Practical Applications of Educational Knowledge Graphs in AI Integration

1. Introduction to Knowledge Graphs

A knowledge graph is a large-scale semantic network, a semantic representation of the real world. Entities are represented as nodes, and the attributes of entities and relationships between entities are represented as edges, forming a web-like graph structure. This structured format is human-readable and machine-friendly, facilitating machine understanding. The large-scale concepts, attributes, and relationships in the graph provide rich semantic information and associations, inherently possessing various characteristics of graphs, allowing for related operations and applications. Initially proposed by Google, the knowledge graph was mainly used to enhance search engine capabilities and improve search quality. Due to its unique characteristics, the knowledge graph plays an important role in various aspects of artificial intelligence.

Practical Applications of Educational Knowledge Graphs in AI Integration

2. Connotation and Classification of Educational Knowledge Graphs

(1) Connotation of Educational Knowledge Graphs

Currently, there is no unified understanding of the concept of educational knowledge graphs in academia; different researchers have interpreted it from various 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, they are a way of semantically organizing resources and their relationships in the educational field in the form of a “graph”; from the perspective of knowledge navigation, educational knowledge graphs can generate personalized learning paths aimed at learning objectives with the support of big data and artificial intelligence; from the perspective of learning cognition, based on knowledge graphs combined with learners’ knowledge mastery status, 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 educational field in a computer-readable way.

(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 involved in the teaching process 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.), as well as textbooks, courses, teaching resources, knowledge themes, knowledge units, teaching objectives, teaching questions, teaching participants, teaching contexts, etc.; (2) Dynamic Reason Graphs (DRG), which represent teaching events or activities as objects and use logical relationships (sequence, causality, inversion, conditions, hierarchy, composition, etc.) as edges to form multi-relational graphs. The following images show static knowledge graphs oriented towards knowledge points and dynamic reason graphs oriented towards activities.

Static Knowledge Graphs Oriented Towards Knowledge Points

Dynamic Reason Graphs Oriented Towards Activities

3. Applications of Educational Knowledge Graphs in the Context of “AI +”

(1) Supporting Intelligent Processing of Educational Big Data

Educational big data is the foundation of artificial intelligence applications in education. The analysis and mining of educational big data is the extraction process from “data” to “knowledge”. Through knowledge graphs, we can integrate the vast and complex big data in the educational field into a semantic knowledge network, solving the problem of data aggregation and integration, and optimizing the process of extracting data value, as shown in the following image.

Application of Knowledge Graphs in Intelligent Processing of Educational Big Data

From the perspective of data aggregation and integration, the low value density and sparse nature of educational big data require the integration of multi-source heterogeneous educational data to ensure the accuracy of analysis results. However, the current educational big data aggregation and integration process lacks unified standards and norms, faces difficulties in dynamic changes of data patterns, and struggles with semantic integration of multi-source heterogeneous data. Problems like “data islands” and “data silos” remain severe. Therefore, there is an urgent need for a flexible, scalable, and intelligent 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, can achieve unified modeling and management of multi-source heterogeneous data to some extent. First, experts and knowledge engineers need to define the standard model of the knowledge graph from the perspectives of teaching, management, and research. Next, big data engineers need to map massive teaching log data, teaching resource data, and learning behavior and assessment data to the standard model. Then, through knowledge verification technology, entities from different data sources need to be integrated. Finally, entity linking technology should be used to 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 MOOC, SPOC, and micro-courses, the way knowledge is acquired has shown cross-end, cross-source, and cross-modal characteristics. Learning resources face serious problems such as fragmentation, disorganization, difficulty in sharing, and lack of associations, making semantic aggregation of learning resources a hot topic in educational technology research.

Knowledge graphs provide new ideas for machines to understand complex learning resources and construct knowledge semantic networks with their semantic associations and intelligent organization capabilities. 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” as a teaching goal serves as the starting point and guiding principle of the teaching process. Its precise positioning determines the effectiveness of teaching design and process. Artificial intelligence technologies represented by knowledge graphs provide new technical means for precise positioning of teaching objectives, mainly reflected in: (1) Based on subject knowledge graphs, it can accurately detect students’ mastery of various knowledge points. With the help of educational big data collection technology, intelligent learning systems can record students’ learning trajectories in assignments, exercises, exams, and Q&A sessions. Combined with learning analytics technology, the mastery of knowledge points by students can be visualized in the form of knowledge graphs, accurately identifying students’ learning weaknesses and weak knowledge points. (2) Combining knowledge tracking technology and relevant educational theories, it can dynamically predict changes in students’ mastery of knowledge points. Each student’s learning status, progress, and knowledge level vary and change dynamically, requiring artificial intelligence technology for dynamic prediction.

(4) Empowering the Construction of Learner Profile Models

User profiling is a modeling method based on user behavior big data, integrating technologies such as text mining, sentiment analysis, knowledge extraction, and data visualization to describe multi-dimensional characteristics of users in a labeled information model. Learner profiling is a special form of user profiling, mainly used to describe learners’ subject knowledge, cognitive abilities, subject literacy, learning styles, and emotional states, serving as the premise and foundation for providing personalized support services. The general process of learner profiling includes four stages: obtaining learning behavior data, analyzing learning behavior data, extracting user labels, and generating user profiles.

(5) Empowering Diagnostic Assessment of Adaptive Learning

The current smart education emphasizes the teaching philosophy of “learner-centered”. However, assessing and diagnosing learners’ true mastery of knowledge and skills remains a challenge faced by global educational informatization. According to cognitive theory, learning is a process of forming and establishing cognitive structures through learners’ psychological processing and information handling in the face of real problem situations, while learning diagnosis is the process of evaluating learners’ cognitive structures through diagnostic testing.

(6) Empowering Personalized Learning Recommendations

Personalized learning is the essential pursuit and value orientation of educational development and is also the best practice of artificial intelligence technology empowering education. However, the current exponential growth of learning resources exacerbates learners’ “cognitive load” and “learning disorientation” issues, highlighting the contradiction between the vast enrichment 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 status, 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 field of education. The 2019 “Innovative Teaching Report” published by the Open University in the UK also pointed out that “robot-assisted learning” will become an innovative teaching method that may emerge in the field of education. Educational robots can serve as learning assistants and intelligent companions for learners, providing intelligent services such as Q&A, navigation, recommendations, questioning, and social interaction, which play an important role in enhancing learning interest, stimulating motivation, and improving learning outcomes. At the same time, educational robots can assist teachers in teaching monitoring, management, and automatic Q&A, extending teachers’ expressive abilities, knowledge transmission capabilities, and communication skills.

Currently, the robots applied in the education field are mainly conversational or chatbots. These robots use machine learning and knowledge graph technologies 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 intent. For example, conversational robots are a complex system that integrates language perception, speech recognition, intelligent decision-making, and automated feedback, involving various artificial intelligence technologies such as natural language processing, knowledge graphs, knowledge reasoning, and enhanced learning, with knowledge graphs playing a decisive role.

The basic structure of a conversational educational robot is shown in the image below, mainly consisting of 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. With the assistance of the knowledge graph, based on deep learning technology, it processes the input through entity recognition, entity linking, coreference resolution, and semantic understanding, ultimately parsing it into slot-value pairs. The dialogue management module integrates learners’ input data and information from the knowledge graph, generating answers to questions through knowledge reasoning, semantic disambiguation, context understanding, and semantic retrieval, which are then fed back to the current learner by the natural language generation module. In this process, the knowledge graph acts as the brain memory system of the educational robot, storing a vast amount of common knowledge in the educational 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.

Basic Structure of Conversational Educational Robots

Deep learning and knowledge graphs are the latest achievements of the research paradigms of artificial intelligence symbolism and connectionism. Integrating these two technologies deeply with education and teaching to improve the quality of precise teaching and personalized service levels has become an inevitable requirement for promoting the development of educational informatization 2.0. This is also an important lever for the transition of smart education from theory to practice. However, purely relying on the intelligent education of deep learning technology presents problems of opacity and inexplicability. Knowledge graphs precisely bridge this gap, driving the development of intelligent education with knowledge at its core. As the foundation for the development of smart education from the “perceptive intelligence” stage to the “cognitive intelligence” stage, knowledge graphs can provide technical support for various educational applications such as intelligent processing of educational big data, semantic aggregation of teaching resources, optimization of smart teaching, construction of learner profile models, diagnostic assessment of adaptive learning, personalized learning recommendations, and intelligent educational robots.

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