Are our institutions digital visitors? What are the impacts on learning and teaching?

As it happens, we’ve been talking and thinking about the Visitor/Resident typology (White & Cornu, 2011) that last couple of weeks. The network gods have been kind, because over night a post titled “The resident web and its impact on the academy” (Lanclos & White, 2015) floats across my Twitter stream. Much food for thought.

It has me wondering

Are universities are digital visitors? If so, what impact is this having on learning and teaching?

Update: more reading and thinking has led to the addition of a section “Branding pushing out social traces”.

Residents and visitors

White & Cornu (2011) describe visitors as those that

understand the Web as akin to an untidy garden tool shed. They have defined a goal or task and go into the shed to select an appropriate tool which they use to attain their goal…Visitors are unlikely to have any form of persistent profile online which projects their identity into the digital space

White & Cornu (2011) describe residents as those that

see the Web as a place, perhaps like a park or a building in which there are clusters of friends and colleagues whom they can approach and with whom they can share information about their life and work. A proportion of their lives is actually lived out online where the distinction between online and off–line is increasingly blurred. Residents are happy to go online simply to spend time with others and they are likely to consider that they ‘belong’ to a community which is located in the virtual…To Residents, the Web is a place to express opinions, a place in which relationships can be formed and extended.

How Universities think about digital learning spaces

@damoclarky and I argued that institutional digital learning is informed by the SET mindset. A mindset that approaches any large, complex problem (like digital learning) with a Tree-like approach. That is, it employs logical decomposition to break the large problem up into its smaller and smaller problems until there is a collection of solvable problems that can allocated to individual units. The units now solve the problems (largely) independently, and each of the small solutions are joined back up together and consequently (hopefully) solve the original big problem.

You can see evidence of this tree-like perspective all over our institutions and the digital learning spaces they produce.

The institutions themselves are divided into hierarchical organisational structures.

What the institution teaches is divided up into a hierarchical structure consisting of programs (degrees), majors, courses, semesters, weeks, lectures, and tutorials.

And more relevant to this argument, the institutional, digital learning space is divided up into separate tools.

At my institution those separate tools include, but are not limited to:

  • the staff/student portal;
  • the Learning Management System;
    In the case of my institution that’s Moodle. Moodle (like many of these systems) is structured into a tree-like collection of modules. The “M” in Moodle stands for Modular.
  • the eportfolio system;
  • the learning object repository system;
  • the library system;
  • the gradebook (Peoplesoft); etc….

Each tool is designed to serve a particular goal, to help complete a specific task.

Hence the tendency for people to see these digital learning spaces “as akin to an untidy garden tool shed” where when they want to do something they “go into the shed to select an appropriate tool which they use to attain their goal” (White & Cornu, 2011).

This collection of separate tools is not likely to be seen as a “place, perhaps like a park or a building in which there are clusters of friends and colleagues whom they can approach and with whom they can” (White & Cornu, 2011) learn.

Of course, there is some awareness of this problem, which leads to a solution.

Brand as unifying solution

Increasingly, the one solution that the corporate university seems able to provide for this “untidy garden tool shed” problem is branding. The idea being that if all the tools use the same, approved, corporate brand then all will be ok. It will be seen as an institutional learning space. With the emphasis explicitly on the institution. It is the institution’s brand that is used to cover the learning space, not the learners and not the teachers. With which I see some problems.

First, is the observation made by Lanclos and White (2015) in the context of the resident web and the academy

scholars will gain a form of currency by becoming perceived as “human” (the extent to which ‘humanness’ must be honest self-expression or could be fabricated is an interesting question here) rather than cloaked by the deliberately de-humanised unemotive academic voice.

In this context the problem isn’t so much the “de-humanised unemotive academic voice” as it is the stultifying, stripping of individuality on the altar of the institutional identity. It doesn’t matter whether you’re learning engineering, accounting, teaching or anything else. It’s the institution and how it wishes to project itself that matters.

Which creates the second problem for which one of my institution’s documents around a large institutional digital learning project provides a wonderful exemplar.

Can you have a digital learning experience that is consistent, brand enhancing, and optimal for each student? I tend to think not. Especially in light of arguments that the diversification and massification of the student body has led universities to shift their education rhetoric from a notion of “one size fits all” to a concept of tailored, flexible learning (Lewis, Marginson et al. 2005).

My current experience is that instead of getting digital learning spaces that support tailored and flexible learning, institutions are more likely to create learning spaces that “have less variety in approach than a low-end fast-food restaurant” (Dede, 2008, p. 58).

Brand pushing out social traces

The visitors/residents typology (White and Cornu, 2011) is particularly interested in whether or not people are leaving social traces of themselves online as they interact with digital learning spaces (well, they are actually focused on the participatory web, but I’ll narrow it a bit). Does the “consistent..brand enhancing” approach to institutional digital learning spaces limit the likelihood of social traces being left? Can institutional digital learning spaces be seen as places people will want to reside within when it’s branded?

It would seem obvious that such a branded space couldn’t be seen as “my space”, especially for students. But what about the impact of teachers. Many teachers – for better or worse – like to customise the learning space (not only for the needs of the students) but also to meet project their personality. Can this be done in a branded digital space?

Impact on learning?

The above points to an institutionally provided (and sometimes mandated) digital learning space that is more likely to resemble and consistently branded, untidy garden tool shed. A perception that is unlikely to be perceived by learners and teachers as a space they would wish to inhabit. Instead, it’s more likely to encourage them to see the learning space as place to visit, complete a task, and leave ASAP. Which would appear likely to negatively impact engagement and learning.

It’s would also appear likely to be a perception that is not going to help institutions address a pressure identified by Lanclos and White (2015)

The academy can no longer simply serve its own communities in the context of the networked Web, and it is under increasing cultural pressure to reach out and appear relevant.

References

Dede, C. (2008). Theoretical perspectives influencing the use of information technology in teaching and learning. In J. Voogt & G. Knezek (Eds.), International Handbook of Information Technology in Primary and Secondary Education (pp. 43–62). New York: Springer.

Lewis, T., S. Marginson, et al. (2005). “The network university? Technology, culture and organisational complexity in contemporary higher education.” Higher Education Quarterly 59(1): 56-75.

White, D., & Le Cornu, A. (2011). Visitors and Residents : A new typology for online engagement. First Monday, 16(9). doi:doi:10.5210/fm.v16i9.3171

Self-assertive and integrative tendencies and the connection to the BAD/SET mindsets

I’ve just started reading Capra & Luigi Luisi (2014), in large part because I think that the shift in scientific thinking they apparently describe in the book may have some useful insights for BAD/SET mindsets and trying to understand and improve digital learning.

In the first chapter they propose

two tendencies – the self-assertive and the integrative – are both essential aspects of all living systems. Neither of them is intrinsically good or bad. What is good, or healthy, is a dynamic balance; what is bad, or unhelathy, is imblance – overemphasis on one tendency and neglect of the other. When we look at our modern industrial culture, we see that we have overemphasized the self-assertive and neglected the integrative tendencies

A perspective that echoes the point @damoclarky and I made in conjunction with digital learning and the BAD/SET mindsets

Capra and Luigi Luisi (2014) then present a table that compare and contrast the two tendencies and their “thinking” and “values”. I’ve split it into two separate

“Thinking” comparison of self-assertive and integrative tendencies (adapted from Capra and Luigi Luisi (2014)
Self-assertive Integrative
rational intuitive
analysis synthesis
reductionist holistic
linear nonlinear
“Values” comparison of self-assertive and integrative tendencies (adapted from Capra and Luigi Luisi (2014)
Self-assertive Integrative
expansion conservation
competition cooperation
quantity quality
domination partnership

They go onto suggest that

  • self-assertive values are generally associated with men
  • the self-assertive tendency is most effectively implemented within hierarchy
  • the integrative tendency aims more towards empowering others
  • best achieved within a network, rather than a hierarchy

References

Capra, F., & Luigi Luisi, P. (2014). The Systems View of LIfe: A Unifying Vision. Cambridge, UK: Cambridge University Press.

The perceived uselessness of the Technology Acceptance Model (TAM) for e-learning

Below you will find the slides, abstract, and references for a talk given to folk from the University of South Australia on 1 October, 2015. A later blog post outlines core parts of the argument.

Slides

Abstract

In a newspaper article (Laxon, 2013), Professor Mark Brown described e-learning as

a bit like teenage sex. Everyone says they’re doing it but not many people are and those that are doing it are doing it very poorly.

This is not a new problem with a long litany of publications spread over decades bemoaning the limited adoption of new technology-based pedagogical practices (e-learning). The dominant theoretical model used in research seeking to understand the adoption decisions of both staff and students has been the Technology Acceptance Model (TAM) (Šumak, Heričko, & Pušnik, 2011). TAM views an individual’s intention to adopt a particular digital technology as being most heavily influenced by two factors: perceived usefulness, and perceived ease of use. This presentation will explore and illustrate the perceived uselessness of TAM for understanding and responding to e-learning’s “teenage sex” problem using the BAD/SET mindsets (Jones & Clark, 2014) and experience from four years of teaching large, e-learning “rich” courses. The presentation will also seek to offer initial suggestions and ideas for addressing e-learning’s “teenage sex” problem.

References

Bichsel, J. (2012). Analytics in Higher Education: Benefits, Barriers, Progress and Recommendations. Louisville, CO. Retrieved from http://net.educause.edu/ir/library/pdf/ERS1207/ers1207.pdf

Box, G. E. P. (1979). Robustness in the Strategy of Scientific Model Building. In R. Launer & G. Wilkinson (Eds.), Robustness in Statistics (pp. 201–236). Academic Press. doi:0-12-4381 50-2

Burton-Jones, A., & Hubona, G. (2006). The mediation of external variables in the technology acceptance model. Information & Management, 43(6), 706–717. doi:10.1016/j.im.2006.03.007

Ciborra, C. (1992). From thinking to tinkering: The grassroots of strategic information systems. The Information Society, 8(4), 297–309.

Corrin, L., Kennedy, G., & Mulder, R. (2013). Enhancing learning analytics by understanding the needs of teachers. In Electric Dreams. Proceedings ascilite 2013 (pp. 201–205).

Davis, F. D. (1986). A Technology Acceptance Model for empirically testing new end-user information systems: Theory and results. MIT.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.

Dawson, S., & McWilliam, E. (2008). Investigating the application of IT generated data as an indicator of learning and teaching performance. Canberra: Australian Learning and Teaching Council. Retrieved from http://moourl.com/hpds8

Ferguson, R., Clow, D., Macfadyen, L., Essa, A., Dawson, S., & Alexander, S. (2014). Setting Learning Analytics in Context : Overcoming the Barriers to Large-Scale Adoption. Journal of Learning Analytics, 1(3), 120–144. doi:10.1145/2567574.2567592

Hannafin, M., McCarthy, J., Hannafin, K., & Radtke, P. (2001). Scaffolding performance in EPSSs: Bridging theory and practice. In World Conference on Educational Multimedia, Hypermedia and Telecommunications (pp. 658–663). Retrieved from http://www.editlib.org/INDEX.CFM?fuseaction=Reader.ViewAbstract&paper_id=8792

Holt, D., Palmer, S., Munro, J., Solomonides, I., Gosper, M., Hicks, M., … Hollenbeck, R. (2013). Leading the quality management of online learning environments in Australian higher education. Australasian Journal of Educational Technology, 29(3), 387–402. Retrieved from http://www.ascilite.org.au/ajet/submission/index.php/AJET/article/view/84

Introna, L. (2013). Epilogue: Performativity and the Becoming of Sociomaterial Assemblages. In F.-X. de Vaujany & N. Mitev (Eds.), Materiality and Space: Organizations, Artefacts and Practices (pp. 330–342). Palgrave Macmillan.

Jasperson, S., Carter, P. E., & Zmud, R. W. (2005). A Comprehensive Conceptualization of Post-Adaptive Behaviors Associated with Information Technology Enabled Work Systems. MIS Quarterly, 29(3), 525–557.

Jones, D., & Clark, D. (2014). Breaking BAD to bridge the reality/rhetoric chasm. In B. Hegarty, J. McDonald, & S. Loke (Eds.), Rhetoric and Reality: Critical perspectives on educational technology. Proceedings ascilite Dunedin 2014 (pp. 262–272). Dunedin.

Kay, A. (1984). Computer Software. Scientific American, 251(3), 53–59.

Kunin, V., Goldovsky, L., Darzentas, N., & Ouzounis, C. a. (2005). The net of life: Reconstructing the microbial phylogenetic network. Genome Research, 15(7), 954–959. doi:10.1101/gr.3666505

Laxon, A. (2013, September 14). Exams go online for university students. The New Zealand Herald.

Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The Technology Acceptance Model: Past, Present, and Future. Communications of the AIS, 12. Retrieved from http://aisel.aisnet.org/cais/vol12/iss1/50

Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing Pedagogical Action: Aligning Learning Analytics With Learning Design. American Behavioral Scientist, 57(10), 1439–1459. doi:10.1177/0002764213479367

Müller, M. (2015). Assemblages and Actor-networks: Rethinking Socio-material Power, Politics and Space. Geography Compass, 9(1), 27–41. doi:10.1111/gec3.12192

Najmul Islam, A. K. M. (2014). Sources of satisfaction and dissatisfaction with a learning management system in post-adoption stage: A critical incident technique approach. Computers in Human Behavior, 30, 249–261. doi:10.1016/j.chb.2013.09.010

Nistor, N. (2014). When technology acceptance models won’t work: Non-significant intention-behavior effects. Computers in Human Behavior, pp. 299–300. Elsevier Ltd. doi:10.1016/j.chb.2014.02.052

Stead, D. R. (2005). A review of the one-minute paper. Active Learning in Higher Education, 6(2), 118–131. doi:10.1177/1469787405054237

Sturgess, P., & Nouwens, F. (2004). Evaluation of online learning management systems. Turkish Online Journal of Distance Education, 5(3). Retrieved from http://tojde.anadolu.edu.tr/tojde15/articles/sturgess.htm

Šumak, B., Heričko, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067–2077. doi:10.1016/j.chb.2011.08.005

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. doi:10.1111/j.1540-5915.2008.00192.x

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204.
Venkatesh, V., Morris, M., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

Digital learning: It’s déjà vu all over again

Below you will find resources associated with a talk titled “Digital Learning: It’s deja vu all over again”. The slides below are the near final set to be presented at the #dLRN15 conference (abstract available below).

Due to time constraints a slightly longer version of the slides has replaced.

Abstract

The initial steps of my university teaching career commenced in the early 1990s teaching information technology courses to on-campus and distance students. For distance students the learning experience was largely print-based with little or no student-student or student-teacher interaction. Like many academics at that time the increasing availability of the Internet sparked explorations into a range of digital learning innovations designed to overcome the limitations of existing institutional teaching methods (Jones, 1996a, 1996b).

Twenty years later and three years ago – long after digital learning had become the norm in higher education – my teaching career continued at a new institution and in a new discipline. Now teaching pre-service teachers in a program proudly proclaiming itself as being amongst the only in Australia to be available entirely online. Once again I found myself teaching both on-campus and “distance” students. Further extending the sense of déjà vu the last three years have been spent exploring a range of digital learning innovations designed to overcome many of the same limitations of existing institutional teaching methods. Digital learning, it’s like déjà vu all over again.

Using this experience and the BAD/SET framework (Jones & Clark, 2014) the session will argue that the institutional implementation of learning and teaching – be it distance education or digital learning – is underpinned by the SET mindset. A mindset that places more emphasis on reuse and scale than on contextually appropriate pedagogical value and thus creates this sense of déjà vu. The session will seek to illustrate how the combination of both the BAD and SET mindsets can offer useful insights for both research and practice into how digital learning might be harnessed institutionally to achieve appropriate and practical outcomes.

References

Jones, D. (1996a). Computing by distance education: Problems and solutions. ACM SIGCSE Bulletin, 28(SI), 139–146.

Jones, D. (1996b). Solving Some Problems of University Education: A Case Study. In R. Debreceny & A. Ellis (Eds.), Proceedings of AusWeb’96 (pp. 243–252). Gold Coast, QLD: Southern Cross University Press.

Jones, D., & Clark, D. (2014). Breaking BAD to bridge the reality/rhetoric chasm. In B. Hegarty, J. McDonald, & S. Loke (Eds.), Rhetoric and Reality: Critical perspectives on educational technology. Proceedings ascilite Dunedin 2014 (pp. 262–272). Dunedin. Retrieved from http://ascilite2014.otago.ac.nz/files/fullpapers/221-Jones.pdf