How do first year students utilize different lecture resources?

Article

Abstract

One of the more noticeable changes to tertiary teaching over the past decade has been the widespread adoption of digital technologies, in particular eLearning platforms and lecture capture technology. However, much of the current knowledge of how students utilise these new technologies and their effect on traditional lecture attendance is simply derived from student surveys rather than comprehensive independent analyses. In this study, we use cluster analysis to identify common lecture resource utilisation patterns for students in four large first-year business subjects. While common usage patterns with respect to lecture attendance, video lecture recording access and download of lecture notes are identified across our subjects, the proportion of students within each of the utilisation clusters varies widely. Business statistics students are much more likely to either attend lectures or view video recordings compared to economics students, many of whom rely solely on the download of lecture notes. In order to gain insight into how student characteristics may affect these utilisation patterns, we develop a predictive model, quantifying the influences of prior academic performance, gender, age, distance from campus and international student status using statistical modelling. We find a strong role for students’ previous academic performance in explaining lecture resource utilisation patterns. Students’ commuting distance to campus is also established as a factor dissuading physical lecture attendance. Contrary to initial expectations, we also found that females and older students tend to rely more heavily on digital resources rather than lecture attendance. It is hoped that these findings can help first-year instructors and University administrators understand the heterogeneity of student lecture engagement patterns within the first-year experience.

Keywords

Cluster analysis Lecture recordings Lecture attendance Learning analytics Student engagement 

References

  1. Abachi, H. (2014). The impact of m-learning on students and educators. Computers in Human Behavior, 30, 491–496.CrossRefGoogle Scholar
  2. Abeyasekera, S. (2003). Chapter 18: Multivariate methods for index construction. Household surveys in developing and transition countries: design, implementation and analysis. United Nations Statistics Division.Google Scholar
  3. Andrietti, V., & Velasco, C. (2015). Lecture attendance, study time, and academic performance: a panel data study. The Journal of Economic Education, 46(3), 239–259.CrossRefGoogle Scholar
  4. Ball, D., & Bass, H. (2002). Toward a practice-based theory of mathematical knowledge for teaching. In Proceedings of the 2002 Annual Meeting of The Canadian Mathematics Education Study Group, queens university may 24-28, edited by Simmt, E., and B. Davis: 3-14.Google Scholar
  5. Bassili, J. N. (2008). Media richness and social norms in the choice to attend lectures or to watch them online. Journal of Educational Multimedia and Hypermedia, 17(4), 453–475.Google Scholar
  6. Becker, W. E. (1997). Teaching economics to undergraduates. Journal of Economic Literature, 35(3), 1347–1373.Google Scholar
  7. Becker, W. E., & Watts, M. (1998). Teaching economics at the start of the 21st century: still chalk-and-talk. American Economic Review, 91(2), 446–451.CrossRefGoogle Scholar
  8. Berenson, M., Levine, D., Szabat, K., O’Brien, M., Watson, J., & Jayne, N. (2015). Basic business statistics. Australia: Pearson.Google Scholar
  9. Biggs, J. (1999). Teaching for quality learning at university. Buckingham: Open University Press.Google Scholar
  10. Bishop, J. L., & Verleger, M. A. (2013). The flipped classroom: a survey of the research. Paper presented at the 120th ASEE Annual Conference and Exposition, Atlanta, June 23–26.Google Scholar
  11. Bongey, S. B., Cizadlo, G., & Kalnbach, L. (2006). Explorations in course-casting: podcasts in higher education. Campus-Wide Information Systems, 23(5), 350–367.CrossRefGoogle Scholar
  12. Bos, N., & Brand-Gruwel, S. (2016). Profiling student behaviour in a blended course: closing the gap between blended teaching and blended learning. In Proceedings of the 8th International Conference on Computer Supported Education (CSEDU 2016) - Volume 2, pp. 65–72.Google Scholar
  13. Bos, N., Groeneveld, C., van Bruggen, J., & Brand-Gruwel, S. (2016). The use of recorded lectures in education and the impact on lecture attendance and exam performance. British Journal of Educational Technology, 47(5), 906–917.CrossRefGoogle Scholar
  14. Brooks, C. (2014). Introductory econometrics for finance. Cambridge: Cambridge University Press.Google Scholar
  15. Brotherton, J. A., & Abowd, G. D. (2004). Lessons learned from eClass: assessing automated capture and access in the classroom. ACM Transactions on Computer-Human Interaction, 11(2), 121–155.CrossRefGoogle Scholar
  16. Cilesiz, S. (2015). Undergraduate students’ experiences with recorded lectures: towards a theory of acculturation. Higher Education, 69(3), 471–493.CrossRefGoogle Scholar
  17. Coates, H., James, R., & Baldwin, G. (2005). A critical examination of the effects of learning management systems on university teaching and learning. Tertiary Education and Management, 11(1), 19–36.CrossRefGoogle Scholar
  18. Cohn, E., & Johnson, E. (2006). Class attendance and performance in principles of economics. Education Economics, 14(2), 211–233.CrossRefGoogle Scholar
  19. Cooke, M., Watson, B., Blacklock, M., Mansah, M., Howard, M., Johnston, A., Tower, M., & Murfield, J. (2012). Lecture capture: first year student nurses’ experiences of a web-based lecture technology. Australian Journal of Advanced Nursing, 29(3), 14–21.Google Scholar
  20. Copley, J. (2007). Audio and video podcasts of lectures for campus-based students: Production and evaluation of student use. Innovations in Education and Teaching International, 44(4), 387–399.CrossRefGoogle Scholar
  21. Davis, S., Connolly, A., & Linfield, E. (2009). Lecture capture: making the most of face-to-face learning. Engineering Education: A Journal of the Higher Education Academy, 4(2), 4–13.CrossRefGoogle Scholar
  22. Durdan, G. C., & Ellis, L. V. (1995). The effects of attendance on student learning in principles of economics. American Economic Review: Papers and Proceedings, 85(2), 343–346.Google Scholar
  23. Ellis, R. A., Steed, A. F., & Applebee, A. C. (2006). Teacher conceptions of blended learning, blended teaching and associations with approaches to design. Australasian Journal of Educational Technology, 22(3), 312–335.CrossRefGoogle Scholar
  24. Garrison, D. R., & Kanuka, H. (2004). Blended learning: uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95–105.CrossRefGoogle Scholar
  25. Gendron, P., & Pieper, P. (2005). Does attendance matter? Evidence from an Ontario ITAL. Unpublished discussion paper, Humber Institute of Technology & Advanced Learning, Toronto http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.124.8735&rep=rep1&type=pdf. Accessed 2 May 2017.
  26. Gonzáles, C. (2010). What do university teachers think eLearning is good for in their teaching? Studies in Higher Education, 35(1), 61–78.CrossRefGoogle Scholar
  27. Gosper, M. V., McNeill, M. A., & Woo, K. (2010). Harnessing the power of technologies to manage collaborative e-learning projects in dispersed environments. Journal of Distance Education, 24(1), 167–186.Google Scholar
  28. Grabe, M., & Christopherson, K. (2007). Optional student use of online lecture resources: resource preferences, performance and lecture attendance. Journal of Computer Assisted Learning, 24(1), 1–10.CrossRefGoogle Scholar
  29. Guney, Y. (2009). Exogenous and endogenous factors influencing students’ performance in undergraduate accounting modules. Accounting Education, 18(1), 51–73.CrossRefGoogle Scholar
  30. Harley, D., Henke, J., Lawrence, S., McMartin, F., Maher, M., Gawlick, M., & Muller, P. (2003). Costs, culture, and complexity: an analysis of technology enhancements in a large lecture course at UC Berkeley.” https://www.researchgate.net/publication/46438019_Costs_Culture_and_Complexity_An_Analysis_of_Technology_Enhancements_in_a_Large_Lecture_Course_at_UC_Berkeley. Accessed 2 May 2017.
  31. Heckman, J. J. (1976). The common structure of statistical models of truncation, sample selection and limited dependent variables. Annals of Economic and Social Measurement, 5, 475–492.Google Scholar
  32. Hew, K. F., & Cheung, W. S. (2014). Students’ and instructors’ use of massive open online courses (MOOCs): motivations and challenges. Educational Research Review, 12, 45–58.CrossRefGoogle Scholar
  33. Horn, P., Jansen, A., & Yu, D. (2011). Factors explaining the academic success of second-year economics students: and exploratory analysis. South African Journal of Economics, 79(2), 202–210.CrossRefGoogle Scholar
  34. Inglis, M., Palipana, A., Trenholm, S., & Ward, J. (2011). Individual differences in students’ use of optional learning resources. Journal of Computer Assisted Learning, 27, 490–502.CrossRefGoogle Scholar
  35. Jones, C. H. (1984). Interaction of absences and grades in a college course. The Journal of Psychology, 116, 133–136.CrossRefGoogle Scholar
  36. Kahu, E. R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38(5), 758–773.CrossRefGoogle Scholar
  37. Kovanovic, V., Gasevic, D., Joksimovic, S., Hatala, M., & Adesope, O. (2015). Analytics of communities of inquiry: effects of learning technology use on cognitive presence in asynchronous online discussions. Internet and Higher Education, 27, 74–89.CrossRefGoogle Scholar
  38. Le, A., Joordens, S., Chrysostomou, S., & Grinnell, R. (2010). Online lecture accessibility and its influence on performance in skills-based courses. Computers and Education, 55, 313–319.CrossRefGoogle Scholar
  39. Leadbeater, W., Shuttleworth, T., Couperthwaite, J., & Nightingale, K. P. (2013). Evaluating the use and impact of lecture recording in undergraduates: evidence for distinct approaches by different groups of students. Computers and Education, 61, 185–192.CrossRefGoogle Scholar
  40. Lin, T., & Chen, J. (2006). Cumulative class attendance and exam performance. Applied Economics Letters, 13(14), 937–942.CrossRefGoogle Scholar
  41. Lust, G., Vandewaetere, M., Ceulemans, E., Elen, J., & Clarebout, G. (2011). Tool-use in a blended undergraduate course: in search of user profiles. Computers and Education, 57, 2135–2144.CrossRefGoogle Scholar
  42. Lyons, A., Reyson, S., & Pierce, L. (2011). Video lecture format, student technological efficacy, and social presence in online courses. Computers in Human Behaviour, 28, 181–186.CrossRefGoogle Scholar
  43. McGarr, O. (2009). A review of podcasting in higher education: its influence on the traditional lecture. Australasian Journal of Educational Technology, 25(3), 309–321.CrossRefGoogle Scholar
  44. O’Flaherty, J., & Phillips, C. (2015). The use of flipped classrooms in higher education: a scoping review. Internet and Higher Education, 25, 85–95.CrossRefGoogle Scholar
  45. Oliver, R. (2008). Engaging first year students using a web-supported inquiry-based learning setting. Higher Education, 55, 285–301.CrossRefGoogle Scholar
  46. Onwuegbuzie, A. J., & Wilson, V. A. (2003). Statistics anxiety: nature, etiology, antecedents, effects and treatments—a comprehensive review of the literature. Teaching in Higher Education, 8(2), 195–209.CrossRefGoogle Scholar
  47. Owston, R., Lupshenyuk, D., & Wideman, H. (2011). Lecture capute in large underegraduate classes: student perceptions and academic performance. Internet and Higher Education, 14, 262–268.CrossRefGoogle Scholar
  48. Ozuorcun, N. C., & Tabak, F. (2012). Is M-learning versus E-learning or are they supporting each other? Procedia – Social and Behavioural Sciences, 46, 299–305.CrossRefGoogle Scholar
  49. Pearce, K., & Scutter, S. (2010). Podcasting of health sciences lectures: benefits for students from a non-English speaking background. Australasian Journal of Educational Technology, 26, 1028–1041.CrossRefGoogle Scholar
  50. Pinder-Grover, T., Green, K. R., & Millunchick, J. M. (2011). The efficacy of screencasts to address the diverse academic needs of students in a large lecture course (pp. 1–28). Winter: Advances in Engineering Education.Google Scholar
  51. Prodanov, V. I. (2012). In-class lecture recording; What Lecture Capture has to Offer the Instructor. https://pdfs.semanticscholar.org/6814/4ee94290cd23e6925b4f4379a3b08c00f0e8.pdf?_ga=1.11246388.1419539888.1472092167. Accessed 2 May 2017.
  52. Pye, G., Holt, D., Salzman, S., Bellucci, E., & Lombardi, L. (2015). Engaging diverse student audiences in contemporary blended learning environments in Australian higher business education: implications for design and practice. Australasian Journal of Information Systems, 19, 1–20.CrossRefGoogle Scholar
  53. Rodgers, J. R. (2001). A panel-data study of the effect of student attendance on university performance. Australian Journal of Education, 45(3), 284–295.CrossRefGoogle Scholar
  54. Romer, D. (1993). Do students go to class? Journal of Economic Perspectives, 7(3), 167–174.CrossRefGoogle Scholar
  55. Ross, T. K., & Bell, P. D. (2007). “No significant difference” only on the surface. International Journal of Instructional Technology and Distance Learning, 4(7), 3–13.Google Scholar
  56. Soong, S. K. A., Chan, L. C. Cheers, C.,& Hu, C. (2006). Impact of video recorded lectures among students. Paper presented at the 23rd annual ascilite conference, Sydney, December 3.Google Scholar
  57. Taplin, R. H., Kerr, R., & Brown, A. M. (2014). Opportunity costs associated with the provision of student services: a case study of web-based lecture technology. Higher Education, 68(1), 15–28.CrossRefGoogle Scholar
  58. Tynjala, P., Valimaa, J., & Sarja, A. (2003). Pedagogical perspectives on the relationship between higher education and working life. Higher Education, 46(2), 147–166.CrossRefGoogle Scholar
  59. Traphagan, T., Kucsera, J. V., & Kishi, K. (2010). Impact of class lecture webcasting on attendance and learning. Educational Technology Research and Development, 58(1), 19–37.CrossRefGoogle Scholar
  60. Trigwell, K., Prosser, M., & Waterhouse, F. (1999). Relations between teachers’ approaches to teaching and students’ approaches to learning. Higher Education, 37, 57–70.CrossRefGoogle Scholar
  61. von Konsky, B. R., Ivins, J., & Gribble, S. J. (2009). Lecture attendance and web based lecture technologies: a comparison of student perceptions and usage patterns. Australasian Journal of Educational Technology, 25(4), 581–595.Google Scholar
  62. Wang, Y. S. (2003). Assessment of learner satisfaction with asynchronous electronic learning systems. Information Management, 41(4), 75–86.CrossRefGoogle Scholar
  63. Wieling, M. B., & Hofman, W. H. A. (2010). The impact of online video lecture recordings and automated feedback on student performance. Computers and Education, 54, 992–998.CrossRefGoogle Scholar
  64. Williams, J., & Fardon, M. (2007). Perpetual connectivity: lecture recordings and portable media players. Paper presented to the ASCILITE conference, Singapore, December 2-5.Google Scholar
  65. Yen, J., & Lee, C. (2011). Exploring problem solving patterns and their impact on learning achievement in a blended learning environment. Computers and Education, 56, 138–145.CrossRefGoogle Scholar
  66. Young, J. R. (2008). The lectures are recorded, so why go to class? The Chronicle of Higher Education, 54(36), A1.Google Scholar
  67. Yuan, L., & Powell, S. (2013). MOOCs and open education: implications for higher education. JISC CETIS. March 2013 http://publications.cetis.org.uk/2013/667.
  68. Zhang, D., Zhou, L., Briggs, R. O., & Nunumaker Jr., J. F. (2006). Instructional video in e-learning: assessing the impact of interactive video on learning effectiveness. Information Management, 43(1), 15–27.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Sydney Business SchoolUniversity of WollongongWollongongAustralia

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