Challenges and opportunities
in e-Learning
Ioana Moisil1, Iulian Pah2
1 “Lucian Blaga “ University os Sibiu, Romania
2 “Babes Bolyai” University, Cluj-Napoca, Romania
[email protected] ; [email protected]
Acknowledgement
This work benefits from funding by the
Romanian Ministry of Education, Research
and Youth
(INFOSOC - CEEX 73/31.07.2006)
challenges
opportunities
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Computer Aided
Learning (CAL)
Computer Aided
Training (CAT)
Computer Aided
Instruction (CAI)
Computer Based
Learning (CBL)
Computer Based
Training (CBT)

e-Learning
Supervised learning
Web 2.0 challenges

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

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Google AdSense Flickr
BitTorrent
Napster
Wikipedia
Blogging
upcoming.org and EVDB
search engine optimization
cost per click
web services
Participation
Wikis
tagging ("folksonomy")
syndication
.A "meme map" of Web 2.0 (from http://www.oreillynet.com)
DANTE
Socio-Cultural Models
implemented through multi-agent
architecture
for
e-learning

Main objective: the development of a
model for the virtual education system,
student centred, that facilitates the
learning through collaboration as a form of
social interaction
Background
Vygotsky's theory of social cognitive
development (1934/1986): described
learning as being embedded within social
events and occurring as a child interacts
with people, objects, and events in the
environment
 Bandura's social learning theory

STUDENT MODEL


an action i is defined by a set of attributes
A. An individual has to decide among a set
I of possible actions:
I = {ik, k=1,2,...,n} and
A k= {aki, i=1,2,....m} (1)
quality-cost function
STUDENT MODEL – cont.

At individual level we are considering that the evaluation
is also influenced by two categories of factors: beliefs
(cognitive) and affects (emotive).
ik = ∑i Bi Ei
(2)
where
ik is an action, k=1,..,n
Bi is the belief that ik posses the attribute aki, i=1,..,m
Ei is the evaluation or utility (desirability) of attribute aki,
i=1,..,m
Intervention/action ik
∑ i B i Ei
Beliefs about
attributes Bi
Evaluation of
attributes Ei
STUDENT MODEL – cont.
Student attitude toward an act, αk, is the sum of the
student's belief strength in the consequences resulting
from performing a certain action (taking a certain
decision) weighted by the evaluation of an anticipated
outcome (positive benefit or avoidance of a negative
consequence):
αk = ∑i βki εki
(3)
where
αk is the attitude toward an action ik, k=1,..,n
βki is the belief that performing ik will lead to an
anticipated outcome i, i=1,..,m
εki is the evaluation or utility (desirability) of the
outcome i, i=1,..,m

STUDENT MODEL – cont.

The influence of the colleagues from the learning environment can
be modeled by introducing the subjective norm:
SN = ∑j NBkj MCkj
(4)
where
SN is the subjective norm - the motivation toward an action ik,
k=1,..,n, as determined by the influence of the group
NBkj is the normative belief that people from the group (j) expect
an individual to perform an action ik will lead to j, j=1,..,n
MCkj is the motivation to comply with the expectation of the
group (j) j, i=1,..,n
The theory of reasoned action
combining the attitude toward an act and the subjective
norm:
DB = f[(BI) = f (αk)w1 + (SN)w2]
(5)
where
DB is the decisional behavior
BI is the behavioral intention
αk is the attitude toward performing the action ik
SN is the subjective norm
w1 and w2 are evaluation weights determined empirically

Student cognitive model


Vygotsky zone of proximal development (ZPD)
four learning stages:
– assistance provided by more capable others (coaches,
experts, teachers);
– self assistance;
– internalization automatization (fossilization); and
– de-automatization: recursiveness through prior
stages.
Vygotsky's theory also claims
"that instruction is most efficient when students engage in
activities within a supportive learning environment and
when they receive appropriate guidance that is mediated by tools"
TUTOR - model
The TUTOR assistant evaluates the educational objectives
of the student and recommends her/him some kind of activities.
The decisions are based on the knowledge of the students’
cognitive profile (which takes into account the social
component). The TUTOR agent interacts with the personal
assistant of the student, with the mediating agent and with the
social agentified environment. As the system is conceived, the
accent is put on collaboration activities between students, which
consist in knowledge exchange, realization of common projects,
tasks’ negotiation, sharing resources, common effort for the
understanding of a subject, problem-solving in-group. The
TUTOR is mainly evolving in a network populated with learning
objects.
Ant social behaviour


Pierre-Paul Grass – stigmergy (indirect, non-symbolic
form of communication,medisated by the
environment; local information)
Deneubourg et al. - studies on pheromone laying and
general ant behavior (double bridge experiment)
→ main source of inspiration for the development of
ant colony optimization (ACO)
ACO
In ACO, a number of artificial ants build solution to an
optimization problem and exchange information on their
quality via a communication scheme that is reminiscent
of the one adopted by real ants.(Dorigo et al., 2006)
ACO algorithms use a population of agents-artificial
ants in order to build a solution to a discrete
optimisation problem. Solutions’ information (individual
connections of the problem) are kept in a global memory
– the pheromone mapping. Specific heuristics can be
considered on a-priori information.
3 processes
ants generation and activity,
 pheromone trail evaporation and
 daemon actions.

TUTOR group-work activities subsystem
Virtual learning environment:

a set G of students’ teams and

a set A of group work activities
consisting of several tasks.
Let
aj A with j=1,…,J be an activity from the set A ;
(1)
gi G with i= 1,…I be a team from the set G, and
(2)
tkji TK , j=1,…,J and i= 1,…I
(3)
be a task of the activity aj that has to be performed
by the team gj.
TUTOR group-work activities subsystem
(cont.)
activity aj :an ordered sequence of tasks
from a set TK = { tkji }.
task tkji : to be performed by the team gi in
a number dji of time units.
N = │TK│- total number of tasks.
Goal: to assign tasks to time intervals in
such a way that no two activities are
performed at the same time by the same
team and that the maximum completion time
of all tasks is minimized.
TUTOR group-work activities subsystem
(cont.)
A team gi G is supposed to have M members
(student-agents):
gi = gim with m=1,.., M (4)
-
all teams have the same number of members
-
there are task-classes
-
the potential assignment of one student to a specific
task is based on the concept of response threshold
combined with a function of the background
knowledge of the student and her decision making
behaviour.
TUTOR group-work activities subsystem
(cont.)
A student-agent, gim , is attracted to a task tji with a
probability P depending on her background
knowledge, beliefs (cognitive), affects (emotive)
and intentions and on response threshold θimj :
P
θ imj
(simj) = simj 2 / (simj2 + θ
2)
imj
(5)
where simj = f (beliefs, affects, intention, qualification-knowledge and
skills).
TUTOR group-work activities subsystem
(cont.)
The response threshold θ imj of the ant-agent gim to the task tkji
is decreased when the agent has performed the task tkji ; at the
same time, thresholds for the other tasks are increasing
proportional to the time t to perform the task.
θ
imj
new
=θ
imj
old
– xij ξΔt + (1-xij) φ Δt
(6)
where:
xij is the time spent by the agent i for the task j,
ξ is a learning coefficient , and
φ is a forgetting coefficient.
If a task is performed, the response threshold is decreased with
a quantity depending of the learning coefficient, in the opposite
situation; the threshold is increased with a function of the
forgetting coefficient.
TUTOR group-work activities subsystem
(cont.)
The state of an ant-agent is evolving from “active” –
performing a task, to “inactive” –idle, and vice versa.
An inactive agent starts to perform a task with a
probability P given by (5). An active agent completes
the task, or abandons it, with a probability p per time
unit :
p = probability (state = “active” → state = “inactive”)
(7)
1/p is the average time spent by an agent in task
performing before giving up the task.

TUTOR group-work activities subsystem
(cont.)
The problem is represented as a weighted graph Q = (TK’, L),
where TK’= TK {tk0} and L is the set of edges that connect
node t0 with the first task of each activity. The vertexes of T are
completely connected, with exception of the nodes of the tasks
from the same activity that are connected sequentially, each
such node being linked only to its direct successor.

There are N (N-1)/2 + │A│ edges. Each edge (k, l) is weighted
by two numbers: τlk - the pheromone level (trail level) and η
kl – the so called visibility and represents the desirability of a
transition from node k to node l. Each ant-agent has associated
a data structure – the tabu-list, that memorises the tasks of an
activity that have been performed at the time moment t.
TUTOR group-work activities subsystem
(cont.)
A transition probability function from node i to
node j for an ant-agent (a-a)k was defined as:
(8)
where allowedk = (N – tabuk)
tabuk is a vector that changes dynamically and contains the tabu-list
of the kth ant.
ά and β are parameters that are used to control the relative importance of
pheromone level and visibility
TUTOR group-work activities subsystem
(cont.)
NR - number of potential active agents,
NRact - number of active agents at the time t,
The variation of the attraction of a task (pheromone
deposit) in a discrete time situation:
simj (t+1) = simj (t) + β - (άNRact) / NR
(9)
The order in which the nodes are visited by each antagent specifies the proposed solution.
Conclusion
“We shape our dwellings and afterwards
our dwellings shape our lives”.
Winston Churchill