ITS-98-effectiveness of agents-pedagogical

On effectiveness of distance learning using LANCA
Guy Gouardères*, Claude Frasson**,
* Université de Pau, IUT Informatique, Bayonne, France
Phone : 514-343 7019
Fax : 514-343 5834
Email : [frasson,]
**Université de Montréal, Département d'informatique et de recherche opérationnelle
2920 Chemin de la Tour, Montréal, H3C 3J7, Québec, Canada
LANCA : Learning Architecture Based on Networked
Cognitive Agents relates to Intelligent Tutoring Systems
(ITS) on the Web and, more particularly, to a system and a
method for assisting the learner involved in distance
learning situation on the Internet using several types of
intelligent Agents. It also relates to knowledge mining by
discovering on the Internet which help appears useful in a
given context.
In this paper, we propose the prime issues when evaluating
several models of the previous cognitive and intelligent
agent working together to improve convivial distance
education using a virtual learning network.
I-Experimental background
The major product in the market is VLN, Virtual Learning
Network, a collaborative network dedicated to the
intelligent application of telecommunications technology.
VLN has no agent, mobile and/or intelligent at all, and can
be just viewed as regular telelearning using discussion
[]. On the
opposite, other innovative products available today in
distance learning on the Web try to combine the use of
different types of intelligent agents with mobile
dissemination of effective synchronous & asynchronous
learning. Among them, the Web Based Intelligent Tutor [C.
Eliot, 1998] and the Web Agent-Based methodology [C. J.
Petrie] seem to be a bit unachieved regarding the actual
performance of the LANCA prototype.
Our present system (LANCA) uses the Internet as a
constructivist learning environment and aims to provide
intelligent assistance to improve both quality of training
and distribution of knowledge in a distance learning
situation. The assistance is based on various intelligent
Agents which act collaboratively to support learning at
different steps. A first Agent (pedagogical Agent), close to
the learner, is able to detect his difficulties using a learner
model and provides local explanations when needed or on
request. A second Agent (dialog Agent) provides access to
other explanations or learners available on the Web, in
synchronous or asynchronous mode. Taking into
consideration all possible helps a third Agent (negotiating
Agent) is in charge of finding and selling explanations
according to a market of requests and helps. A fourth Agent
( moderator) in charge of determining which explanation
was finally useful to serve as a permanent source of
explanation for future learners, (see Fig 1.).
The goal of this paper is to present an overview about the
resulting performances on real tests between two different
sites in Montreal and Bayonne (France). We will compare
the response time in the different sites. Technical details
related to the achieved architecture can be found in Frasson
& al., [1998].
II-How to evaluate the efficiency of the improved
learning process in a mixed society of human &
artificial Agents ?
We elaborated on previous findings by exploring how
various helps and explanations resource resulting from the
agents activity can improve the performance of learners
during individual or collaborative problem solving. We
tested two main hypotheses using a multi-agent problem
solving simulation testbed:
(1) an agent decides to present useful or profitable helps
only if it reduces overall problem solving effort
(2) an agent can use its own evaluation context to intervene
or keep silent when assessing the individual or collective
learner performance on a given problem
III- Description of the model to evaluate performance
In this work, we adopt a definition of performance
evaluation in accordance with the critical goals for LANCA
objectives: first, individual versus collective management
of aids
and second, synchronous versus asynchronous
Agent (1)
Agent server
De Service
Agent (n)
Dialog (n)
Dialog (2)
Dialog (1)
(1) Agent
(2) Agent
(n) Agent
Fig. 1 :General architecture of LANCA
This performance measure assumes that the agents are
working together as a team (Actors), and are attempting to
maximize performance of their goal, the learner. In this
way, the performance is the difference between an objective
measure of the utility of different types and source of helps,
and their induced cost measure in terms of collaborative
agent effort to be effective in a distance learning situation.
in the learner profile. The mastering of the learner in a
problem solving situation is measured using a unique
parameter, the SCORE. This SCORE is calculated using
the parameters of the learner profile [ Fig 2 ] and takes into
account whether or not the user learning performance is
improved or not with a given aid (as just a "useful" help
or, at the opposite, as a "decisive" one).
On one hand, the calculation of the communicative effort,
which specifies the cost of evaluating user elementary
interaction for resolving a difficulty, is coupled with the
calculation of the retrieval effort to get the right aid in the
right time - i.e. the correct behavior of a human (teacher,
learner,..) or software agent (pedagogical, tutor,….).
IV- Selected protocol and experiments
On the other hand, the profitability of an agent intervention
is evaluated by taking into account the resulting evolution
According to the previous model to evaluate agent
performance, we experimented within two ranges
resources; low for agents with limited resource (staying on
the learner station) and high for agents with unlimited
resource (located in the server station such as Negociating
or Moderator Agents).
We have selected a sequence of learner manipulations
involving all the functional capabilities of the LANCA
system, all the Agents and all the types of exercises. In
particular, we have identified three types of exercises:
 with multiple choices (MCE) : the solution is among a
list of exclusive choices;
 with multiple multiple choices (MMCE) : the solution is
among a sequence of responses with binary exclusive
 with several multiple choices (ESCM) : involving
several steps including more than two choices of
Each type of exercise can be performed using different
strategy :
 Tutor
 Companion;
 Troublemaker;
 Book
V- Experimental results
We determined that a sample size of 50 to 100 dialogues
per experimental condition is adequate for determining
whether a collaborative scenario between agents affects
performance. To collect these samples we have tested 100
dialogues with the appropriate parameter settings, yielding
a performance distribution for each strategy and a set of
assumptions tested.
The comparisons of problem-solving dialogue strategies is
presented in annex (table 1) The selected example
illustrates the differences of agent timing performances
using the same strategies, first, for a distant learner on the
server (in Montreal, Canada), second, for a distant learner
acting at the same time on the user station (in Bayonne,
We were gracefully surprised by the very low decreasing of
ratio performance when distance and traffic on the network
is obviously increasing. This is due to the transfer on the
local station of the effectiveness the "intelligent" guidance
of the learner. In fact, the local session in Bayonne nearly
reach the same performance as the one in Montreal.
Another good surprise occurs when, using a centralized
trace on the server in Montreal, (see Fig 2), we can
dynamically track the precise time and context in which an
help became "useful" or "decisive" for a given solving
problem situation according to a learner profile and
behavior. (see Fig. 2)
[Creating ibm.aglets.tahiti.Tahiti]
********** Pedagogical Agent: Run **********
********** Negociator Agent: Receive Service Agent ID:e128816373c9066b**********
********** Service Agent: Receive: Get course **********
********** Service Agent: Course Name: howToCreateItsOwnPageOnTheInternet ******
********** Service Agent: Receive: Kernel: explanation **********
********** Service Agent: Concept: File menu: Save **********
********** Service Agent: Strategy: tutor **********
********** Service Agent: Level: weak **********
>>>>>>>>>>> Service Agent: S C O R E = 1020 <<<<<<<<<<<
********** Service Agent: Receive: Kernel: explanation **********
********** Service Agent: Concept: File menu: Save as **********
********** Service Agent: Strategy: companion **********
********** Service Agent: Level: weak **********
>>>>>>>>>>> Service Agent: S C O R E = 1054 <<<<<<<<<<<
********** Negociator Agent: Receive: Search market: explanation **********
********** Negociator Agent: search local database **********
********** Negociator Agent: search market: explanation **********
********** Negociator Agent: Receive: Asynchronous explanation **********
********** Negociator Agent: Receive: Search market: learner **********
********** Negociator Agent: search market: learner **********
********** Negociator Agent: Receive: Synchronous explanation **********
********** Negociator Agent: concept: File menu: Save **********
********** Negociator Agent: Receive: Synchronous explanation **********
********** Negociator Agent: concept: File menu: New document **********
********** Service Agent: Receive: Kernel: explanation **********
********** Service Agent: Concept: <TITLE> tag **********
********** Service Agent: Strategy: tutor **********
********** Service Agent: Level: average **********
>>>>>>>>>>> Service Agent: S C O R E = 1108 <<<<<<<<<<<
********** Service Agent: Receive: Kernel: explanation **********
********** Service Agent: Concept: button 8 **********
********** Service Agent: Strategy: tutor **********
********** Service Agent: Level: average**********
>>>>>>>>>>> Service Agent: S C O R E = 1128<<<<<<<<<<<
Fig. 2: Tracking the learner SCORE change according to
more and more decisive helps.
These first experiments have focused on decisions that
have to do for evaluating that a given agent (human or
artificial) is, just in time, bringing up (or not) useful or
decisive helps. Currently, we are revisiting our previous
definition of performance evaluation in distance
collaborative problem solving. This performance measure
must be completed and improved by adding more fine
grained agent parameters, as what are immediate benefits in
automatically change of strategy as tutor, companion or
troublemaker into the pedagogical agent when the dialog
agent is connecting to different types of external helps
(forum, direct video chat-room, etc..)….
The critical question that remains is how the agent
moderator goes about calculating adequacy of agent
interventions and evaluating profitability of the prescript
A first solution has been tested. The moderator agent
receives success explanations from all users connected to
network data via each negotiating agent, and compiles
statistical data in order to generate a signal indicating the
utility of the trial messages. Then, corresponding trial
explanations can be stored. The moderator agent may
remove trial explanation messages which have been found
as not used, or order trial explanation messages which are
found useful in explaining particular types of subject
matter. Their ranking or importance is increased to provide
the most relevant trial explanation messages first.
In future work, the moderator agent may include a
screening procedure for determining whether the proposed
new explanation is worthy of being entered into the
reminding kernel of explanation in the database.
Cohen, P. R. 1995. Empirical Methods for Artificial
Intelligence. Boston: MIT Press.
Guinn, C. I. 1994. Meta-Dialogue Behaviors: Improving
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Hanks, S.; Pollack, M.; and Cohen, P. 1993. Benchmarks,
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Web-Based Intelligent Tutor for Basic Anatomy,
Frasson C., Mengelle T., Aimeur E., Gouardères G., 1996,
"An Actor-based Architecture for Intelligent Tutoring
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Annex : Table 1 ( see trace extract in Fig. 2, "change strategy to companion" )
Current parallel actions
(in Montreal & Bayonne)
First Netscape message (agent load)
Pedagogical Agent<strategy Tutor>
Button<Exercises according to..>
Question 1.18
<&amp> choice(correct)
Select exercise 1.2
Answer reply 2 et 3
<help> request
Dialog agent
Select exercise 1.3
Answer.3 (incorrect)
<help> request
<Explanation> from Kernel DB
Answer.2 (correct)
Change of strategy (Companion)
Select exercise 1.10
-------------------------------------Select exercise 1.7
Incorrect choice
<Explanation> from the Market
Correct answer
Change strategy (TroubleMaker)
Select exercise 1.14
Answer. 1 (correct)
Delay in Montreal (at 10 h
15 sec
14 sec
Delay in Bayonne(at 4 h P.M.)
20 sec
17 sec
10 sec
7 sec
4 sec
15 sec
30 sec
7 sec
5 sec
12 sec