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As a next step, all recorded user input actions are
translated in terms of discrete input actions related to
the microphone and to the keyboard. The human
experts of the empirical study (Alepis et al 2007)
have identified which input action from the
keyboard and the microphone could led them in the
successful recognition of possible emotional states.
From the input actions that appeared in the
experiment we have used those that were proposed
by the majority of the human experts.
In particular considering the keyboard we have:
a) a student types normally b) a student types
quickly (speed higher than the usual speed of the
particular user) c) a student types slowly (speed
lower than the usual speed of the particular user) d)
a student uses the backspace key often e) a student
hits unrelated keys on the keyboard f) a student does
not use the keyboard.
Considering the students’ basic input actions
through the microphone we have 7 cases: a) a
student speaks using strong language b) a student
uses exclamations c) a student speaks with a high
voice volume (higher than the average recorded
level) d) a student speaks with a low voice volume
(low than the average recorded level) e) a student
speaks in a normal voice volume f) a student speaks
words from a specific list of words revealing an
emotion g) a student does not say anything.
4 ANIMATED AGENTS
Elliot et al. (Elliot, 1999) suggest that animated
agents in an educational environment will be more
effective teachers if they display and understand
emotions. More specifically they point out that:
1. An animated agent should appear to care
about students and their progress
2. An animated agent should be sensitive to the
student’s emotions
3. An animated agent should foster enthusiasm
in the student for a subject matter
4. An animated agent may make learning more
fun
However, even if new multimodal capabilities
like 3D-video and speech synthesis have made
pedagogical personas more human-like, there is also
a great need in determining “how” (what exactly
should the pedagogical persona do) and “when” (in
which situation) an animated agent should
act/behave in each part of the tutoring process.
An expected contribution of our system is to
affect positively the educational process of students
who learn programming languages. More
specifically, the system should motivate the students
for the purpose of learning more efficiently and also
more enjoyable. In (Soldato and Du Boulay, 1995) it
is suggested that a tutoring system must react with
the purpose of motivating distracted, less confident
or discontented students, or sustaining the
disposition of already motivated students.
At the same time a system’s critical long-term
objective is to operate as an educational tool that
implements affective functionalities in order to assist
teachers in using user-friendlier, thus more
communicable, educational e-learning applications.
In view of the above our system also
incorporates an affective module that relies on
animated agents. The system modells possible
emotional states of users-students and proposes
tactics for improving the interaction between the
animated agent and the student who uses the
educational application. The system may suggest
that the animated agent should express a specific
emotional state to the student for the purpose of
motivating her/him while s/he learns. Accordingly,
the agent becomes a more effective teacher. Table 1
illustrates event variables that are used as triggers
for the activation of the animated agents. Each time
a trigger condition takes place the animated agent
uses a certain tactic in order to communicate
emotionally with the user for pedagogical reasons.
Table 1: Event variables for the activation of animated
agents.
Event variables
a mistake (the user may receive an error
message by the application or navigate
wrongly)
• many consecutive mistakes
• absence of user action for a period of time
• action unrelated to the main application
• correct interaction
• many consecutive correct answers (related
to a specific test)
• many consecutive wrong answers (related
to a specific test)
• user aborts an exercise
• user aborts reading the whole theory
• user requests help from the persona
• user takes a difficult test
• user takes an easy test
• user takes a test concerning a new part of
the theory
• user takes a test from a well known part of
the theory
COMBINING TWO DECISION MAKING THEORIES FOR AFFECTIVE LEARNING IN PROGRAMMING COURSES
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