This document summarizes a study on designing energy interventions for student accommodation. Researchers recruited 100 student practitioners who conducted focus groups with 300 participants. Thematic analysis of the data identified 5 categories: student experience, energy consumption, barriers to saving, behavioral solutions, and design suggestions. Quotes within each category provided insights. For example, barriers included not knowing normal consumption levels and not paying utility bills. The broad range of experiences uncovered design implications and requirements for pilot energy interventions.

1. “Experience Centered
Design of Energy
Interventions for Shared
Student
Accommodation”
Conor Linehan, Derek Foster, Shaun
Lawson
Maureen Schoonheyt, Katrin Heintze
Email: defoster@Lincoln.ac.uk
@derekfoster

2. “One participant told me
that instead of getting his
window fixed he just turned
his heating on more often”
“participant one has
no interest in the
consequences of
their actions”
“I save energy to save
money, not the planet”
“meh” ?

3. Sustainable?

4. Overview
• Context
• Background
• Study Approach
• Method
• Results

5. University of Lincoln
• UK University in Lincoln, England
• 1037 students across 17 ‘official’ accommodation
blocks
1,734,020 kg
6,007,972 kWh
1,202,693 kWh
£300,508 p.a

6. Why?
• Promote more sustainable energy-use practices in
official student accommodation
• Lower UoL CO2 footprint and support UoL strategic
plan for improved sustainability engagement
• Embed sustainability in the curriculum at UoL
• Contribute to corpus of HCI sustainability literature

7. Background
• HCI sustainability research suggests people have a
poor understanding of their consumption habits*
• Evolution of energy monitors has rebooted digital
monitoring for the home focussing on feedback
• Environmental psychology studies indicate that
feedback can motivate reductions
• So HCI + Psychology == behaviour change
interventions for sustainable practices?
• Sounds great in principle, but what about the
practical application of such interventions?
• Bates, O., Clear, A. K., Friday, A., Hazas, M., & Morley, J. Accounting for energy-reliant services within everyday life at home. In Proc Pervasive Computing
(2012), 107-124.
• Darby, S. (2006). The effectiveness of feedback on energy consumption. A Review for DEFRA,486, 2006.
• Toth, N., Little, L., Read, J. C., Fitton, D., & Horton, M. (2012). Understanding teen attitudes towards energy consumption. Journal of Environmental

8. Anton Gustafsson and Magnus Gyllenswärd. 2005. The power-aware cord: energy awareness through ambient information display. In CHI '05
extended abstracts on Human factors in computing systems (CHI EA '05). ACM, New York, NY, USA, 1423-1426.
Petkov, Petromil, Köbler, Felix, Foth, Marcus, & Krcmar, Helmut (2011) Motivating domestic energy conservation through comparative,
community-based feedback in mobile and social media. In Proceedings of the 5th International Conference on Communities & Technologies
(C&T 2011), ACM, Brisbane, pp. 21-30.
Background
• Mobile and ambient monitoring systems

9. • Domestic energy studies delivering socially-mediated live
energy feedback on social platforms*
Background
Derek Foster, Shaun Lawson, Mark Blythe, and Paul Cairns. 2010. Wattsup?: motivating reductions in domestic energy consumption using
social networks. In Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries (NordiCHI '10). ACM,
New York, NY, USA, 178-187.
Derek Foster, Conor Linehan, Shaun Lawson, and Ben Kirman. 2011. Power ballads: deploying aversive energy feedback in social media. In

10. Background
Injunctive
Norm
(Is your consumption
good or bad?)
Descriptive
Norm
(How much energy are you
using compared to others?)

11. Jon Froehlich, Leah Findlater, Marilyn Ostergren, Solai Ramanathan, Josh Peterson, Inness Wragg, Eric Larson, Fabia Fu, Mazhengmin Bai, Shwetak
Patel, and James A. Landay. 2012. The design and evaluation of prototype eco-feedback displays for fixture-level water usage data. In Proceedings of
the SIGCHI Conference on Human Factors in Computing Systems (CHI '12). ACM, New York, NY, USA, 2367-2376.
Matthias Laschke, Marc Hassenzahl, Sarah Diefenbach, and Marius Tippkämper. 2011. With a little help from a friend: a shower calendar to save
water. In CHI '11 Extended Abstracts on Human Factors in Computing Systems (CHI EA '11). ACM, New York, NY, USA, 633-646.
Background
• Domestic water monitoring visualisation

12. Eric B. Hekler, Predrag Klasnja, Jon E. Froehlich, and Matthew P. Buman. 2013. Mind the theoretical gap: interpreting, using, and developing behavioral
theory in HCI research. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '13). ACM, New York, NY, USA, 3307-
3316.
Jon Froehlich, Leah Findlater, and James Landay. 2010. The design of eco-feedback technology. In Proceedings of the SIGCHI Conference on Human
Factors in Computing Systems (CHI '10). ACM, New York, NY, USA, 1999-2008.
Carl DiSalvo, Phoebe Sengers, and Hrönn Brynjarsdóttir. 2010. Mapping the landscape of sustainable HCI. In Proceedings of the SIGCHI Conference on
Background
• HCI sustainability review papers

13. Background
• Froehlich et al review paper revealed a number of
issues with HCI sustainability studies:
• Short study length
• - no longitudinal return to baseline
• - novelty effect
• Difficult to validate behaviour change
• Lack of evidenced-based behaviour change methods
• Highlighted importance of working with
psychologies to help bridge the theory-design gap
• It’s very hard to do!

14. Background
• Some limited HCI research
has been undertook that
looks at student energy
consumption habits*
• Mainly feedback
interventions
• Findings indicated
environmental concerns
were not a priority
Oliver Bates, Adrian K. Clear, Adrian Friday, Mike Hazas, and Janine Morley. 2012. Accounting for energy-reliant services within everyday life at home.
In Proceedings of the 10th international conference on Pervasive Computing (Pervasive'12), Judy Kay, Paul Lukowicz, Hideyuki Tokuda, Patrick
Olivier, and Antonio Krüger (Eds.). Springer-Verlag, Berlin, Heidelberg, 107-124.
Odom, W., Pierce, J., & Roedl, D. Social Incentive & Eco-Visualization Displays: Toward Persuading Greater Change in Dormitory Communities. In
Workshop Proc. OZCHI (2008).

15. Study Approach
• Builds upon previous HCI research in area
• Study adopted both a participatory design and
practitioner-led inquiry approach
• 100 students were recruited as ‘practitioner
researchers’ who recruited a further 300 participants
• Experiential data elicited to inform design process
• Large body of data collected
• Thematic analysis carried out on data by authors of
this work to identify clusters of
experiences, perceptions and attitudes

16. Study Approach
“Design a technology-led and
socially-enabled energy
intervention; that is both
engaging and cool, for students
in official accommodation
blocks, that encourages more
sustainable energy-use
practices.“
• A design challenge was presented, the focus of all
research carried out by the student practitioners:

17. Study Approach
• Researchers were presented with the design
challenge
• User experience-centred practices were used to
understand and address the challenge
• Focus groups were initially conducted in order to
elicit user requirements for the design challenge
• A variety of techniques were used within the focus
groups

18. Study Approach
• 100 focus groups carried out
semi-structured
interviews, 53
questionnaires, 40
card sorting, 26
particapatory design
tasks, 15
diary studies, 13
cultural probes, 4 cool walls, 4
0
10
20
30
40
50
60
Instances of technique used in focus groups

19. Study Approach
• Each researcher produce a thematic analysis of their
focus group data and paper prototype
• Each thematic analysis carried out produce 3
themes, typically with one paragraph describing
each theme
• The authors then carried out an inductive thematic
analysis on all of the researchers themes as one
corpus of data
• The data presented next represents both the
subjective, experiential information from
participants, plus our interpretation of the data

20. Better connected?

21. Better connected?

22. Study Approach
• Unit of analysis at sentence level
• 1,760 units analysed
• First pass created 87 conceptual labels
• These were grouped on similarity to create 34
categories
• A further pass created 5 distinct categories
• All labels grouped under one of the main categories

23. Results
• 146 labelsStudent
Experience
• 232 labelsEnergy
Consumption
• 291 labelsBarriers to
Saving
• 209 labelsBehavioural
Solutions
• 450 labelsDesign
Suggestions

24. Results
• From a purely descriptive perspective we can see
that discussion around the design aspects of the
challenge were the most common
• Can also see there was a lot of discussion around
barriers to saving energy
– more on this later!
• Can look upon these 5 distinct categories as the
design implications for student energy
interventions
• Example quotes from each category are now
discussed

25. Results
Student Experience
socialisin
g
drinking
alcohol
computer
games
using social media
cool
YOL

26. Results
Energy Consumption
“With each student spending the majority of
time in their rooms…. each room will have
electrical appliances/devices turned on, on
standby or charging up”
“most of the time the
students are at home….
some kind of technology
is always being used”
“One participant
told me that
instead of getting
his window fixed
he just turned his
heating on more
often”

27. Results
Barriers to Saving
“students may not be too familiar with
existing terminology or whether their
current energy consumption level is
particularly high or low” ”
“there is not a defined
scale of how much I
should and shouldn‟t be
using”
“the fact that we don‟t
have to pay just makes
us like „meh, we might
as well make the most
of it‟”

28. Results
Behavioural Solutions
“A reward system
whereby at the end of
each month, the person
who saved the most is
rewarded”
“the best way of
making people
change their
behaviour is to turn it
into a competition”
“£50 to everyone in
the flat that saves
the most energy…”

29. Results
Design Suggestions
“Although this is a good
idea one of the
disadvantages could be
that there may be
rebellious students who
want to boast how much
energy they can use”
“With each student spending the majority of
time in their rooms…. each room will have
electrical appliances/devices turned on, on
standby or charging up”
“a method of
turning a light
bulb off
without them
interacting
with it”

30. Results
• Breadth and scope of experiences and reflections is
powerful – sometimes at odds with itself
• Thematic analysis supports ‘making sense’ of
chaotic qualitative data
• Realistic and grounded findings of the barriers to
successful intervention uptake and adherence
• One size does not fit all*
• A novel approach that promotes openness in the
complex area of energy use practices
Helen Ai He, Saul Greenberg, and Elaine M. Huang. 2010. One size does not fit all: applying the transtheoretical model to energy feedback technology
design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '10). ACM, New York, NY, USA, 927-936.

31. Results
• Each theme produces a cluster of related user-
requirements or design implications
• Requirements can be used to design energy
interventions across pilot studies
• Currently working on implementing a range of pilot
interventions from findings
• Developed an opendata platform to publish
Lincoln accommodation energy data to open
standards, every 30 minutes
Helen Ai He, Saul Greenberg, and Elaine M. Huang. 2010. One size does not fit all: applying the transtheoretical model to energy feedback technology
design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '10). ACM, New York, NY, USA, 927-936.

33. Thanks!
Derek Foster
Lecturer in Computer Science
Lincoln Social Computing (LiSC) Research Centre
School of Computer Science
University of Lincoln
Email: defoster@Lincoln.ac.uk
@derekfoster
Questions?