The Trends, Dilemmas, and Boosting Mechanisms of Data-Driven Innovative Development in Ideological and Political Education

Data-driven approaches are a new engine for the development of the times, providing conditions and space for the innovative development of ideological and political education, enhancing its precision and digital-intelligence level, and boosting its move toward digitalization. Xi Jinping emphasized: “Promote the deep integration of the traditional advantages of ideological and political work with information technology, and enhance its contemporary relevance and appeal.” [1]1 Xi Jinping, “Speech at the National Conference on Ideological and Political Work in Colleges and Universities” (December 7, 2016). The data-driven innovative development of ideological and political education essentially embodies the internal unity of value rationality and instrumental rationality. Facing the practical demand for deep integration of modern information technology and education, ideological and political education must seize the data-driven opportunity of the times, rationally apply big data technology, promptly prevent and resolve technological crises, and construct an educational mechanism for ideological and political education within “technology for good,” thereby enhancing the effectiveness of ideological and political education.

数据驱动是时代发展的新引擎，为思想政治教育提供了创新发展的条件和空间，提高了思想政治教育的精准度和数智化水平，助推思想政治教育迈向数字化。习近平强调：“推动思想政治工作传统优势同信息技术高度融合，增强时代感和吸引力。”[1]数据驱动思想政治教育创新发展本质上体现了价值理性与工具理性的内在统一，面对现代信息技术与教育深度融合的现实诉求，思想政治教育要抓住数据驱动的时代机遇，合理运用大数据技术，及时防范化解技术危机，在“技术向善”中构建思想政治教育育人机制，提升思想政治教育效果。

I. Presentation of Trends in Data-Driven Innovative Development of Ideological and Political Education

一、数据驱动思想政治教育创新发展的态势呈现

1. The process of openness and sharing is accelerating. Data-driven technology not only pushes the educational model from one-way indoctrination to full participation and collaboration but also expands the educational field from the real world to holographic time-space, which places higher demands on the openness and sharing of the data resource system for ideological and political education. On the one hand, data drives the opening of information in ideological and political education. The opening of data information resources is key to breaking information closure and an important prerequisite for information sharing. The degree of data openness is deepening with the development of data technology. Educational subjects can use data-driven technology to aggregate and integrate data information such as educational cloud platforms, digital education applications, and high-quality educational resources, connecting and communicating various types of information across different levels and regions, thereby achieving interconnection and intercommunication of educational information, assisting in precise assessment of educational objects, and transforming the advantages of data information openness into functional advantages for ideological and political education. On the other hand, data drives the sharing of meaning in ideological and political education. “Meaning sharing refers to the activity in which, within the process of online interpersonal interaction, people understand, accept, absorb others’ thoughts, and reconstruct themselves based on sharing social space resources in the network.” [2]2 Source of this quotation not identified in the original text. In the process of data information opening, people continuously input, understand, absorb, and reshape their own values, outlining a more three-dimensional, multi-dimensional, and intuitive ideological form, thereby expanding the scope of meaning sharing in ideological and political education. Currently, data-driven technology reshapes the environment, concepts, resources, methods, and forms of ideological and political education, further promoting the combination of instrumental rationality and value rationality, explicit factors and implicit factors, objective data and subjective experience, and grand themes and micro-narratives, thereby further expanding the breadth and depth of meaning sharing in ideological and political education.

1. 开放共享进程加快。数据驱动技术不仅推动了教育模式从单向灌输走向全员协同，也促进了教育场域从现实世界走向全息时空，这对思想政治教育数据资源体系的开放共享提出了更高层次的要求。一方面，数据驱动思想政治教育信息开放。数据信息资源开放是打破信息封闭的关键，也是信息共享的重要前提。数据开放的程度正随着数据技术的发展日益加深，教育主体可以利用数据驱动技术将教育云平台、数字化教育应用、优质教育资源等数据信息汇聚整合，沟通对接不同层次、不同地区的各类信息，进而实现教育信息的互联互通，助力精准研判教育对象，助推数据信息开放的优势转化为思想政治教育的功能优势。另一方面，数据驱动思想政治教育意义共享。“意义共享指在网络人际互动过程中，人们在分享网络社会空间资源的基础上理解、接受、吸纳他人的思想并重构自我的活动。”[2]在数据信息开放的过程中，人们不断输入、理解、吸收、重塑自己的价值观念，勾勒出更加立体、多维、直观的思想体式，扩大了思想政治教育意义共享的范围。当前，数据驱动技术重塑着思想政治教育的环境、理念、资源、方法、形式等，进一步促进了工具理性与价值理性、显性因素与隐性因素、客观数据与主观体验、宏大主题与微小叙事的结合，进一步拓展了思想政治教育意义共享的广度和深度。

2. Technological empowerment continues to advance. On the one hand, data-driven technology empowers a precision-oriented path. Traditional forms of manual information collection and analysis are gradually being replaced by intelligent data analysis, and the precision and intelligence level of ideological and political education are increasingly improving. Precise identification, precise analysis, precise supply, precise evaluation, and precise feedback constitute the complete path of data-driven technology empowering ideological and political education. Educational subjects, based on the data traces of educational objects, precisely identify their ideological dynamics and behavioral trajectories, model and analyze scattered, trivial, and subtle dynamic data information, thereby grasping the individual differences and needs of educational objects, providing them with differentiated educational methods and precisely supplying educational content. Then, based on feedback from educational objects, they further evaluate the actual educational effects, precisely feed back the achievements and problems of ideological and political education, and use this as a basis for optimizing educational methods and strategies. On the other hand, data-driven technology empowers immersive experiences. Technologies such as VR, AR, and MR make it possible to construct virtual-real symbiotic simulation environments and teaching practice scenarios. Multi-dimensional and three-dimensional narrative space fields can effectively enhance the immersive experience of educational objects. Educational subjects can, through intuitive and three-dimensional scenario simulations, strengthen the sense of presence of educational objects, transforming abstract text, rigid reasoning, and dogmatic instruction into knowable, perceptible, and visualizable scene existences, enhancing the immersive experience and the feeling of being on the scene for educational objects. By using virtual-real symbiotic means to restore and reconstruct the objective world, by enhancing the two-way interaction between educational subjects and objects through panoramic interactive forms, and by narrowing the educational distance through emotional resonance, this helps educational objects more profoundly internalize emotional experiences into value concepts. Technological empowerment of ideological and political education enhances the initiative and participation of both educational subjects and objects, helping them deepen value cognition and consolidate ideological consensus through interactive perception.

2. 技术赋能持续推进。一方面，数据驱动技术赋能精准化进路。传统人工信息收集分析的形式逐渐被智能化数据分析替代，思想政治教育精准化、智能化水平日益提升。精准识别、精准分析、精准供给、精准评估、精准反馈构成了数据驱动技术赋能思想政治教育的完整进路。教育主体依据教育对象的数据痕迹精准识别其思想动态和行为轨迹，建模分析零散、琐碎、细微的动态数据信息，从而把握教育对象的个性差异和需求，为其提供分众化教育方式、精准供给教育内容，再根据教育对象的情况反馈，进一步评估教育实际效果，精准反馈思想政治教育成效及问题，并以此为思想政治教育方法和策略优化提供依据。另一方面，数据驱动技术赋能沉浸式体验。VR、AR、MR等技术使得虚实相生的仿真环境与教学实践场景构建成为可能，多维立体的叙事空间场域，能够有效增强教育对象的沉浸式体验。教育主体可以通过直观立体的情景模拟，强化教育对象的在场感，将抽象的文字、生硬的说理、教条的训导转化为可知、可感、可视化的场景存在，增强教育对象的沉浸式体验和身临其境之感。以虚实相生的手段还原重构客观世界、以全景化交互形式增强教育主客体的双向互动、以情感共鸣拉近教育距离，从而帮助教育对象更加深刻地将情感体验内化为价值观念。技术赋能思想政治教育，增强了教育主客体的积极性和参与度，帮助他们在交互感知中深化价值认知、凝聚思想共识。

3. Cross-border integration is increasingly deepening. First, it promotes the cross-border integration of educational thinking. The subjects and objects of ideological and political education are gradually beginning a transformation from traditional “on-site” education to quantitative thinking model education. Educational subjects can use data-driven technology to collect, process, and analyze relevant data of educational objects, quantify the basic situation of educational objects, and integrate the data information chain of educational objects. At the same time, educational subjects can, based on dynamic data chains, analyze, judge, and predict the ideological status and behavioral tendencies of educational objects in real time. Second, it promotes the cross-border integration of educational forms. Modern information technology updates the forms of ideological and political education. Emerging platforms such as self-media, short videos, and information websites are flourishing. These platforms precisely match data information resources based on academic ties, geographical ties, professional ties, and interest ties, and accordingly provide targeted resource information services, facilitating communication and interaction among users. Third, it promotes the cross-border integration of educational methods. Data-driven technology promotes the transformation of educational methods, shifting educational methods from focusing on random samples to full data, from pursuing precision to accommodating hybridity, and from judging causal relationships to analyzing correlational relationships. This is conducive to combining traditional educational methods with data methods, transforming sampling survey methods, strengthening full-sample analysis, and formulating scientific, personalized, and precise educational strategies.

3. 跨界融合日益深入。第一，推动教育思维的跨界融合。思想政治教育主客体逐步开启了从传统“在场”教育向量化思维模式教育的转变。教育主体可以利用数据驱动技术收集、处理、分析教育对象的相关数据，量化教育对象的基本情况、集成教育对象的数据信息链。同时，教育主体可以依据动态数据链实时分析、研判和预测教育对象的思想状况和行为倾向。第二，推动教育形式的跨界融合。现代信息技术更新着思想政治教育的形式，新兴自媒体、短视频、信息网站等平台蓬勃发展，这些平台根据学缘、地缘、业缘、趣缘等精准匹配数据信息资源，并据此提供具有针对性的资源信息服务，促成用户间的交流互动。第三，推动教育方法的跨界融合。数据驱动技术促进了教育方法转型，将教育方法从关注随机样本转换到全数据、从追求精准性转换到兼容混杂性、从判断因果关系转换到分析相关关系，这有利于将传统教育方法与数据方法相结合，转化抽样调查方式，加强全样本分析，制定具有科学化、个性化、精确化的教育策略。

II. Realistic Dilemmas in Data-Driven Innovative Development of Ideological and Political Education

二、数据驱动思想政治教育创新发展的现实困境

1. Data barriers cause the dislocation of data sharing in ideological and political education. First, data silos affect the acquisition and supply of educational data. Data barriers block smooth communication between data subjects, and the interest games among multiple data subjects exacerbate data fragmentation. Therefore, the information of ideological and political education forms scattered, independent, and non-interactive data silos. The closed and exclusive nature of data is not conducive to improving the efficiency and level of ideological and political education. The interconnection and sharing of data information resources affect the acquisition of educational data resources. Difficulty in data acquisition directly affects the precision of profiling educational objects, thereby affecting educational decision-making and educational outcomes. At the same time, data silos also affect the precise supply of educational data. Each database stores and defines data independently, with data being mutually incompatible and lacking correlation. This results in unbalanced and asymmetric data mastery of educational objects among educational subjects, and educational subjects cannot precisely match data needs with each other. The imbalance between supply and demand of educational data exacerbates the dislocation of educational data sharing. Second, data monopoly affects the circulation and application of educational data. Data monopoly includes monopolies over data elements, data collection and storage carriers, data analysis tools, and data benefits. In the era of big data, whoever possesses data holds the priority. For ideological and political education to maintain a competitive advantage among major digital platforms, it must possess powerful educational data sources and data chains. However, platforms that currently hold data access rights are better able to identify the personalities, interests, preferences, and psychology of educational objects, thereby providing services and products that match needs, inducing and manipulating the consciousness and behavior of educational objects. Currently, various social trends of thought [3]3 “Social trends of thought” (社会思潮) refers to diverse and often competing ideological currents, such as liberalism, nationalism, or postmodernism, that circulate in society and may challenge official Marxist discourse. are making a comeback through digital platforms. They use their data monopoly advantages to continuously spread harmful言论 in cyberspace, seriously disrupting the ecological environment of ideological and political education. Long-term data monopoly will exacerbate the closed state of data barriers, block the normal circulation and application of data, and obstruct the pathways for educational objects to obtain true knowledge.

1. 数据壁垒致使思想政治教育数据共享失位。第一，数据孤岛影响教育数据的获取供给。数据壁垒阻隔了数据主体间的畅通交流，多方数据主体的利益博弈加剧了数据割据，因此，思想政治教育信息形成了分散独立、缺乏互动性的数据孤岛。数据的封闭独享不利于提高思想政治教育的效率和水平。数据信息资源的互通互享影响着教育数据资源的获取，数据获取困难直接影响着教育对象画像的精准度，进而影响困难教育决策及教育成效。同时，数据孤岛还影响着教育数据的精准供给。各数据库间各自存储、各自定义，数据之间互不相融、缺乏关联性，这就使得教育主体对教育对象数据掌握不平衡、不对称，教育主体之间无法精准对接数据需求。教育数据的供需失衡，加剧了教育数据共享失位。第二，数据垄断影响了教育数据的流转运用。数据垄断包括对数据要素、数据收集存储载体、数据分析工具、数据利益等的垄断。大数据时代，谁拥有了数据，谁就掌握了优先权。思想政治教育要在各大数字平台中保持竞争优势就必须拥有强大的教育数据源和数据链。然而，当下掌握数据访问权限的平台更能找准教育对象的个性、兴趣、偏好、心理，进而提供需求匹配的服务和产品，诱导、操纵教育对象的意识和行为。当前，各类社会思潮借助数字平台卷土重来，他们利用数据垄断优势，在网络空间持续散布不良言论，严重扰乱了思想政治教育生态环境。长期的数据垄断将加剧数据壁垒的封闭状态、阻隔数据之间的正常流转运用、堵塞教育对象获得真知的途径。

2. Data risks induce disorder in data security for ideological and political education. First, data leakage infringes on the privacy rights of education subjects. Data information is likely to be intercepted, identified, and cracked by application backends, which is extremely detrimental to protecting the privacy of education subjects, causing great distress to their lives and work. Similarly, when education subjects use intelligent voice recognition, image recognition, text recognition, and other data analyses to study the ideological dynamics, learning behaviors, and effects of education objects, there is also a risk of data information leakage. Second, data abuse destroys the boundaries of data use. Data abuse usually occurs when education objects are unaware or "passively consent"; platforms, without the consent of education objects, improperly use their data information in unauthorized ways. For ideological and political education to achieve its expected effects, it is closely related to the collection of full-sample data, but this does not mean that education subjects can mine education objects' data without limits or boundaries. Education subjects must mine and use data within prescribed boundaries and be vigilant against data abuse and privacy infringement. Data abuse has triggered concerns among education objects about big data technology, highlighting its negative effects. Third, data theft oversteps data security regulations. Tempted by data benefits, some personnel within the education system take risks, intercepting, stealing, and misappropriating large amounts of personal data information, brazenly crossing the red line of data security, openly overstepping data security regulations, and seriously threatening the privacy security and legitimate rights of education objects.

2. 数据风险诱发思想政治教育数据安全失序。第一，数据泄露侵犯了教育对象的隐私权。数据信息很可能被应用后台拦截、识别、破解，极不利于保护教育对象的隐私保护，这给教育对象的生活工作造成了极大的困扰。同样，教育主体在运用智能语音识别、图像识别、文本识别等数据分析教育对象的思想动态学习行为及效果时，也存在数据信息泄露的风险。第二，数据滥用破坏了数据使用边界。数据滥用通常发生在教育对象不知情或“被自愿”的情况下，平台未经教育对象同意，采用不当方式私自使用教育对象数据信息。思想政治教育要达到预期效果与全样本数据的收集紧密相关，但这并不意味着教育主体能无限制、无边界地挖掘教育对象数据，教育主体必须要在规定界限范围内挖掘、使用数据，警惕数据滥用和隐私侵犯。数据滥用引发了教育对象对大数据技术的担忧，凸显了大数据技术的负面效应。第三，数据窃取僭越了数据安全法规。在数据利益的诱惑下，部分教育系统内部人员铤而走险，拦截、窃取、盗用大量个人数据信息，肆意踏过数据安全红线，公然僭越数据安全法规，严重威胁了教育对象的隐私安全和正常权益。

3. Data anxiety leads to the failure of data effectiveness in ideological and political education. First, excessive reliance on data can easily lead to a situation where ideological and political education emphasizes "quantity" while neglecting "quality." Data quantification makes it easy for education subjects to shift their focus from the essence of education to data results, over-trusting data feedback information, and even relying on data information to make educational decisions. Over time, this will weaken the thinking ability and judgment of education subjects. For education objects, long-term evaluation of themselves based on quantitative data can also easily produce data anxiety; quantitative data charts clearly reflect the gap from ideal data, invisibly imposing enormous psychological pressure and depressive emotions on education objects. This excessive reliance essentially deviates from the original intention of using data technology to enhance the data effectiveness of ideological and political education. Second, excessive rejection of data is not conducive to improving the quality and efficiency of ideological and political education. Some education subjects maintain a high level of vigilance toward emerging technologies, constantly warning themselves with the possibility of "technological alienation," over-doubting big data technology, over-concerned about its negative effects, believing that big data technology itself has many uncertainties, and neglecting its positive role. Additionally, education subjects who reject data believe that behavioral data cannot fully and accurately reflect the ideological dynamics of education objects, and whether data can precisely reflect the ideological and behavioral dynamics of education objects remains unknown. This excessive risk awareness also hinders the innovative development of ideological and political education.

3. 数据焦虑导致思想政治教育数据动力失效。第一，过度依赖数据易导致思想政治教育重视“数”忽略“质”的局面。数据量化极易使教育主体由关注教育本质转向关注数据结果，过度信任数据反馈信息，甚至依赖数据信息做出教育决策，长此以往，将会削弱教育主体的思考力和判断力。于教育对象而言，长期以量化数据评价自身也易产生数据焦虑，数据量化图表清晰可见地反映了与理想数据的差距，无形中给教育对象造成了巨大的心理压力和压抑情绪。这种过度依赖本质上与利用数据技术提升思想政治教育数据效力的初衷相背离。第二，过度排斥数据不利于思想政治教育提质增效。部分教育主体对新兴技术保持较高的警惕性，他们时刻以“技术异化”的可能性警示自己，过度怀疑大数据技术，过度在意大数据技术的负面效应，认为大数据技术本身存在诸多不确定性，忽视了大数据技术的积极作用。此外，排斥数据的教育主体认为行为数据并不能完全准确地反映教育对象的思想动态，数据能否精准反映教育对象的思想行为动态也不得而知。这种过度风险意识同样阻碍了思想政治教育的创新发展。

III. Mechanism Construction for Data-Driven Innovation and Development in Ideological and Political Education

三、数据驱动思想政治教育创新发展的机制构建

1. Identify the key basis points for data interoperability and construct a data-sharing mechanism for ideological and political education. First, ensure the identity recognition of multiple education subjects and establish trust relationships among them. The prerequisite for identity recognition among education subjects is trust relationships; mutual trust can promote positive interaction, thereby constructing a data-sharing mechanism for ideological and political education. Data sharing is a collective action that maintains relationships among education subjects, providing a platform for their identity recognition and further consolidating trust relationships among them. Maintaining trust relationships among education subjects is conducive to promoting the creation of a data-sharing platform for ideological and political education, aggregating various data resource information, and constructing an education data matrix, which can to some extent break down barriers to data information exchange.

1. 找准数据互通的关键基点，构建思想政治教育数据共享机制。一要确保教育多元主体的身份认同，建立主体间的信任关系。教育主体间身份认同的前提是主体间的信任关系，相互信任方能助推良性互动，进而构建思想政治教育数据共享机制。数据共享是教育主体间关系维系的集体行动，其为教育主体身份认同提供了平台，进一步巩固了教育主体间的信任关系。教育主体之间保持信任关系，有利于推动思政数据共享平台创建，集聚各类数据资源信息，构建教育数据矩阵，这可以在一定程度上破解数据信息交流的壁垒。

Second, clarify the data-sharing list for ideological and political education and standardize cross-domain communication and collaboration. Data sharing in ideological and political education requires drafting a clear list of items and content for sharing, delineating the types and scope of shareable data, and excluding risky data information in advance. Data sharing involves multiple subjects, units, departments, industries, etc., and requires the formation of a unified and orderly self-maintenance system to effectively manage and ensure the smooth flow of data sharing in a transparent, procedural, and standardized manner. Through hierarchical and categorical management of data, differentiated processing should be applied to data of different levels and categories, clarifying the limits of rights and responsibilities in data sharing, and effectively allocating shared data resources. At the same time, the security risks of data sharing must be considered, the credibility of entities participating in data sharing must be assessed, and the responsibilities and obligations of data-sharing subjects must be clarified, thereby standardizing cross-domain communication and collaboration.

二要明确思想政治教育数据的共享清单，规范跨领域的沟通协作。思想政治教育数据共享需要拟定明确的共享清单名目和内容，划定可共享数据的类型和范围，事先排除具有风险性的数据信息。数据共享涉及多元主体、单位、部门、行业等，需要形成统一有序的自维护体系，以透明化、流程化、规范化的方式有效管理、保障数据共享的流转畅通。要通过对数据分级分类管理，针对不同等级、不同类别的数据进行差异化处理，明确数据共享的权责限度，有效配置共享数据资源。同时，还要考虑数据共享的安全风险，评估参与数据共享主体的可信度，明确数据共享主体的责任和义务，以此规范跨领域的沟通协作。

Third, determine the behavioral norms for subjects of ideological and political education and maintain the self-ordering of each organization. The key to forming self-ordering lies in group members jointly maintaining and consciously adhering to behavioral norms, which requires both self-discipline among group members and mutual supervision among groups, forming a community of shared interests to jointly maintain the order and authority of self-ordering. The behavioral norms for data sharing among education subjects, on the one hand, should clarify the data-sharing list, delineating the boundaries, content, paths, methods, types, conditions, etc., of data sharing; on the other hand, should clarify the specific operational procedures for data sharing, such as "in what way to collect data," "who collects it," and "how to clean and compare it after collection," so as to maintain organizational order through standardized operations.

三要确定思想政治教育主体的行为准则，维护各组织的自为秩序。自为秩序得以形成的关键在于群体成员共同维护、自觉遵守行为准则，既要求群体成员自我约束，又要使群体之间相互监督，彼此之间结成利益共同体，共同维护自为秩序的有序和权威。教育主体之间数据共享的行为准则，一方面，要明确数据共享清单，划定数据共享的边界范围、内容、路径、方式、类型、条件等；另一方面，要厘清数据共享具体操作流程，如，“以何种方式收集数据”“由谁收集”“收集完毕后如何清洗比对”等，以规范化操作维护组织秩序。

2. Strengthen the key fulcrum of data technology and construct a data security mechanism for ideological and political education. First, utilize data processing technology to enhance self-diagnosis of data security in ideological and political education. Data processing technology is key to handling massive data information and is also an important guarantee for the security and order of various links such as data mining, data collection, data processing, data analysis, data storage, and data feedback. When applying data processing technology to ideological and political education work, it is necessary to strictly control each link of data use to prevent data leakage, data abuse, data theft, and other problems. At the same time, based on the large amount of data information generated during the education process, analyze, diagnose, and evaluate the ideological conditions and behavioral dynamics of education objects, predict the development process, promptly screen urgent data information, and use important reminders from urgent data to avoid risks and prevent problems before they occur, thereby enhancing the self-diagnosis of data security in ideological and political education.

2. 强化数据技术的重要支点，构建思想政治教育数据安全机制。第一，利用数据处理技术，增强思想政治教育数据安全自我诊断。数据处理技术是应对海量数据信息的关键，也是数据挖掘、数据收集、数据加工、数据分析、数据存储、数据反馈等各个环节安全有序的重要保障。数据处理技术应用到思想政治教育工作中，要严格把关数据运用的各个环节，防止数据泄露、数据滥用、数据窃取等乱象发生。同时，依据教育过程中产生的大量数据信息，分析、诊断、评估教育对象的思想情况、行为动态，预判发展进程，及时筛选紧急数据信息，根据紧急数据的重要提醒规避风险，防患于未然，以此增强思想政治教育数据安全的自我诊断。

Second, utilize data perception technology to promote self-feedback on data security in ideological and political education. First, integrate various perception data sources, collect security data, strengthen the perception and screening of security data from various education terminals, education network links, education systems, education applications, education services, etc., effectively collect and integrate security data, and lay the foundation for self-feedback on data security. Second, eliminate various types of hidden danger data information, build a security database, establish a defense system based on the collected data chain, promptly investigate various data information, uniformly manage and store dangerous data information, use encryption technology to clean and destroy it, package and store security data, and build a security database. Third, use open-source distributed technology to architect a data perception platform, achieve real-time monitoring and risk early warning, comprehensively break through information silos, establish a danger detection model, effectively identify, analyze, and handle security threats, enhance the overall security perception capability of the ideological and political education data security system, and promote self-feedback on data security in ideological and political education.

第二，利用数据感知技术，促进思想政治教育数据安全自我反馈。一要整合各类感知数据源，采集安全数据，加强对各类教育终端、教育网络链路、教育系统、教育应用、教育服务等安全数据的感知和筛查，有效采集整合安全数据，打好数据安全自我反馈的基础。二要排除各类隐患数据信息，建设安全数据库，在采集数据链条的基础上建立防御系统，及时排查各类数据信息，统一管理储存具有危险性的数据信息，采用加密技术进行清理和销毁，打包贮存安全数据，建设安全数据库。三要利用开源分布式技术架构数据感知平台，实现实时监测和风险预警，全方位打通信息孤岛，建立危险检测模型，有效识别、分析、处理安全威胁，提升思想政治教育数据安全系统整体的安全感知能力，促进思想政治教育数据安全自我反馈。

Third, using data encryption technology to achieve self-protection of data security in ideological and political education. Educational subjects must take data encryption technology as the core, raise the security level and defense capability of the data system for ideological and political education, and strengthen the self-protection of data security in ideological and political education. For example, when storing and sending educational data, use node encryption technology to prevent problems such as loss, omission, interception, and tampering of data information during the upload and download process, ensuring the safe storage and transmission of educational data. In addition, link encryption technology can be used to perform secondary encryption on the entire process of each node, strengthening the supervision of the entire process in the circulation of educational data and ensuring the safe and smooth flow of the data circulation process; end-to-end encryption technology can be used to directionally and precisely transmit data information, reducing security risks in the data circulation process.

第三，利用数据加密技术，实现思想政治教育数据安全自我保护。教育主体要以数据加密技术为核心，提高思想政治教育数据系统的安全等级和防御能力，强化思想政治教育数据安全自我保护。例如，在存储和发送教育数据时利用节点加密技术，防止数据信息在上传下载过程中出现丢失、遗漏、截取、篡改等问题，确保教育数据安全保存、传输。此外，可以利用链路加密技术，对各个节点的全过程进行二次加密，强化教育数据流转中全过程监管，保障数据流转过程的安全畅通；利用端到端的加密技术，定向精准传输数据信息，减少数据在流转过程中的安全隐患。

3. Focusing on the ultimate landing point of data education, constructing a data-driven mechanism for ideological and political education. First, implement precise profiling of educational objects to enhance the concept of personalized education. “People” are always the essence of technological application; the instrumentality of data technology must be fully utilized, grasping the reasonable scope and limits of big data technology application to serve the growth and development of educational objects. Educational objects are living individuals with unique identifiers; their actual circumstances and growth needs require teaching according to their aptitude. By implementing precise profiling of educational objects, educational services are improved through personalized customization. Focus on the core elements of precise profiling, including value concepts, life conduct, group relationships, etc. Then, based on the internal dimensional information of the precise profile, accurately identify the actual needs of educational objects, precisely allocate educational resources, and provide comprehensive precision drip-irrigation services, thereby meeting the personalized growth needs of educational objects.

3. 聚焦数据育人的最终落点，构建思想政治教育数据动力机制。其一，实施教育精准画像，增强个性化教育理念。“人”始终是技术运用的本质，要充分发挥数据技术的工具性，把握大数据技术运用的合理范围和限度，服务教育对象的成长成才。教育对象是具有独特标识的生命个体，其实际情况和成长需要因材施教，对教育对象实施精准画像，通过个性定制完善教育服务。聚焦精准画像的核心要素，包括价值观念、生活品行、群体关系等。再根据精准画像内在维度信息，精准识别教育对象的实际需要，精准配置教育资源，提供全方位的精准滴灌服务，以此满足教育对象个性化成长需求。

Second, build an intelligent data platform to configure integrated educational resources. On the basis of data intelligence interconnection, establish an intelligent data platform, use the platform’s integration advantages to gather educational resources, and consolidate educational synergy from both quantitative and qualitative levels. Merge similar types of data resources, analyze and archive different types of data resources, avoid the disorder and chaos of data resources, reduce tedious and unnecessary repetitive research, break down data information barriers, and maximize the utility of data resources. At the same time, the construction of an intelligent data platform also requires the collaborative efforts of various forces, including schools, governments, society, enterprises, students, professional technical personnel, etc., to open up all levels of channels for the data platform and fully absorb and integrate different types and levels of data resources.

其二，打造智能数据平台，配置集成化教育资源。在数据智联互通的基础上，搭建智能数据平台，以平台的集成优势汇聚教育资源，从量、质层面凝聚教育合力。对同类型数据资源合并同类项，分析归档不同类型数据资源，避免数据资源的杂乱无序，减少烦琐且不必要的重复性研究，打通数据信息壁垒，发挥数据资源的最大效用。同时，智能数据平台的搭建还需要各方力量的共同协作，需要学校、政府、社会、企业、学生、专业技术人员等协同发力，打通数据平台畅通的各级渠道，充分吸纳整合不同类型层级的数据资源。

Third, create diverse learning scenarios to foster a digital-intelligent education ecosystem. Tap into technical elements such as big data and VR virtual simulation to enrich the learning scenarios of ideological and political education, reshape the ideological and political education classroom, adopt emerging educational forms that educational objects enjoy, and actively promote the transformation of education and teaching from instrumentalization to humanization through novel, innovative, lively, and vivid methods. Educational subjects should combine traditional educational forms with modern educational forms, deeply integrate online learning, use virtual simulation scenario creation to strengthen contextual and immersive experiences, evoke students’ emotional resonance through embodiment and a sense of presence, shorten the psychological distance between teachers and students, and mobilize students’ enthusiasm and interactivity in participating in educational practice activities, thereby improving the quality and efficiency of ideological and political education and fostering a digital-intelligent education ecosystem.

其三，创设多元学习场景，营造数智化教育生态。挖掘大数据、VR虚拟仿真等技术要素，丰富思想政治教育的学习场景，重塑思想政治教育课堂，采用教育对象喜闻乐见的教育新兴形式，以新颖、创新、活泼、生动的方式积极推进教育教学从工具化走向人本化。教育主题要将传统教育形式与现代教育形式相结合，深度融入在线学习，利用虚拟仿真的场景创设，强化情景化和沉浸式体验，以具身性、在场感引发学生情感共鸣，拉近师生之间的心理距离，调动学生参与教育实践活动的积极性和互动性，以此为思想政治教育提质增效，营造数智化教育生态。

(Author profiles: Yan Jie, Deputy Secretary of the General Party Branch of the Department of History and Culture, Lüliang University; Zhang Xiao, Professor, School of Marxism, Shanxi University of Finance and Economics)

（作者简介：闫婕，吕梁学院历史文化系党总支副书记；张晓，山西财经大学马克思主义学院教授）

(Fund project: This article is a phased research result of the National Social Science Fund’s special project on ideological and political courses in higher education, “Research on the Path to Improving the Quality of Ideological and Political Theory Course Teachers” (21VSZ094))

（基金项目：本文系国家社科基金高校思政课研究专项“思想政治理论课教师素质提升路径研究”（21VSZ094）的阶段性研究成果）

Online editor: Tongxin

网络编辑：同心