An Analysis of Precision Teaching in Ideological and Political Courses Based on Knowledge Graphs

The digitalization of education is an inevitable choice for actively adapting to the new round of technological revolution and industrial transformation, and it is the necessary path for promoting innovative development in education. Knowledge graphs, with their unique methods of knowledge organization and expression, are gradually becoming an important lever for the digital transformation of education. In February 2023, at the World Digital Education Conference, Minister of Education Huai Jinpeng stated in his speech: "Use massive data to form learner profiles and educational knowledge graphs, to better achieve teaching students in accordance with their aptitude." [1]1 "Teaching students in accordance with their aptitude" (因材施教) is a classical Chinese educational principle, traditionally attributed to Confucius, emphasizing that instruction should be tailored to the individual characteristics and abilities of each student. As a key tool in digital education, knowledge graphs play an important role in precision teaching in ideological and political courses, and are of great significance for teachers of ideological and political courses to accurately grasp student needs and enhance the targetedness and effectiveness of teaching. This article intends to conduct an in-depth analysis of how knowledge graphs can empower precision teaching in ideological and political courses, and to explore the construction framework and process of knowledge graphs for ideological and political courses. On this basis, combining the specific requirements of ideological and political courses, it explores practical pathways for promoting precision teaching in ideological and political courses based on knowledge graphs.

教育数字化是主动适应新一轮科技革命和产业变革的必然选择，是推动教育创新发展的必由之路。知识图谱以其独特的知识组织与表达方式，逐渐成为教育数字化转型的重要抓手。2023年2月，教育部部长怀进鹏在世界数字教育大会上的演讲中指出：“运用海量数据形成学习者画像和教育知识图谱，更好地实现因材施教。”知识图谱作为数字化教育中的关键工具，在思政课精准教学中扮演着重要角色，对思政课教师精准把握学生需求，提升教学针对性和实效性具有重要意义。本文拟对知识图谱何以赋能思政课精准教学进行深入分析，并探讨思政课知识图谱的构建框架与流程，在此基础上，结合思政课的具体要求探索基于知识图谱推动思政课精准教学的实践路径。

I. How Can Knowledge Graphs Empower Precision Teaching in Ideological and Political Courses?

一、知识图谱何以赋能思政课精准教学？

A knowledge graph refers to a graph-based, structured, visualizable, and adjustable semantic network formed by connecting all different types of information together. Compared with traditional textual forms, graph structures possess advantages that string sequences lack, namely the ability to more vividly and precisely express complex semantics and rich knowledge. As an important branch of artificial intelligence, knowledge graphs complement big data and deep learning. In the teaching of ideological and political courses, the excellent semantic processing capabilities and interconnected organizational efficiency of knowledge graphs bring powerful empowering effects to precision teaching. Precision teaching refers to the teaching subject, guided by meticulous and accurate thinking methods and working approaches, controlling each operational link and each participating element of teaching. Accurately grasping students is the prerequisite and foundation of precision teaching, precisely supplying teaching content is the core link of precision teaching, and precisely evaluating teaching effects is an important guarantee for deeply advancing precision teaching.

知识图谱是指所有不同种类的信息连接在一起而形成的基于图的结构化、可视化、可调整的语义网络。相较于传统的文本形式，图结构具有字符串序列不具备的优势，即能够更为形象精确地表达复杂的语义和丰富的知识。作为人工智能的重要分支，知识图谱与大数据、深度学习相辅相成。在思政课教学中，知识图谱所具备的卓越语义处理能力和互联组织效能，为精准教学带来了强大的赋能作用。精准教学是指教学主体以精细、准确的思维方式和工作方法为指引，把控教学各运行环节、各参与要素。精准把握学生是精准教学的前提和基础，精准供给教学内容是精准教学的核心环节，精准评价教学效果是深入推进精准教学的重要保障。

(I) Assisting in Grasping Student Group Characteristics and Individual Traits

（一）助力把握学生群体特征和个性特点

Accurately grasping students' ideological characteristics and behavioral tendencies, and educating and guiding students in a targeted manner, is the foundation for ensuring the effectiveness of ideological and political education. Each student is a unique individual, with their own interests, learning styles, and personality traits; the group characteristics and thinking traits of students from different majors also vary. In the classroom, students hope to explore issues from different perspectives, express personalized views, and aspire to achieve personalized development in the educational process. Therefore, teachers of ideological and political courses must not only master students' cognitive patterns and common demands but also deeply analyze students' group characteristics and individual traits, formulate personalized teaching plans, implement refined teaching, and enhance the effectiveness of education. However, the class sizes for ideological and political courses are generally larger than those for specialized courses. Due to the course selection system, the majors of students in teaching classes are diverse, and individual differences are significant. Additionally, teachers of ideological and political courses generally have heavy teaching tasks, making it difficult to meet the practical teaching requirements of ideological and political courses by understanding each student's learning characteristics and needs through traditional methods.

准确把握学生的思想特征和行为趋向，有针对性地教育和引导学生是保证思想政治教育取得实效的基础。每个学生都是独特的个体，具有各自的兴趣爱好、学习风格和个性特征，不同专业的学生群体个性和思维特点也不尽相同。在课堂中，学生希望从不同角度探讨问题，表达个性化的看法，渴望在教育过程中得到个性化的发展。因此，思政课教师既要掌握学生的认知规律与普遍诉求，又要深入分析学生的群体特征与个性特点，制定个性化教学方案，实施精细化教学，增强育人实效。然而，思政课课堂教学规模普遍比专业课大，由于采用选课制，教学班级中学生的专业各不相同，个体差异较大。此外，思政课教师教学任务普遍比较繁重，以传统的方式了解每一个学生的学习特点和需求已很难满足思政课的现实教学要求。

Knowledge graphs, with their unique advantages, can assist teachers of ideological and political courses in precisely capturing students' group characteristics and individual differences. First, knowledge graphs utilize technologies such as multi-source data collection, integration, processing, and multimodal analysis to collect data reflecting students' behavior, emotions, attitudes, and cognition through channels such as teaching systems, learning platforms, social media, and student interactions, and perform preprocessing operations such as cleaning, deduplication, and standardization on the data to ensure data quality. Second, natural language processing technology is used to parse students' textual feedback, discussion content, etc., extract key information and viewpoints, and through built-in analysis algorithms, automatically analyze students' emotional tendencies toward the course, learning behaviors, learning states, etc., enabling a more precise and comprehensive understanding of students' emotional attitudes and learning interests regarding the course content. Third, cluster analysis is performed on the data, and by drawing network graphs to display the associations and interactions between students, key nodes and subgroups within the student group are discovered, allowing for a deeper understanding of different types of students. Using knowledge graphs, teachers, on the basis of a comprehensive and precise understanding of individual and group characteristics of students, can provide personalized teaching suggestions and resource recommendations for students, better meeting their learning needs.

知识图谱以其特有的优势可以协助思政课教师精确捕捉学生的群体特征及个体差异。首先，知识图谱利用多源数据采集整合与处理、多模态分析等技术，通过教学系统、学习平台、社交媒体、学生互动等渠道收集反映学生行为、情感、态度和认知方面的数据，并对数据进行清洗、去重、标准化等预处理操作，确保数据质量。其次，利用自然语言处理技术解析学生的文本反馈、讨论内容等，提取关键信息和观点，通过内置分析算法，自动分析学生对课程的情感倾向、学习行为、学习状态等，可以更精准、全面地了解学生对课程内容的情感态度和学习兴趣等状况。再次，对数据进行聚类分析，并通过绘制网格图展示学生之间的关联和互动，发现学生群体中的关键节点和子群体，更深入地了解不同类型的学生。利用知识图谱，教师在全面精准了解学生个体及群体特征的基础上，可以为学生提供个性化的教学建议和资源推荐，更好地满足学生的学习需求。

(II) Achieving Coherent and Targeted Supply of Content

（二）实现内容贯通性、针对性供给

Precise content supply can effectively enhance the effectiveness of ideological and political courses. How to achieve coherent and targeted content supply based on students' age and learning stage characteristics is an important link in improving the effectiveness of ideological and political courses.

精准的内容供给能有效提高思政课实效性。如何依据学生的年龄和学段特点，贯通性、针对性地实现内容供给是提高思政课实效性的重要环节。

Each ideological and political course in higher education has its specific teaching objectives and content, but the overall teaching objective is to enhance the sense of mission and responsibility of college students, help them establish a correct worldview, outlook on life, and value system, strengthen their belief in Marxism, and enhance their ideological, political, theoretical, and emotional identification with socialism with Chinese characteristics. Therefore, attention must be paid to internal coherence in the teaching process of each ideological and political course. Teachers must not only recognize the characteristics and differences of each ideological and political course but also consider the connections and coherence between them. At the same time, different students have different demands for the same course; some students may have higher requirements for theoretical depth, while others may be more concerned with the application of theory in real life. This requires teachers of ideological and political courses to precisely supply content based on course characteristics and student needs.

高校各门思政课有其具体的教学目标和内容，但总的教学目标都是增强大学生的使命担当，帮助大学生树立正确的世界观、人生观和价值观，坚定马克思主义信仰，增强对中国特色社会主义的思想认同、政治认同、理论认同和情感认同。因此，各门思政课在教学过程中要注意内在衔接。教师既要认识到各门思政课的特点与区别，也要考虑到各门思政课之间的衔接与贯通。同时，不同学生对同一课程的诉求也不尽相同，有的学生可能对理论深度有更高的要求，有的学生则更关注理论在现实生活中的应用。这就要求思政课教师依据课程特点和学生需求精准地供给内容。

Knowledge graphs can present, through visual forms such as graphs and charts, the rich and specific, coherently focused, and internally logically related knowledge systems of different ideological and political courses. By extracting, representing, associating, and visually presenting knowledge systems, knowledge graphs provide technical support for ensuring the coherence of teaching content, thereby offering important support for the coherent supply of teaching content. First, knowledge graphs can structurally present key knowledge points and theoretical viewpoints extracted from textbooks and historical documents, forming data formats that computers can understand and compute. Second, using technologies such as entity recognition and association, entities that represent the same or similar content are identified and relationships are established, achieving the fusion and association of different knowledge. Third, using graph databases specifically for storing and querying entities and relationships, students can discover the internal connections between knowledge points of different courses through queries. Finally, knowledge graphs, by collecting student learning data, identifying personalized needs, and combining recommendation algorithms, customize appropriate teaching resources and activities for students, providing technical support for strengthening the targeted supply of ideological and political courses. First, through algorithmic technologies such as entity recognition and relationship extraction, relationships between students, courses, and knowledge points are identified from the collected data. Second, using data collection technologies, student learning behavior data is collected, and multimodal tools are used to analyze learning data; using semantic analysis and reasoning combined with learning preferences, students' personalized needs are inferred, and on this basis, personalized learning graphs are constructed for students. Third, based on personalized learning needs and the information in the knowledge graph, recommendation algorithms are used to recommend suitable reading materials, course resources, or practical activities for students.

知识图谱可以通过图形、图表等可视化形式呈现不同思政课丰富而具体、侧重有衔接、内在逻辑关联的知识体系。知识图谱通过提取与表示、关联与可视化呈现知识体系，为确保教学内容的贯通提供技术支持，从而为贯通性供给教学内容提供重要支撑。首先，知识图谱可以对从教材、历史文献提取的关键知识点、理论观点等进行结构化呈现，形成计算机可理解和计算的数据格式。其次，利用实体识别与关联等技术，找出表示相同或相似的实体并建立关系，实现不同知识的融合和关联。再次，利用专门存储和查询实体与关系的图数据库，让学生通过查询能发现不同课程知识点之间的内在联系。最后，知识图谱通过收集学生学习数据、识别个性化需求，并结合推荐算法，为学生定制合适的教学资源和活动，为加强思政课针对性供给提供技术支持。一是通过实体识别和关系抽取等算法技术，从收集到的数据中识别出学生、课程、知识点之间的关系。二是利用数据收集技术，收集学生的学习行为数据，并利用多模态工具分析学习数据，利用语义分析与推理并结合学习偏好，推断出学生的个性化需求，并在此基础上为学生构建个性化学习图谱。三是基于个性化学习需求和知识图谱中的信息，使用推荐算法为学生推荐合适的阅读材料、课程资源或实践活动。

(III) Promoting the Optimization of Evaluation Toward the Entire Process and All Elements

（三）促进评价向全过程、全要素优化

The evaluation of ideological and political education refers to the value judgment made by evaluators on all elements and the entire process of ideological and political education, based on certain evaluation standards and using a combination of qualitative and quantitative methods. Effect evaluation is an important part of ideological and political courses. Scientific evaluation is not only an important guarantee for advancing the construction of ideological and political courses but also an important basis for improving their effectiveness. Comprehensive evaluation includes requirements for both whole-process evaluation and all-element evaluation. The teaching process of ideological and political courses covers links such as pre-class, in-class, and post-class, and the teaching effect is also related to links such as pre-class preparation, classroom implementation, and post-class feedback and reflection. Whole-process evaluation includes evaluating each link, such as pre-class preparation, classroom implementation, and post-class feedback and reflection. All-element evaluation refers to the comprehensive evaluation of elements such as the teaching object, teaching subject, and teaching content; all-element evaluation concerns the coherence and integration degree of evaluation among these elements.

思想政治教育评价是指评价者依据一定的评价标准，运用定性与定量相结合的方法对思想政治教育全要素、全过程进行价值判断。效果评价是思政课的重要一环。科学评价既是推进思政课建设的重要保障，也是提高思政课实效性的重要依据。全面评价包含全过程评价和全要素评价两个方面的要求。思政课教学过程覆盖课前、课中和课后等环节，思政课教学效果也与课前准备、课堂实施、课后反馈与反思等环节相关。全过程评价包含对课前准备、课堂实施、课后反馈与反思等各环节进行评价。全要素评价是指对教学对象、教学主体、教学内容等要素进行全面评价，全要素评价关系到各要素间评价的连贯性和融合度。

Using knowledge graphs for whole-process evaluation can provide teachers with more coherent and precise evaluation information. Before class, the knowledge graph matches the teacher’s prepared materials and lesson plans, as well as students’ preview content, with course objectives, quantitatively assessing the students’ preview situation and the teacher’s lesson preparation effectiveness. During class, data mining techniques are used to collect data on students’ learning behaviors; sensor devices analyze data such as students’ gaze duration to monitor their attention levels in the classroom, and this is combined with data on the teacher’s behaviors during lectures, interactions, and other processes to evaluate classroom teaching effectiveness. After class, multi-dimensional data such as student assignments are analyzed in conjunction with the knowledge structure in the knowledge graph to evaluate the quality of student assignments and their understanding and application of knowledge points, thereby obtaining more comprehensive information on teaching effectiveness.

利用知识图谱进行全过程评价能为教师提供更连贯、更精准的评价信息。课前，知识图谱将教师准备的备课资料和教案、学生的预习内容与课程目标进行匹配，量化评估学生预习情况和教师备课效果。课中，利用数据挖掘技术收集学生学习行为数据，通过传感器设备分析学生的注视时间等数据，监测学生课堂的注意力水平等，结合教师在课堂讲授、互动等过程中的行为数据评价课堂教学效果。课后，结合知识图谱中的知识结构分析学生作业等多维度数据，评价学生作业质量及其对知识点的理解和应用程度，从而更全面地获取教学效果信息。

Using knowledge graphs for all-factor evaluation can provide teachers with more systematic and comprehensive evaluation information. Regarding content factors, relationship and attribute extraction techniques are used to extract key attributes of the content, such as difficulty, importance, and timeliness, to assess the quality of the content and the coherence and completeness of knowledge points. By analyzing resource annotation data, click-through rates, download volumes, etc., the quality and utilization rate of teaching resources are evaluated. Regarding the learner factor, student assignment, test, and interaction data are combined, and relationship extraction techniques are used to analyze students’ mastery of knowledge points, comparing learning outcomes with teaching objectives to evaluate student learning effectiveness. Regarding the method factor, classroom interaction data from the knowledge graph are used to compare and analyze student participation and feedback under different teaching methods, evaluating the effectiveness of teaching methods. Additionally, by collecting student evaluations of the teacher and using natural language processing techniques to analyze data such as the teacher’s lesson plans, lecture content, and student classroom interaction feedback, the degree of student recognition and acceptance of the teacher’s lecture content is evaluated, providing teachers with intuitive teaching suggestions.

利用知识图谱进行全要素评价能为教师提供更系统、更全面的评价信息。针对内容要素，通过关系与属性抽取技术提取内容中的关键属性，如难度、重要性、时效性等，评估内容的质量、知识点的连贯性和完整性。通过分析资源的标注数据、点击率、下载量等，评价教学资源的质量、利用率。针对受教育者要素，结合学生的作业、测试和互动数据，通过关系抽取技术分析学生对知识点的掌握情况，对比学生学习结果和教学目标，评估学生学习效果。针对方法要素，利用知识图谱中的课堂互动数据，对比分析不同教学方法下学生的参与度和反馈情况，评估教学方法的有效性。此外，通过收集学生对教师的评价，利用自然语言处理技术分析教师的教案、课堂讲授内容、学生课堂互动反馈等数据，评估学生对教师讲授内容的认可度和接受度，为教师提供直观的教学建议。

II. The Construction Framework and Process of Knowledge Graphs for Ideological-Political Courses

二、思政课知识图谱的构建框架与流程

(A) The Construction Framework of Knowledge Graphs for Ideological-Political Courses

（一）思政课知识图谱的构建框架

The training objective is the guiding principle for implementing the fundamental task of fostering virtue through education [2]2 “Fostering virtue through education” (立德树人, lide shuren) is a central slogan in Chinese education policy, emphasizing moral character cultivation as the foundation of talent development. and formulating talent cultivation plans. The training objective is typically set based on the nature of the discipline to which the course belongs, social needs, and student characteristics, emphasizing the knowledge, skills, and qualities that students should master and enhance after completing the course. The indicators of the training objective should fully consider social vocational ability requirements on the basis of focusing on the professional knowledge system. Knowledge graphs for ideological-political courses must be constructed around the training objective. As a key course for implementing the fundamental task of fostering virtue through education, the training objective of ideological-political courses should revolve around the mission of educating people for the Party and the nation, meeting the needs of students’ comprehensive development, and promoting students’ healthy growth.

培养目标是落实立德树人根本任务、制订人才培养方案的纲领。培养目标通常根据课程所属学科的性质、社会需求和学生特点来设定，强调学生在完成课程学习后应掌握的知识、技能和需要提升的素质。培养目标的指标设定应在关注专业知识体系的基础上充分考虑社会职业能力需求。思政课知识图谱必须围绕培养目标进行构建。思政课作为落实立德树人根本任务的关键课程，其培养目标要围绕为党育人、为国育才的使命，满足学生全面发展的需求，促进学生的健康成长。

The knowledge graph must be deeply linked to the training objective to provide support for the effective implementation of ideological-political course teaching. First, the training objective provides directional guidance for the construction of the knowledge graph. By linking with the training objective, the knowledge graph can precisely optimize knowledge content, structure, resources, etc., thereby improving the quality of ideological-political course teaching. Second, the knowledge graph presents course objectives in a visual manner, pointing the way for students to master knowledge and enhance their abilities and qualities. Third, following the training objective can help teachers design teaching content and select teaching methods, enhancing the effectiveness and appeal of ideological-political courses. The knowledge graph for ideological-political courses should be constructed around three core elements: knowledge content, relational relationships, and mapping relationships.

知识图谱必须深度关联培养目标，为思政课教学的有效开展提供支持。其一，培养目标为知识图谱的构建提供方向指引。通过与培养目标的关联，知识图谱能精准优化知识内容、结构、资源等，提升思政课教学质量。其二，知识图谱通过可视化方式呈现课程目标，为学生掌握知识、提升能力素养指明方向。其三，以培养目标为遵循，能助力教师设计教学内容和选择教学方法，提升思政课的实效性和吸引力。思政课知识图谱应围绕知识内容、关联关系及映射关系三大核心要素进行构建。

At the level of knowledge content, the knowledge graph encompasses a knowledge cluster, a capability library, and a problem library. The knowledge cluster refers to a collection of related knowledge points that together form a complete knowledge domain or topic. The capability library covers the abilities and skills students need when learning and mastering specific knowledge clusters. The problem library revolves around three core questions—“what, why, and how”—and encompasses theoretical knowledge confusions, practical application confusions, ideological and emotional confusions, and personal development confusions within the problem system. The problem library is a collection of problems constructed based on the knowledge cluster and capability library, used to test students’ mastery of knowledge and their application abilities. These three major sections establish and construct the objective system, capability system, problem system, and knowledge system on the basis of refining the training objectives of ideological-political courses, ensuring that these systems serve the teaching objectives of ideological-political courses more precisely. In the context of ideological-political courses, the knowledge system refers to all the knowledge content involved in teaching and their interrelationships. The knowledge cluster is the basic unit of the knowledge system, linking various knowledge points through the basic theories, principles, and concepts of each course field to form a tight knowledge network. The capability system covers core capability requirements such as basic cognition, critical thinking, value judgment, and innovation and creation. The capability library is an important component of the capability system. The problem system is a collection of problems constructed based on the objective and capability systems, used to test students’ learning outcomes and capability development. The problem library, as an important part of the knowledge graph, provides specific materials and tools for the implementation of the problem system. The objective system clarifies the overall teaching objectives and phased objectives. The construction of the knowledge graph should serve the realization of these objectives, ensuring that students can achieve the predetermined knowledge, ability, and quality requirements through learning.

在知识内容层面，知识图谱囊括知识簇、能力库与问题库。知识簇指一系列关联知识点的集合，它们共同构成完整的知识领域或主题。能力库涵盖学生在学习和掌握特定知识簇时所需的能力和技能。问题库围绕“是什么、为什么、怎么办”三大核心问题展开，涵盖问题体系中的理论知识困惑、实践应用困惑、思想情感困惑和个人发展困惑。问题库是基于知识簇和能力库构建的问题集合，用于检测学生对知识的掌握程度和应用能力。这三大板块是在细化思政课培养目标的基础上，确立并构建目标体系、能力体系、问题体系及知识体系，确保这些体系更加精准地服务于思政课教学目标。在思政课的语境下，知识体系指教学中所涉及的全部知识内容及其相互关系。知识簇是知识体系的基本组成单元，以各课程领域的基本理论、原理及概念为纽带，串联起各知识点，形成紧密的知识网络。能力体系涵盖基础认知、批判性思维、价值判断及创新创造等核心能力要求。能力库是能力体系的重要内容。问题体系是基于目标和能力体系构建的问题集合，用于检测学生的学习效果和能力发展情况。问题库作为知识图谱的重要组成部分，为问题体系的实施提供具体的材料和工具。目标体系明确了教学的总体目标和分阶段目标。知识图谱的构建应服务于实现这些目标，确保学生能够通过学习达成预定的知识、能力和素质要求。

In terms of relational relationships, the knowledge graph analyzes in detail the intrinsic connections between knowledge and knowledge, problem and problem, and capability and capability. In the field of ideological-political courses, the relationships between knowledge are mainly reflected in the logical relationships between basic principles, concepts, and theories, such as inclusion, subordination, and progression. The relationships between problems are mainly reflected in the combination of theoretical knowledge problems, practical application problems, ideological and emotional value problems, and personal development problems. The relationships between capabilities are reflected in the fact that basic cognitive ability is a key capability, which then leads to the mastery of critical thinking ability, innovation and creation ability, etc.

在关联关系方面，知识图谱详细分析知识与知识、问题与问题、能力与能力之间的内在联系。在思政课领域，知识之间的关联主要体现在基本原理、概念及理论之间的逻辑关系，如包含、从属、递进等关系。问题之间的关联主要体现在理论知识、实践应用、思想情感价值、个人发展问题的组合上。能力之间的关联体现在基础认知能力是关键能力，进而掌握批判性思维能力、创新创造能力等。

The mapping relationship is another important link in the construction of the knowledge graph, achieving an effective connection between objectives, capabilities, problems, and knowledge. Key capability requirements revolve around the teaching objective system, while capability development is assessed through problem decomposition and task completion; the design of knowledge points needs to rely on the systematic layout of problems. The knowledge graph transforms teaching objectives into specific capability requirements, which are then transformed into a problem system, and finally mapped onto the knowledge system. This process not only helps teachers precisely grasp teaching objectives but also guides students’ learning process in a targeted manner, ensuring the effective achievement of teaching objectives.

映射关系是知识图谱构建的又一重要环节，它实现了目标、能力、问题与知识之间的有效对接。关键能力要求围绕教学目标体系，而能力发展则通过问题分解和任务完成来评估，知识点的设计需要依托问题的系统布局。知识图谱将教学目标转化为具体的能力要求，进而转化为问题体系，并最终映射到知识体系。这一过程不仅有助于教师精准把握教学目标，更能有针对性地指导学生的学习过程，确保教学目标的有效达成。

(B) The Construction Steps of Knowledge Graphs for Ideological-Political Courses

（二）思政课知识图谱的构建步骤

The construction of knowledge graphs for ideological-political courses focuses on three core steps: First, determining the knowledge framework, where subject domain experts, through fine classification and organization of knowledge points, transform the complex knowledge system into a graph data structure that computers can efficiently recognize and parse, laying a solid foundation for subsequent digital teaching. Second, developing and annotating teaching resources, which involves connecting the structured textbook chapter content with the nodes of the knowledge graph, building a bridge that facilitates students in finding knowledge points by following the graph and promotes the deep integration of teaching resources. Through structured processing such as careful editing of content and fine slicing of video resources, teaching resources become closely linked to specific knowledge points, enhancing the diversity and interactivity of learning paths. Third, optimizing the knowledge graph and teaching resource library, by reviewing the rationality of the knowledge modeling structure and dynamically adjusting the quantity of resources, continuously improving the effectiveness of the knowledge graph to ensure it accurately reflects the internal logic of knowledge while meeting the practical needs of precision teaching. We take the course “Ideological and Moral Cultivation and Legal Foundation” (hereinafter referred to as “Daofa”) as an example to elaborate on the specific steps of constructing a knowledge graph for ideological-political courses.

思政课知识图谱的构建聚焦三个核心步骤：一是确定知识框架，由学科领域专家通过对知识点的精细分类与整理，将纷繁复杂的知识体系转化为计算机能够高效识别与解析的图数据结构，为后续的数字化教学奠定坚实的基础。二是开发与标注教学资源，即将结构化的教材章节内容与知识图谱的节点相连接，构建起一个既便于学生按图索骥查找知识点，又促进教学资源深度整合的桥梁。通过对内容的精心编辑、视频资源的细致切片等结构化处理，教学资源得以与具体知识点紧密关联，提升了学习路径的多样性与互动性。三是知识图谱及教学资源库优化，通过对知识建模结构的合理性审查与资源数量的动态调整，不断优化知识图谱的效能，确保其既能准确反映知识的内在逻辑，又能满足精准教学的实际需求。我们以“思想道德与法治”（以下简称“道法”）课为例，详细阐述思政课知识图谱构建的具体步骤。

1. Determining the Knowledge Framework

1.确定知识框架

The “Morality and Law” course can be divided into four modules: the Life Module, the Values Module, the Morality Module, and the Rule-of-Law Module. The content of each module can be categorized into knowledge requirements, ability requirements, and literacy requirements. Knowledge requirements are an important basis for constructing the knowledge graph of the “Morality and Law” course. Since the “granularity” of knowledge points affects the difficulty of resource development and the scale of the system, and based on fully absorbing the opinions of subject-matter experts, knowledge points are divided into levels one through five, with annotations for prerequisite, subsequent, and related knowledge points. First-level knowledge points are the modules in the course textbook, such as Module 1 “Comprehending the True Meaning of Life and Grasping the Direction of Life” and Module 4 “Studying the Thought on the Rule of Law and Enhancing Rule-of-Law Literacy”; second-level knowledge points are refinements of module content, such as “A Correct Outlook on Life” and “The Characteristics and Operation of Socialist Law”; third-level knowledge points are refinements of sub-concepts involved in second-level knowledge points, such as “A Positive and Enterprising Attitude toward Life” and “The Operation of China’s Socialist Law”; fourth- and fifth-level knowledge points are refined from the specific content of the textbook, such as “Life Requires Earnestness” and “Lawmaking.” “Dialectically Viewing the Contradiction between Ideals and Reality” is a prerequisite knowledge point for “The Long-Term, Arduous, and Tortuous Nature of Realizing Ideals”; that is, if a student has problems mastering the knowledge point “The Long-Term, Arduous, and Tortuous Nature of Realizing Ideals,” the cause can be traced back to the prerequisite knowledge point “Dialectically Viewing the Contradiction between Ideals and Reality.”

“道法”课可分为人生篇、价值篇、道德篇和法治篇四个模块，每个模块的内容可分为知识要求、能力要求和素养要求。知识要求是“道法”课知识图谱构建的重要依据。由于知识点“颗粒”大小关系到资源开发的难易程度和体系大小，因此在充分吸纳学科领域专家意见的基础上，将知识点划分为一至五级，并进行前置、后置以及关联知识点的标注。一级知识点是课程教材中的模块，如模块1“领悟人生真谛把握人生方向”和模块4“学习法治思想提升法治素养”等；二级知识点是对模块内容的提炼，如“正确的人生观”、“社会主义法律的特征和运行”等；三级知识点是对二级知识点中所涉及的次概念进行提炼，如“积极进取的人生态度”、“我国社会主义法律的运行”等；四级至五级知识点则是从教材的具体内容中提炼的，如“人生需认真”、“法律制定”等。“辩证看待理想与现实的矛盾”是“实现理想的长期性、艰巨性和曲折性”的前置知识点，也就是说如果学生对“实现理想的长期性、艰巨性和曲折性”知识点的掌握出现了问题，可以从“辩证看待理想与现实的矛盾”这个前置知识点中寻找原因。

After knowledge sorting, it is necessary to determine the “entity” types, the relationships between “entities,” “attributes,” and “attribute values.” For the “Morality and Law” course, “entities” include basic concepts (such as rule of law, social morality, etc.), basic theories (such as the Marxist view of the masses, etc.), basic principles (such as the relationship between law and morality, etc.), critical thinking, innovation ability, practical application (such as case analysis, etc.), social responsibility, and rule-of-law literacy. Relationships between “entities” include prerequisite (e.g., the prerequisite knowledge point for “The Dialectical Relationship between the Individual and Society” is “The Marxist Understanding of Human Essence”), subsequent (e.g., the subsequent knowledge point for “The Dialectical Relationship between the Individual and Society” is “Noble Life Pursuits”), and related (e.g., the related knowledge point for “Correctly Understanding Human Essence” is “The Marxist Understanding of Human Essence”). “Attributes” include knowledge point name (e.g., the first-level knowledge point name is “Shouldering the Great Task of Rejuvenation and Becoming a New Era Talent”), definition (e.g., “The Connotation of the New Era” is a conceptual knowledge point), importance (e.g., key points, difficult points, exam points), etc. “Attribute values” refer to the specific values taken by attributes, that is, the description of specific knowledge points.

知识梳理后要确定“实体”类型、“实体”之间的关系、“属性”及“属性值”等。对“道法”课而言，“实体”包括基本概念（如法治、社会公德等）、基础理论（如马克思主义群众观等）、基本原理（如法律与道德的关系等）、批判思维、创新能力、实践应用（如案例分析等）、社会责任、法治素养。“实体”间的关系包括前置（如“个人与社会的辩证关系”的前置知识点是“马克思主义关于人的本质的认识”）、后置（如“个人与社会的辩证关系”的后置知识点是“高尚的人生追求”）、关联（如“正确认识人的本质”的关联知识点是“马克思主义关于人的本质的认识”）等。“属性”包括知识点名称（如一级知识点名称为“担当复兴大任成就时代新人”）、定义（如“新时代的内涵”就是概念性知识点）、重要性（如重点、难点、考点）等。“属性值”是指对属性的具体取值，也就是对具体知识点的描述。

2. Developing and Annotating Teaching Resources

2.开发与标注教学资源

The educational value of knowledge graphs is mainly reflected through the development and annotation of teaching resources that match the knowledge. Generally speaking, teaching resources include knowledge point resources and assessment resources. In terms of developing knowledge point resources, the main basis is the textbook content, extracting the core content of knowledge points, supplemented by images and other aids to help students understand, with important content being emphasized and annotated. Knowledge point resources should be developed following the logic of “problem introduction → knowledge point explanation → confusion resolution,” focusing on cultivating students’ key abilities, moral cultivation, rule-of-law concepts, etc., ensuring that core competencies are integrated into resource development. In terms of developing assessment resources, test questions are an important guarantee for accurately evaluating and pushing relevant knowledge points and resource learning. In addition to testing students’ degree of mastery, depth of understanding, and application ability regarding course knowledge points, test questions should also focus on evaluating students’ core competencies. Therefore, test questions should include multiple question types such as multiple-choice questions, true/false questions, short-answer questions, essay questions, and case analysis questions, ensuring the comprehensiveness and targeting of assessment around the knowledge content of the five modules. The following requirements should be noted in developing assessment resources: covering all knowledge points as much as possible, with an even distribution of difficulty, and developing comprehensive test items. The verification and annotation of resources are key to achieving precise push. The content of verification and annotation mainly includes question type, difficulty, question stem, options, answers, and knowledge points, and knowledge labels are sorted according to the degree of relationship. In the process of verifying knowledge labels and their sorting for different types of resources, multiple rounds of review by multiple teachers are required to ensure the accuracy of the association between resources and knowledge points and the scientific nature of the sorting.

知识图谱的教育价值主要通过与知识匹配的教学资源开发与标注来体现。一般而言，教学资源包括知识点资源和测评资源。在知识点资源开发方面，主要以教材内容为依据，提炼知识点最核心的内容，配以图片等帮助学生理解，对重要的内容可以进行强调和标注。知识点资源应以“问题导入→知识点讲解→困惑解决”为逻辑进行开发，注重培养学生的关键能力、道德修养、法治观念等，确保将核心素养融入资源开发中。在测评资源开发方面，测验题是精准评判和推送相关知识点和资源学习的重要保障。测验题除了检测学生对课程知识点的掌握程度、理解深度以及应用能力，更应注重评价学生的核心素养。因此，测验题应包括选择题、判断题、简答题、论述题、案例分析题等多种题型，围绕五个模块的知识内容确保测评的全面性和针对性。测评资源开发要注意以下要求：尽量覆盖全部知识点，难度分布均匀，开发综合类试题。资源的核对与标注是实现精准推送的关键。核对与标注的内容主要包括题目类型、难度、题干、选项、答案、知识点，并根据关系程度对知识标签进行排序。在给不同类型的资源核定知识标签及排序的过程中，需要经过多位教师多轮审核，以确保资源与知识点之间关联的准确性以及排序的科学性。

3. Optimization of the Knowledge Graph and Teaching Resource Library

3.知识图谱及教学资源库优化

Based on students’ learning behavior data, the platform can automatically identify and adjust the correlation coefficients between knowledge points in the knowledge graph. For example, if a student frequently searches for legal provisions related to a certain moral principle after learning it, the platform will increase the correlation coefficient between these two knowledge points, reflecting the close connection between them. Through feedback from learning big data, the platform can identify which entity relationships are frequently queried or cited by students, thereby further optimizing these relationships. As students’ learning progress advances and their abilities improve, the platform will dynamically adjust the recommendation strategy, ensuring that the recommended teaching resources always remain synchronized with students’ learning needs.

基于学生的学习行为数据，平台可以自动识别并调整知识图谱中各知识点之间的关联系数。例如，如果学生在学习某个道德原则后，频繁地搜索与该原则相关的法律条文，那么平台就会增加这两个知识点之间的关联系数，反映出它们之间的紧密联系。通过学习大数据的反馈，平台能够识别出哪些实体关系被学生频繁查询或引用，从而进一步优化这些关系。随着学生学习进度的推进和能力的提升，平台会动态调整推荐策略，确保推荐的教学资源始终与学生的学习需求保持同步。

III. Practical Pathways for Promoting Precision Teaching in University Ideological-Political Courses through Knowledge Graphs

三、以知识图谱推动高校思政课精准教学的实践路径

To better implement the fundamental task of fostering virtue through education [3]3 “Fostering virtue through education” (立德树人) is a core educational principle of the CPC, emphasizing the cultivation of moral character and talent in students as the fundamental task of education. in ideological-political courses and achieve the connotative development of ideological-political course construction, it is necessary to promote the integration of ideological-political courses with knowledge graphs. Using multimodal analysis, algorithms, and human-machine collaboration technology, this promotes the precise understanding of students, precise supply of content, and precise evaluation of teaching effectiveness in ideological-political courses, thereby enhancing the targeting and practical effectiveness of ideological-political courses while improving the level of precision teaching.

为了更好地落实思政课立德树人根本任务、实现思政课建设内涵式发展，要推动思政课与知识图谱的融合，运用多模态分析、算法，结合人机协同技术推动思政课精准把握学生、精准供给内容以及精准评价教学效果，在提高精准教学水平的同时提升思政课的针对性和实效性。

(I) Precise Profiling and Customizing Personalized Learning Paths

（一）精准描摹画像，定制个性化学习路径

Multimodal analysis technology in knowledge graphs is a technology that associates data from different media and modalities, such as text, images, and audio, with entities and relationships in the knowledge graph, and performs deep understanding and analysis. In ideological-political course teaching, data related to student behavior, such as voice, images, video, text, and browsing data, are processed, organized, and classified to form specific metadata. To facilitate ideological-political course teachers in examining student profiles from the perspective of course objectives, metadata can be divided into five dimensions: knowledge, emotion, ability, behavior, and thought, resulting in knowledge metadata, emotional metadata, ability metadata, behavioral metadata, and thought metadata. Knowledge metadata mainly describes students’ degree of mastery of ideological-political course knowledge points, their understanding of the knowledge system, and their application of knowledge, collected through multimodal data such as test scores, homework analysis, and classroom interaction. Emotional metadata mainly describes students’ learning attitudes, self-cognition, and mental health, collected through multimodal data such as question responses, classroom interaction, exams, and questionnaires. Ability metadata mainly describes students’ cognitive, memory, practical, and innovative abilities, collected through multimodal data such as project assignments and practical activities. Behavioral metadata mainly describes students’ learning habits, classroom participation, and learning strategies, collected through multimodal data such as classroom observation, learning logs, and interactive participation. Thought metadata mainly reflects students’ values and ways of thinking, collected through multimodal data such as discussion records and homework. After technical processing, key metadata can be integrated into a unified multimodal analysis platform, which flexibly outputs and presents multi-dimensional student profiles according to the needs of educators and administrators, supporting personalized teaching and educational decision-making.

知识图谱中的多模态分析技术是一种将来自不同媒介和模态的数据如文本、图像、音频等与知识图谱中的实体、关系进行关联，并进行深度理解和分析的技术。在思政课教学中，对学生行为数据关联的语音、图像、视频、文本、浏览等数据进行加工、整理、分类，从而形成特定的元数据。为方便思政课教师以课程目标审视学生画像，可以从知识、情感、能力、行为、思想五个维度将元数据分为知识元数据、情感元数据、能力元数据、行为元数据、思想元数据。知识元数据主要通过收集来自测试成绩、作业分析、课堂互动等多模态数据来描述学生对思政课知识点的掌握程度、知识体系的理解情况和知识应用等情况。情感元数据主要通过收集来自问题回答、课堂互动、考试、问卷调查等多模态数据来描述学生的学习态度、自我认知和心理健康等。能力元数据主要通过项目作业、实践活动等多模态数据来描述学生认知、记忆、实践、创新等各项能力。行为元数据主要通过收集课堂观察、学习日志、互动参与等多模态数据来描述学生的学习习惯、课堂参与、学习策略等情况。思想元数据主要通过收集来自讨论记录、作业等多模态数据来反映学生的价值观和思维方式等。经过技术处理，关键元数据可被整合至统一的多模态分析平台，该平台根据教育者和管理者需求，灵活输出并呈现多维度的学生画像，助力个性化教学与教育决策。

By using multimodal tools to analyze student profiles that reflect explicit behaviors and implicit emotions, teachers of ideological and political courses can gain a more comprehensive and dynamic understanding of students, provide them with personalized learning paths, achieve differentiated instruction, and enhance the precision of teaching in ideological and political courses. First, teachers conduct pre-class questionnaire surveys to understand students’ cognition of the theoretical and practical issues involved in the current chapter, combine this with multimodal tools to collect students’ pre-class behavioral data, and ascertain students’ knowledge mastery, thereby conducting targeted teaching in the classroom. Second, through online learning platforms and classroom interaction tools, using technologies such as facial expression recognition and speech analysis, metadata is obtained across dimensions including knowledge, emotion, ability, behavior, and thought. Through methods such as cluster analysis and association rule mining, the learning characteristics and types of different students are identified, generating profiles that reflect students’ basic information, knowledge characteristics, emotional characteristics, ability characteristics, behavioral representations, and thought trends, such as “systematic consolidation type,” “precise and efficient type,” and “curious exploration type.” Teachers must clarify the meaning and characteristic manifestations of each profile label: the “systematic consolidation type” exhibits a planned, organized learning style that emphasizes practice and consolidation, demonstrating strong self-drive; the “precise and efficient type” shows strong self-cognitive ability and goal orientation, focusing on the targeted nature and effectiveness of learning, and is adept at using assessment results to adjust learning strategies; the “curious exploration type” enjoys driving learning through problem-solving and relishes the sense of achievement brought by the exploration process. Finally, based on the profiles, teachers design teaching strategies to meet the diverse needs of different types of students. Specifically: the learning path for “systematic consolidation type” students is to first listen to the teacher’s lecture, then systematically learn the knowledge points, and finally consolidate through practice. For the learning habits of this type of student, teachers can adopt a teaching strategy of “micro-lecture videos + supporting test questions + evaluation matching test results,” designing a learning path of “video guidance—exercise reinforcement” for students. The learning path for “precise and efficient type” students is “pre-test and post-test cyclical learning.” For students with this characteristic, teachers can adopt a teaching strategy of “test evaluation + resource recommendation,” meaning teachers can assess students’ initial levels through a pre-test on knowledge points, then after students learn the relevant knowledge points and teaching resources, conduct a post-test to evaluate their learning outcomes, and then push supplementary teaching resources to fill gaps. The learning path for “curious exploration type” students is “inquiry-based learning.” This type of student has a strong thirst for knowledge and exploratory spirit. Facing this type of student, teachers can adopt a teaching strategy of “problem chain-driven,” that is, proposing a list of questions related to the knowledge points to stimulate students’ curiosity, encouraging students to autonomously learn relevant teaching resources, actively seek answers to the questions, and deepen their understanding of the knowledge points by reporting learning outcomes.

思政课教师通过多模态工具分析反映学生显性行为和内隐情感的学生画像，可以更全面、动态地了解学生，为其提供个性化学习路径，实现分类教学，提升思政课教学精准度。首先，教师在课前进行问卷调查，了解学生对本章节所关涉的理论和现实问题的认知，结合多模态工具收集学生课前行为数据，了解学生知识掌握情况，从而在课堂上进行有针对性的教学。其次，通过在线学习平台、课堂互动工具，利用面部表情识别、语音分析等技术获取围绕知识、情感、能力、行为、思想等维度的元数据。通过聚类分析、关联规则挖掘等方法，识别不同学生的学习特点和类型，生成反映学生基本信息、知识特征、情感特征、能力特征、行为表征和思想动向的画像，如“系统巩固型”、“精准高效型”、“好奇探索型”等。教师必须明确每种画像标签的含义和特征表现，“系统巩固型”表现出一种有计划、有条理且注重实践巩固的学习方式，展现出较强的自我驱动力；“精准高效型”展现出较强的自我认知能力和目标导向性，注重学习的针对性和有效性，善于利用评估结果来调整学习策略；“好奇探索型”喜欢通过解决问题来驱动学习，享受探究过程带来的成就感。最后，教师依据画像，设计教学策略来满足不同类型学生的多样化需求。具体来说：“系统巩固型”学生的学习路径是习惯先听教师讲授，再系统学习知识点，最后结合练习加以巩固。针对该类学生的学习习惯，教师可采取“微课视频+配套试题+与试题结果匹配的评价”的教学策略，为学生设计“视频导学—习题强化”的学习路径。“精准高效型”学生的学习路径则是“前测后评式循环学习”，针对具有这一特点的学生，教师可采取“测试评价+资源推荐”的教学策略，即教师可以通过对学生知识点的前测，评估学生的初始水平，在学生学习相关知识点和教学资源后再进行知识点的后测，评估其学习效果，再查漏补缺地推送相关教学资源，补齐不足。“好奇探索型”学生的学习路径则是“探究式学习”，这类学生有较强的求知欲和探索精神。面对这一类型的学生，教师可采取“问题链驱动”的教学策略，即提出与知识点相关的问题清单，激发学生好奇心，鼓励学生自主学习相关教学资源，主动探寻问题答案，以汇报学习成果的方式深化对知识点的理解。

(II) Efficiently Utilizing Resources and Precisely Recommending Learning Content

（二）高效利用资源、精准推荐学习内容

An algorithm refers to a sequence of steps for operating on data according to certain rules, a set of clear instructions for solving problems. The algorithms of knowledge graphs are mainly used to construct, represent, query, and reason about entities and relationships. Key algorithm types include entity recognition and relation extraction. Entity recognition involves identifying and classifying given texts, while relation extraction involves extracting relationships between entities from texts. Teachers should use entity recognition to identify key entities, such as concepts and theories, from a large amount of teaching materials for ideological and political courses, accurately grasping the teaching content. They should use relation extraction to identify relationships between entities, such as causal and parallel relationships, establishing the nodes and links of the knowledge graph in ideological and political courses, thereby constructing a complete knowledge system.

算法是指按照一定规则对数据进行操作的步骤序列，是解决问题的一系列明确指令。知识图谱的算法主要用于构建、表示、查询以及推理实体与关系，关键算法类型包括实体识别和关系抽取，实体识别是识别并分类给定的文本，关系抽取是从文本中提取实体间的关系。教师要利用实体识别从大量的思政课教学材料中识别出关键实体，如概念、理论等，准确把握教学内容。利用关系抽取识别实体间的关系，如因果、并列关系，建立思政课中知识图谱的节点和链接，从而构建完整的知识体系。

Knowledge graphs embed diversified algorithmic tools. On the one hand, they can promote the efficient utilization of teaching resources. Through graph algorithm analysis, teachers of ideological and political courses can rationally allocate and use various teaching resources. On the other hand, they can achieve personalized content recommendations. Based on students’ actual needs and preferences, graph algorithms can tailor personalized learning content for them, thereby greatly enhancing the learning experience and teaching effectiveness. Additionally, teachers can use the algorithmic association technology of knowledge graphs to connect originally isolated teaching resources such as learning courseware, teaching videos, online question banks, and reference books with knowledge points, weaving them into a vast and organic teaching resource network. They can also jointly create digital resource libraries with practical teaching venues such as museums and red culture memorial halls, forming a three-dimensional, large-scale resource repository.

知识图谱内嵌多元化的算法工具，一方面，可以促进教学资源的高效利用，凭借图谱算法分析，思政课教师可以合理地配置和使用各类教学资源；另一方面，可以实现个性化的内容推荐，依据学生的实际需求和偏好，图谱算法能为其量身打造个性化的学习内容，从而极大地提升学习体验感和教学效果。此外，教师可以利用知识图谱的算法关联技术，将原本孤立的学习课件、教学视频、在线题库、参考书籍等教学资源与知识点连接，编织成一个庞大而有机的教学资源网络，并与博物馆、红色文化纪念馆等实践教学场馆联合打造数字资源库，形成立体化、规模化的资源库。

Teachers of ideological and political courses can use the multiple types of algorithmic tools embedded in knowledge graphs to drive the efficient allocation and precise utilization of teaching resources, providing students with rich and precise learning materials. They can also use knowledge base tools at any time to integrate and update resources, facilitating students in conducting targeted learning through learning platforms. Teachers of ideological and political courses should fully utilize expert systems, combined with the teaching Q&A system of knowledge graphs, to carry out personalized content recommendations. By leveraging the advantages of the knowledge graph recommendation engine algorithm, teachers analyze the dynamic trace big data from students’ questioning sessions, analyze students’ existing knowledge structures and dynamic needs, accurately identify students’ knowledge blind spots and weak areas, and provide students with professional learning guidance and suggestions. This precision teaching that meets students’ needs can undoubtedly provide students with clustered, customized personalized learning content, improving students’ learning efficiency.

思政课教师可以利用知识图谱内嵌的多类型算法工具，驱动教学资源的高效配置与精准利用，为学生提供丰富而精准的学习材料，并随时利用知识库工具实现资源的整合与更新，便于学生通过学习平台开展针对性学习。思政课教师应充分利用专家系统，结合知识图谱的教学答疑系统，开展个性化内容推荐。教师利用知识图谱推荐引擎算法优势，分析学生在提问环节中的动态痕迹大数据，分析学生已有的知识结构和动态需求，精准识别学生的知识盲点和薄弱环节，为学生提供专业化的学习指导和建议。这种满足学生需求的精准教学无疑能为学生提供聚类式、定制式的个性化学习内容，提高学生的学习效率。

(III) Assessing Real-Time Learning Situations and Providing Immediate Feedback on Teaching and Learning Outcomes

（三）评估实时学情，即时反馈教与学成效

Human-machine collaboration based on knowledge graphs is an efficient mode of cooperation. It utilizes the organizing, classifying, and modeling capabilities of knowledge graphs to promote effective cooperation and collaborative work between humans and machines. Human-machine collaboration based on knowledge graphs can, through online collaboration, multi-dimensional interaction, and immediate feedback, jointly act on the evaluation of all elements and the entire process of teaching in ideological and political courses, promoting precise evaluation of teaching in ideological and political courses and improving the quality of teaching in ideological and political courses.

以知识图谱为基础的人机协同是一种高效的合作方式，它利用知识图谱的整理、分类、建模能力，促进人类与机器之间的有效合作与协同工作。以知识图谱为基础的人机协同可以通过在线协作、多维互动以及即时反馈，共同作用于思政课教学的全要素与全过程评价，推动思政课教学精准评价，提升思政课教学质量。

In terms of online collaboration, teachers of ideological and political courses can, through online learning systems that record the complete process of student collaboration and discussion, combined with knowledge graphs to track the evolution of associations between topics and knowledge points, understand students’ discussion threads and internal logical reasoning. Taking the course “Outline of Modern Chinese History” as an example, when discussing the historical significance of the May Fourth Movement, teachers can use knowledge graphs to track the evolution of associations between the May Fourth Movement and knowledge points such as the New Culture Movement and the Northern Expedition, understanding students’ reasoning logic during discussions and their ability to draw upon and connect related knowledge. At the same time, when students discuss “the contemporary value of the May Fourth Spirit,” teachers analyze their unique viewpoints, analytical perspectives, argumentation logic, etc., to evaluate the depth and breadth of student discussions. Furthermore, by analyzing the flow and sharing of knowledge within student teams, teachers comprehensively evaluate the collaborative ability of student teams. Teachers can also evaluate students’ communication skills by analyzing factors such as students’ speaking frequency, clarity of content expression, and speed of feedback response, combined with the entities and relationships of the knowledge graph.

在线协作方面，思政课教师可通过记录学生协作与讨论完整过程的在线学习系统，结合知识图谱追踪话题与知识点之间的关联演变，了解学生的讨论线索和内在逻辑运思。以“中国近现代史纲要”课为例，当探讨五四运动的历史意义时，教师可以通过知识图谱，追踪五四运动与新文化运动、北伐战争等知识点之间的关联演变，了解学生在讨论中的运思逻辑及对相关知识的牵引和关联能力。同时，学生在讨论“‘五四精神’的当代价值”时，分析他们提出的独特观点、分析视角、论证逻辑等，以此来评估学生讨论的深度和广度。此外，分析学生团队内部的知识流动与共享状况，全面评价学生团队的协作能力。教师还可通过分析学生的发言频率、内容表达清晰度、反馈响应速度等，结合知识图谱的实体和关系，评价学生的沟通能力。

At the level of multi-dimensional interaction, online learning systems can not only track students’ learning trajectories on the platform, including access time, dwell time, and navigation paths, but also capture students’ physiological, psychological, and behavioral characteristics during learning, converting this information into intuitive data and models. For example, when teaching the chapter “The War of Resistance Against Japan,” teachers can use the system to collect students’ multi-dimensional interaction data on related historical events and figures, such as students’ likes, comments, and shares on the heroic deeds of resistance fighters. Combined with knowledge graphs and human-machine collaboration tools, they identify, collect, and organize information such as speech, images, and videos from the classroom, deeply analyze students’ interaction preferences and intensity on key events of the “War of Resistance Against Japan,” and thereby gain insights into students’ attitudinal tendencies, emotional characteristics, learning habits, and theoretical doubts.

多维互动层面，在线学习系统不仅能够追踪到学生在平台上的学习轨迹，包括访问时间、停留时长、跳转路径等，还能捕捉学生在学习时的生理、心理及行为特征，并将这些信息转化为直观的数据和模型。如在讲述“抗日战争”这一章节时，教师可以通过系统收集学生对相关历史事件和人物的多维互动数据，如学生对抗战英雄事迹的点赞、评论和分享等，结合知识图谱和人机协同工具，识别、收集和整理课堂中的语音、图像及视频等信息，深度分析学生在“抗日战争”各个关键事件上的互动偏好、强度，进而洞察学生的态度倾向、情感特征、学习习惯和理论疑惑等。

In terms of instant feedback, a learning platform based on knowledge graphs can achieve automated grading, enabling teachers to obtain feedback on students’ mastery of knowledge points and learning effectiveness. After students complete assignments in ideological and political courses, teachers, by combining standard answers provided by the test question resource bank, the correlation recommendations between knowledge and learning resources in the knowledge graph, the logical reasoning paths of the learning system, and the system’s detailed records of the entire process of students completing assignments—such as thinking time, revision records, and search and help-seeking behaviors—comprehensively assess students’ logical thinking, argumentation strategies, and the accuracy, completeness, and innovativeness of their assignments during the problem-solving process. Teachers then adjust their teaching strategies based on the precise data feedback provided by the system, combined with students’ individual differences and learning characteristics. Furthermore, by comparing students’ assignment performance at different stages, and combining the learning paths and difficulty gradients in the knowledge graph, teachers can track the trajectory of students’ thinking growth on a certain important knowledge point. This enables teachers to more comprehensively evaluate students’ learning effectiveness and also provides a basis for adjusting subsequent teaching plans and methods.

即时反馈方面，基于知识图谱的学习平台可实现自动化批改，使教师获得关于学生知识点掌握程度、学习效果等方面的反馈。学生完成思政课作业后，教师结合试题资源库提供的标准答案、知识图谱中知识与学习资源间的关联推荐、学习系统的逻辑推理路径以及系统详尽记录的学生完成作业的全过程数据，如思考时间、修改记录、搜索和求助行为等，全面评估学生在解题过程中的逻辑思维、论证策略以及学生作业的准确性、完整性和创新性。教师则根据系统提供的精准数据反馈，结合学生的个体差异和学习特点，对教学策略进行调整。此外，通过对比学生在不同阶段完成的作业情况，结合知识图谱中的学习路径和难度梯度，追踪学生在某一重要知识点上的思维成长轨迹。教师能够更全面地评价学生的学习效果，也为后续教学计划和方法的调整提供依据。

Against the backdrop of rapid digital technology development, digital education has become an important engine driving educational innovation. As an important tool of digital education, knowledge graphs, through integration with cutting-edge technologies such as digital profiling, algorithms, and human-machine collaboration, can empower precise teaching in ideological and political courses. Teachers of ideological and political courses should fully leverage the advantages of knowledge graphs to diagnose students’ knowledge and ability levels, accurately grasp learning conditions, and customize personalized learning paths for students based on their needs; utilize their powerful algorithmic advantages to efficiently use teaching resources and precisely push teaching content; and combine human-machine collaboration technology to monitor in real time and comprehensively evaluate the entire process of students’ online learning, capture students’ learning dynamics in real time, and precisely grasp students’ learning progress and effectiveness. Knowledge graphs can not only promote the systematic innovation of teaching concepts and models in ideological and political courses, fostering the innovative development of ideological and political course teaching, but also make ideological and political courses more aligned with the pulse of the times, meet students’ personalized needs, and promote students’ comprehensive development.

在数字技术快速发展的时代背景下，数字教育已成为推动教育创新的重要引擎。知识图谱作为数字教育的重要工具，通过与数字画像、算法和人机协同等前沿技术的融合，能为思政课精准教学赋能。思政课教师要充分发挥知识图谱的优势，诊断学生知识能力水平、精准把握学情，针对学生需求为其定制个性化学习路径；利用其强大的算法优势，高效利用教学资源，精准推送教学内容；结合人机协同技术，实时监测与全面评估学生在线学习全过程，实时捕捉学生学习动态，精准把握学生学习进度与学习效果。知识图谱不仅能推动思政课教学理念与教学模式的系统性革新，促进思政课教学的创新发展，而且让思政课更加贴合时代脉搏，满足学生个性化需求，促进学生全面发展。

(The authors are professors and doctoral supervisors at the School of Marxism, Southwest Jiaotong University.)

（作者系西南交通大学马克思主义学院教授、博士生导师）

Online Editor: Ma Jingren

网络编辑：马京仁