How do first year students utilize different lecture resources?

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.

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Notes

  1. 1.

    Namely the motivation, ability, attendance and achievement models (Jones 1984).

  2. 2.

    Students with missing values for any of the variables were removed from the analysis. Likewise, students who did not attend the final exam were excluded.

  3. 3.

    We have no insightful explanation for this observation except to note that microeconomics is an elective first-year subject while the other three subjects are compulsory core subjects.

  4. 4.

    Except in the case of macroeconomics, where we previously observed a much lower average mark.

  5. 5.

    With the exception of cluster 3 in the statistics subject, denoted with an asterisk in Table 2.

  6. 6.

    ‘High’ frequency is defined as more than 50% of lectures (> 6).

References

  1. Abachi, H. (2014). The impact of m-learning on students and educators. Computers in Human Behavior, 30, 491–496.

    Article  Google 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.

  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.

    Article  Google 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.

  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.

    Article  Google 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.

  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.

    Article  Google 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.

  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.

    Article  Google 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.

    Article  Google Scholar 

  16. Cilesiz, S. (2015). Undergraduate students’ experiences with recorded lectures: towards a theory of acculturation. Higher Education, 69(3), 471–493.

    Article  Google 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.

    Article  Google Scholar 

  18. Cohn, E., & Johnson, E. (2006). Class attendance and performance in principles of economics. Education Economics, 14(2), 211–233.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  29. Guney, Y. (2009). Exogenous and endogenous factors influencing students’ performance in undergraduate accounting modules. Accounting Education, 18(1), 51–73.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  35. Jones, C. H. (1984). Interaction of absences and grades in a college course. The Journal of Psychology, 116, 133–136.

    Article  Google Scholar 

  36. Kahu, E. R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38(5), 758–773.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  40. Lin, T., & Chen, J. (2006). Cumulative class attendance and exam performance. Applied Economics Letters, 13(14), 937–942.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  45. Oliver, R. (2008). Engaging first year students using a web-supported inquiry-based learning setting. Higher Education, 55, 285–301.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  54. Romer, D. (1993). Do students go to class? Journal of Economic Perspectives, 7(3), 167–174.

    Article  Google 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.

  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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

  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.

    Article  Google 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.

    Article  Google Scholar 

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O’Brien, M., Verma, R. How do first year students utilize different lecture resources?. High Educ 77, 155–172 (2019). https://doi.org/10.1007/s10734-018-0250-5

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Keywords

  • Cluster analysis
  • Lecture recordings
  • Lecture attendance
  • Learning analytics
  • Student engagement