Patterns in students’ usage of lecture recordings: a cluster analysis of self-report data

  • Daniel Ebbert Institute for Psychology in Education, University of Münster, Münster, Germany https://orcid.org/0000-0003-3666-7205
  • Stephan Dutke Institute for Psychology in Education, University of Münster, Münster, Germany
Keywords: lecture recording, lecture capture, approaches to learning, educational technology evaluation, self-regulated learning

Abstract

Students’ usage of lecture recordings can be characterised by usage frequency, repetitiveness and selectivity in watching, lecture attendance, and social context and location in which students watch the lecture recordings. At the University of Münster (Germany), the lecture recording service was evaluated over three semesters. The data were combined and used for a cluster analysis with the aim of being able to describe the students’ distinct usage patterns. The cluster analysis was performed using partitioning around medoids with Gower distance. Five clusters of students were identified, which differed mainly on the amount of lecture recordings watched, whether the lecture recordings were watched completely or partially, whether the recordings were watched once or multiple times, and the number of lectures the students missed. The five clusters are interpreted as representing different ways of utilising lecture recordings. The clustering provides a basis for investigating the usage of lecture recordings in the context of different approaches to learning and learning strategies.

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References


American Psychological Association (2017) Ethical Principles of Psychologists and Code of Conduct, American Psychological Association [online] Available at: https://www.apa.org/ethics/code/


Bacro, T. R. H., Gebregziabher, M. & Fitzharris, T. P. (2010) ‘Evaluation of a lecture recording system in a medical curriculum’, Anatomical Sciences Education, vol. 3, no. 6, pp. 300–308. doi: 10.1002/ase.183


Beasley, T. M. & Schumacker, R. E. (1995) ‘Multiple regression approach to analyzing contingency tables: post hoc and planned comparison procedures’, The Journal of Experimental Education, vol. 64, no. 1, pp. 79–93. doi: 10.1080/00220973.1995.9943797


Conover, W. J. & Iman, R. L. (1979) Multiple-Comparisons Procedures. Informal Report, Los Alamos Scientific Lab, Los Alamos, [online] Available at: https://www.osti.gov/biblio/6057803


De Boer, J. & Tolboom, J. (2008) ‘How to interpret viewing scenarios in log files from streaming media servers’, International Journal of Continuing Engineering Education and Life-Long Learning, vol. 18, no. 4, pp. 432–445. doi: 10.1504/IJCEELL.2008.019643


Dolch, C. & Zawacki-Richter, O. (2018) ‘Are students getting used to learning technology? Changing media usage patterns of traditional and non-traditional students in higher education’, Research in Learning Technology, vol. 26. doi: 10.25304/rlt.v26.2038


Dommeyer, C. J. (2017) ‘Lecture capturing: its effects on students’ absenteeism, performance, and impressions in a traditional marketing research course’, Journal of Education for Business, vol. 92, no. 8, pp. 388–395. doi: 10.1080/08832323.2017.1398129


Draper, M. J., Gibbon, S. & Thomas, J. (2018) ‘Lecture recording: a new norm’, The Law Teacher, vol. 52, no. 3, pp. 316–334. doi: 10.1080/03069400.2018.1450598


Dunn, O. J. (1961) ‘Multiple comparisons among means’, Journal of the American Statistical Association, vol. 56, no. 293, pp. 52–64. doi: 10.1080/01621459.1961.10482090


Edwards, M. R. & Clinton, M. E. (2018) ‘A study exploring the impact of lecture capture availability and lecture capture usage on student attendance and attainment’, Higher Education, vol. 77, no. 3, pp. 403–421. doi: 10.1007/s10734-018-0275-9


Elliott, C. & Neal, D. (2016) ‘Evaluating the use of lecture capture using a revealed preference approach’, Active Learning in Higher Education, vol. 17, no. 2, pp. 153–167. doi: 10.1177/1469787416637463


European Federation of Psychologists’ Associations (2005) Meta-Code of Ethics, [online] European Federation of Psychologists’ Associations. Available at: http://ethics.efpa.eu/metaand-model-code/meta-code/


Gosper, M., et al., (2008) The Impact of Web-Based Lecture Technologies on Current and Future Practices in Learning and Teaching. [online] Strawberry Hills, NSW: Australian Learning and Teaching Council, pp. 1–7. Available at: https://www.mq.edu.au/lih/altc/wblt/docs/report/ce6-22_final2.pdf


Gower, J. C. (1971) ‘A general coefficient of similarity and some of its properties’, Biometrics, vol. 27, no. 4, pp. 857–871. doi: 10.2307/2528823


Gyllen, J. G., et al., (2019) ‘Accuracy in judgments of study time predicts academic success in an engineering course’, Metacognition Learning, vol. 14, no. 2, pp. 215–228. doi: 10.1007/s11409-019-09207-6


Hope, A. C. A. (1968) ‘A simplified Monte Carlo significance test procedure’, Journal of the Royal Statistical Society. Series B (Methodological), vol. 30, no. 3, pp. 582–598. doi: 10.1111/j.2517-6161.1968.tb00759.x


Kalnikaitė, V. & Whittaker, S. (2010) ‘Beyond being there? Evaluating augmented digital records’, International Journal of Human-Computer Studies, vol. 68, no. 10, pp. 627–640. doi: 10.1016/j.ijhcs.2010.05.003


Kaufman, L. & Rousseeuw, P. J. (1990) ‘Partitioning around medoids (Program PAM)’, in Finding Groups in Data: An Introduction to Cluster Analysis, eds L. Kaufman & P. J. Rousseeuw, John Wiley & Sons, Hoboken, NJ, pp. 68–125. doi: 10.1002/9780470316801.ch2


Kruskal, W. H. & Wallis, W. A. (1952) ‘Use of ranks in one-criterion variance analysis’, Journal of the American Statistical Association, vol. 47, no. 260, pp. 583–621. doi: 10.1080/01621459.1952.10483441


Marton, F. & Säljö, R. (1976) ‘On qualitative differences in learning I – outcome and process’, British Journal of Educational Psychology, vol. 46, no. 1, pp. 4–11. doi: 10.1111/j.2044-8279.1976.tb02980.x


Mark, K. P. & Vrijmoed, L. L. P. (2017) ‘Does lecture capturing improve learning? A data driven exploratory study on the effectiveness of lecture capture on learning in a foundation IT course’, Proceedings of 2016 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2016, Bangkok, pp. 338–344. doi: 10.1109/TALE.2016.7851818


Mundform, D. J., et al., (2011) ‘Number of replications required in Monte Carlo simulation studies: a synthesis of four studies’, Journal of Modern Applied Statistical Methods, vol. 10, no. 1, pp. 19–28. doi: 10.1109/TALE.2016.7851818


Newland, D. B. (2017) Lecture capture in UK HE 2017. A HeLF Survey Report, Heads of eLearning Forum, Brighton [online] Available at: https://drive.google.com/open?id=0Bx0Bp7cZGLTPRUpPZ2NaaEpkb28


Nordmann, E., et al., (2018) ‘Turn up, tune in, don’t drop out: the relationship between lecture attendance, use of lecture recordings, and achievement at different levels of study’, Higher Education, vol. 77, no. 6, pp. 1065–1084. doi: 10.1007/s10734-018-0320-8


O’Callaghan, F. V., et al., (2017) ‘The use of lecture recordings in higher education: a review of institutional, student, and lecturer issues’, Education and Information Technologies, vol. 22, no. 1, pp. 399–415. doi: 10.1007/s10639-015-9451-z


Rousseeuw, P. J. (1987) ‘Silhouettes: a graphical aid to the interpretation and validation of cluster analysis’, Journal of Computational and Applied Mathematics, vol. 20, pp. 53–65. doi: 10.1016/0377-0427(87)90125-7


Traphagan, T., Kucsera, J. V. & Kishi, K. (2010) ‘Impact of class lecture webcasting on attendance and learning’, Educational Technology Research and Development, vol. 58, no. 1, pp. 19–37. doi: 10.1007/s11423-009-9128-7


Vajoczki, S., et al., (2011) ‘Students approach to learning and their use of lecture capture’, Journal of Educational Multimedia and Hypermedia, vol. 20, no. 2, pp. 195–214. https://www.learntechlib.org/primary/p/36105/


Walls, S. M., et al., (2010) ‘Podcasting in education: are students as ready and eager as we think they are?’, Computers & Education, vol. 54, no. 2, pp. 371–378. doi: 10.1016/j.compedu.2009.08.018


Whitley-Grassi, N. E. (2017) Evaluating Student Use Patterns of Streaming Video Lecture Capture in a Large Undergraduate Classroom, Dissertation Thesis, Walden University.


Wiese, C. & Newton, G. (2013) ‘Use of lecture capture in undergraduate biological science education’, Canadian Journal for the Scholarship of Teaching and Learning, vol. 4, no. 2, pp. 1–24. doi: 10.5206/cjsotl-rcacea.2013.2.4


Williams, A. E., Aguilar-Roca, N. M. & O’Dowd, D. K. (2016) ‘Lecture capture podcasts: differential student use and performance in a large introductory course’, Educational Technology Research and Development, vol. 64, no. 1, pp. 1–12. doi: 10.1007/s11423-015-9406-5


Woo, K., et al., (2008) ‘Web-based lecture technologies: blurring the boundaries between face-to-face and distance learning’, Research in Learning Technology, vol. 16, no. 2, pp. 81–93. doi: 10.3402/rlt.v16i2.10887


Zupancic, B. & Horz, H. (2002) ‘Lecture recording and its use in a traditional university course’, Proceedings of the 7th Annual Conference on Innovation and Technology in Computer Science Education - ITiCSE’02, Arhus, pp. 24–24. doi: 10.1145/637610.544424
Published
2020-01-09
How to Cite
EbbertD., & DutkeS. (2020). Patterns in students’ usage of lecture recordings: a cluster analysis of self-report data. Research in Learning Technology, 28. https://doi.org/10.25304/rlt.v28.2258
Section
Original Research Articles