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

一、建设需求1.1地区教育系统亟待进行信息共享、系统融合当前我国教育信息化发展进入2.0时代，区域教育信息化也进入新的发展阶段，需要借助创新技术持续推动教育信息化的进步。在众多区域，教育信息化发展取得了巨大成功：教学环境越来越好、教育资源越来越丰富、信息化教学越来越普及、公共教育平台的服务水平越来越高且覆盖面越来越广。但由于区域教育信息化具有应用场景多样、业务逻辑繁复、需求差异显著等特点，促使区域不得不持续建设越来越多的应用系统进行应对；在众多区域都积累了大量的建设于不同时期、针对不同目标、交由不同厂商、采用不同技术路线实现的教育信息化系统，这些系统在解决问题的同时也给用户带来了巨大的困扰与挑战。（1）地区教育局日常使用的信息化系统数量过多（总量达数十个），熟悉、管理和使用全部的系统较为困难；（2）经常需要在多个系统之间导入、导出和转化数据；（3）重要数据需要在多个系统中录入；（4）同一个指标在不同系统中数据统计结果不一致，需承受大量的人工分析与处理工作等。整理制作郎丰利。大数据为地区教育信息化提供了系统性解决问题的手段。教育大数据实现了数据的统一采集，使所有的应用系统都成为数据采集端，弥补了特定数据只能由专门的信息化系统采集的不足，解决了系统重复冗余的问题；实现了数据的统一交换，使数据能够在系统与中心之间非实时交换，极大地降低了数据交换技术的复杂度，解决了数据高效流转的问题；实现了离线数据的统一加工，使数据能够被全面清洗、集中规整并以统一的口径进行统计，规避了相同指标在不同系统中含义不一致的现象，解决了数据冲突问题；实现了离线数据与在线数据的统一利用，使数据可以跨场景、多角度、全方位地呈现和发掘规律，扩大了数据应用场景，极大地提升了数据的内在价值。以大数据技术为基础开展知识挖掘、数据分析、学习效果评价等服务，为实现差异化教学、精细化管理和智能化服务提供了可能。智慧校园的深度应用为提升中小学校管理效率、促进优化治理、实现真正以人为本的个性化服务奠定了良好基础，有助于推动教育管理从经验型、粗放型、封闭型向精细化、智能化、可视化转变。因此，区域发展教育大数据是教育信息化进一步深化发展的必然结果。1.2新型教学环境需要创新的教学内容和方法全面引入智能技术的智慧校园必将广泛普及，绿色节能管控、精细化治理、个性化服务将全面实现，从而引发学校治理水平的大幅度提升。新型教育技术的引入将彻底改变多媒体教室的定义，传统具有多媒体内容呈现和有限互动能力的教室必将升级为更加智能、更具个性化、更支持智能交互和差异化教学的智能教室。今后课堂的边界逐渐模糊，物理学习空间和网络虚拟学习空间将全面衔接，教学方法将更加多样化，以信息技术为支撑的探究式、讨论式、参与式教学和混合式学习等新型教与学方法将逐步普及。大数据成为实现大规模个性化学习的重要支撑，大数据革新了学生的学习、教师的教学，为实现大规模的个性化自适应学习提供了重要途径。1.3教育元素的改变需要教育评价模式的更新新型教育形态的变化，教育评价体系必然发生根本性变化，知识传授型教育将向能力培养型教育转变，被动识记型学习将向主动探究型学习转变；强调主体性、个性化，注重以人为本、开放多元、全面发展的教育理念将进一步得到彰显。以大数据和人工智能为支撑的精准化评价和智能诊断将全面普及，教学评价的手段更加智能，面向学习全过程，基于智能评价的学习干预将取得更好效果。教育管理与督导评估也将因为大数据、人工智能技术的引入而变得更加科学、即时、精细化。1.4新型社会形态促使人才培养模式急需转变信息化社会，知识生产和流动速度加快、知识更新更加频繁，对人才的知识、能力、素质的要求更高，而且智能技术的发展改变了很多行业的基本业态，让很多工作都可以由智能机器代替人来完成，导致社会对人才的需求与以往产生很大不同。智能社会更需要的是具备批判性思维能力、协作沟通能力、创新能力的个性化人才。信息时代的学习者是数字原生代，他们在智能手机、平板电脑等各种智能设备的环绕中成长，在数字化科技的熏陶下生活，他们十分依赖通过互联网获取信息和开展人际交流。在学习方面，他们的知识获取渠道极为丰富，阅读习惯呈现出非线性特点，学习方式高度个性化，对课程质量要求很高，使用数字化学习设备和资源对他们来说几乎是与生俱来的本领。大数据应用技术是实现教育多样性、个性化的有效手段，使我们能够精细刻画每一个学习者的个体特征，从而在提供大规模教育的同时，针对每个学习者的特定需求实现差异化的教育供给，以此解决长期困扰教育界的规模化与个性化相矛盾的问题，实现高质量的教育均衡和高水平的终身教育。二、设计思路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。2.1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。2.2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。2.3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。2.4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。三、建设目标3.1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。3.2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。3.3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。3.4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。3.5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。3.6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。3.7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。

1. Construction Requirements
1.1 Urgent Need for Information Sharing and System Integration in Regional Education Systems
At present, the development of education informatization in China has entered the 2.0 era, and regional education informatization has also entered a new development stage, requiring the use of innovative technologies to continuously promote the progress of education informatization. Many regions have achieved great success in the development of education informatization: teaching environments have been increasingly improved, educational resources have become richer, information-based teaching has become more popular, and the service level and coverage of public education platforms have become higher and wider. However, due to the characteristics of regional education informatization such as diverse application scenarios, complex business logic, and significant differences in demands, regions have to continuously build more and more application systems to cope with these challenges. Many regions have accumulated a large number of education informatization systems built in different periods, targeting different goals, developed by different vendors, and adopting different technical routes. While solving problems, these systems have also brought great troubles and challenges to users:
(1) Regional education bureaus use dozens of information systems on a daily basis, making it difficult to familiarize themselves with, manage and operate all of them;
(2) Data often needs to be imported, exported and transferred across multiple systems;
(3) Important data needs to be entered repeatedly in multiple systems;
(4) The same indicator may produce inconsistent statistical results across different systems, requiring extensive manual analysis and processing.
Compiled by Lang Fengli.
Big data provides a systematic solution for regional education informatization. Education big data enables unified data collection, turning all application systems into data collection terminals, making up for the shortcoming that specific data can only be collected by dedicated information systems, and solving the problem of redundant systems; it enables unified data exchange, allowing non-real-time data exchange between systems and the center, greatly reducing the complexity of data exchange technology and solving the problem of efficient data circulation; it enables unified processing of offline data, allowing comprehensive data cleaning, centralized standardization and unified-caliber statistics, avoiding inconsistent meanings of the same indicator across different systems and solving data conflicts; it enables unified utilization of offline and online data, allowing cross-scenario, multi-angle and all-round presentation of data and discovery of laws, expanding data application scenarios and greatly enhancing the intrinsic value of data. Services such as knowledge mining, data analysis and learning effect evaluation based on big data technology provide possibilities for differentiated teaching, refined management and intelligent services. The deep application of smart campuses has laid a good foundation for improving the management efficiency of primary and secondary schools, promoting optimized governance and realizing people-oriented personalized services, helping transform education management from experience-based, extensive and closed modes to refined, intelligent and visualized ones. Therefore, developing education big data in regions is an inevitable outcome of the further deepening of education informatization.
1.2 Innovative Teaching Content and Methods Needed for New Teaching Environments
Smart campuses fully integrated with intelligent technologies will be widely popularized, and green energy conservation management, refined governance and personalized services will be fully realized, greatly improving school governance levels. The introduction of new educational technologies will completely redefine the multimedia classroom: traditional classrooms with only multimedia content presentation and limited interaction capabilities will be upgraded to smart classrooms that are more intelligent, personalized and support intelligent interaction and differentiated teaching. In the future, the boundaries of classrooms will gradually blur, physical learning spaces and online virtual learning spaces will be fully connected, teaching methods will become more diverse, and inquiry-based, discussion-based, participatory teaching and blended learning supported by information technology will gradually become popular. Big data has become an important support for large-scale personalized learning, revolutionizing students' learning and teachers' teaching, and providing an important way to realize large-scale personalized adaptive learning.
1.3 Updated Education Evaluation Models Needed for Changes in Educational Elements
Changes in new educational forms will inevitably lead to fundamental changes in the education evaluation system: knowledge-transmission-based education will transform into competency-cultivation-based education, and passive rote learning will transform into active inquiry-based learning; the education philosophy emphasizing subjectivity, personalization, people-orientation, openness, diversity and all-round development will be further highlighted. Precise evaluation and intelligent diagnosis supported by big data and artificial intelligence will be fully popularized, making teaching evaluation more intelligent, covering the whole learning process, and learning intervention based on intelligent evaluation will achieve better effects. Education management and supervision evaluation will also become more scientific, real-time and refined with the introduction of big data and artificial intelligence technologies.
1.4 Urgent Transformation of Talent Training Models Driven by New Social Forms
In the information society, the production and circulation of knowledge are accelerating, and knowledge updates are more frequent, putting higher requirements on talents' knowledge, abilities and qualities; the development of intelligent technologies has changed the basic business models of many industries, allowing many jobs to be replaced by intelligent machines, leading to great differences in social demand for talents compared with the past. Intelligent societies more urgently need personalized talents with critical thinking, collaborative communication and innovation capabilities. Learners in the information age are digital natives, who grow up surrounded by smart devices such as smartphones and tablets, live under the influence of digital technology, and rely heavily on the Internet to obtain information and communicate with others. In terms of learning, they have extremely rich access to knowledge, their reading habits present nonlinear characteristics, their learning methods are highly personalized, they have high requirements for course quality, and using digital learning devices and resources is almost an innate ability for them. Big data application technology is an effective means to realize educational diversity and personalization, enabling us to finely depict the individual characteristics of each learner, thus providing differentiated education supply for each learner's specific needs while delivering large-scale education, solving the long-standing contradiction between scale and personalization in the education sector, and achieving high-quality education balance and high-level lifelong learning.
2. Design Ideas
Education big data technology provides a brand-new scientific tool for educational activities such as teaching, learning and management decision-making, and strongly promotes educational reform.
2.1 Focus on Data Value
Education data collection and in-depth analysis technologies cover all application systems of education services, including teaching, management, research, training, etc., paying attention to both the identification of correlation and the determination of causality. Through data analysis technologies, actual problems in education are discovered, assisting users in interpreting and understanding data analysis results, more accurately evaluating the current situation and predicting future trends. The results of data analysis will be integrated into the daily management and services of schools, providing refined and intelligent services for teachers and students.
2.2 Adhere to Integration and Innovation
Give play to technological advantages, transform traditional modes, and promote the in-depth integration of new technologies with education and teaching, truly realizing the transition from the integrated application stage to the innovative development stage. Comprehensively collect, accurately analyze and rationally utilize education big data, shift from "based on limited cases" to "based on comprehensive data", and promote the transformation of education decision-making from experience-based and extensive modes to refined and intelligent ones.
2.3 Improve Teachers' and Students' Data Literacy
Integrate multiple applications, provide rich, diverse and personalized professional services, improve the information literacy of teachers and students, and promote the expansion from technology application to ability and quality. No longer only focus on students' academic scores, but pay more attention to their physical and mental health, academic progress, personalized skills, growth experience, etc. Establish good information thinking among teachers and students, and cultivate their ability to apply information technology to solve problems in teaching, learning and life.
2.4 Transform Thinking Modes
The classroom is the main front for promoting the connotative development of schools. Carry out classroom observation, data collection and analysis for teachers through big data applications, obtain evaluation results, formulate corresponding improvement measures, and continuously promote the improvement of teachers' teaching level. Promote the in-depth integration of big data technology and teacher education, and promote teachers' professional development and all-round reform and innovative development of education and teaching.
3. Construction Goals
3.1 Build an Education Big Data Platform to Lead Educational Applications
Education big data is supported by "centralized data, shared information, interoperable business and unified application", and an education data application platform will be established. All applications will be unified under the big data platform, realizing the mode of collecting data from various application platforms, converging them to the data center for unified storage, unified calling by the application layer, and processing according to different scenarios. The education big data platform provides multi-level and all-round comprehensive application services for different groups such as education managers, teachers, parents and students, comprehensively building a multi-dimensional education big data informatization system that combines "teaching, learning, management, evaluation, testing, practice" with "education management institutions, schools, teachers, parents and students". Comprehensively connect users' basic data, integrate big data such as academic data, comprehensive education management information and teacher information, and carry out various comprehensive multi-dimensional analyses and data pivot analyses. Education managers, teachers, students and parents can access various educational applications on the platform according to their roles, permissions and application scenarios.
3.2 Provide Personalized Teaching Services
Through the analysis of students' annual academic scores, course selections, activity participation and other data, in addition to tracking their academic progress, we can also analyze the learning needs and styles of different students, and then provide personalized teaching adapted to students' characteristics:
(1) Carry out intervention for students with learning difficulties through data analysis; teachers can monitor students' academic performance through the student data system and provide intervention guidance;
(2) Obtain real-time intelligent feedback on students' learning results; through classroom behavior recording and analysis tools, teachers can timely obtain students' learning status and adjust teaching activities;
(3) When students choose minor courses or extracurricular projects, big data technology can provide personalized suggestions suitable for students;
(4) Improve daily teaching work based on big data analysis; teachers can analyze students' social behavior data to carry out team and group learning more effectively, optimize learning plans and schedules.
3.3 Reform the Development Model of Teaching and Learning and Improve Teachers' and Students' Data Literacy
In-depth analysis and mining of daily teaching process, behavior and result data such as teachers' teaching log data, teaching resource data, teaching interaction data, teaching evaluation data, teaching effect data, teacher continuing education data and teaching tool usage data will help establish teachers' data literacy, assist teachers in better obtaining student feedback, discover each student's interests and weak points, optimize teaching models and strategies, and realize personalized teaching. It will help teachers effectively predict students' exam scores and development trends, timely intervene and guide students' learning and development; help teachers make comprehensive and objective evaluations of students, promoting the transformation of education evaluation methods from "empiricism" to "data-driven"; help teachers make more scientific and accurate education decisions, improve work efficiency and students' academic scores; help teachers identify deficiencies and problems in their own professional skills, improve professional abilities and research levels, and adapt to the new requirements of the data-driven teaching era.
In-depth mining and analysis of six types of student data including student log data, score early warning data, teacher-student evaluation data, online discourse data, ethical and privacy data and multi-modal data will help establish students' data literacy. 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. Help students better obtain learning feedback, discover their own learning advantages and disadvantages, optimize learning methods, and realize refined learning; help students better predict learning development trends and guide them to make academic career plans; help students more accurately track and evaluate their learning process, providing data support for students' comprehensive quality evaluation; help students discover and learn efficient learning methods, improve adaptive learning ability and cultivate lifelong learning habits.
3.4 Promote the Development of School Education Informatization and Solve Development Problems
To promote the rapid development of regional teaching driven by big data application services, education administrative departments, education big data service enterprises and primary and secondary schools should work together, focusing on five aspects:
(1) Carry out special training on data literacy to improve teachers' data awareness and data processing capabilities;
(2) Build a smart learning platform based on big data to support teachers in carrying out data-driven precise teaching;
(3) Carry out data-driven teaching demonstration projects to explore new models of data-driven teaching;
(4) Build a data-driven teaching practice community to spread data-driven teaching culture;
(5) Carry out special research on data-driven teaching to lead the continuous in-depth development of data-driven teaching.
3.5 Leverage Big Data to Promote the Transformation of Education Research
Modern education uses empirical data to study specific educational issues, and then guides policies and practices based on research results. Big data technology provides convenience for large-scale education empirical research, promoting the transformation of education research into "data-intensive scientific research":
(1) Carry out long-term longitudinal tracking research: For example, Vigdor took 1500 teachers in North Carolina as the research object, tracked and analyzed the impact of students' academic evaluation results from the 1997-1998 academic year to the 2007-2008 academic year on teachers' salaries, and put forward reform suggestions for teacher remuneration based on performance appraisal;
(2) Carry out large-scale cross-sectional comparative research: 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 education databases, targeting 92% of college students across the United States, analyzing the impact of high school learning on college academic performance by understanding the relationship between school enrollment status, student demographic characteristics and college enrollment, and the academic performance of students with similar high school academic performance in college. Such longitudinal tracking research and large-scale cross-sectional comparative research cannot be realized without the support of big data.
3.6 Promote Scientific Education Decision-Making Based on Big Data
Comprehensive regional basic education quality monitoring not only allows decision-makers to understand the overall situation and changing trends of education, but also analyzes the relationship between family background, education programs, school teaching and students' academic performance, thereby affecting the allocation of regional education resources and the implementation of subsidized projects:
(1) Plan school layout and resource allocation using education big data: Regions and schools can analyze student demographic data to obtain the trend of school-age population changes in the region, so as to scientifically plan school layout and resource allocation in the region;
(2) Improve school performance evaluation methods: Evaluate the quality of school running or project implementation based on overall school and individual student academic data, and analyze the school's strengths and weaknesses;
(3) Promote home-school cooperation: Through the use of smart classroom feedback tools, teachers can upload students' classroom performance and task completion status in real time, allowing schools and parents to timely understand and communicate with the school about students' situations;
(4) Improve school management efficiency: Use student management software in activities such as student attendance, dining and school bus operation, automatically record data and propose improvement plans through data analysis;
(5) Reform teacher evaluation and appointment methods: Scientifically evaluate teachers' professional level and development potential by analyzing the academic performance of students taught by teachers and indicators such as teachers' professional beliefs, professional development and social services.
3.7 Carry out Education Big Data Services to Benefit All Educational Participants
Through the regional education ecology monitoring big data platform covering all K12 stages (primary school, junior high school, senior high school), comprehensively track and diagnose the teaching and learning status of classes, schools and regions. Provide real-time analysis of the teaching quality of the entire region and each school, and intelligently provide education optimization plans for education authorities at all levels in the region, intelligently tracking the quality of basic education in the entire region. Provide precise teaching and improvement basis for all teachers in the region, and provide students and parents with ways, improvement channels, resources and methods to understand their own learning status. Provide an interactive platform for teaching and learning content discussion and communication under 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

该数据集提供了一个全面的智慧教育大数据解决方案，重点解决教育信息化中的系统融合与数据共享问题，并通过大数据技术实现个性化教学和教育管理优化。方案涵盖了从数据采集到应用服务的完整流程，旨在提升教育质量和效率。