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(especially when encountering decision points)
and comparing them to those of experts. This
approach has been applied to train aircraft pilots
to a higher level of skill compared to other pilot
training approaches reducing training cost (i.e. no
presence of a human flight instructor is required).
The approach can also be utilized in other domains
involving training in a simulator such as training
of automobile driving students, medical students,
air traffic controllers, etc.
A series of patents involve a goal-based IES
utilizing RBR to tutor in a real business simulated
environment (Beams & Harris, 2006; Beams &
Harris, 2007; Bertrand, Zorba, Conant, 2002;
Bertrand, O'Connor & Rosenfeld, 2004; Ber-
trand & Nichols, 2006; Bertrand & Wills, 2008;
Nichols, 2004; Lannert, 2006; Bertrand, Zorba &
Conant, 2006; Nichols, Gilchrist & Poon, 2006).
Previous systems did not provide a creative learn-
ing environment by encompassing real business
simulations in rules. A training session involves
steps such as the following (Beams & Harris,
2006): receiving information indicative of a
goal, presenting information indicative of a goal,
receiving/analyzing/evaluating learner responses,
providing feedback to assist learners in achieving
the goal, presenting remedial information. RBR
is used to dynamically generate learner-tailored
feedback simulating real-world environment and
interactions. Such feedback may involve audio,
video, graphics, animations, email/telephony/chat
information, etc. User input may correspond to
collective actions and the system includes logic
simulating a business outcome from such collec-
tive actions (Bertrand, Zorba & Conant, 2006).
Collaborative sessions can be established in which
a learner is provided with feedback from at least
one other user in order to assist in goal achieve-
ment. Multiple 'roles' are also available for a
learner to learn from each simulated environment
from multiple viewpoints (Beams & Harris, 2007).
The approach can be executed on a plurality of
servers connected through a network (Beams &
Harris, 2006; Beams & Harris, 2007).
Bloom et al. (1997) present an approach
to tutoring Customer Service Representatives
(CSRs). CSRs handle all types of customer calls
regarding products/services and need thorough
training and on-the-job experience to become
proficient. The approach simulates CSR working
environment enabling trainees to learn job pro-
cedures and exercise conversing with customers.
Domain knowledge is rich and includes, among
others, discourse grammar describing all possible
conversations supported and rules describing
actions to be taken in response to specific situ-
ations. Conversations are syntactically correct
sequences through discourse grammar made up
of rule sequences.
Mitry (2001) presents an approach offering
lessons within simulated environments involv-
ing natural attractions and providing lifelike and
real-time consequences of learner actions. The
user solves problems/puzzles and is rewarded for
his/her performance. Neural networks evaluate
user performance and produce system feedback.
System feedback is synchronized to typographical/
graphical/pictorial illustrations simulating reality
and providing dynamic interactions.
Patents Involving Combination
of IES with LMS Technologies
Chakraborty (2006) presents an integration of
LMS and AEHS technologies. Basic components
are: learning content storage unit, user modeling
unit, a personalization unit for personalizing
learning content and a user interface. The learn-
ing content can be broken into modules within
the SCORM framework called Assignment Units
(AUs). The composition of AUs in learning pages
varies according to user model. The content storage
unit stores various data fragments that constitute
the AUs organized as XML fragments. The user
modeling unit continuously keeps track of char-
acteristics such as sophistication level, interaction
with the system, interest level and others. The
system observes user behavior/interaction col-
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