京公网安备 11010802034615号
经营许可证编号:京B2-20210330
美国将大数据应用于国际学生能力评估计划(PISA)_数据分析师
大数据是教育产业重塑商业模式,促使政府、商业组织和社会企业家通力合作将实证、创意、资源整合起来成就全民终身教育的基础。因此未来教育界的巨头将是那些能够把学术权威与信息和社交网络的协同效应结合起来的领军者。更为重要的是,这将使人们在运用大数据的基础上进行应用创新。这要求体制上的协同创新,要采取更有进取、更完善的公共政策,来改变目前教育界弊病:工业化的组织模式、官僚的和以应诉导向的工作方式和策略。
这不仅仅是增加教育透明度和公共责任的问题,甚至可以说这不是主要问题。简单地把数据公布于众不能改变学生学习,老师授课和学校运作的模式。信息公开并不能自然而然地引领我们运用大数据改革教育方法。相反这一做法经常造成民众和政府在信息的控制和所有权方面的对立情绪。
运用大数据实现教育产业转型的前提是摒弃我们社会的“只读”模式。透明和合作并举。目前的情况是,坐在大办公楼里一角的某位教育专家制定了规则,成千上百名学生和老师只能遵从,没有人知道这些决定是怎么来的。如果我们能分享数据、培育民间创新和实验、开拓创造性文化,大数据可以实现大范围的信任。难怪世界经济合作与发展组织(OECD)一项关于成人技能的最新调查显示:一个人的读写能力越好,就越容易信任他人。
协同消费就是很好的一个印证。如今,我们与陌生人共享他们的汽车,甚至是房子。协同消费使人人都可以成为小微企业家,其发展驱动力在于建立与陌生人的信任。想想我们在商业世界里的行为,我们在信任他人的基础上提供信息,心甘情愿地交出信用卡数据,和各个商业行业中可信的陌生人建立联系。教育界的数据分享离我们还非常遥远。
但是这应该是我们努力的方向。几年前我们引入了国际学生能力评估计划(PISA),一项针对各国15岁青少年可比较技能的全球调研。PISA提供了大量有关教育质量的数据。PISA计划使公共教育政策的制定更加透明、高效,帮助教育力量的分配重获平衡。在微观层面,仍存有很多质疑:老师认为这是政府又一个想控制他们的问责工具。那我们该做什么?今年我们实施了“我的PISA”项目,将PISA的分析工具分发到学校。现在每个学校可以用它与全球各地相似或完全不同的学校进行比较分析。
突然间原有的状态发生了改变;学校开始使用这些数据。例如,美国弗吉尼亚州费尔法克斯郡的十所学校的校长和老师们围绕第一份报告的结论开始了长达一年的讨论。在当地教育部门(和OECD)的帮助下,他们将开始第二轮分析,进行深入的数据挖掘,更好地了解如何相互类比,并和世界各地的其他学校进行类比。这些校长和老师不再把自己看作全球舞台上的观众,而是合作的队友。换言之,在费尔法克斯郡,大数据正在建立大范围的信任。
英语原文:
Big data is the foundation on which education can reinvent its business model and build the coalition of governments, businesses, and social entrepreneurs that can bring together the evidence, innovation and resources to make lifelong learning a reality for all. So the next educational superpower might be the one that can combine the hierarchy of institutions with the power of collaborative information flows and social networks. More than anything else, this will hinge on getting people to generate innovative applications on top of big data. It’s about the co-creation of governance, about delivering more progressive and better policies than the industrial work organisation and the bureaucratic and litigation-oriented tools and strategies that we are used to in education.
This isn’t just or even mainly about improved transparency and public accountability in education. Throwing education data into the public space does not change the ways in which students learn, teachers teach and schools operate. It does not lead to people doing anything with that data and transforming education in ways that will actually change education practice. On the contrary, it often results simply in adversarial relationships between civil society and government over the control and ownership of information.
The prerequisite for using big data as a catalyst to change education practice is to get out of the “read-only” mode of our societies. It’s about combining transparency with collaboration. The way in which educational institutions often work is that you have a single expert sitting somewhere in a corner who determines the application of rules and regulations affecting hundreds of thousands of students and teachers – and nobody can figure out how those decisions were made. Big data can lead to big trust if we make that data available, train civic innovators, experiment, create a maker culture. It is no surprise that OECD’s new Survey of Adult Skills shows that the more proficient people are in literacy, the more they trust others.
Collaborative consumption provides a great example of this. These days, people share their cars and even their apartments with strangers. Collaborative consumption has made people micro-entrepreneurs – and its driving engine is building trust between strangers. Think about it: in the business world, we have evolved from trusting people to provide information, to willingly handing over credit card data, to connecting trustworthy strangers in all sorts of marketplaces. We are light-years away from that when it comes to data about education.
But here’s how we can get a little closer. Some years ago we created PISA, a global survey that examines the skills of 15-year-olds in ways that are comparable across countries. PISA has created huge amounts of big data about the quality of schooling outcomes. PISA has also helped to change the balance of power in education by making public policy in the field of education more transparent and more efficient. At the micro-level, there were still a lot of sceptics: teachers thought this was just another accountability tool through which governments wanted to control them. So what did we do? This year we put in place a kind of “MyPISA” – PISA-type instruments that we circulated out into the field. Now every school can figure out how it compares with other schools anywhere else in the world, schools that are similar to them or schools that are very different.
Suddenly, the dynamic has changed; schools are beginning to use that data. Ten schools in Fairfax county in Virginia, for example, have started a year-long discussion among principals and teachers based on the results of the first reports. With the help of district offices (and the OECD), they will be conducting secondary analyses to dig deeper into their data and understand how their schools compare with each other and with other schools around the world. Those principals and teachers are beginning to see themselves as teammates – not just spectators – on a global playing field. In other words, in Fairfax county, big data is building big trust.
数据分析咨询请扫描二维码
若不方便扫码,搜微信号:CDAshujufenxi
在数据分析中,“正态分布” 是许多统计方法(如 t 检验、方差分析、线性回归)的核心假设 —— 数据符合正态分布时,统计检验的 ...
2025-10-28箱线图(Box Plot)作为展示数据分布的核心统计图表,能直观呈现数据的中位数、四分位数、离散程度与异常值,是质量控制、实验分 ...
2025-10-28在 CDA(Certified Data Analyst)数据分析师的工作中,“分类变量关联分析” 是高频需求 —— 例如 “用户性别是否影响支付方式 ...
2025-10-28在数据可视化领域,单一图表往往难以承载多维度信息 —— 力导向图擅长展现节点间的关联结构与空间分布,却无法直观呈现 “流量 ...
2025-10-27这个问题问到了 Tableau 中两个核心行级函数的经典组合,理解它能帮你快速实现 “相对位置占比” 的分析需求。“index ()/size ( ...
2025-10-27对 CDA(Certified Data Analyst)数据分析师而言,“假设检验” 绝非 “套用统计公式的机械操作”,而是 “将模糊的业务猜想转 ...
2025-10-27在数字化运营中,“凭感觉做决策” 早已成为过去式 —— 运营指标作为业务增长的 “晴雨表” 与 “导航仪”,直接决定了运营动作 ...
2025-10-24在卷积神经网络(CNN)的训练中,“卷积层(Conv)后是否添加归一化(如 BN、LN)和激活函数(如 ReLU、GELU)” 是每个开发者都 ...
2025-10-24在数据决策链条中,“统计分析” 是挖掘数据规律的核心,“可视化” 是呈现规律的桥梁 ——CDA(Certified Data Analyst)数据分 ...
2025-10-24在 “神经网络与卡尔曼滤波融合” 的理论基础上,Python 凭借其丰富的科学计算库(NumPy、FilterPy)、深度学习框架(PyTorch、T ...
2025-10-23在工业控制、自动驾驶、机器人导航、气象预测等领域,“状态估计” 是核心任务 —— 即从含噪声的观测数据中,精准推断系统的真 ...
2025-10-23在数据分析全流程中,“数据清洗” 恰似烹饪前的食材处理:若食材(数据)腐烂变质、混杂异物(脏数据),即便拥有精湛的烹饪技 ...
2025-10-23在人工智能领域,“大模型” 已成为近年来的热点标签:从参数超 1750 亿的 GPT-3,到万亿级参数的 PaLM,再到多模态大模型 GPT-4 ...
2025-10-22在 MySQL 数据库的日常运维与开发中,“更新数据是否会影响读数据” 是一个高频疑问。这个问题的答案并非简单的 “是” 或 “否 ...
2025-10-22在企业数据分析中,“数据孤岛” 是制约分析深度的核心瓶颈 —— 用户数据散落在注册系统、APP 日志、客服记录中,订单数据分散 ...
2025-10-22在神经网络设计中,“隐藏层个数” 是决定模型能力的关键参数 —— 太少会导致 “欠拟合”(模型无法捕捉复杂数据规律,如用单隐 ...
2025-10-21在特征工程流程中,“单变量筛选” 是承上启下的关键步骤 —— 它通过分析单个特征与目标变量的关联强度,剔除无意义、冗余的特 ...
2025-10-21在数据分析全流程中,“数据读取” 常被误解为 “简单的文件打开”—— 双击 Excel、执行基础 SQL 查询即可完成。但对 CDA(Cert ...
2025-10-21在实际业务数据分析中,我们遇到的大多数数据并非理想的正态分布 —— 电商平台的用户消费金额(少数用户单次消费上万元,多数集 ...
2025-10-20在数字化交互中,用户的每一次操作 —— 从电商平台的 “浏览商品→加入购物车→查看评价→放弃下单”,到内容 APP 的 “点击短 ...
2025-10-20