测评大数据支持下的学习反馈设计研究

测评大数据是基于测评活动获得的学习数据的集合,基于测评大数据分析的学习反馈是发挥大数据优势、提高学习效率的有效途径。本研究从测评大数据的视角探讨了大数据应用于学习反馈环节的机遇与挑战,对当前测评大数据的应用方式及问题进行了分析;从数据优势与教学经验有机结合的思路出发,建构了基于测评大数据分析的学习反馈系统框架,包括:反馈内容设计、反馈环境设计、反馈机制设计三个维度。研究认为:测评大数据为当前学习者状态分析和教学优化提供了更客观的数据支持,基于测评大数据的学习反馈分析与规律总结是教育大数据在基础教育领域的价值所在,把握学习状态是开展下一阶段学习的基础。

学习测评大数据支撑下面向知识点的学习预警建模与仿真

学习测评是教学环节中的重要活动模块,对基于学生测评所产生的数据进行收集分析有助于挖掘学生真实的学习状态,明晰学生对知识点的掌握情况。以测评大数据为基础,以学生知识点综合掌握情况为研究内容,以贝叶斯网络为分析方法,通过节点权重分析及因果关系分析形成预警模型网络结构,在参数学习和条件概率分析的基础上形成面向知识点的学习预警模型。以某学校高一数学三次考试中的500条测评数据作为样本数据,采用5折交叉验证法对模型的有效性进行验证,采用因果推理和诊断推理对知识点预测结果进行仿真验证分析。研究结果表明,所构建的预警模型涵盖1个总目标层节点、5个指标层节点和20个数据层节点,通过调整模型中的节点能够有效观测不同条件下指标层和目标层知识点的掌握情况,并进行准确预警;通过灵敏度分析得出学习预警需要关注的核心节点,其影响程度从大到小依次为:核心知识点掌握情况>相关知识点掌握情况>各难度题目掌握情况;交叉使用主次指标排队分类法和专家打分法确定节点不同预警状态概率间的权重并以此计算判别值,按需调整其阈值以对学习者进行个性化类别划分与状态识别。

测评大数据支持下的有效教学研究

有效教学是一种教学理想,是"有效果""有效率""有效益"的教学。传统有效教学研究侧重于采用思辨性研究方法,基于研究者和教师的主观判断与教学经验进行研究,研究结果缺乏数据的支撑,教学是否有效很难证明。教育大数据为教学分析与诊断提供了客观的数据支持,为有效教学的实现提供了可测量可能。测评大数据是教育大数据的子集,它是学生在常态性练习作业和考试测评等活动中产生的过程性数据和结果性数据,具有常态化、易采集、结构化程度高的优势。测评大数据支持下的有效教学,力求体现个性化、精准性、效率性等特征,主要包括精准把握学习起点、个性化定制学习目标、动态组织教学内容、灵活调整教学策略、即时评价与个性化反馈、推荐个性化学习资源这六个环节。

教育评价中的技术与控制——大数据教育测评的社会学审视

在信息技术、知识经济的时代,技术在教育评价中的应用越来越广泛,大数据教育测评成为了当前评价的主流趋势。然而,技术在给教育带来便利的同时也在侵蚀着人们的思想,无处不在的技术作为新的控制形式正在营造一种教育的虚假需求,加剧了教育的功利化和工具性,走向了技术理性迷途。从社会学视角分析大数据教育测评之为何、如何,剖析技术在教育评价中的控制机制,并据此提出未来展望。要想摆脱技术的控制和束缚,教育评价需要走上一条通往审美的道路,恢复感性在技术主导的教育中的地位,实现理性和感性的统一,回归教育的本体价值,使教育成为一门充满人性和人情的艺术性事业。

餐饮服务质量的测评方法

餐饮服务认证的基本测评方法包括认证员审核、神秘顾客体验、行业专家核查、大数据测评四种方法。本文还介绍了对餐饮服务基本要求、服务资源、服务过程、服务结果的测评方法,以及测评结果的计算方法。

教师增值性评价探索:现实隐忧与可为路径

教师增值性评价具有看重学生的进步、考虑生源因素、剥离出教师对学生成长的“净效应”等价值,在国际社会广为使用。增值性评价在我国并未推广的原因在于,实行增值性评价面临着可能加重“唯分数”的导向、评价模型的复杂性压抑教师主体性、教育数据收集困难、教师无法自我确证引发对公正性的怀疑等方面的现实隐忧。探索增值性评价,破解现实隐忧的路径在于:树立“五育并举”导向的增值评价理念;因地制宜地采用合适的增值评价模式;构建数据采集系统,尝试利用大数据测评;提升教师自身的评价能力和自主权,避免绩效崇拜;将增值评价与其他评价有机结合,建构科学的教师评价体系。

Measuring and evaluating SDG indicators with Big Earth Data

The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic,social,and environmental action.A comprehensive indicator system to aid in the systematic implementation and monitoring of progress toward the Sustainable Development Goals(SDGs)is unfortunately limited in many countries due to lack of data.The availability of a growing amount of multi-source data and rapid advancements in big data methods and infrastructure provide unique opportunities to mitigate these data shortages and develop innovative methodologies for comparatively monitoring SDGs.Big Earth Data,a special class of big data with spatial attributes,holds tremendous potential to facilitate science,technology,and innovation toward implementing SDGs around the world.Several programs and initiatives in China have invested in Big Earth Data infrastructure and capabilities,and have successfully carried out case studies to demonstrate their utility in sustainability science.This paper presents implementations of Big Earth Data in evaluating SDG indicators,including the development of new algorithms,indicator expansion(for SDG 11.4.1)and indicator extension(for SDG 11.3.1),introduction of a biodiversity risk index as a more effective analysis method for SDG 15.5.1,and several new high-quality data products,such as global net ecosystem productivity,high-resolution global mountain green cover index,and endangered species richness.These innovations are used to present a comprehensive analysis of SDGs 2,6,11,13,14,and 15 from 2010 to 2020 in China utilizing Big Earth Data,concluding that all six SDGs are on schedule to be achieved by 2030.

Analysis and assessment of bridge health monitoring mass data——progress in research/development of “Structural Health Monitoring”

The "Structural Health Monitoring" is a project supported by National Natural Science Foundation for Distinguished Young Scholars of China(Grant No.50725828).To meet the urgent requirements of analysis and assessment of mass monitoring data of bridge environmental actions and structural responses,the monitoring of environmental actions and action effect modeling methods,dynamic performance monitoring and early warning methods,condition assessment and operation maintenance methods of key members are systematically studied in close combination with structural characteristics of long-span cable-stayed bridges and suspension bridges.The paper reports the progress of the project as follows.(1) The environmental action modeling methods of long-span bridges are established based on monitoring data of temperature,sustained wind and typhoon.The action effect modeling methods are further developed in combination with the multi-scale baseline finite element modeling method for long-span bridges.(2) The identification methods of global dynamic characteristics and internal forces of cables and hangers for long-span cable-stayed bridges and suspension bridges are proposed using the vibration monitoring data,on the basis of which the condition monitoring and early warning methods of bridges are developed using the environmental-condition-normalization technique.(3) The analysis methods for fatigue loading effect of welded details of steel box girder,temperature and traffic loading effect of expansion joint are presented based on long-term monitoring data of strain and beam-end displacement,on the basis of which the service performance assessment and remaining life prediction methods are developed.