Supple Interfaces for Education: can Affective
Interactions improve Learning via Educational Games?
Cristina Conati
Department of Computer Science
University of British Columbia
2366 Main Mall
Vancouver, B.C. Canada V6T 1Z4
email: conati@cs.ubc.ca
Phone: (604) 822-4632
Fax: (604) 822-575
As our society becomes more and more technologically savvy, kids turn to
computer games for their entertainment. Its not uncommon for kids to spend hours a
day in front of the computer playing games [1]. Why? -because they’re fun! What
could be more attractive for educators than to capitalize on the captivating nature of
computer games to teach academic concepts and supplement classroom activities?
Several authors have suggested that video and computer games have potential as
educational tools [2, 3], and many educational games are already in use in
classrooms [1, 4]. However, although educational games are motivating, there are
few studies which indicate that they promote learning [4, 5].
In fact, it is often the case that students do not learn when playing educational
games, unless these games are coupled with additional supporting activities [6, 4, 7,
1]. However, students are often not under the direct supervision of a teacher when
playing educational games in school, as computer resources are often limited to one
or two computers per classroom [1], and students must use the computers
sequentially.
This makes it difficult for a classroom teacher to support students during game
play and encourage them to think carefully about their actions.
Although games are highly motivating -a property that is extremely desirable in
any educational activity -the fast-paced game environment can work against
learning as students rush to complete levels and don’t pause to engage in proactive
thinking about the underlying instructional material. This is further perpetuated by
the fact that students are also often reluctant to ask for help when playing a game or
working at the computer[e.g., 8].
Even though an educational game’s rules are based on instructional content, it is
often possible for students to play a game sucessfully without reasoning about the
underlying domain knowledge that the game is supposed to teach [9]. Conati and
Lehman [9] show results indicating that students will often use superficial heuristics
to progress in a game, rather than reasoning. Baker et. al. [10] coin the term
”gaming the system”, although not specifically for educational games, to describe
behaviour in which students advance by systematically taking advantage of
regularities in the software’s feedback and help. Many students will play in this way
unless expressely guided to think about their actions by an instructor or other
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educational agent. All of these factors point to a problem that although students find
educational games very engaging, unless game play is supported, they do not learn.
We argue that computer-provided individualized support based on careful
assessment of student learning during game play can help overcome this limitation
and make educational games a truly innovative form of learning. The testbed for
our research is the educational game Prime Climb, a computer game which aims to
teach 6th and 7th grade students about number factorization. We propose adding to
Prime Climb an emotionally intelligent pedagogical agent that uses a student’s
game actions to track her evolving knowledge, and uses this as a basis for
generating timely, tailored interventions to trigger constructive reasoning.
Providing this support during game play can be extremely challenging however,
because it requires careful tradeoffs between fostering learning and maintaining a
positive affective state. An agent which intervenes too often or with the wrong
advice will frustrate the student and jeopardize the motivating advantage of using a
game rather than a traditional tutoring system to convey the instructional content.
Thus, we believe that it is crucial for the agent to have accurate models of both
student learning and affect.
In support for the above claim, we propose to present
results from an
empirical evaluation showing that when the Prime Climb pedagogical agent relies
solely on an accurate model of student learning [10] to decide how to intervene
during game playing, it fails to increase the game’s pedagogical effectiveness. We
will present data indicating that a likely cause for this result is that the agent does
not explicitly take into account student affect in its interventions.
But even if one believes, like we do, that taking student affect into account is
important when trying to foster learning with game-based interactions, little
knowledge exist about how affective information should be used. One of our goals
in participating in this workshop is to gain a better understanding of how one can
foster positive user affect during interaction with electronic games in general, as a
starting point to understand how to leverage student affect to support learning with
electronic games that are educational in nature.
A more general goal is to initiate a discussion on the general merit of including
affective components to human computer interaction. In particular, we would like to
gain insights on the following questions: when is it useful/necessary to provide an
interface with affective components, and when is it detrimental? How can we
measure the long term, intangible effects (either positive or negative) of adding
affect to human computer interaction?
REFERENCES
[1] A. McFarlane, A. Sparrowhawk, and Y. Heald. Report on the
educational use of games; an exploration by teem of the
contribution which games can make to the education process.
Technical report, Cambridge, 2002.
[2] T.W. Malone and M.R. Lepper. Making learning fun: A taxonomy of
intrinsic motivations for learning. Aptitude, learning and
instruction: Volume III Cognative
[3] V.J. Shute. A comparison of learning environments: All that
glitters... Computers
as Cognitive Tools, S.P.L. and S.J.Derry, editors, pages 47–73,
1993.
[4] J.M. Randel, B.A. Morris, C.D. Wetzel, and B.V. Whitehill. The
effectiveness of games for educational purposes: a review of the
research. Simulation and Gaming, 25:261–276, 1992.
390
[5] C.R. Beal, W. L. Johnson, R. Dabrowski, and S. Wu.
Individualized feedback and simulation-based practice in the
tactical language training system: An experimental evaluation. In
AIED ’05: Proceedings of the 12th International conference on
Artificial Intelligence in Education, pages 747–749, 2005.
[6] M. Klawe. When does the use of computer games and other
interactive multimedia software help students learn mathematics?
NCTM Standards 2000 Technology Conference, Arlington, VA, 1998.
[7] H. Leemkuil, T. de Jong, R. de Hoog, and N. Christoph. Km
quest: A collaborative internet-based simulation game. Simulation
and Gaming, 34(1):89–111, 2003.
[8] C. Conati and J.F. Lehman. Efh-soar: Modeling education in
highly interactive microworlds. Lecture Notes in Artificial
Intelligence. Advances in Artificial
Intelligence, AI-IA, 1993.
[9] R.S. Baker, A.T. Corbett, K.R. Koedinger, and A.Z. Wagner. Offtask behavior in the cognitive tutor classroom: when students ”game
the system”. In CHI ’04: Proceedings of the SIGCHI conference on
Human factors in computing systems, pages 383–390, New York, NY,
USA, 2004. ACM Press.
[10] Manske M. and Conati C. (2005). Modelling Learning in
Educational Games. Proceedings of AIED 05, Proceedings of the 12th
International Conference on AI in Education, Amsterdam, July 19-23.
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