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

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 material

We 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.

The web based learning, teaching and assessment materials will help UK social science departments to either start or expand their QM teaching activity at undergraduate level.
They will make it easier for departments to increase the contact hours associated with QM training, give undergraduates more space in which to practice and develop their QM skills, and to concentrate their support efforts upon the students who most need it.
They will help disseminate best practice in undergraduate QM teaching across UK universities and encourage the shift of methodology towards a more central place in the undergraduate curriculum.
They will encourage more students to consider secondary data analysis in their project work and give the the skills necessary to do such analysis.
They will increase the supply of QM proficient social science graduates able to properly consider and use QM approaches in PG study or in employment.