Solving the quantitative skills gap: a flexible learning call to arms!

References

• Allgood, S. (2001). Grade targets and teaching innovations. Economics of Education Review, 205, 485–493.
   Google Scholar
• Bergmann, J., & Sams, A. (2012). Flip your classroom: Reach every student in every class every day. Virginia: ISTE & ACSD.
   Google Scholar
• Berrett, D. (2012). How ‘flipping’ the classroom can improve the traditional lecture. The Chronicle of Higher Education. Retrived from https://www.chronicle.com/article/How-Flipping-the-Classroom/130857
   Google Scholar
• Bishop, J., & Verleger, M. (2013). The flipped classroom: A survey of the research. Paper presented at the 120th ASEE Annual Conference & Exposition Paper ID #6219, Atlanta, Georgia.
   Google Scholar
• Blair, E., Maharaj, C., & Primus, S. (2016). Performance and perception in the flipped classroom. Education and Information Technologies, 21, 1465–1482.
   Web of Science ®Google Scholar
• Bligh, D. (2000). What’s the use of lectures? San Francisco: Jossey-Bass.
   Google Scholar
• British Academy. (2012). Society counts: Quantitative skills in the social sciences and humanities. London: Author.
   Google Scholar
• British Academy. (2015). Count us in: Quantitative skills for a new generation. London: Author.
   Google Scholar
• Brown, B., & Liedholm, C. (2002). Can web courses replace the classroom in principles of microeconomics? American Economic Review Papers and Proceedings, 922, 444–448.
   Google Scholar
• Carey, E., Hill, F., Devine, A., & Szücs, D. (2016). The chicken or the egg? The direction of the relationship between mathematics anxiety and mathematics performance. Frontiers in Psychology, 6, 1987.
   PubMed Web of Science ®Google Scholar
• Caviglia-Harris, J. (2016). Flipping the undergraduate classroom: Using online videos to enhance teaching and learning. Southern Economic Journal, 81, 321–331.
   Google Scholar
• Chen, J., & Lin, T. (2012). Do supplemental online recorded lectures help students learn microeconomics? International Review of Economics Education, 111, 6–15.
   Google Scholar
• Cook, S. (2006). Understanding the construction and interpretation of forecast evaluation statistics using computer-based tutorial exercises. Economics Network Ideas Bank. Retrieved from https://www.economicsnetwork.ac.uk/showcase/cook_forecas
   Google Scholar
• Cook, S. (2013a). Time series decomposition: A practical example using a classic data set. Economics Network Ideas Bank. Retrieved from https://www.economicsnetwork.ac.uk/showcase/cook_timeseries
   Google Scholar
• Cook, S. (2013b). Forecast combination: A tool for teaching and demonstrating the underlying issues. Economics Network Ideas Bank. Retrived from https://www.economicsnetwork.ac.uk/showcase/cook_combination
   Google Scholar
• Cook, S. (2014a). Forecast encompassing as a means of forecast comparison. Economics Network Ideas Bank. Retrived from https://www.economicsnetwork.ac.uk/showcase/cook_encompassing
   Google Scholar
• Cook, S. (2014b). Directional forecasting, forecast accuracy and making profits. Economics Network Ideas Bank. Retrived from https://www.economicsnetwork.ac.uk/showcase/cook_directional
   Google Scholar
• Cook, S. (2016a). Forecasting using exponential smoothing: Developing understanding via replication and contemporary applications. Economics Network Ideas Bank. Retrived from https://www.economicsnetwork.ac.uk/showcase/cook_smoothing
   Google Scholar
• Cook, S. (2016b). Visual identification of ARIMA models. Economics Network Ideas Bank. Retrived from https://www.economicsnetwork.ac.uk/showcase/cook_arima
   Google Scholar
• Cook, S. (2016c). Analysing forecasting bias: Significance, methods and samples. Economics Network Ideas Bank. Retrived from https://www.economicsnetwork.ac.uk/showcase/cook_bias
   Google Scholar
• Dawson, P. (2014). Skills in mathematics and statistics in economics and tackling transition. New York, NY: Higher Education Academy.
   Google Scholar
• Department of Business, Innovation and Skills. (2013). Market assessment of public sector information. London: Author.
   Google Scholar
• Dowker, A., Sarkar, A., & Looi, C. (2016). Mathematics anxiety: What have we learned in 60 years? Frontiers in Psychology, 7, 508.
   PubMed Web of Science ®Google Scholar
• Economics Network. (2012). National economics students survey 2012 report. Bristol: Author.
   Google Scholar
• Economics Network. (2015). National economics students survey 2014–15 report. Bristol: Author.
   Google Scholar
• Folley, D. (2010). The lecture is dead long live the e-lecture. Electronic Journal of e-Learning, 8, 93–100.
   Google Scholar
• French, S., & Kennedy, G. (2017). Reassessing the value of university lectures. Teaching in Higher Education, 22, 639–654.
   Web of Science ®Google Scholar
• Gibbs, G. (2013, November). Lectures don’t work, but we keep using them. Can a demonstrably ineffective pedagogic form still be put to good use? Times Higher Education. Retrived from https://www.timeshighereducation.com/news/lectures-dont-work-but-we-keep-using-them/2009141.article
   Google Scholar
• Gordon, N. (2014). Flexible Pedagogies: Technology-Enhanced Learning. New York, NY: Higher Education Academy. Retrived from https://www.heacademy.ac.uk/flexible-pedagogies-technology-enhanced-learning
   Google Scholar
• Guerrero, S., Beal, M., Lamb, C., Sonderegger, D., & Baumgartel, D. (2015). Flipping undergraduate finite mathematics: Findings and implications. Primus, 25, 814–832.
   Google Scholar
• Harackiewicz, J., Barron, K., Carter, S., Lehto, A., & Elliot, A. (1997). Predictors and consequences of achievement goals in the college classroom: Maintaining interest and making the grade. Journal of Personality and Social Psychology, 736, 1284–1295.
   Google Scholar
• Hembree, R. (1990). The nature, effects and relief of mathematics anxiety. Journal of Research in Mathematics Education, 21, 33–46.
   Web of Science ®Google Scholar
• Hoyles, C., Wolf, A., Molyneux-Hodgson, S., & Kent, P. (2002). Mathematical skills in the workplace: Final report to the science technology and mathematics council. London: Institute of Education, University of London; Science, Technology and Mathematics Council.
   Google Scholar
• Kwak, D., Menezes, F., & Sherwood, C. (2015). Assessing the impact of blended learning on student performance. Economic Record, 91, 91–106.
   Web of Science ®Google Scholar
• Macher, D., Paechter, M., Papousek, I., Ruggeri, K., Freudenthaler, H., & Arendasy, M. (2013). Statistics anxiety, state anxiety during an examination, and academic achievement. British Journal of Educational Psychology, 83, 535–549.
   PubMed Web of Science ®Google Scholar
• MacInnes, J., Breeze, M., de Haro, M., Kandlik, M., & Karels, M. (2016). Measuring up: International case studies on the teaching of quantitative methods in the social sciences. London: The British Academy.
   Google Scholar
• Mason, G., Nathan, M., & Rosso, A. (2015). State of the Nation: A review of evidence on the supply and demand of quantitative skills. London: The British Academy & NIESR.
   Google Scholar
• Matheson, R., & Sutcliffe, M. (2017). Creating belonging and transformation through the adoption of flexible pedagogies in masters level international business management students. Teaching in Higher Education, 22, 15–29.
   Web of Science ®Google Scholar
• McFarlin, B. (2008). Hybrid lecture-online format increases student grades in an undergraduate exercise physiology course at a large urban university. Advances in Physiology Education, 32, 86–91.
   PubMed Web of Science ®Google Scholar
• National Union of Students 2008. NUS student experience report. Retrieved from http://www.nus.org.uk/PageFiles/4017/NUS_StudentExperienceReport.pdf
   Google Scholar
• Olitsky, N., & Cosgrove, S. (2013). The effect of blended courses on student learning: Evidence from introductory economics courses. International Review of Economics Education, 15, 17–31.
   Google Scholar
• Olitsky, N., & Cosgrove, S. (2016). The better blend? Flipping the principles of microeconomics classroom. International Review of Economics Education, 16, 1–11.
   Google Scholar
• Paechter, M., Macher, D., Martskvishvili, K., Wimmer, S., & Papousek, I. (2017). Mathematics anxiety and statistics anxiety: Shared but also unshared components and antagonistic contributions to performance in statistics. Frontiers in Psychology, 8, 1196.
   PubMed Web of Science ®Google Scholar
• Pokorny, M., & Pokorny, H. (2005). Widening participation in higher education: Student quantitative skills and independent learning as impediments to progression. International Journal of Mathematical Education in Science and Technology, 36, 445–467.
   Google Scholar
• Randles, R., Fligner, M., Policello, G., & Wolfe, D. (1980). An asymptotically distribution-free test for symmetry versus asymmetry. Journal of the American Statistical Association, 75, 168–172.
   Web of Science ®Google Scholar
• Ryan, A., & Tilbury, D. (2013). Flexible pedagogies: New pedagogical ideas. New York, NY: Higher Education Academy. Retrived from https://www.heacademy.ac.uk/flexible-pedagogies-new-pedagogical-ideas
   Google Scholar
• Schmidt, H., Wagener, S., Smeets, G., Keemink, L., & van der Molen, H. (2015). On the use and misuse of lectures in higher education. Health Professions Education, 1, 12–18.
   Google Scholar
• Sparks, R. (2013). Flipping the classroom: An empirical study examining student learning. Journal of Learning in Higher Education, 92, 65–70.
   Google Scholar
• Terry, N., & Lewer, J. (2003). Campus, online, or hybrid: An assessment of instruction modes. Journal of Economics and Economic Education Review, 41, 23–34.
   Google Scholar
• Watson, D., Webb, R., Cook, S., & Arico, F. (2015). Death of the pedagogue: Pluralism and non-didacticism. International Journal of Pluralism and Economics Education, 5, 242–255.
   Google Scholar
• Webb, R., Watson, D., & Cook, S. (2018). Is flipping the future? Assessing the effectiveness of two types of flipping the classroom. Under review, mimeo.
   Google Scholar