Learning Styles as Planning, and Learning Styles as Situated Action

Continuing my thoughts about how learning styles could affect people’s encounters with machines, I wanted to examine Suchman’s Planning and Situated Action (1987 and 2007) in an educational context. Curriculum as experienced by humans might have similarities that can help inform about interaction as experienced between individual and groups of humans and machines.

Firstly, it appears learning style models have weaknesses. A review of 13 of the most influential learning style models (Coffield et al, 2004) highlights a lack of theoretical rigour, conceptual confusion and poor quality in learning style models, and an over-reliance on categorisation schemes. Attempts to categorise and then design pedagogy around these feels much more like planning, than planning with situated action. It underplays the idea that “lessons are always co-constructed by teacher and students together, through the unfolding actions and interactions” (Lemke, 1985). Coffield et al (2004) do not rule out the existence of learning styles; their primary concerns are with the research field, and use of learning styles to dictate interventions.

Wells (2003) provides an early discussion of “situated enactment of learning and teaching” highlighting the non-deterministic nature of plans. Whilst undertaking recent team-based activities in our MRes Digital Civics modules, each person does not use a single consistent learning style. Instead it is more fluid – an improvisation based on the materials, objectives and most importantly the other participants – just like Suchman’s analysis (1987) of photocopier users when they try to help each other. These interactions vary session-to-session, and group-to-group. Fortunately, our combined group agency is not entirely pre-scripted and predictable. It is situated action influenced by our experiences, conventions, traditions, knowledge, collaborations, and of course the instructor and lesson plan.

There are equivalences in the dynamics of (human-human) teacher-learner interaction with machine-human interaction – from an initial imbalance of understanding/knowledge, agency emerges during interpretation. The photocopier (Suchman, 1987) was trying to teach “users” its plan, and various interfaces, guides and handbooks were simply alternative methods of broadcasting a fixed plan to address different imagined learning styles.

Furthermore, teachers and learners are not the same, and Suchman (1987) proposes that machines and humans are complementary rather than equivalent. The result (knowledge) again is not a fixed pre-determined outcome, but like Suchman’s flexible and moving “boundaries”, instead is something which is relational, situational and changes over time.

The outcome of an interaction cannot simply be pre-planned, but needs to consider the context, and the interactions between all the participating parties at the time. Human learning styles should inform research in Human Computer Interaction (HCI).

By considering the ways people approach and make sense of unfamiliar problems with other humans and technology, we reveal alternative approaches to how interpretation is encountered, working our way towards better solutions. In turn we can embrace some degree of ambiguity so digital technology is permitted to understand and facilitate people’s actions and circumstances, rather than pre-define these encounters. Learning styles are considerations in both planning and situated action.


Jay L. Lemke. 1985. Using Language in the Classroom (Specialised curriculum: language & learning). Deakin University Press, Australia. ISBN 0730003086.

Frank C. Coffield, David V. M. Moseley, Elaine Hall, Kathryn Ecclestone. 2004. Learning Styles and Pedagogy in Post‐16 Learning: Findings of a Systematic and Critical Review. Learning and Skills Research Centre, London.

Lucy Suchman. 1987. Plans and Situated Actions: The Problem of Human–Machine Communication. Cambridge University Press. ISBN 0521337399.

Lucy Suchman. 2007. Human-Machine Reconfigurations – Plans and Situated Actions. 2nd Edition. Cambridge University Press. ISBN: 052167588X.

Gordon Wells. 2003. Lesson Plans and Situated Learning-and-Teaching. Journal of the Learning Sciences, 12:2, 265-272.


Author’s own. School reports.

The Interpretation of HCI

HCI as an encounter

Human Computer Interaction (HCI) turns out to be a Pandora’s Box of cross-disciplinary (Reeves, 2015) viewpoints, ideas, approaches, methods and theories, with a very wide range of potential research topics. The features of ambiguity (Gaver et al, 2003) and flexibility in how people interpret their computer technology encounters (Sengers and Gaver, 2006) have caught my imagination. The suggestion that there should not necessarily be “a single correct way to interpret a computer system” (Gaver et al, 2003) is fascinating to me since it is in contrast with my initial assumptions that there could always be a single or best way.

Interpretation in the display of information

In making available weather data summaries for a local community newsletter a few years ago, I was restricted to textual presentation at that time. I was surprised by how different people reacted to the presentation of numbers versus written prose, even when the contained facts were identical. I had not realised the effect of different learning styles, thinking there was instead a best way. Miller (2001) suggests individual “learning preferences and styles” have a “significant impact on how students learn”.

Interpretation in the use of computer technology

It appears to make sense that individual preferences and styles also have a role in encounters between people and computer technology. How computer technology that includes this idea of flexibility of interpretation is encountered (found, chosen, seen, used and changed by people), might not suit everyone, and this will depend upon the context, their cultural expectations, their experiences, their knowledge and their preferences. Designing for multiple interpretations will require greater effort, and thinking about learning styles might be a way to consider these opportunities in some king of existing framework. The consideration of multiple interpretations will also be of help in designing systems that want to avoid ambiguity, and that could be another area to investigate.

The issue of interpretation has become a more noticeable issue as the machines have moved from the computer room, to the workplace desk, to our homes, to our mobile devices, to our apparel, and onto objects with behaviours (Levillain and Zibetti, 2017) and in the future components of our bodies and minds.

There is a large volume of prior work around learning styles in the educational field such as the use of online and other e-learning systems (and of course about what learning styles computer science students exhibit), but much less about how learning styles of people in the wider world affect their relationship with machines. Some notable exceptions are using cognitive styles as a way of modelling user preferences (Brown et al, 2006) and considering learning style when evaluating web pages (Papaeconomou et al, 2008).

It seems the learning styles of individuals ought to influence their interaction with computer technology, and this is an area I would like to consider further.


Brown, Elizabeth; Brailsford, Tim; Fisher, Tony; Moore, Adam; Ashman, Helen. (2006) Reappraising cognitive styles in adaptive web applications. WWW ’06 Proceedings of the 15th international conference on World Wide Web. Pages 327-335.

Gaver, William W.; Beaver, Jacob; Benford, Steve. (2003) Ambiguity as a resource for design. CHI ’03 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ACM, New York, pages 233-240.

Levillain, Florent; Zibetti, Elisabetta. (2017) Behavioral objects: the rise of the evocative machines. Journal of Human-Robot Interaction archive. Volume 6 Issue 1, May 2017. Pages 4-24.

Miller, Pamela. (2001) Learning Styles: The Multimedia of the Mind. Research Report.

Papaeconomou, Chariste; Zijlema, Annemarie F.; Ingwersen, Peter. (2008) Searchers’ relevance judgments and criteria in evaluating web pages in a learning style perspective. IIiX ’08 Proceedings of the second international symposium on Information interaction in context. ACM, New York, pages 123-132.

Reeves, Stuart. Human-computer interaction as science. (2015) In Proceedings of The Fifth Decennial Aarhus Conference on Critical Alternatives, AA ’15, pages 73-84. Aarhus University Press, August 2015

Sengers, Phoebe; Gaver, Bill. (2006) Staying open to interpretation: engaging multiple meanings in design and evaluation. DIS ’06 Proceedings of the 6th conference on Designing Interactive systems. ACM, New York, pages 99-108.


Author’s own. Exhibit in Farmiloe Building. (2014) Clerkenwell Design Festival. London. Designer unknown.

Edited 23 and 24 Oct 2018 to add photo credit.

Edited 30 Oct to include publication year in text references.