"Emotion and games in technology-enhanced learning" presentation at the 2012 Joint European Summer School on Technology Enhanced Learning in Estoril, Portugal

1. Emotion and games in
technology-enhanced learning
JTEL 2012
Kostas Karpouzis
National Technical University of Athens
kkarpou@cs.ntua.gr

2. a bit of theory
the quest for Flow (and fun)

3. a theory of fun
• Raph Koster
– lead designer of Ultima
Online
– creative director of Star Wars
Galaxies
– http://www.theoryoffun.com
/theoryoffun.pdf
– http://www.raphkoster.com

13. a theory of fun
• ‘all games are edutainment’
– ‘some games teach spatial relationships (e.g.
Tetris)’
– ‘some teach you to explore (Super Mario)’
– ‘some teach you how to aim (FPSs)’
– some teach you to share/cooperate (Farmville)
– ‘players seeking to advance in a game will always
try to optimize what they are doing’

15. a theory of fun
• ‘We talk so much about emergent gameplay,
non-linear storytelling, or about player-
entered content. They’re all ways of increasing
the possibility space, making self-refreshing
puzzles’
• So, what is it that makes a game ‘fun’?

16. the concept of Flow
– a state of concentration or complete absorption with the
activity at hand and the situation. It is a state in which
people are so involved in an activity that nothing else
seems to matter (Csikszentmihalyi,1990)
– “Being completely involved in an activity for its own sake.
The ego falls away. Time flies. Every action, movement,
and thought follows inevitably from the previous one, like
playing jazz. Your whole being is involved, and you're
using your skills to the utmost”

18. flow revisited
• the ‘holy grail’ of
game design
• just the right amount
of challenge
• making a game very
hardgamers quit
• making a game very
easygamers bored

19. flow revisited
• it’s not about the
graphics
• or the controller
• or the franchise (e.g.
sports games)
• just ask Rovio
– makers of Angry Birds
– $80M/yr, 600M dl’s

20. flow revisited
• ‘smart’ games adapt
to player skill and
engagement
• keeping them coming
back for more
• at the end of the
day…

22. what about serious games?
• our first activity as
children
• ‘fun’ and ‘flow’ are a
given

23. what about serious games?
• our first activity as
children
• ‘fun’ and ‘flow’ are a
given
• best material to
teach social skills

24. what about serious games?
• our first activity as
children
• ‘fun’ and ‘flow’ are a
given
• best material to
teach social skills
• but schools fail to
capitalize on that

25. gamification
• the best one way to influence player behaviour
• include game design elements in non-game contexts

26. gamification
• image by Sebastian Deterding
• Bunchball white paper: http://info.bunchball.com/gamification-101/

27. gamification
• in Foursquare, users
earn points for
check-ins and other
activities
• leaderboards and
badge display
enhance competition

28. gamification
• replace ‘check-in’ with,
e.g., ‘recycle’
– gamification in the real
world

29. gamification
• replace ‘check-in’ with,
e.g., ‘recycle’
– gamification in the real
world
• what happens when
badges and rewards
are taken away?
– open research question

30. in a nutshell
• games provide challenge and fun to players
– or should be adapted to do so
• fun not always equal to entertainment
– the case of serious/learning games
• player experience: function of skill,
performance and challenge

31. user modelling

32. player personalities
• Richard Bartle
– co-creator of MUD1
(the first MUD)
• Bartle Test of Gamer
Psychology
– series of questions to
players of MMOs into
categories based on
gaming preferences

33. player personalities

34. player personalities
• Achievers: players who prefer to gain
points, levels, equipment and other
concrete measurements of succeeding
in a game
• Explorers: players who prefer
discovering areas, creating maps and
learning about hidden places

35. player personalities
• Socializers: players who choose to play
games for the social aspect, rather than
the actual game itself
• Killers: players who like to …‘club’ other
players
– They thrive on competition with other
players, and prefer fighting them to
scripted computer-controlled opponents.

36. player personalities
• Bartle Quotient totals 200% across all
categories, with no single category exceeding
100%
• A person may score "100% Killer, 50%
Socializer, 40% Achiever, 10% Explorer“
– indicating preference to fight people compared to
other aspects of gameplay
• but…

37. player personalities
• these are self-reported characteristics
– mostly refer to what players would prefer to do
and not necessarily what they actually do when
playing
• game genre-specific
– and different for single- and multi-player gaming
• offer little in terms of adaptation
– mostly refer to game mechanics and features

38. back to the drawing board
• what can we model?
– and how?
• definition of ‘affective computing’
– ‘affective Computing is computing that relates to,
arises from, or deliberately influences emotion or
other affective phenomena’ -- Roz Picard, 1995
– ‘a set of observable manifestations of a
subjectively experienced emotion’ -- Merriam-
Webster’s dictionary

39. observable manifestations

40. observable manifestations

41. hypothesis
• ‘shallow’ treatment
– i.e. not as far as ‘personality’, sticking to ‘affect’
• identify/track user reactions
– facial expressions and gestures, body movements
and stance, hand and body expressivity (for
whole-body interaction)
• relate those to events in the game

42. hypothesis
• ideally, we could identify the players’ stress
level (via the ‘observable manifestations’) and
their skill level (via their performance)
• and cluster those to identify player types
– for the particular game genre!
• or use them to adapt the game
– make it easier for players ‘in distress’
– or harder for players in the verge of boredom

43. hypothesis
• why bother with both affect and
performance?
• why are players standing still?
– is it flow (immersion) or boredom?
• or why do they move around?
– is it immersion (e.g. in a racing game) or lack of
engagement?
• remember: Flow  skill AND engagement

44. we’ve covered affect;
what else is there?
• cognitive models (Gray, 2007)
– evaluate why players behave in the way that they do, or
conversely to control computer-driven AI (Funge, 1999)
• cultural models
– “collective programming of the mind which distinguishes
the members of one group or category of people from
another” (Hofstede, 1996)
– not necessarily related to player origin or descent (e.g.
sub-cultures)

45. we’ve covered affect;
what else is there?
• learner models
– students motivation strongly linked to learning (Malone
and Lepper, 1987)
– demographic information and personality  understand
and predict the student’s learning behavior
– demographics (gender), personality (Big 5: openness,
conscientiousness, extraversion, agreeableness,
neuroticism), goal orientation (performance based on
result or mastery on skill), and presence (involvement with
the system) (McQuiggan et al., 2010)

46. in a nutshell
• games provide an ideal medium to induce and
capture affective interactions
• well-designed games bring out different (and
valuable!) reactions from players
• gaming is a multi-faceted activity
– thus, player models are usually detailed
• player affect tells us a lot about the game

47. lessons learned
the Siren project

48. conflict resolution games
• Siren aims to
produce a conflict
resolution serious
game
– for 10-14 y.o.
children
– in school
environments

49. conflict resolution games
The life cycle of conflict (Swanstrom and Weissmann, 2005)

50. conflict resolution games
• during escalation, negative emotions are present
• cannot use neg. emotions to indicate stress  adaptation

51. conflict resolution games
• rather, use estimated emotion to identify where players
are in this figure (which phase)

52. conflict resolution games
• and produce content to ‘push’ users towards de-escalation
• learning objective of the game!

53. conflict resolution games
• sensed affect can be used to identify player
performance
– i.e. whether players actually ‘move’ towards
resolving the conflict
• but which emotions are relevant?
• negative vs positive
• is that enough for all game genres?

54. in a nutshell
• player affect is genre-dependent
• reflects many qualities from the user model
• many open research questions
• single- vs multi-player
• easy to find people to play games
– yay!

55. thank you!
Kostas Karpouzis
kkarpou@cs.ntua.gr