multi-source heterogeneous educational data to ensure the accuracy of analysis results. However, the current educational big data faces serious issues such as a lack of unified standards and norms in the aggregation and integration process, difficulties in dynamic changes of data patterns, and challenges in semantic integration of multi-source heterogeneous data, with problems like “data islands” and “data silos” still prevalent. Therefore, there is an urgent need for a flexible, scalable, and intelligently adaptive data model to achieve multi-dimensional deep integration of existing data. Knowledge graphs, as a lightweight data model with semantic associations and dynamic scalability capabilities, can achieve unified modeling and management of multi-source heterogeneous data to a certain extent. First, discipline experts and knowledge engineers need to define the standard model of knowledge graphs from the perspectives of teaching, management, and research; second, big data engineers need to map massive teaching log data, teaching resource data, and learning behavior and assessment data to the standard model; third, knowledge validation techniques should be used to integrate entities from different data sources; finally, entity linking techniques should associate data with various resource and knowledge bases.
(2) Supporting Semantic Aggregation of Teaching Resources
In recent years, with the emergence of ubiquitous learning environments and open educational resources such as MOOCs, SPOCs, and micro-courses, the way knowledge is acquired has shown cross-end, cross-source, and cross-modal characteristics. Learning resources face serious issues such as being scattered and disordered, fragmented knowledge, difficulty in sharing, and missing associations, making semantic aggregation of learning resources a hot topic in educational technology research.
Knowledge graphs, with their ability for semantic association and intelligent organization and aggregation, provide new ideas for machines to understand complex learning resources and build knowledge semantic networks. They are one of the key technologies for organizing, representing, and managing massive educational resources and achieving educational resource integration.
(3) Enhancing the Efficiency of Smart Teaching
Supporting “teaching based on learning” means that teaching objectives serve as the starting point and guiding principle for the teaching process, and their precise positioning determines the effectiveness of teaching design and processes. Artificial intelligence technologies represented by knowledge graphs provide new technical means for the accurate positioning of teaching objectives, mainly reflected in: (1) Based on the subject knowledge graph, it can accurately detect students’ mastery of various knowledge points. With the help of educational big data collection technologies, intelligent learning systems can record students’ learning trajectories in assignments, exercises, exams, and Q&A sessions; combined with learning analytics technologies, the mastery of knowledge points can be visualized in the form of knowledge graphs, allowing for precise identification of students’ learning weaknesses and fragile knowledge points. (2) Combining knowledge tracking technologies and relevant educational theories, it can dynamically predict changes in students’ mastery of various knowledge points. Each student’s learning status, progress, and knowledge level vary and change dynamically, requiring artificial intelligence technologies for dynamic prediction.
(4) Empowering the Construction of Learner Profiling Models
User profiling is a modeling method based on user behavior big data, integrating techniques such as text mining, sentiment analysis, knowledge extraction, and data visualization to describe users’ multi-dimensional features through labeled information models. Learner profiling is a special form of user profiling, mainly used to describe learners’ knowledge in subjects, cognitive abilities, subject literacy, learning styles, and emotional states, serving as the premise and foundation for personalized support services. The general process of learner profiling includes four stages: acquiring learning behavior data, analyzing learning behavior data, extracting user tags, and generating user profiles.
(5) Empowering Adaptive Learning Diagnostics
Current smart education increasingly emphasizes a “learner-centered” teaching philosophy, yet assessing and diagnosing learners’ true mastery of knowledge and skills remains a global challenge in educational informationization. According to cognitive theory, learning is the process of forming and establishing cognitive structures through learners’ psychological processing and information handling in real problem situations, while learning diagnostics is the process of assessing learners’ cognitive structures through diagnostic tests.
(6) Empowering Personalized Learning Recommendations
Personalized learning is the essential pursuit and value orientation of educational development, and it is also the best practice for artificial intelligence technology empowering education. However, the exponential growth of learning resources intensifies learners’ “cognitive load” and “learning disorientation” issues, highlighting the contradiction between the vast richness of learning resources and the insufficient supply of personalized learning services. Personalized learning recommendations can suggest appropriate personalized learning resources and learning paths based on learners’ current knowledge states, becoming the key and foundation for achieving precise personalized learning.
(7) Empowering Intelligent Educational Robots
The “Horizon Report” has predicted for several years that teaching robots will become a key technology that continuously impacts the education field; the 2019 “Innovative Teaching Report” released by the Open University in the UK also pointed out that “robot-assisted learning” could become an innovative teaching method that may emerge in the education field. Educational robots can serve as learning assistants and intelligent companions for learners, providing intelligent services such as answering questions, navigation, recommendations, inquiries, and social interaction, which plays an important role in enhancing learning interest, stimulating learning motivation, and improving learning outcomes. At the same time, educational robots can assist teachers in teaching monitoring, management, and automatic Q&A, extending teachers’ expression capabilities, knowledge transmission abilities, and communication skills.
Currently, the robots used in the education field are mainly conversational or chatbots. These robots use machine learning and knowledge graph technologies to enable machines to understand human language, responding to learners’ questions or completing specific tasks through simple logical reasoning and rule-matching processes based on in-depth analysis of human intentions. For example, a conversational robot is a complex system that integrates language perception, speech recognition, intelligent decision-making, and automatic feedback, involving various artificial intelligence technologies such as natural language processing, knowledge graphs, knowledge reasoning, and reinforcement learning, with knowledge graphs playing a decisive role.
The following image shows the basic structure of conversational educational robots, which mainly includes four parts: natural language processing module, dialogue management module, knowledge graph module, and natural language generation module. The basic processing flow is as follows: the natural language processing module converts the text and voice data input by learners into internal representations for the machine, and with the assistance of the knowledge graph, processes the input for entity recognition, entity linking, reference resolution, and semantic understanding based on deep learning technologies, ultimately parsing it into slot-value pairs; the dialogue management module integrates learners’ input data and information from the knowledge graph, generating answers through operations such as knowledge reasoning, semantic disambiguation, context understanding, and semantic retrieval, which are finally fed back to the current learner by the natural language generation module. In this process, the knowledge graph acts as the memory system of the educational robot, storing a vast amount of common knowledge in the education field, as well as data on learners’ emotional states, interests, preferences, and knowledge skills, enabling the robot to possess memory, thinking, reasoning, and judgment capabilities similar to the human brain.
Deep learning and knowledge graphs are the latest achievements of the symbolic and connectionist research paradigms in artificial intelligence. Integrating these two technologies deeply with education and teaching to improve the quality of precision teaching and personalized service levels has become an inevitable requirement for promoting the development of educational informationization 2.0. This is also an important lever for transforming intelligent education from theory to practice. However, relying solely on deep learning technologies for intelligent education presents issues of opacity and lack of interpretability, while knowledge graphs precisely bridge this gap, driving the development of intelligent education with knowledge at its core. As the foundation for the transition of intelligent education from the stage of “perceptual intelligence” to “cognitive intelligence”, knowledge graphs can provide technical support for various educational applications, including intelligent processing of educational big data, semantic aggregation of teaching resources, optimization of smart teaching, construction of learner profiling models, adaptive learning diagnostics, personalized learning recommendations, and intelligent educational robots.
Source: Hexi University
Disclaimer: Some texts and images on this site are sourced from the internet, and copyright belongs to the original authors. If there is any infringement, please contact us for deletion.
[WeChat users, please return to the previous page, and in the lower left corner of the dialogue box, reply “8779” to indicate that you have read this article. Web users can directly reply “8779”.]