智慧教育大数据信息化顶层设计及智慧运用建设全套解决方案

   本方案教育大数据以“数据集中、信息共享、业务共通、应用统领”为支撑，建立教育数据应用平台；所有应用统一于大数据平台，实现数据从各应用平台采集，汇聚到数据中心统一存储，应用层统一调用，分场景进行加工处理的模式。 教育大数据技术为教学、学习及管理决策等教育活动提供了全新的科学工具，并有力地推动了教育的变革。聚焦数据价值教育数据采集与深度分析技术全覆盖教育业务各应用系统中，涵盖教学、管理、科研、培训等，既注重相关关系的识别，又强调因果关系的确定，通过数据分析技术发现教育中实际存在的问题，辅助用户解读和理解数据分析结果，更准确评价当前现状，预测未来趋势。将数据分析的结果融入学校的日常管理与服务之中，为师生提供精细化与智能化服务。坚持融合创新发挥技术优势，变革传统模式，推进新技术与教育教学的深度融合，真正实现从融合应用阶段迈入创新发展阶段。全面收集、准确分析、合理利用教育大数据，从“基于有限个案”向“基于全面数据”转变，推动教育决策从经验型、粗放型向精细化、智能化转变。提升师生素养整合多元应用，提供丰富、多样、个性化专业服务，提升师生信息素养，推动从技术应用向能力素质拓展，不再仅仅注重学生的学习成绩，而更加关注身心健康、学业进步、个性技能、成长体验等方面。建立师生良好信息思维，培养应用信息技术解决教学、学习、生活中问题的能力。3.4转化思维模式课堂作为推动学校内涵发展的主阵地，通过大数据应用对教师进行课堂观察、数据采集和分析，得出测评结果，然后制定相应的提升措施，不断促进教师教学水平的提升。推动大数据技术与教师教育产生深度融合，促进教师专业发展、教师教育教学全方位变革与创新发展。建设目标打造教育大数据平台，统领教育应用教育大数据以“数据集中、信息共享、业务共通、应用统领”为支撑，建立教育数据应用平台；所有应用统一于大数据平台，实现数据从各应用平台采集，汇聚到数据中心统一存储，应用层统一调用，分场景进行加工处理的模式。教育大数据平台，为教育管理者、教师、家长、学生等不同对象提供多层次、全方位的综合应用服务，综合构建“教、学、管、评、测、练”与“教育管理机构、学校、老师、家长、学生”相结合的多维教育大数据信息化体系。全面打通用户的基础数据，融合学业数据、教育管理综合信息、教师信息等大数据，进行全面多维的各类分析、数据透视。教育管理者、教师、学生、家长都能通过教育大数据平台，根据角色、权限及应用场景的不同，享用平台中各类教育应用。提供个性化教学服务通过对学生历年学业成绩、课程选修、活动参与等数据分析，除了追踪学生学业进步情况外，还可以从中分析不同学生的学习需求和风格，进而提供适应学生特点的个性化教学。一是通过数据分析对学习困难学生进行干预，教师通过学生数据系统监控学生学业表现进行干预性指导。二是获得学生学习结果的即时智能反馈。通过课堂行为记录与分析工具，教师可以及时获得学生学习情况并调整教学活动。三是在学生选择辅修课程或课外项目时，大数据技术可以提供适合学生的个性化建议。四是基于大数据分析改进日常教学工作。教师可以通过分析学生社交行为数据，更有效地开展团队和小组学习，优化学习计划和日程安排。变革教与学发展模式，提升师生数据素养对教师教学日志数据、教学资源数据、教学互动数据、教学评价数据、教学效果数据、教师继续教育数据、教学工具使用数据等日常教学过程、行为、结果数据的深入分析与挖掘建立教师数据素养，帮助教师更好地获得学生反馈，发现每位学生的兴趣点和薄弱点，以优化教学模式，改进教学策略，实现个性化教学；有助于教师有效预测学生考试成绩及发展趋势，及时干预并指导学生的学习与发展；有助于教师对学生做出全面客观的评价，推动教育评价方式从“经验主义”走向“数据主义”；有助于教师的教育决策更加科学准确，提高工作效率与学生的学习成绩；有助于教师发现自身专业技能的不足和问题，提升专业能力和研究水平，适应数据驱动教学时代的新要求。对学生日志数据、成绩预警数据、师生评价数据、在线话语数据、伦理隐私数据、多模态数据等六类数据的进行深入的挖掘分析,建立学生数据素养。培养学生在数据感知和采集、组织和管理、处理与分析、共享与协同创新等方面的能力，以及在数据的生产、管理和发布过程中的道德与行为规范。帮助学生更好地获取学习反馈，发现自身学习的优劣势，优化学习方式方法，实现精细化学习；帮助学生更好的预估学习发展趋势，指导学生做好学业生涯规划；帮助学生更精确的进行学习过程跟踪与学习过程评价，为学生综合素质评价提供数据支撑；帮助学生发现和学习高效学习方式方法，提升自适应学习能力，培养终身学习习惯。促进学校教育信息化发展，破解发展难题为了推进大数据应用服务驱动地区教学的快速发展，教育行政部门、教育大数据服务企业、中小学校应当协同发力，重点从五个方面推进实施，包括：开展数据素养专题培训，提高教师数据意识与数据处理能力；打造基于大数据的智慧学习平台，支撑教师开展数据驱动的精准教学；开展数据驱动教学示范项目，探索数据驱动教学新模式；构建数据驱动教学实践共同体，传播数据驱动教学文化；开展数据驱动教学专题研究，引领数据驱动教学持续深入发展。挖掘大数据推动教育研究转型现代教育运用实证数据研究教育具体问题，再基于研究结果指导政策与实践。大数据技术为大规模教育实证研究提供便利，推动教育研究转型为“数据密集型科研”。一是利用纵向数据开展长期性追踪研究。维格多以北卡罗莱州1500名教师为对象，跟踪分析1997—1998学年至2007—2008学年学生学业测评结果对教师工资薪酬的影响，提出基于绩效考核的教师酬金改革建议。二是开展大规模横向比较研究。美国“全国学生中心”(NSC)开展的大学阶段学业成就与高中阶段学业间关系研究，依托各州和联邦教育数据库，以全美92%的在校大学生为对象，通过了解不同学校入学情况、学生人口特征与大学入学的关系、高中阶段学业成绩相似学生在大学后的学业表现等内容，分析高中阶段学习对大学学业的影响。这种跨年度的追踪研究和大规模的横向比较研究，没有大数据的支撑是难以实现的。基于大数据推动教育科学决策全面的地区基础教育质量监测不仅让决策者了解教育的整体状况和变化趋势，还通过分析家庭背景、教育项目和学校教学与学生学业成绩间的关系，进而影响地区教育资源分配与资助性项目的实施。一是运用教育大数据规划学校布局与资源分配，地区和学校通过分析学生人口学数据，得出本地区学龄人口变动趋势，从而科学规划本地区学校布局与资源分配。二是改进学校绩效评估办法。基于学校整体与学生个体学业数据，评价学校的办学质量或项目实施质量，分析学校的优势与弱势领域。三是推动家校合作。通过使用智慧课堂反馈工具，教师可以实时上传本节课学生课堂表现和任务完成情况，学校和家长借此可以及时了解并与校方交流学生情况。四是提高学校管理效率。在学生出勤、用餐及校车运营等活动中使用学生管理软件，自动记录并通过数据分析提出改进方案。五是改革教师评聘方式。通过分析教师任教学生的学业成绩以及教师的职业信仰、专业发展、社会服务等指标，科学评估教师专业水平与发展潜能。开展教育大数据服务，惠及全体教育参与者通过涵盖K12范围内的小学、初中、高中全线贯通的区域教育生态监控大数据平台，全面跟进诊断班级、学校、区域教育的教与学现状；为全区各级各类教育主管部门实时分析全区域、各校教学质量，并智能提供教育优化方案，智慧化跟进全区基础教育质量；为全区所有教师提供精准教学和改进依据、为全区学生和家长提供了解自己学习现状的途径及改进渠道、资源和方法；提供基于移动互联网环境下的教与学内容探讨、交流互动的互动平台，形成教师、学生、家长三个群体之间的兴趣聚合和广泛讨论。

Built on the principles of "data centralization, information sharing, business interoperability and unified application leadership", this educational big data solution establishes an educational data application platform. All applications are unified under the big data platform, following the model where data is collected from various application platforms, aggregated and stored centrally in the data center, uniformly invoked by the application layer, and processed in a scenario-specific manner. Educational big data technology provides brand-new scientific tools for educational activities such as teaching, learning and management decision-making, and has strongly promoted educational transformation. Focusing on data value, the technologies of educational data collection and in-depth analysis cover all application systems of educational business, including teaching, management, scientific research and training. While paying attention to the identification of correlations, they also emphasize the determination of causal relationships. Through data analysis technologies, actual problems in education are identified, users are assisted in interpreting and understanding data analysis results, more accurate evaluation of current status is achieved, and future trends are predicted. The results of data analysis are integrated into the daily management and services of schools, providing refined and intelligent services for teachers and students. Adhering to integration and innovation, leveraging technical advantages, transforming traditional models, and promoting the deep integration of new technologies with education and teaching, we truly realize the transition from the integrated application stage to the innovative development stage. By comprehensively collecting, accurately analyzing and rationally utilizing educational big data, we shift from "based on limited cases" to "based on comprehensive data", and promote the transformation of educational decision-making from empirical, extensive mode to refined and intelligent mode. Improving teachers' and students' literacy: Integrating multiple applications to provide rich, diverse and personalized professional services, enhancing the information literacy of teachers and students, and promoting the expansion from technical application to ability and quality. We no longer only focus on students' academic performance, but pay more attention to physical and mental health, academic progress, individual skills, growth experience and other aspects. We aim to cultivate good information thinking among teachers and students, and train their ability to apply information technology to solve problems in teaching, learning and daily life. 3.4 Transforming Thinking Mode: The classroom, as the main position for promoting the connotative development of schools, uses big data applications to conduct classroom observation, data collection and analysis for teachers, obtain evaluation results, and then formulate corresponding improvement measures to continuously enhance teachers' teaching level. We promote the deep integration of big data technology with teacher education, and facilitate teachers' professional development and all-round transformation and innovative development of their education and teaching. Construction Goals: Building an educational big data platform to unify educational applications. Built on the principles of "data centralization, information sharing, business interoperability and unified application leadership", an educational data application platform is established. All applications are unified under the big data platform, following the model where data is collected from various application platforms, aggregated and stored centrally in the data center, uniformly invoked by the application layer, and processed in a scenario-specific manner. The educational big data platform provides multi-level and all-round comprehensive application services for different stakeholders including educational administrators, teachers, parents and students. It comprehensively constructs a multi-dimensional educational big data informatization system that combines "teaching, learning, management, evaluation, assessment, practice" with "educational management institutions, schools, teachers, parents and students". It fully interconnects users' basic data, integrates big data such as academic data, comprehensive educational management information and teacher information, and conducts comprehensive and multi-dimensional analysis and data drill-down. Educational administrators, teachers, students and parents can access various educational applications on the platform according to their roles, permissions and application scenarios. Providing personalized teaching services: Through data analysis of students' annual academic performance, course selections, activity participation and other information, in addition to tracking students' academic progress, we can also analyze the learning needs and styles of different students, and then provide personalized teaching tailored to students' characteristics. 1. Intervening with students who have learning difficulties through data analysis: Teachers monitor students' academic performance via the student data system to provide intervention guidance. 2. Obtaining real-time intelligent feedback on students' learning outcomes: Through classroom behavior recording and analysis tools, teachers can timely obtain students' learning status and adjust teaching activities. 3. Providing personalized recommendations for students when they choose minor courses or extracurricular projects, powered by big data technology. 4. Improving daily teaching work based on big data analysis: Teachers can analyze students' social behavior data to carry out team and group learning more effectively, and optimize learning plans and schedules. Transforming the development mode of teaching and learning, and improving teachers' and students' data literacy: Through in-depth analysis and mining of teachers' daily teaching process, behavior and outcome data, including teaching log data, teaching resource data, teaching interaction data, teaching evaluation data, teaching effect data, teacher continuing education data and teaching tool usage data, we establish teachers' data literacy. This helps teachers better obtain student feedback, identify each student's interests and weaknesses, optimize teaching models and strategies, and realize personalized teaching. It also enables teachers to effectively predict students' exam scores and development trends, intervene timely and guide students' learning and development; helps teachers make comprehensive and objective evaluations of students, and promote the transformation of educational evaluation from "empiricism" to "data-drivenism"; helps teachers make more scientific and accurate educational decisions, improve work efficiency and students' academic performance; helps teachers identify deficiencies and problems in their own professional skills, enhance professional ability and research level, and adapt to the new requirements of the data-driven teaching era. For students: Through in-depth mining and analysis of six types of data including student log data, score early warning data, teacher-student evaluation data, online discourse data, ethical and privacy data and multimodal data, we establish students' data literacy. We cultivate students' abilities in data perception and collection, organization and management, processing and analysis, sharing and collaborative innovation, as well as their moral and behavioral norms in the process of data production, management and release. This helps students better obtain learning feedback, identify their own learning advantages and disadvantages, optimize learning methods and achieve refined learning; helps students better predict learning development trends and guide them to make academic career plans; helps students conduct more accurate learning process tracking and evaluation, providing data support for students' comprehensive quality evaluation; helps students discover and learn efficient learning methods, improve adaptive learning ability and cultivate lifelong learning habits. Promoting the informatization development of schools and solving development difficulties: To promote the rapid development of regional teaching driven by big data application services, educational administrative departments, educational big data service enterprises and primary and secondary schools should work together, focusing on promoting implementation from five aspects: 1. Carrying out special training on data literacy to improve teachers' data awareness and data processing capabilities; 2. Building a big data-based smart learning platform to support teachers in carrying out data-driven precise teaching; 3. Launching data-driven teaching demonstration projects to explore new models of data-driven teaching; 4. Constructing data-driven teaching practice communities to disseminate data-driven teaching culture; 5. Conducting special research on data-driven teaching to guide the continuous and in-depth development of data-driven teaching. Unleashing the potential of big data to promote the transformation of educational research: Modern education uses empirical data to study specific educational issues, and then guides policies and practices based on research results. Big data technology facilitates large-scale educational empirical research, promoting the transformation of educational research into "data-intensive scientific research". 1. Conducting long-term longitudinal tracking research using longitudinal data: For example, Vigdor took 1500 teachers in North Carolina as subjects, tracked and analyzed the impact of students' academic assessment results from the 1997-1998 academic year to the 2007-2008 academic year on teachers' salaries, and put forward reform recommendations for teacher remuneration based on performance appraisal. 2. Conducting large-scale cross-sectional comparative research: For instance, the U.S. National Student Clearinghouse (NSC) conducted a study on the relationship between college academic achievement and high school academic performance. Relying on state and federal educational databases, taking 92% of the college students in the United States as subjects, they analyzed the impact of high school learning on college academic performance by understanding the relationship between school enrollment status, student demographic characteristics and college enrollment, as well as the academic performance of students with similar high school academic performance after entering college. Such cross-year tracking research and large-scale cross-sectional comparative research would be impossible without the support of big data. Promoting scientific educational decision-making based on big data: Comprehensive regional basic education quality monitoring not only enables decision-makers to understand the overall status and changing trends of education, but also analyzes the relationship between family background, educational programs, school teaching and students' academic performance, thereby affecting the allocation of regional educational resources and the implementation of subsidized projects. The specific measures include: 1. Using educational big data to plan school layout and resource allocation: Regions and schools analyze student demographic data to obtain the trend of school-age population changes in the region, so as to scientifically plan the school layout and resource allocation in the region. 2. Improving school performance evaluation methods: Evaluating the quality of school operation or project implementation based on overall school and individual student academic data, and analyzing the strengths and weak areas of schools. 3. Promoting home-school cooperation: By using smart classroom feedback tools, teachers can upload students' classroom performance and task completion status of this class in real time, enabling schools and parents to timely understand and communicate about students' situations. 4. Improving school management efficiency: Using student management software in activities such as student attendance, dining and school bus operation to automatically record data and propose improvement plans through data analysis. 5. Reforming teacher evaluation and employment methods: Scientifically evaluating teachers' professional level and development potential by analyzing the academic performance of students taught by teachers, as well as indicators such as teachers' professional beliefs, professional development and social services. Providing educational big data services to benefit all educational participants: Through the regional educational ecology monitoring big data platform covering all stages of K12 (primary school, junior high school and senior high school), we comprehensively track and diagnose the teaching and learning status of classes, schools and regional education. We provide real-time analysis of the teaching quality of the entire region and each school for educational administrative departments at all levels in the region, and intelligently provide educational optimization plans to intelligently track the quality of basic education in the region. We provide precise teaching and improvement basis for all teachers in the region, provide ways, improvement channels, resources and methods for students and parents to understand their own learning status. We also provide an interactive platform for teaching and learning content discussion and communication based on the mobile Internet environment, forming interest aggregation and extensive discussions among teachers, students and parents.

搜集汇总

背景与挑战

背景概述

该数据集提供了一套完整的智慧教育大数据解决方案，通过集中管理教育数据并实现多场景分析应用，支持个性化教学和科学决策。方案涵盖教学、管理、科研等多个领域，旨在通过数据分析优化教育服务，提升师生信息素养和教学质量。