Designing for student wellbeing: challenging assumptions about where our students learn

Authors

  • Debbie Holley Bournemouth University
  • David Biggins Bournemouth University

DOI:

https://doi.org/10.47408/jldhe.vi27.938

Keywords:

Student wellbeing, hidden learning spaces, technostress, analytics

Abstract

Student wellbeing has been foregrounded during the recent Covid-19 pandemic but this is broad brush and contested with different models being followed across the sector. One aspect of concern is the extent to which access to the technology institutions require students to use contributes to additional stress. A student survey (n=30) in one UK HEI revealed the ‘hidden spaces’ where students learn, and the findings indicate that the formal institutional Virtual Learning Environment (VLE), with its sophisticated learning analytics, did not fully capture the student experience. This work was followed up with a digital wellbeing survey (n=172) and, by drawing together the two datasets, we report on a more nuanced student experience. Initial findings indicate a schism between formal and informal spaces where students learn, especially within our institutional reporting of students working online. Examples include students using their own preferred tools such as WhatsApp, Trello, and Slack to communicate outside the formal channels; these behaviours thereby devalue the validity of the VLE datasets that student-facing staff are encouraged to use for decision-making. This paper offers insights into accessing and interpreting data in ways that are more useful for academics, learning developers, and learning designers, and suggests ways in which we can effectively frame student support by putting the ‘real’ student experience at the centre of our practice.

Author Biographies

Debbie Holley, Bournemouth University

Debbie Holley is Professor of Learning Innovation at Bournemouth University. A National Teaching Fellow and a Principal Fellow of AdvanceHE, she is a passionate educator, with expertise in learning design and blending learning to engage a diverse student body. Supporting student writing, drawing on creative practice in her own teaching, and influencing the teaching of others aligns her practice with the LD community, where she regularly presents and supports the annual conference, contributes to the #Take5 blog series, and formerly served on the ALDinHE National Steering Group. Her research in simulation, augmented reality, and immersive worlds reality influence national policy.

David Biggins, Bournemouth University

David Biggins is a Senior Lecturer at Bournemouth University and Senior Fellow of AdvanceHE. David worked as a Technology Enhanced Learning Theme Leader and Academic Learning Designer where he developed a TEL Toolkit, contributed to the design, deployment, and staff training for a new VLE, and led projects in staff skills development, curriculum design, the learning culture, and other institution-wide initiatives. A particular interest for David is the use of data to promote student success, learning analytics, and data visualisation. David’s praxis has developed through his interactions with the LD community and his research is frequently presented at ALDinHE conferences.

References

Advance HE (2022) Student academic experience survey. Available at: https://www.advance-he.ac.uk/knowledge-hub/student-academic-experience-survey-2022 (Accessed: 18 February 2023).

Alvarez Jr., A. V. (2021) ‘Rethinking the digital divide in the time of crisis’, Globus Journal of Progressive Education, 11(1), pp.26-28.

Becker, S. A., Brown, M., Dahlstrom, E., Davis, A., DePaul, K., Diaz, V. and Pomerantz, J. (2018) NMC horizon report: 2018 higher education edition. Available at: https://cit.bnu.edu.cn/docs/2018-09/20180918163624337480.pdf (Accessed: 18 February 2023).

Biggins, D., Holley, D. and Supa, M. (2022) ‘From tools to wellbeing: a proposed digital learning maturity model (DLMM)’, 16th International Technology, Education and Development Conference: INTED2022 Proceedings. Online 7-8 March. IATED Digital Library, pp.4687-4696. https://doi.org/10.21125/inted.2022.1235.

Braun, W. J. and Murdoch, D. J. (2021) A first course in statistical programming with R. Cambridge: Cambridge University Press.

Brennan, P. (2021) ‘Data visualization with the programming language R’, The Biochemist, 43(5), pp.8-14. https://doi.org/10.1042/bio_2021_174.

Brod, C. (1984) Technostress: the human cost of the computer revolution. Reading, Mass.: Addison-Wesley.

Citizens Advice Bureau (2021) Life through lockdown: what Citizens Advice data tells us about the year everything changed, March. Available at: https://www.citizensadvice.org.uk/Global/CitizensAdvice/Covd-19%20Data%20trends/Citizens%20Advice_Life%20Through%20Lockdown%20(1).pdf (Accessed: 18 February 2023).

Colvin, C., Dawson, S., Wade, A. and Gasevic, D. (2017) ‘Addressing the challenges of institutional adoption’, Handbook of Learning Analytics, 1, pp.281-289.

Corrin, L., Kennedy, G., French, S., Buckingham Shum, S., Kitto, K., Pardo, A., West, D., Mirriahi, N. and Colvin, C. (2019) The ethics of learning analytics in Australian higher education. Available at: https://www.researchgate.net/publication/332263485_The_Ethics_of_Learning_Analytics_in_Australian_Higher_Education_DISCUSSION_PAPER_PREPARED_BY?enrichId=rgreq-a9442f19d110372a98e526367e519370-XXX&enrichSource=Y292ZXJQYWdlOzMzMjI2MzQ4NTtBUzo3NDUyNDA5MDY0MzI1MTRAMTU1NDY5MDY4NjkzMA%3D%3D&el=1_x_2&_esc=publicationCoverPdf (Accessed: 18 February 2023).

Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J. and Vuorikari, R. (2016) Research evidence on the use of learning analytics: implications for education policy. Seville: Joint Research Centre. https://doi.org/10.2791/955210.

Ferguson, R. and Clow, D. (2017) ‘Learning analytics: avoiding failure’, Educause Review Online, 31. Available at: http://oro.open.ac.uk/50385/3/50385.pdf (Accessed: 18 February 2023).

Foster, K. (2021) ‘Paving the road for the future of data analytics’, Jisc, 5 November. Available at https://www.jisc.ac.uk/blog/paving-the-road-for-the-future-of-data-analytics-05-nov-2021 (Accessed: 21 February 2023).

Guzmán-Valenzuela C., Gómez-González C., Tagle A. R. and Lorca-Vyhmeister A. (2021) ‘Learning analytics in higher education: a preponderance of analytics but very little learning?’, International Journal of Educational Technology in Higher Education, 18(1), pp.1-9. https://doi.org/10.1186/s41239-021-00258-x.

Hassan, Z. A., Schattner, P. and Mazza, D. (2006) ‘Doing a pilot study: why is it essential?’ Malaysian family physician: the official journal of the Academy of Family Physicians of Malaysia, 1(2-3), p.70. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4453116/ (Accessed: 21 February 2023).

Hathaway, W. E. (1985) ‘Hopes and possibilities for educational information systems’, Information Systems Conference. Available at: https://eric.ed.gov/?id=ED253560 (Accessed: 21 February 2023).

Heitz, C., Laboissiere, M., Sanghvi, S. and Sarakatsannis, J. (2020) ‘Getting the next phase of remote learning right in higher education’, McKinsey & Company. Available at https://www.mckinsey.com/industries/public-sector/our-insights/getting-the-next-phase-of-remote-learning-right-in-higher-education (Accessed: 18 February 2023).

Helsper, E. (2021) The digital disconnect: the social causes and consequences of digital inequalities. London: Sage Publications.

Hernández-Leo, D., Martinez-Maldonado, R., Pardo, A., Muñoz-Cristóbal, J. A. and Rodríguez-Triana, M. J. (2019) ‘Analytics for learning design: a layered framework and tools’, British Journal of Educational Technology, 50(1), pp.139-152. https://doi.org/10.1111/bjet.12645.

IBM (2020) Random forest. Available at: https://www.ibm.com/cloud/learn/random-forest (Accessed: 18 February 2023).

Ifenthaler, D. and Yau, J. Y. K. (2020) ‘Utilising learning analytics to support study success in higher education: a systematic review’, Educational Technology Research and Development, 68(4), pp.1961-1990. https://doi.org/10.1007/s11423-020-09788-z.

Kaliisa, R., Kluge, A. and Mørch, A. I. (2022) ‘Overcoming challenges to the adoption of learning analytics at the practitioner level: a critical analysis of 18 learning analytics frameworks’, Scandinavian Journal of Educational Research, 66(3), pp.367-381. https://doi.org/10.1080/00313831.2020.1869082.

Kernohan, D. and Dickenson, J. (2022) ‘Anxious students want to us to help them spend time with and learn with each other’, WONKHE, 9 June. Available at https://wonkhe.com/blogs/anxious-students-want-to-us-to-help-them-spend-time-with-and-learn-with-eachother/ (Accessed: 18 February 2023).

Khalil, M., Prinsloo, P. and Slade, S. (2022) ‘A comparison of learning analytics frameworks: a systematic review’, LAK22: 12th International Learning Analytics and Knowledge Conference. Online, USA 21-25 March. https://doi.org/10.1145/3506860.3506878.

Killen, C. and Langer-Crane, M. (2021) ‘Student digital experience insights survey’, Jisc. Available at: https://www.jisc.ac.uk/reports/student-digital-experience-insights-survey-2020-21-uk-higher-education-findings (Accessed: 18 February 2023).

Kleimola, R. and Leppisaari, I. (2022) ‘Learning analytics to develop future competences in higher education: a case study’, International Journal of Educational Technology in Higher Education, 19(1), pp.1-25. https://doi.org/10.1186/s41239-022-00318-w.

Kollom, K., Tammets, K., Scheffel, M., Tsai, Y., Jivet, I., Muñoz-Merino, P. J., Moreno-Marcos, P. M., Whitelock-Wainwright, A., Calleja, A. R., Gasevic, D., Kloos, C. D., Drachsler, H. and Ley, T. (2021) ‘A four-country cross-case analysis of academic staff expectations about learning analytics in higher education’, The Internet and Higher Education, 49. https://doi.org/10.1016/j.iheduc.2020.100788.

Li, L. and Wang, X. (2021) ‘Technostress inhibitors and creators and their impacts on university teachers’ work performance in higher education’, Cognition, Technology & Work, 23(2), pp.315-330. https://doi.org/10.1007/s10111-020-00625-0.

Lim, L. A., Dawson, S., Gašević, D., Joksimović, S., Pardo, A., Fudge, A. and Gentili, S. (2021) ‘Students’ perceptions of, and emotional responses to, personalised learning analytics-based feedback: an exploratory study of four courses’, Assessment & Evaluation in Higher Education, 46(3), pp.339-359. https://doi.org/10.1080/02602938.2020.1782831.

Macfadyen, L. P. (2022) ‘Institutional implementation of learning analytics: current state, challenges, and guiding frameworks’, in Lang, C., Friend Wise, A., Merceron, A., Gaševic, D. and Siemens, G. (eds.) The Handbook of Learning Analytics. 2nd edn. Vancouver: SOLAR.

Mutimukwe, C., Viberg, O., Oberg, L.M. and Cerratto-Pargman, T. (2022) ‘Students' privacy concerns in learning analytics: model development’, British Journal of Educational Technology, 53(4), pp.932-951. https://doi.org/10.1111/bjet.13234.

National Union of Students (2020) Covid-19 and students survey report. Available at: https://www.nusconnect.org.uk/resources/covid-19-and-students-survey-report (Accessed: 18 February 2023).

Oana, I. E., Schneider, C. Q. and Thomann, E. (2021) Qualitative comparative analysis using R: a beginner's guide. Cambridge: Cambridge University Press.

Office for National Statistics (2021) ‘Consistency needed: care experience students and higher education’, Insight, 9 April. Available at: https://www.officeforstudents.org.uk/publications/consistency-needed-care-experienced-students-and-higher-education/ (Accessed: 18 February 2023).

Office for National Statistics (2022) ‘Schools, attainment and the role of higher education’, Insight, 13, April. Available at: https://www.officeforstudents.org.uk/media/24ac9501-234f-4f34-bf44-edb13e5282c5/insight-brief-13-schools-attainment-and-the-role-of-higher-education.pdf (Accessed: 18 February 2023).

Office for Students (2020) ‘“Digital poverty” risks leaving students behind’, 3 September. Available at: https://www.officeforstudents.org.uk/news-blog-and-events/press-and-media/digital-poverty-risks-leaving-students-behind (Accessed: 18 February 2023).

Pardo, A., Ellis, R. A. and Calvo, R. A. (2015) ‘Combining observational and experiential data to inform the redesign of learning activities’, In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge. New York 16-20 March. New York: Association for Computing Machinery, pp.305-309. https://doi.org/10.1145/2723576.2723625.

Persico, D. and Pozzi, F. (2015) ‘Informing learning design with learning analytics to improve teacher inquiry’, British Journal of Educational Technology, 46(2), pp.230-248. https://doi.org/10.1111/bjet.12207.

R Studio (2022) R Markdown. Available at: https://rmarkdown.rstudio.com/ (Accessed: 18 February 2023).

Rimal, Y. (2021) ‘Reproducible academic writing and interactive data visualization using R Markdown (R Programming Flex-Dashboard: Flex_Dashboard Packages’, in Singh Rathore, V., Dey, N., Piuri, V., Babo, R., Polkowski, Z. and Tavares, J. M. (eds.) Rising threats in expert applications and solutions. Singapore: Springer, pp.603-615. https//doi.org/10.1007/978-981-15-6014-9_73.

Robinson, L., Cotten, S. R., Ono, H., Quan-Haase, A., Mesch, G., Chen, W., Schulz, J., Hale, T. M. and Stern, M. J. (2015) ‘Digital inequalities and why they matter’, Information, Communication & Society, 18(5), pp.569-582. https://doi.org/10.1080/1369118X.2015.1012532.

Shoufan, A. (2019) ‘Estimating the cognitive value of YouTube's educational videos: a learning analytics approach’, Computers in Human Behavior, 92, pp.450-458. https://doi.org/10.1016/j.chb.2018.03.036.

Stake, R. E. (1995) The art of case study research. Thousand Oaks, CA: Sage.

Staples, T. L. (2022) ‘Expansion and evolution of the R programming language’, arXiv, 2208.12382. https://doi.org/10.48550/arXiv.2208.12382.

Student Minds (2021) University mental health: life in a pandemic. Available at: https://www.studentminds.org.uk/lifeinapandemic.html (Accessed: 18 February 2023).

Susnjak, T., Ramaswami, G. S. and Mathrani, A. (2022) ‘Learning analytics dashboard: a tool for providing actionable insights to learners’, International Journal of Educational Technology in Higher Education, 19(1), pp.1-23. https://doi.org/10.1186/s41239-021-00313-7.

Tsai, Y. S., Rates, D., Moreno-Marcos, P. M., Muñoz-Merino, P. J., Jivet, I., Scheffel, M., Drachsler, H., Kloos, C. D. and Gašević, D. (2020) ‘Learning analytics in European higher education – trends and barriers’, Computers & Education, 155, p.103933. https://doi.org/10.1016/j.compedu.2020.103933.

Viberg, O., Hatakka, M., Bälter, O. and Mavroudi, A. (2018) ‚The current landscape of learning analytics in higher education’, Computers in human behavior, 89, pp.98-110. https://doi.org/10.1016/j.chb.2018.07.027.

Vidoni, M. C. (2021) ‘Software engineering and R programming: a call for research’, R J., 13(2), p.600. Available at: https://www.researchgate.net/profile/Melina-Vidoni/publication/358583892_Software_Engineering_and_R_Programming_A_Call_for_Research/links/620d90716c472329dced9b1a/Software-Engineering-and-R-Programming-A-Call-for-Research.pdf (Accessed: 21 February 2023).

Vuorikari Rina, R., Kluzer, S. and Punie, Y. (2022) ‘DigComp 2.2: The digital competence framework for citizens with new examples of knowledge, skills and attitudes’, EconPapers, JRC128415. Available at: https://econpapers.repec.org/paper/iptiptwpa/jrc128415.htm (Accessed: 21 February 2023).

Wasson, B., Hanson, C. and Mor, Y. (2016) ‘Grand challenge problem 11: empowering teachers with student data’, in Eberle, J., Lund, K., Tchounikine, P. and Fischer, F. (eds.) Grand challenge problems in technology-enhanced learning II: MOOCs and beyond. Cham: Springer, pp. 55-58. https://doi.org/10.1007/978-3-319-12562-6_12.

Downloads

Published

27-04-2023

How to Cite

Holley, D. and Biggins, D. (2023) “Designing for student wellbeing: challenging assumptions about where our students learn ”, Journal of Learning Development in Higher Education, (27). doi: 10.47408/jldhe.vi27.938.

Issue

Section

Papers