What began with research on the interactions of individuals with computer technology, transformed to the social, and now also society. From the precision of one, to the ambiguity of the many, from a universal machine to a universe of possibilities.

Early computing pioneers were very much aware of computational limits, recognising the pivotal role humans have in the overall system. With an innate human preference for shiny new things, initially research in Computer Science and later Human Computer Interaction (HCI) understandably concentrated efforts on the machine side of these systems, rather than the human and social aspects.

The move to the social, especially Suchman’s work on Plans and Situated Actions saw a shift in emphasis, with greater influence given to the human side and context of the interaction. The seeds of co-production appear in Suchman’s work, contributing to our current research, as well as the turn to the civic where it is recognised citizens can collectively contribute to social justice, including how local services are provided.

The interdisciplinarity of HCI is a natural fit with the multi-complexity of society – neither is a homogenous mix. HCI research has benefitted from looking to and connecting with the alternative, the niche, the outliers. Socio-technical research cannot be defined with precision; we cannot assume what problems need to be solved, nor what the solutions will be.

Future research should keep open as many avenues as possible, consider what we may have overlooked in the past, and seek outliers and oddities. Our research should not attempt to follow well-worn paths nor focus on the shiny new. It is suggested that examining the mundane, the ordinary and the simple may provide worthwhile insights – big things have small beginnings.

Photographs: Author’s own taken at The National Museum of Computing, Bletchley Park. The electro-mechanical (left to right): reconstructed Turing-Welchman Bombe machine wiring; Tony Sale with his rebuilt Mk 2 Colossus; reconstructed Turing-Welchman Bombe machine drums.

Multiplicity of Perspectives

The foundations of HCI are not that much younger than the foundations of Computer Science itself. The interdisciplinarity of HCI mirrors the story of people central to computing’s journey, many of whom were unconventional, alternative, outsiders and somewhat controversial within the societal norms they lived in. This acceptance of the unusual and different perspectives has helped drive innovation in HCI, and in turn this can be used to foster innovation in society.

My own first encounters with computers were learning to program at school aged 13. This incorporated using a sharp pencil to write BASIC on coding sheets, which were sent to the University of Edinburgh for keying onto punch cards and submission to “the computer”. The printed output a week later was disappointingly often “Syntax error at line 10”.

From the earlier times of remote interactions, HCI has been transformed. The HCI field has broken out of “disciplinary boundaries” (Shneiderman, 2011) to become an interdisciplinary area with a dynamic and fluid mix of researchers, with backgrounds in computer science, information studies, psychology, sociology, political science and art. It is very like design which Manzini (2015a) says is a “third way of doing things between hard science and human science”. Carroll (2008-2018) writes HCI “is the intersection of the cultural, the social, the cognitive, and the aesthetic with computing and information technology”.

So how did HCI get to where we are now?

In the Beginning

We have come a long way. Charles Babbage’s interaction with computational machines was very much physical, with the construction of the analogue machines using mechanical parts. His colleague Ada Lovelace published theories (Lovelace, 1843) which are now recognised as the first computer algorithms. Lovelace’s insights may not have been possible without a broad interdisciplinary background. She was a mathematician, an engineer, a writer, an illustrator, and… a computer scientist. Lovelace’s contributions were largely overlooked until more recently, but opinion is that she had greater insight than Babbage and “went beyond number-crunching to see possibilities for wider applications” (Füegi and Francis, 2015).

Babbage might be known as the “father of computing”, but it is Alan Turing who is recognised as the “father of the modern computer” (Hodges, 1983). Wartime efforts spurred on developments in computing. Turing had little concern for status or class and privilege structures (Hodges, 1983). With a wide-ranging background in in science, maths and philosophy, his research work’s considerable legacy included the concept of a universal machine, and also the absolute limits of the computable.

In those initial years of computing, the interface between humans and computers soon started to change from electronic rewiring to printed or punched paper, but interaction was still limited to programming, operating the hardware, and utilising the output (Grudin, 2012).

Christopher Strachey, another outsider at the periphery of traditional society, was invited to the University of Manchester by his friend Turing. True multimedia output may first have occurred in 1951 when Strachey programmed a draughts game which played the national anthem on termination. Recently restored recordings are the earliest known computer-generated music. Strachey was the University of Oxford’s first Professor of Computer Science. This is where I later studied a computer science master’s degree conversion course, in the early days of the public internet.

In 2016 Hodges invited me, as a friend, to his Strachey Lecture – The Once and Future Turing. I was intrigued to see the inclusion of a photo from the Turing Archive of a computer-printed personal message, annotated and sent by Turing to his associate and close friend Robin Gandy, demonstrating early “consumer computing”. During the same lecture, Hodges explained in his discussion of the decision problem (Turing, 1936), that Turing “invented the mentality of computer programming” from the rigour of mathematical foundations, and that he was not thinking just about the mind of the programmer, but analysing “the action of the human mind and what it is a person is doing when they are computing”.

Early formative steps in HCI came from the fields of research into human engineering, human factors and ergonomics, where the emphasis was on efficiency of action. What is seen as one of the first papers in the HCI field, but well before the term “HCI” was introduced, Shackel (1959) describes how ergonomic design principles can be used to reduce mistakes when “[re-programming] from one problem to solve another”.

With the development of vacuum-tube displays, screens provided greater interaction flexibility, and the humans interacting with the computers expanded beyond individual mathematicians and technicians. As we shall see later, this expansion of the “user base” was, and continues to be one of the main driving factors in the changing scope of HCI research.

From One to Many

Even in the early years, visionaries like Nelson (1960) foresaw the vast inter-connectedness we have today and Engelbart (1962) contemplated human augmentation by machines. In 1965 it was Nelson who introduced the term hypertext, and twenty years later I became a member of the Edinburgh Home Computer Club and first went online, joining Micronet 800 and other services on BT’s Prestel videotext service. It was almost another ten years when in 1994 I created my first web home page, something which afterwards turned into a commercial career.

What began primarily as research into user interaction design, HCI has changed, morphed, and reinvented itself many times. Invigorated by its interdisciplinarity, the field comprises an ever-shifting cornucopia of viewpoints and visions. From a human factors/engineering approach, this shifted to a cognitive approach drawn from psychology, then to alternative cognitive methods, more social approaches including situated action, ethnomethodology and ethnography, and then drawing theory from a wider range of fields, including activity theory and grounded theory (Rogers, 2012, and Harrison et al, 2007).

These viewpoints and visions have also reflected the changing role of the “user”. Initially firmly rooted in the consideration of human factors and ergonomics from an operator’s point of view, this changed as the outputs of the machines were driven by economic needs to management information systems, where the understanding of typically printed output became a more prevalent driver. Cognitive and design approaches developed, drawing on experience from other fields, to inform how software is developed (Carroll, 2003) – the user as a programmer.

Driven by the vast expansion of computing in the 1980s into workplaces, the home and the mass consumerisation in the 1990s and 2000s, there was an increase in ideas drawn from social sciences, with less emphasis on traditional cognitive approaches. This change to the scale of “user” affected research perspectives. HCI had originally focused on individual interactions, but this has expanded to include groups of people, and more recently to vast numbers of people (through ideas like ubiquitous computing, social computing and the growth of the data society). In a constructivist approach both the past (knowledge) and current situation impinge on our response/interactions and societal-scale groupings can multiply this effect.

Consider one theory from the late 1980s which examined how the context of the interaction between humans and technologies leads to intelligent action. Suchman (2006) wrote, in a discussion of critiques, that the term user “singularizes what is actually a multiplicity and fails to differentiate actors with very different relations to a given artifact”.

The Social with Plans and Situated Actions

Suchman (1985, 2006) touched on a topic close to Turing’s previously mentioned consideration of “what a person is doing when they are computing” (1936) which resonates in Turing’s later work on intelligent machines (1948).

Suchman studied photocopier users as they tried to assist each other. In Plans and Situated Actions (P&SA), she challenged the genius role of plans in human-computer systems highlighting that the “attribution of knowledge and agency” should not assume the discrete nature of machines and humans with some sort of pre-planned boundary. Rather the boundary between humans and machines materialises during the encounter, and will vary based on the participants, and the setting. She described how we can learn from interactions during encounters, and how the boundaries between humans and machines systems can change over time and not be distinct. This ambiguity of interpretation is an avenue for exploration, not avoidance.

When we consider more than one-to-one encounters, the one-to-many and many-to-many, the framing needs a wider viewpoint (Suchman, 2006) that places less emphasis on individual transactions (regardless whether by machines or by humans) because agency resides “neither in us nor in our artifacts but in our intra-actions”. These interactions vary session-to-session, and group-to-group. The 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 plan.

From Contextual to Societal Co-Production

The move from HCI in the laboratory, to fieldwork, and now to the civic (Boehner and DiSalvo, 2016, and Vlachokyriakos at al, 2016) embedded in communities (Wright and Olivier, 2017), shifts the research role from being remote expert to working with and for individuals and communities. It is a step beyond the change form “user” to “social” approaches. It is a move to a broader societal approach, because the issues relevant to the people involved are about their lives – their needs, their concerns and their aspirations. These are inherently linked with service provision by the state, by markets, and by the third sector.

The discrete devices dreamt of in ubiquitous computing and the multi-located data everywhere fail to envision this inter-relational creation of agency, nor the ever-changing boundaries of a multitude of people with a multitude of machines, nor their potential multiple interpretations of their computer technology encounters (Sengers and Gaver, 2006). Like the various perspectives on personal data, different groups and individuals and society itself perceive different imprecise and shifting boundaries from an initial imbalance of understanding/knowledge as “agency emerges through interpretation” (Suchman, 2006) where the result is not pre-determined.

As researchers immersing ourselves, we become part of this fluid interconnected system, where problems and issues move into and out of focus at the same time as network connections and dependencies change, and where opportunities to use digital technologies may arise. The capacity for action is “relational, dynamic and collective” (Aanestad, 2003).

We need neither privileged experts nor privileged machines. The interdisciplinarity of HCI fits well with a multifaceted human society. Without defined privilege there is greater scope for inclusion of a wide range of beliefs, theories and ideas. Using co-production techniques, the plan and the action can emerge from a more ambiguous starting point.

Suchman proposed that machines and humans are complementary rather than equivalent. The outcome is not a fixed pre-determined outcome, but like Suchman’s flexible and moving “boundaries”, instead there 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. Similarly in our research with people (Wright and Olivier, 2017), the “problem” and the “solution” cannot be pre-planned. We do not have the solutions defined, nor the problems defined.

Co-production in encounters, and in design of systems, provides greater agency to individuals. Rather than being part of someone else’s opaque system, people can contribute to the creation their own systems where the social justice related properties of fairness, transparency, openness are also considered in the wider picture. This requires investment in local communities “building trust and, critically, distributing power”.

Full Spectrum HCI

Working as an applied engineer of sorts for most of my working life, much of my progression has been through gathering and applying knowledge as facts, in essence knowledge that can be learnt and therefore tested (Sfard, 1998). Engineering, with its emphasis on the “genius” role, likes to build bodies of knowledge around generalised methods. In contrast, in HCI we need to allow for, even promote ambiguity, a topic of surprise to me when it appeared early in our HCI for Digital Civics course. Solutions can arise from the participants, with their ideas, thoughts, skills and digital machines.

Various HCI approaches, visions and viewpoints provide alternative insights into issues. In a similar way, Digital Civics is an unconventional part of the HCI family – somewhat alternative and willing to challenge norms. It is not important exactly what Digital Civics is, but much more how it does it and the societal outcomes. Pushing the viewpoint, scope and framing wider to society allows us to consider socio-technical systems that go well beyond the absolute limits of machine computability identified by Turing (1936).

Returning to the earliest person in this story, Lovelace, the “first programmer” published her work during first-wave feminism (Bardzell, 2010). Suchman also suggested there is possibly much to learn from alternative theories such as found in art, and in feminist approaches (Rode, 2011), including the role of gender, equity, diversity, social justice, care and art.

Some characters in the historical references here have been towards the edges of society – outliers, their lives and motivations somewhat ambiguous. Society is comprised of individuals, and they are not uniform. There is great variety. It is comprised of unique individuals. With uniqueness comes a wide spectrum of ideas, thoughts, contributions. We should be inspired to look at the fringes, the specialist, the alternative niches in HCI and wider academia, to find novel views and ideas which might fit with this distributed local service provision.

Distributed Co-Produced Socio-Technical Systems

Suchman stated the important question is how to configure assemblages where humans and machines intra-act “responsibly and generatively” through acts of engagement. There is ambiguity here too (Gaver et al. 2003) in “the problem” and “the solution” which require people to participate in shared meaning making (Mikalsen et al, 2018). This is not ambiguity in the sense of speculative design (Wong and Khovanskaya, 2018) but ambiguity in the need, the process, the techniques, and the outcomes.

While it is attractive to undertake future research on the shiny new, there are problems of interest in what might seem the mundane and the ordinary. Innovation is not just invention – it can also be found in maintenance and care. People’s everyday lives. As Manzini said (2015a) we should be enabling people rather than services. These are not quite either “issue-based” or “ideologically based” publics (Le Dantec, 2016), but rather publics based on a widespread commonality. Possibly fundamentals such as accommodation, food, clothing and sanitation, but ones which cross social divides due to common needs. Not everyone is a parent; not everyone needs care; not everyone is unemployed.

So-called simple problems should be a target area of research interest, rather than the more glamourous complex problems. Research into these sometimes ignored or forgotten issues may identify commonalities that cross social, political and cultural boundaries (distributed issues) which could be fertile ground for social innovation (distributed solutions). They might reveal interesting hidden entanglements which are old or new, but just not shiny.

Distributed implies a wide network of individuals augmented by digital systems, forming a cohesive socio-technical public. The agency is diffusely spread throughout, built upon their intra-actions. It is not a uniform consistent mesh, but rather a flexible network of humans and technology, which has in-built resilience, varying granularities and structures, dynamic interconnections, and which can “handle change and stability simultaneously” (Duit et al, 2010).

This is not big HCI (Rogers, 2012) – instead it is about embracing the radical, and co-designing social cohesion through a distributed approach. Like the boundaries between humans and computers, the boundaries between researchers and “participants” need to be situational, contextual and allow for changes over time. People are also part of infrastructure. As a starting point we may want to consider how local council services might be provided through a distributed, rather than centralised approach, as well as being relational rather than transactional. Such work could “move from small-scale experimentation to solutions that are available to all” (Cottam, 2018).

Conclusion

Early theoretical understandings of the absolute limits of computability and the relevance of the human participant in socio-technical systems have come to the fore again in situated social perspectives. Keeping an open mind, allowing for a wide range of contributions from a diverse group of participants – researchers and non-researchers – simply reflects society. Interdisciplinarity is a benefit in HCI, so the multi-complexity of society should also be embraced in Digital Civics. We should consider the non-shiny and the simple, and what we might have missed in the rearview mirror, because the easily ignored may mean it has much wider applicability.

Future research should identify publics of commonality, the matters of everyday life we often take for granted, and empower those to undertake change that redistributes and develops a citizen economy. These may uncover solutions that are more sustainable and replicable. Issues that might seem to be simple could in fact shed light on social innovation for seemingly more wicked problems (Wright and Olivier, 2017).

Manzini’s emphasis on what design is, and who can do it, (2015b) is central to our thinking about social innovation, and building a sustainable and resilient culture. It was fitting that Shackel’s paper, considered to be the first in the field of HCI, was published in the journal called “Design”.

Photographs: Author’s own. The socio-technical (left to right): pedestrians on a street in Oxford, United Kingdom; view of Bogotá, Colombia; people shopping at a market in Barjac, France.

References

Margunn Aanestad. 2003. The camera as an actor: Design-in-use of telemedicine infrastructure in surgery. Computer Supported Cooperative Work.
https://doi.org/10.1023/A:1022492210898

Shaowen Bardzell. 2010. Feminist HCI : Taking Stock and Outlining an Agenda for Design. In SIGCHI Conference on Human Factors in Computing Systems (CHI’10).
https://doi.org/10.1145/1753326.1753521

Kirsten Boehner, Carl DiSalvo. 2016. Data, Design and Civics: An Exploratory Study of Civic Tech. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems.
https://doi.org/10.1145/2858036.2858326

John M Carroll. 2008-2018. Series Editor. Introduction: Synthesis Lectures on Human-Centered Informatics. Morgan & Claypool.
https://www.morganclaypool.com/toc/hci/1/1

John M Carroll. 2003. Introduction: Toward a multidisciplinary science of human-computer interaction. In Carroll, J M, Ed., HCI Models, Theories and Frameworks. Morgan Kaufmann, San Francisco, CA.

Hilary Cottam. 2018. Radical Help: How we can remake the relationships between us and revolutionise the welfare state. Little, Brown Book Group.

Christopher A Le Dantec. 2016. Designing Publics (Design Thinking, Design Theory). The MIT Press.

Andreas Duit, Victor Galaz, Katarina Eckerberg, Jonas Ebbesson. 2010. Governance, complexity, and resilience. Global Environmental Change.
https://doi.org/10.1016/j.gloenvcha.2010.04.006

Douglas C Engelbart. 1962. Augmenting Human Intellect: A Conceptual Framework.
https://web.stanford.edu/dept/SUL/library/extra4/sloan/mousesite/EngelbartPapers/B5_F18_ConceptFrameworkInd.html

John Fuegi, Jo Francis. 2015. Lovelace & Babbage and the creation of the 1843 ‘Notes’. Ada User Journal.
https://doi.org/10.1109/MAHC.2003.1253887

William W Gaver, Jacob Beaver, Steve Benford. 2003. Ambiguity as a resource for design. In Proceedings of the conference on Human factors in computing systems – CHI ’03.
https://doi.org/10.1145/642651.642653

Jonathan Grudin. 2012. A moving target: The evolution of human-computer interaction. The Human-Computer Interaction Handbook–Fundamentals, Evolving Technologies, and Emerging Application.
https://www.microsoft.com/en-us/research/wp-content/uploads/2017/01/HCIhandbook3rd.pdf

Steve Harrison, Deborah Tatar, and Phoebe Sengers. 2007. The three paradigms of HCI. In the Conference on Human Factors in Computing Systems (CHI2007).
https://www.researchgate.net/publication/215835951_The_three_paradigms_of_HCI

Andrew Hodges. 1983. Alan Turing: The Enigma. Burnett Books with Hutchinson.

Ada Lovelace. 1843. Translator’s notes to Sketch of The Analytical Engine Invented by Charles Babbage by L. F. MENABREA of Turin, Officer of the Military Engineers from the Bibliothèque Universelle de Genève, October, 1842, No. 82 With notes upon the Memoir by the Translator Ada Augusta, Countess of Lovelace. Retrieved 07 Dec 2018.
http://www.fourmilab.ch/babbage/sketch.html

Ezio Manzini. 2015a. Design, When Everybody Designs. Medea Talk, 10 Sep 2015, YouTube retrieved 18 Nov 2018.
https:// www.youtube.com/watch?v=Ap-vE53CdFI

Ezio Manzini. 2015b. Design, When Everybody Designs: An Introduction to Design for Social Innovation. Translated by Rachel Coad.

Marius Mikalsen, Babak A Farshchian, Yngve Dahl. 2018. Infrastructuring as Ambiguous Repair: A Case Study of a Surveillance Infrastructure Project. Computer Supported Cooperative Work: CSCW: An International Journal.
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Theodor H Nelson. 1960. Vision of Interconnectedness.

Antti Oulasvirta, Kasper Hornbæk. 2016. HCI Research as Problem-Solving. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems – CHI ’16.
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Jennifer A Rode. 2011. A theoretical agenda for feminist HCI. Interacting with Computers.
https://doi.org/10.1016/j.intcom.2011.04.005

Yvonne Rogers. 2012. HCI Theory: Classical, Modern, and Contemporary. Synthesis Lectures on Human-Centered Informatics.
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Phoebe Sengers, Bill Gaver. 2006. Staying Open to Interpretation: Engaging Multiple Meanings in Design and Evaluation. In In Proc.of DIS’06.
https://doi.org/10.1145/1142405.1142422

A Sfard. 1998. On Two Metaphors for Learning and the Dangers of Choosing Just One. Educational Researcher.
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Brian Shackel. 1959. Ergonomics for a computer. Design 120: 36-39.

Ben Shneiderman. 2011. Claiming success, charting the future: micro-HCI and macro-HCI. Interactions.
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Lucy A Suchman. 1985. Plans and Situated Actions: The Problem of Human Machine Communication.

Lucy Suchman. 2006. Human-machine reconfigurations: Plans and situated actions, 2nd edition.
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Alan M Turing. 1936. On Computable Numbers, with an Application to the Entscheidungsproblem. Mathematica.
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Alan M Turing. 1948. Intelligent Machinery.
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Vasillis Vlachokyriakos, Clara Crivellaro, Christopher A Le Dantec, Eric Gordon, Pete Wright, Patrick Olivier. 2016. Digital Civics: Citizen Empowerment With and Through Technology. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems – CHI EA ’16.
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Richmond Y Wong, Vera Khovanskaya. 2018. Speculative Design in HCI: From Corporate Imaginations to Critical Orientations Richmond. In New Directions in Third Wave Human-Computer Interaction: Volume 2 – Methodologies.

Peter Wright, Patrick Olivier. 2017. Digital Civics and the Cities Challenge. Open Lab, Newcastle University.

Header photograph

Author’s own. Close-up from “Turing IV – Machinery”, Turing Suite, Eduardo Paolozzi , 2000, colour silkscreen print, hors commerce originally given to Hodges by Paolozzi. Incorporating excerpts from Hodges 1983 and Turing 1948.

Quotations

Verbatim from each source cited.

The language used around HCI research into digital health and wellbeing monitors, trackers and coaches reinforces the idea of information ownership by the data subjects themselves. We read about personal informatics, quantified self, life logging, self-tracking, and personal enhancement.

These labels suggest an intimacy and a sense of possession. Are we to believe personal informatics are exemplars of Suchman’s complementary machines and humans, where the outcome is relational, situational and can change over time? People’s capacity to act is reconfigured as they interact, but is their agency being thwarted by lack of awareness?

Neither an individual view, nor a peer-to-peer view, seem to capture the richness of relationships, or the variety of motivations, or the range of use. As individuals interact with their technology and social clusters, and as they experience, curate and share their lived data, we need a broader perspective (Kuutti, 1996) which examines how other parties in the data universe interact. The situation boundaries are wider than the person and their apps.

Personal informatics extends beyond the individuals themselves and their own social clusters. The data have value as assets to the organisation providing the services; it also has value to other organisations who might want to use the data for legitimate or improper purposes, and finally it has societal value as interpreted by local and national government, regulators and other bodies (Watson and Leach, 2010).

Individuals have little knowledge of the ways their data might be used, and how or when this might affect them negatively, as it flows through these different parties. It is not so much an ambiguity in explanation, but a complete hopelessness in understanding and control, despite changing legislation and regulation or the existence of privacy notices. The situation they believe they are in is far removed from reality – the plans exist and the individuals are not in control.

If citizens cannot achieve an understanding and insight into what is happening in personal informatics and the trade-offs being made (Pirolli and Russell, 2011), what hope is there that they can take intelligent action? Personal informatics is a long way from individual sensemaking, and currently more akin to data collection sensors for exploitation by other parties – individuals as instrumentation.

Fortunately, the individual-centric research view is changing, with research into the social motivations of these technologies such as understanding the social contexts and practices. Our models, methods, techniques need to perceive the wider picture to understand what is happening and what the side-effects are, at both individual and societal levels. In the meantime, change the language from Personal Informatics – it is Exposure Informatics.

References

Chris Elsden, David S. Kirk, Abigail C. Durrant. 2016. A Quantified Past: Toward Design for Remembering With Personal Informatics. Human-Computer Interaction.
https://doi.org/10.1080/07370024.2015.1093422

Kari Kuutti. 1996. Activity Theory as a Potential Framework for Human-Computer Interaction Research. Context and Consciousness: Activity Theory and Human Computer Interaction, MIT, Massachusetts, USA.

Peter Pirolli, Daniel M Russell. 2011. Introduction to this Special Issue on Sensemaking. Human–Computer Interaction 26, 1–2: 1–8.
https://doi.org/10.1080/07370024.2011.556557

Colin Watson, John Leach. 2010. The Privacy Dividend : the business case for investing in proactive privacy protection. UK Information Commissioner’s Office.
https://ico.org.uk/media/1042345/privacy-dividend.pdf

Photo

Author’s own. Cyclists participating in Sky Ride London 2010.

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.

References

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.
http://www.leerbeleving.nl/wp-content/uploads/2011/09/learning-styles.pdf

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.
https://doi.org/10.1207/S15327809JLS1202_6

Photograph

Author’s own. School reports.

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.

References

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.
https://doi.org/10.1145/1135777.1135827

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.
https://doi.org/10.1145/642611.642653

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.
https://doi.org/10.5898/JHRI.6.1.Levillain

Miller, Pamela. (2001) Learning Styles: The Multimedia of the Mind. Research Report.
https://eric.ed.gov/?id=ED451140

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.
https://doi.org/10.1145/1414694.1414722

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
https://doi.org/10.7146/aahcc.v1i1.21296

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.
https://doi.org/10.1145/1142405.1142422

Photograph

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.

For nearly a decade primary school children in the North East have learned about fitness and nutrition through Newcastle United Foundation’s Match Fit programme. Now, a digital civics project aims to enhance this six-week programme by using digital technologies to further increase the fitness and health awareness of primary school children.

Students taking part in Match Fit learn about nutrition and exercise, as well as taking part in extensive physical activity, all inspired by the fitness regime of Newcastle United’s footballers.

ThinkActive.io takes this project one stage further by using sensors to measure the movement of the children to see just how active they are. Inexpensive fitness trackers report on step counts over the course of the programme to evidence behaviour change.

The goal of ThinkActive.io is to is to engage students with their own activity and nutritional data to enhance data literacy and help them learn about how health and fitness can be supported by technology. The data collected through the programme can also be used as an engaging educational resource by teachers; hopefully a more critical understanding of data provides students with the skills to be engaged digital citizens.

For more information please contact Andy Garbett.

Bootlegger bridges the gap between professional filmmakers and people with no prior filming experience wishing to record video on their mobile phone. Improvements to camera technology have made mobile phones a viable tool for filming, but Bootlegger makes larger and more complex projects much more accessible for citizen filmmakers.

Crucially, Bootlegger coordinates different people contributing to a single project, with all of their footage uploaded to an editable archive. This makes it easy to capture multiple views of a place or topic, different stages of an event, or synchronised shots from people distributed around the world. The crew for each project can be recruited through the Bootlegger app and contribute to the design of the film shoot. The app also guides the crew members through the filming process itself, providing templates for close-ups, wide shots and many other scenarios. The shoot can even be planned beforehand, with different roles allocated and different templates set up, and Bootlegger provides tools for editing the video afterwards as well.

Bootlegger has been used in multiple digital civics research areas, ranging from education to neighbourhood planning and consultation projects. It fits within the broader digital civics theme of using technology to connect people with common needs and interests to work together on joint projects.

For more information please contact Tom Bartindale.

Professor, researcher, author. And organiser of the Tiree Tech Wave, bringing technological experimentation to a remote Scottish island. Alan Dix is among the most influential figures within human-computer interaction, and his career is as varied as it is distinguished. He is an author of one of the key HCI textbooks and has extensive experience of teaching, currently at the University of Birmingham.

Alan has recently hosted the thirteenth Tiree Tech Wave, which he describes as “an opportunity to work, talk and make with others without any set criteria or objectives.” Technologists, artists, designers, activists and philosophers came together on the Inner Hebridean island to experiment.

“Of course,” Alan continued, “precisely because there are no objectives, exciting things happen – both practical and theoretical, including numerous collaborations, projects and publications.” A best paper award at CHI 2015 was given to a paper that arose from a Tiree project.

Alan was inspired to initiate the tech waves after moving to Tiree and discovering “that there was something about the vast open horizon that opened up the mind. After all, the wild Celtic fringe is where scholarship was kept alive through the Dark Ages.”

If the remoteness of Tiree helps to free developers from the distractions of their everyday lives, it also offers inspiration of a more direct kind. Alan explained: “If the islands – and indeed other remote areas – are to survive and be living communities, then digital technology will play a significant part.”

Alan is interested in “technology at the edge” and believes the tech waves help to bring the latest ideas to the Tiree community in a mutually-beneficial exchange. In 2013 he spent three months walking over a thousand miles across Wales and, amongst other things, investigated issues of broadband connectivity and mobile coverage. He was shocked by what he found.

“Some countries have embraced digital access as a core infrastructure for modern society, but in the UK, despite eGovernment and digital commerce making the internet an essential part of citizenship, in practice connectivity is simply a matter of economics,” he said. “In particular, all along the coast there is ample mobile coverage outwards to sea, where wealthy yachts-folk sail, but little or none on the land where poorer rural communities strive.”

The intervening four years has seen numerous initiatives to improve rural connectivity, but Alan is still dismayed at the technological isolation of many rural areas. He believes change will only come with “a drastic change of heart” from the government, but argues that software designers could also do more to cater to areas with low connectivity.­­­

Alan’s recent interest in the role of technology in rural areas is by no means the extent of his research, however. Over the past 30 years Alan has worked in “pretty much every aspect of human-computer interaction”, and currently works for Talis, software developers for higher education. Alan specialises in learning analytics in the reuse of materials from MOOCs, massive open online courses, for flipped class learning.

He says that this role “often overlaps” with his teaching, which recently has centred more around technology in rural settings and “how open data can be consumed and, perhaps more importantly, produced by small communities to inform and empower them.”

This variety within HCI is mirrored by his moves between different disciplines altogether. “I actually ended up in HCI almost by accident,” Alan explained. “I was originally a mathematician, and was part of the British Team to the XX International Mathematical Olympiad. After a period working in agricultural engineering research and then Cobol programming, I returned to academia to work on an Alvey project on formal methods in interactive systems… and the rest is history!” Today, Alan combines elements of his mathematical and statistical background with his HCI teaching.

850,000 people in the UK are currently living with dementia, but new technologies can offer ways to help them and their families. With Create4Dementia, an online competition delivered by digital civics researchers, these technologies could be designed by the local community.

As well as proposing ideas for technologies to help enrich the lives of people with dementia, members of the local community will be able to vote on and discuss each other’s ideas and shape each stage of the design process. The most popular ideas will go forward to a judging panel, which will include experts from Dementia Care and Sunderland Software City, partners in the project.

Even the development of the technologies themselves will be open to the public. Software developers will submit bids to make the winning design a reality, which the community will be able to scrutinise. Ultimately the process will lead to a new technology for people with dementia, their carers and families, designed by people with experience of dementia, whether in the personal or professional lives.

“It’s exciting to be a part of a process which aims to give the designing power explicitly back to the people who will benefit from the technology at hand,” said Shaun Lawson, Professor of Social Computing at Northumbria University.

Kellie Morrissey, another member of the LaunchSpot team, added: “People with dementia are often underestimated – they’re often still able to contribute in many meaningful ways to their families and to their communities. However, with quite limited treatment available at the moment, it’s really important that we pay attention to the sensitive design of new technologies to help people with dementia live happier, more connected lives for longer.”

Create4Dementia is the first in a series of competitions to design technologies for mental health, all run through the LaunchSpot platform developed by Ed Jenkins at Open Lab. This allows for community participation in every stage of the development process.

Shaun continued: “Create4Dementia by LaunchSpot is the first foray into doing this on a wider scale than our usual academic workshops, and of course with the potential for real life impact at the end of the process.”

For more information please contact Kellie Morrissey.

Dmitry’s research is into human attitudes towards robots. He discusses the reactions of people to three different types of robots: abstract, zoomorphic and humanoid, and explores how issues such as privacy and security could influence our choices about letting robots into our lives.