Factors influencing students’ perceptions of their quantitative skills

References

• Matthews KE, Belward S, Coady C, Rylands L, Simbag V. The state of quantitative skills in undergraduate science education: findings from an Australian study. Sydney (Australia): Office for Learning and Teaching (OLT); 2012.
   Google Scholar
• American Association for the Advancement of Science. AAAS. Vision and change in undergraduate biology education: a call to action. Washington (DC); 2011.
   Google Scholar
• American Association of Medical Colleges. AAMC. Scientific foundations for future physicians. Washington (DC); 2009.
   Google Scholar
• Augustine NR. Rising above the gathering storm: energizing and employing America for a brighter future. Washington (DC): The National Academies; 2007.
   Google Scholar
• National Research Council, NRC. BIO2010: Transforming undergraduate education for future research biologists. Washington (DC): National Academies Press; 2003.
   Google Scholar
• NRC. A New Biology for the 21st Century. Washington (DC): The National Academies; 2009.
   Google Scholar
• Obama B. Remarks by the President at the National Academy of Sciences Annual Meeting. In: Room TWHB, editor. Washington (DC): National Academy of Sciences; 2009.
   Google Scholar
• Fazackerley A, Richmond T. Science fiction? Uncovering the real level of science skills at school and university. London: Policy Exchange; 2009.
   Google Scholar
• Koenig J. A survey of the mathematics landscape within bioscience undergraduate and postgraduate UK higher education. Cambridge: University of Cambridge; 2011.
   Google Scholar
• Brown G. Review of education in mathematics, data science and quantitative disciplines: report to the Group of Eight Universities. Canberra (Australia): The Group of Eight; 2009.
   Google Scholar
• Chubb I. Health of Australian science: office of the chief scientist. Canberra: Australian Government; 2012.
   Google Scholar
• Lyons T, Quinn F. Choosing science: understanding the declines in senior high school science enrolments. New South Wales (Australia): National Centre of Science, ICT, University of New England; 2010.
   Google Scholar
• Rubinstein H. A national strategy for mathematical sciences in Australia. Melbourne (Australia): Australian Mathematical Sciences Institute; 2009.
   Google Scholar
• Berlin DF, Hyonyong L. Integrating science and mathematics education: historical analysis. Sch Sci Math. 2005;105:15–24.
   Google Scholar
• Belward S, Matthews KE, Rylands L, Coady C, Adams, P, & Simbag V. A study of the Australian tertiary sector's portrayed view of the relevance of quantitative skills in science. In: Clark J, Kissane B, Mousley J, Spencer T & Thornton S, editors. Mathematics: Traditions and [New] Practices: AAMT-MERGA. Alice Springs (Australia): Australian Association of Mathematics Teachers (AAMT) and the Mathematics Education Research Group of Australasia (MERGA); 2011.
   Google Scholar
• Bialek W, Botstein D. Introductory science and mathematics education for 21st-century biologists. Science. 2004;303:788–790.
   PubMed Web of Science ®Google Scholar
• Matthews KE. The hidden experience: mathematics in science. In: Hudson P, Chandra V, King D, Lee T, editors. Brisbane: International Conference of STEM Education; 2010.
   Google Scholar
• Wieman C. Why not try a scientific approach to science education? Change: Magazine Higher Learn. 2007;39:9–15.
   Google Scholar
• Wood WB. Revising the AP biology curriculum. Science. 2009;325:1627–1628.
   PubMed Web of Science ®Google Scholar
• Anderson W, Banerjee U, Drennan C, Elgin S, Epstein I, Handelsman J, Hatfull G, Losick R, O’Dowd D, Olivera B. Changing the culture of science education at research universities. Science. 2011;331:1.
   Web of Science ®Google Scholar
• Wieman C, Perkins K, Gilbert S. Transforming science education at large research intensive universities: a case study in progress. Change: Magazine Higher Learn. 2010; 42: 6–14.
   Google Scholar
• Brakke DF. Improving undergraduate biology and science education on a broad scale. Alexandria (VA): Association for Women in Science; 2011. p. 36.
   Google Scholar
• Gross L. Presentation at Iowa State University. Contact author for transcript, http://www.tiem.utk.edu/~gross/-rectalks, 1996.
   Google Scholar
• Jungck JR. Ten equations that changed biology: mathematics in problem-solving biology curricula. Bioscene. 1997;23:11–21.
   Google Scholar
• Hastings A, Arzberger P, Bolker B, Collins S, Ives AR, Johnson NA, Palmer MA. Quantitative bioscience for the 21st century. Biosci. 2005;55:511–517.
   Web of Science ®Google Scholar
• Hastings A, Palmer MA. A bright future for biologists and mathematicians? Science.2003;299: 2003–2004.
   PubMed Web of Science ®Google Scholar
• Matthews KE, Adams P, Goos M. Putting it into perspective: mathematics in the undergraduate science curriculum. Int J Math Educ Sci Technol. 2009;40:891–902.
   Google Scholar
• Steen LA. Math and Bio 2010: linking undergraduate disciplines. Washington (DC): Mathematical Association of America. 2005.
   Google Scholar
• Wood L, Solomonides I. Different disciplines, different transitions. Math Educ Res J. 2008;20:117–134.
   Google Scholar
• Britton S, New PB, Sharma MD, Yardley D. A case study of the transfer of mathematics skills by university students. Int J Math Educ Sci Technol. 2005;36:1–13.
   Google Scholar
• LeBard R, Thompson R, Micolich A, Quinnell R. Identifying common thresholds in learning for students working in the ‘hard’ discipline of science. In: Hugman A, editor. Motivating science undergraduates: ideas and interventions. Sydney:Uniserve Science, The University of Sydney; 2009. p. 72–77.
   Google Scholar
• Tariq VN. Defining the problem: mathematical errors and misconceptions exhibited by first-year bioscience undergraduates. Int J Math Educ Sci Technol. 2008;39:889–904.
   Google Scholar
• Bridgeman AJ, Schmid S. Collaborative laboratory for quantitative data analysis. Paper presented at the Proceedings of the Australian Conference on Science and Mathematics Education; 2010; Sydney, Australia.
   Google Scholar
• Gyuris E, Everingham Y, Sexton J. Maths anxiety in a first year introductory quantitative skills subject at a regional Australian university – establishing a baseline. Int J Innov Sci Math Educ. 2012;20:42–54.
   Google Scholar
• Familari M, Elliott K, Watson R, Matthews KE. Embedding case studies into statistical teaching to enhance quantitative skills of biomedicine students. Int J Innov Sci Math Educ. 2012;20:44–56.
   Google Scholar
• Matthews KE, Adams P, Goos M. Using the principles of BIO2010 to develop an introductory, interdisciplinary course for biology students. CBE–Life Sci Educ. 2010;9:290–297.
   PubMed Web of Science ®Google Scholar
• Tariq V, Jackson V. Biomathtutor: evaluation of a new multimedia e-learning resource to support mathematics in the biosciences. Int J Math Educ Sci Technol. 2008;39:1003–1021.
   Google Scholar
• Thompson KV, Nelson KC, Marbach-Ad G, Keller M, Fagan WF. Online interactive teaching modules enhance quantitative proficiency of introductory biology students. CBE–Life Sci Educ. 2010;9:277–283.
   PubMed Web of Science ®Google Scholar
• Wenner JM, Burn HE, Baer EM. The math you need, when you need it: online modules that remediate mathematical skills in introductory geoscience courses. J Coll Sci Teaching. 2011;41:16–24.
   Google Scholar
• Caudill L, Hill A, Hoke K, Lipan O. Impact of interdisciplinary undergraduate research in mathematics and biology on the development of a new course integrating five STEM disciplines. CBE–Life Sci Educ. 2010;9:212–216.
   PubMed Web of Science ®Google Scholar
• Duncan SI, Bishop P, Lenhart S. Preparing the ‘new’ biologist of the future: student research at the interface of mathematics and biology. CBE–Life Sci Educ. 2010;9:311–315.
   PubMed Web of Science ®Google Scholar
• Miller JE, Walston T. Interdisciplinary training in mathematical biology through team-based undergraduate research and courses. CBE–Life Sci Educ. 2010;9:284–289.
   PubMed Web of Science ®Google Scholar
• Fairweather J. Linking evidence and promising practices in science, technology, engineering, and mathematics (STEM) undergraduate education. Washington (DC): Board of Science Education, National Research Council, The National Academies; 2008.
   Google Scholar
• Yorke M, Knight P. Curricula for economic and social gain. Higher Educ. 2006;51:565–588.
   Google Scholar
• Barnett R, Coate K. Engaging the curriculum in higher education. London: Open University Press; 2004.
   Google Scholar
• Barr RB, Tagg J. From teaching to learning – a new paradigm for undergraduate education. Change: Mag Higher Learn. 1995;27:12–26.
   Google Scholar
• Rylands L, Simbag V, Matthews KE, Coady C, Belward S. Scientists and mathematicians collaborating to build quantitative skills in undergraduate science. Int J Math Educ Sci Technol. 2013:1–12.
   Google Scholar
• Sriraman B, English LD. Theories of mathematics education: seeking new frontiers. Berlin/Heidelberg: Springer; 2010.
   Google Scholar
• Matthews KE, Hodgson Y. The science students skills inventory: capturing graduate perceptions of their learning outcomes. Int J Innov Sci Math Educ. 2012;20:24–43.
   Google Scholar
• Bransford J. How people learn: brain, mind, experience, and school. Washington (DC): National Academies Press; 2000.
   Google Scholar
• New P, Britton S, Sharma M, Brew A. Researching the transferability of mathematical skill. Paper presented at the Proceedings of the Australian Conference on Science and Mathematics Education; 2001; Sydney, Australia.
   Google Scholar
• Hurney CA, Brown J, Griscom HP, Kancler E, Wigtil CJ, Sundre D. Closing the loop: involving faculty in the assessment of scientific and quantitative reasoning skills of biology majors. J Coll Sci Teaching. 2011;40:18–23.
   Google Scholar
• Bowman NA. Validity of college self-reported gains at diverse institutions. Educ Res.2011;40:22–24.
   Web of Science ®Google Scholar
• Douglass JA, Thomson G, Zhao C-M. The learning outcomes race: the value of self-reported gains in large research universities. Higher Educ. 2012;64:317–335.
   Google Scholar
• Berk RA. Thirteen strategies to measure college teaching. Sterling (VA): Stylus; 2006.
   Google Scholar
• Kember D, Ginns K. Evaluating teaching and learning: a practical handbook for colleges, universities and the scholarship of teaching. New York: Routledge; 2012.
   Google Scholar
• Pugh KJ, Linnenbrink-Garcia L, Koskey KL, Stewart VC, Manzey C. Motivation, learning, and transformative experience: a study of deep engagement in science. Sci Educ. 2010;94:1–28.
   Web of Science ®Google Scholar
• Vittinghoff E, Shiboski SC, Glidden DV, McCulloch CE. Regression methods in biostatistics: linear, logistic, survival, and repeated measures mode. Statistics for Biology and Health. New York: Springer; 2005.
   Google Scholar
• Tabachnick BG, Fidell LS. Using multivariate statistics. 5th ed. Boston: Pearson; 2007.
   Google Scholar
• Tariq VN, Durrani N. Factors influencing undergraduates’ self-evaluation of numerical competence. Int J Math Educ Sci Technol. 2012;43:337–356.
   Google Scholar
• Breen S, Cleary J, O’Shea A. An investigation of the mathematical literacy of first year third-level students in the Republic of Ireland. Int J Math Educ Sci Technol. 2009;40:229–246.
   Google Scholar
• Carmichael C, Taylor JA. Analysis of student beliefs in a tertiary preparatory mathematics course. Int J Math Educ Sci Technol. 2005;36:713–719.
   Google Scholar
• Hackett G, Betz NE. An exploration of the mathematics self-efficacy/mathematics performance correspondence. J Res Math Educ. 1989;20:261–273.
   Google Scholar
• Tariq VN, Qualter P, Roberts S, Appleby Y, Barnes L. Mathematical literacy in undergraduates: role of gender, emotional intelligence and emotional self-efficacy. Int J Math Educ Sci Technol. 2013;1–17.
   Google Scholar
• Wilson TM, MacGillivray HL. Counting on the basics: mathematical skills among tertiary entrants. Int J Math Educ Sci Technol. 2007;38:19–41.
   Google Scholar
• Mura R. Sex-related differences in expectations of success in undergraduate mathematics. J Res Math Educ. 1987;18:15–24.
   Google Scholar
• Hudson P, Matthews KE. Identities and transformational experiences for quantitative problem solving: gender comparisons of first-year university science students. J Sci Math Educ Southeast Asia. 2012;35:22–46.
   Google Scholar
• Adams T, Banks M, Davis D, Dickson J. The Hobsons retention project: context and factor analysis report. Melbourne: Tony Adams and Associates; 2010.
   Google Scholar
• Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84:191–215.
   PubMed Web of Science ®Google Scholar