The development of new generation artificial intelligence technologies such as deep learning, knowledge graphs, and reinforcement learning is driving the “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 education informatization 2.0. From the research paradigm of 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 development stage of artificial intelligence, knowledge graphs are an important foundation for the advancement of artificial intelligence 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, construction of learner profile models, adaptive learning diagnostics, personalized learning recommendations, and intelligent educational robots.
1. Introduction to Knowledge Graphs
A knowledge graph is a large-scale semantic network, which is a semantic representation of the real world. Entities are represented as nodes, and the attributes of entities and the relationships between entities are represented as edges, forming a networked graph structure. This structured form is recognizable by humans and friendly to machines, facilitating machine understanding. The large-scale concepts, attributes, and relationships among 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, the knowledge graph was primarily used to improve search engine capabilities and enhance search quality. Due to its unique characteristics, the knowledge graph can play an important role in various aspects of artificial intelligence.
2. The 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 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 subject knowledge ontologies; from the resource management perspective, educational knowledge graphs are a way to semantically organize resources and their relationships in the education field using a “graph” format; from the knowledge navigation perspective, educational knowledge graphs can generate personalized learning paths aimed at learning goals under the support of technologies such as big data and artificial intelligence; from the perspective of learning cognition, based on knowledge graphs and learner knowledge mastery state information, it can form learners’ cognitive schemas; from the knowledge base perspective, 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 the following 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.) or can refer broadly to textbooks, courses, teaching resources, knowledge themes, knowledge units, teaching objectives, teaching problems, teaching participants, teaching contexts, etc.; (2) Dynamic Reason Graphs (DRG), which represent teaching events or activities as objects, with logical relationships (sequential, causal, inversion, conditional, hierarchical, composition, etc.) as edges, forming a multi-relational graph. The following figures respectively show static knowledge graphs aimed at knowledge points and dynamic reason graphs aimed at activities.
Knowledge Point-Oriented Educational Knowledge Graph
Activity-Oriented Educational Knowledge Graph
3. Applications of Educational Knowledge Graphs in the Perspective of AI+
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 vast and complex educational big data into a semantic knowledge network, solving the problems of data aggregation and fusion, and optimizing the process of extracting data value, as shown in the figure below.
Application of Knowledge Graphs in Intelligent Processing of Educational Big Data
From the perspective of data aggregation and fusion, the characteristics of educational big data include low value density and data sparsity, necessitating the integration of multi-source heterogeneous educational data to ensure the accuracy of analysis results. However, the current educational big data aggregation and fusion process lacks unified standards and norms, faces difficulties in dynamic changes of data patterns, and has serious issues such as “data silos” and “data chimneys”. Therefore, there is an urgent need for a flexible, scalable, and intelligent adaptive data model to achieve multi-dimensional deep fusion 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, subject experts and knowledge engineers need to define the standard model of the knowledge graph 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 verification technology should be used to merge entities from different data sources; finally, entity linking technology should be utilized to associate data with various resource libraries 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 severe problems such as dispersion, knowledge fragmentation, difficulties in sharing, and lack of associations, making the semantic aggregation of learning resources a hot topic in educational technology research.
Knowledge graphs, with their ability for semantic associations and intelligent organization, provide a new approach 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, achieving integration of educational resources.
3. Enhancing Efficiency of Smart Teaching
Supporting “teaching based on learning” as the starting point and guiding principle of the teaching process, its precise positioning determines the effectiveness of teaching design and process. Technologies represented by knowledge graphs provide new technical means for the precise positioning of teaching objectives, mainly manifested in: (1) Based on subject knowledge graphs, it can accurately detect students’ mastery of knowledge points. With the help of educational big data collection technology, intelligent learning systems can record students’ learning trajectories in various stages such as assignments, exercises, exams, and Q&A combined with learning analytics technology, the mastery level of students on knowledge points can be visualized in the form of knowledge graphs, allowing precise identification of students’ learning shortcomings and weak knowledge points. (2) Combined with 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 are different and dynamically changing, 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 users’ multi-dimensional features in a labeled information model. Learner profiling is a special form of user profiling, mainly used to describe learners’ knowledge, 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: acquisition of learning behavior data, analysis of learning behavior data, extraction of user labels, and generation of user profiles.
5. Empowering Adaptive Learning Diagnostics
The current smart education emphasizes a “learner-centered” teaching philosophy. However, how to assess and diagnose 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 real problem situations, while learning diagnostics is the process of evaluating 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 empowering education with artificial intelligence technology. However, the exponentially growing learning resources have exacerbated learners’ “cognitive load” and “learning navigation” issues, highlighting the contradiction between the vast richness of learning resources and the insufficient supply of personalized learning services. Personalized learning recommendations can recommend suitable personalized learning resources and paths based on the learner’s current knowledge state, becoming the key and foundation for achieving precise personalized learning.
7. Empowering Intelligent Educational Robots
The “Horizon Report” has continuously predicted that teaching robots will become a key technology that will continuously impact the education sector; the 2019 “Innovative Teaching Report” published by the Open University in the UK also pointed out that “robot-assisted learning” will become a potential “innovative teaching method” in the education field. Educational robots can serve as learning assistants and intelligent companions, providing intelligent services such as Q&A, navigation, recommendations, questioning, and social interaction, which are crucial for enhancing learning interest, stimulating learning motivation, and improving learning effectiveness. At the same time, educational robots can assist teachers in teaching monitoring, management, and automated Q&A, extending teachers’ expressive, knowledge transmission, and communication abilities.
Currently, the robots applied in the education sector are mainly conversational or chatbots. These robots utilize technologies such as machine learning and knowledge graphs 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, in which knowledge graphs play a decisive role.
The basic structure of a conversational educational robot is shown in the figure 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 the learner into the machine’s internal representation. With the assistance of the knowledge graph, it processes the input through deep learning technology for entity recognition, entity linking, reference resolution, and semantic understanding, ultimately parsing it into slot-value pairs; the dialogue management module integrates the learner’s input data with information from the knowledge graph, generating answers to questions through 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 serves as the memory system of the educational robot, storing massive 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, 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 symbolic and connectionist research paradigms of artificial intelligence. Integrating these two technologies deeply into education and teaching to improve the quality of precise teaching and the level of personalized services has become an inevitable requirement for promoting the development of education informatization 2.0. This is also an important lever for moving smart education from theory to practice. However, relying solely on intelligent education through deep learning technology has issues such as the black box problem and lack of explainability. Knowledge graphs precisely bridge this gap, driving the development of smart education with knowledge at its core. As the foundation for the development of smart 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 profile models, adaptive learning diagnostics, personalized learning recommendations, and intelligent educational robots.
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