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A blended learning course in quantitative methods for UK sociology, social policy and political science undergraduates.

为英国社会学、社会政策和政治学本科生开设的定量方法混合学习课程。

基本信息

批准号：
ES/J012025/1
负责人：
Ailsa Henderson
金额：
$ 9.82万
依托单位：
University of Edinburgh
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2012
资助国家：
英国
起止时间：
2012 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FJ012025%2F1
关键词：
blended learning course quantitative methods

项目摘要

We will reconfigure and expand University of Edinburgh's 20 credit M level blended learning course 'Core Quantitative Data Analysis' (CQDA) to provide approximately 60 credits worth of learning resources for use under a creative commons license by any UK HEI for training undergraduates in QM. These resources may be used 'off the shelf' as a complete course in QM (and accessible as such by any UK HEI undergraduate), or individual components can be integrated into existing courses by QM teaching staff. The CQDA course has been developed over five years, using resources from an Edinburgh University E-learning initiative, to deliver training in QM up to ERSC PG generic requirements level. It is based on the philosophy that students learn QM best when they are presented as a way of resolving problems encountered in research, and learned by repeated practice using real data. It follows Tukey (1977) and Marsh's (1982) emphasis on data exploration, but includes a critical discussion of inference and the value and limits of hypothesis testing. It is highly interactive, using screen animation, videos and question/feedback tests requiring students to use real data to solve problems in order to complete each module. The course aims to show students the relevance and value of QM, as well as giving them confidence to apply QM in practice. The course has delivered improved and excellent results from the cohorts of students taking it (around 150 each year), praise from externals and extremely positive feedback from students. A feature of the course is the way it allows limited staff time to be concentrated on helping students who need most support.To maximise usability in diverse curriculum contexts, we will migrate the existing course learning resources, authored in Adobe Dreamweaver and Captivate, to a user-friendly open access content management system that will allow teachers and course organisers to rearrange and customise the material to their particular needs with the minimum of effort. Edinburgh will host the resources on its servers, downloadable by users in other HEIs. We will produce new discipline specific content for sociology, criminology, social policy and political science students. We will re-write modules to base all examples and exercises on European Social Survey and World Bank teaching datasets that will not require student or teacher registration for use (currently the course uses GHS, BSAS, SSAS and other datasets). We will separate the course material from the software platform to be used (currently SPSS) allowing the course to be used with SPSS, Stata or R. We will add a much larger volume of self/tutor test exercises at the end of each module which may be used for either self- or summative assessment, and use learning assessment exercises within modules to route students through relevant support materialWe will adapt the course content to undergraduate level by producing additional modules on sources of data, on data visualisation, and on secondary data analysis, and by adapting the course materials to a wider range of student abilities. One of the current features of the course is its ability to use its web format to present material that will engage stronger students, but which can safely be ignored by weaker students wishing to concentrate on the basics, and to incorporate material that weaker students require but which stronger students can skip past. The web learning modules thus have a flexibility that is difficult to achieve in lecture or seminar formats. We will monitor the use of the learning materials, revise and review them in the light of teachers' and students' feedback, and continue to maintain the learning resources server and links for three years. At the end of this period we will curate and archive the materials so that they continue to be available for use.

我们将重新配置和扩展爱丁堡大学的20学分M级混合学习课程“核心定量数据分析”（CQDA），以提供约60学分的学习资源，供任何英国高等教育机构在QM培训本科生的知识共享许可下使用。这些资源可以被用来“现成的”作为一个完整的课程在QM（和访问任何英国高等教育学院本科），或个别组件可以整合到现有的课程由QM教学人员。CQDA课程已经开发了五年，使用爱丁堡大学电子学习计划的资源，提供质量管理培训，达到ERSC PG通用要求水平。它是基于这样的理念，即学生学习质量管理最好的时候，他们提出了解决问题的研究中遇到的，并通过反复实践使用真实的数据学习。它遵循Tukey（1977）和Marsh（1982）对数据探索的强调，但包括对推理和假设检验的价值和限制的批判性讨论。它是高度互动的，使用屏幕动画，视频和问题/反馈测试，要求学生使用真实的数据来解决问题，以完成每个模块。本课程旨在向学生展示质量管理的相关性和价值，并让他们有信心在实践中应用质量管理。该课程已经从参加它的学生群体（每年约150人）中获得了改进和优异的结果，外部的赞扬和学生的非常积极的反馈。该课程的一个特点是，它允许有限的工作人员时间集中在帮助最需要支持的学生。为了在不同的课程环境中最大限度地提高可用性，我们将迁移现有的课程学习资源，在Adobe Dreamweaver和Captivate中创作，对于用户-一个友好的开放式内容管理系统，允许教师和课程组织者根据他们的特殊需要重新安排和定制材料，最小的努力。爱丁堡大学将在其服务器上托管这些资源，供其他高等院校的用户下载。我们将为社会学，犯罪学，社会政策和政治学学生制作新的学科特定内容。我们将重新编写模块，使所有示例和练习都基于欧洲社会调查和世界银行的教学数据集，这些数据集不需要学生或教师注册即可使用（目前课程使用GHS，BSAS，SSAS和其他数据集）。我们将把课程材料与要使用的软件平台（目前是SPSS）分开，允许课程与SPSS，Stata或R一起使用。我们将在每个模块的末尾增加大量的自我/导师测试练习，这些练习可用于自我评估或总结性评估，并在模块中使用学习评估练习，通过相关支持材料引导学生。我们将通过制作有关数据源，数据可视化和二级数据分析的额外模块，使课程内容适应本科水平。并使课程材料适应更广泛的学生能力。该课程目前的特点之一是能够使用其网络格式来呈现能够吸引较强学生的材料，但希望专注于基础知识的较弱学生可以安全地忽略这些材料，并结合较弱学生需要的材料，但较强的学生可以跳过。因此，网络学习模块具有讲座或研讨会形式难以实现的灵活性。我们会监察学习材料的使用情况，并根据教师和学生的意见修订和检讨，以及继续维持学习资源服务器和连结三年。在此期间结束时，我们将对材料进行整理和存档，以便继续使用。