Session Information
22 SES 10 A, Teaching, Learning and Assessment in Higher Education
Paper Session
Contribution
In the UK, various reports have highlighted the lack of suitably trained social science students with the quantitative skills required either for the workplace or for higher levels of study, and this problem is evident in many countries across Europe. The problem begins in schools and is compounded by recent trends away from quantitative social science in Universities (with the exception of economics and psychology departments) (ESRC et al., 2010; Hodgen et al., 2010; MacInnes, 2010; ACME, 2011).
As part of a coordinated response to this skills shortage, funding councils in the UK supported a curriculum initiative programme to improve numeracy, statistical literacy and students’ engagement with quantitative methods. The ‘Convincing Stories? Numbers as Evidence in the Social Sciences’ unit about which we report here was set up as part of the initiative. The unit was run over 12 weeks, for 2 hours per week. It was available to all first year undergraduates across the university, but was specifically aimed at social science students. Seventy-five students signed up for the unit, from a range of disciplines including Geography, Childhood Studies, Social Policy, Sociology, and Psychology. Each week, the unit aimed to introduce a statistical concept to students, through the use of a contemporary story or problem. Examples included an overview of the manipulation of data and charts in newspaper stories, consideration to whether the reform of University finance in England (the introduction of the £9000 per year fees) were quite as ‘progressive’ as the Government White Paper claimed, the introduction of ideas around sampling and measurement, through discussion of community cohesion, and regression analysis through different conceptualisations of good parenting.
Teaching quantitative methods to students in the sciences and social sciences brings a number of potential problems to the fore. Many students in the social sciences are anxious about their ability to work quantitatively, and do not enjoy it (e.g. Bridges et al, 1998; Jackson and Johnson, 2013), or struggle to see its relevance (Hannover and Kessels, 2004). These factors can affect the students’ level of engagement, motivation and success in their learning (Murtonen et al, 2008; Ramos and Carvalho, 2011).
Teaching across disciplinary divides is also widely acknowledged in the research literature as potentially problematic. Academic disciplines can be seen as different cultures, with their own epistemologies, languages, assumptions, and methods of teaching (Bradbeer, 1999; Nikitina, 2005; Spelt et al, 2009). Universities are usually structured according to subjects (Eisen et al, 2009), with little opportunities to work in a cross-disciplinary way. As students become inculcated in their “home” discipline (and English education becomes specialised at a relatively early stage), transgressing disciplinary boundaries and working with students from other departments can become extremely difficult (Woods, 2007).
The range of students taking the Convincing Stories unit, therefore, creates an interesting dynamic. They are from different disciplines, each with their own disciplinary interests, and are in the process of being enculturated into very different ways of thinking about quantitative methods. Teaching such a range of students presents an exciting challenge.
In their study in a Portuguese university, Ramos and Carvalho (2011) highlighted the range of abilities and attitudes towards quantitative methods that can exist among undergraduate students. There is still much to explore, however, about how this relates to the teaching of quantitative methods, particularly to a cohort from across a range of disciplines. The current study provides a deeper understanding of first-year undergraduates’ attitudes towards and confidence in quantitative methods, and how this underpins their engagement with the field.
Method
Expected Outcomes
References
Advisory Committee on Mathematics Education (2011) Mathematical Needs in the Workplace and in Higher Education. London: ACME. Bradbeer, J. (1999). Barriers to interdisciplinarity: Disciplinary discourses and student learning. Journal of Geography in Higher Education, 23(3), 381–396. Bridges, G., Gillmore, G., Pershing, J. and Bates, K. (1998). Teaching quantitative research methods: A quasi-experimental analysis. Teaching Sociology, 26(1), 14-28. Eisen, A., Hall, A., Soon Lee, T., & Zupko, J. (2009). Teaching water: Connecting across disciplines and into daily life to address complex societal issues. College Teaching, 57(2), 99–104. ESRC, HEFCE and HEFCW (2010). National Strategy for Building a World Class Social Science Research Base in Quantitative Methods. Swindon: ESRC. Garfield, J. and Ben-Zvi, D. (2007). How students learn statistics revisited: A current review of research on teaching and learning statistics. International Statistical Review, 75(3), 372-396. Hannover, B. and Kessels, U. (2004). Self-to-prototype matching as a strategy for making academic choises. Why high school students do not like math and science? Learning and Instruction, 14(1), 51–67. Hodgen J., Pepper D., Sturman L., and Ruddock G. (2010), Is the UK an outlier? An International Comparison of Upper Secondary Mathematics education. London: Nuffield Foundation. Jackson, D. and Johnson, E. (2013). A hybrid model of mathematics support for science students emphasizing basic skills and discipline relevance. International Journal of Mathematical Education in Science and Technology, 44(6), 846-864. Keebaugh, A., Darrow, L., Ran, D. and Jamerson, H. (2009). Scaffolding the science: Problem based strategies for teaching interdisciplinary undergraduate research methods. International Journal of Teaching and Learning in Higher Education, 21(1), 118-126. MacInnes, J. (2010) Proposals to Support and Improve the Teaching of Quantitative Research Methods at Undergraduate Level in the UK, Final Report to ESRC. Murtonen, M., Olkinuora, E., Tynjälä, P. and Lehtinen, E. (2008). “Do I need research skills in working life?”: University students’ motivation and difficulties in quantitative methods courses. Higher Education, 56, 599-612. Nikitina, S. (2005). Pathways of interdisciplinary cognition. Cognition and Instruction, 23(3), 389–425. Ramos, M. and Carvalho, H. (2011). Perceptions of quantitative methods in higher education: Mapping student profiles. Higher Education, 61, 629-647. Spelt, E., Biemans, H., Tobi, H., Luning, P. and Mulder, M. (2009). Teaching and learning in interdisciplinary higher education: A systematic review. Educational Psychology Review, 21, 365-378. Woods, C. (2007). Researching and developing interdisciplinary teaching: Towards a conceptual framework for classroom communication. Higher Education, 54(6), 853–866.
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