Modelling User Linguistic Communicative Competences for Individual and Collaborative Learning

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25 Οκτ 2013 (πριν από 3 χρόνια και 9 μήνες)

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181

Modelling User Linguistic Communicative Competences
for Individual and Collaborative Learning


Timothy Read
1

& Elena Bárcena
2

1. Depart
a
mento de Lenguajes y Sistemas Informáticos, UNED, Spain.

Email: tread@lsi.uned.es

2. Departamento de Filologías Extranjeras y sus Lingüísticas, UNED, Spain.

Email:
mbarcena@flog.uned.es



Abstract

In this article an innovative framework for use in Intelligent Computer Assisted
Language Learning (henceforth, ICALL) systems (as developed
by the ATLAS
1

research group
2
) is presented in terms of the different models that compose it. It is
argued that suc
h a

general framework

allows

the design and development of
ICALL systems in a technologically, pedagogically and linguistically robust
fashion, thereby avoiding the use of ad hoc knowledge models, which prove
difficult to
move
from one system to another.

S
uch a framework has been
designed to overcome three problems present in most second language learning
systems: the oversimplification and reduction of the vastness and complexity of
the learning domain to a few formal linguistic aspects (studied in closed
and
decontextualized activities), the lack of underlying pedagogic principles, and the
complexity of automatic language parsing and speech recognition. The
framework attempts to capture and model the relevant pedagogic, linguistic and
technological element
s for the effective development of
second language
(henceforth, L2)

competence. One of the goals were that any ICALL system
developed around this framework would structure the complex network of
communicative language competences (linguistic, pragmatic and

sociolinguistic)
and processes (reception, production and interaction) within the L2 learning
process in a causal quantitative way, adapting such process to the progress made
by a given student.


1. Introduction

T
he need to be able to understand and
communicate in languages other
than our own native tongue is
an important skill in our modern networked



1

http://atlas.uned.es. Artificial Intelligence
Techniques for Linguistic Applications (reference no
FFI2008
-
06030
).

2

This research is part of the
I
-
AGENT (Intelligent Adaptive Generic ENglish

Tutor) project,
and has been
funded by the Spanish
Ministry of Education (grant
reference code
:
FFI2008
-
06030
)
.


182

society. As anyone who has dedicated any time and effort to learning a
secon
d language
know
s
, progress is slow, and any break from the learning
process
causes such progress to be lost with alarming speed
. Over the past
decade e
-
Learning has gone from being a minority learning approach used
in some areas

of distance education to becoming

a key part of the
educational process followed in the majority of tra
di
tional face
-
to
-
face
learning institutions
. This transformation is argued to result from two
changes in our modern society: fi
rstly, to satisfy the need for
lifelong
learning
,

where mature students need
/ want to continue their studies

and
update their kno
wledge, competences and skills
without the commitment
of attending taught classes. Secondly, with the proliferation of broadband
technology and the availability of low price computing hardware, students
are used
to
having access to
all sorts of
Internet
-
ba
sed services, and
generate a demand for them
to fulfil p
art of their

educational needs

too
. It
is not
,

therefore
,

su
r
prising that
there is a lot of interest in using e
-
Learning
systems for L2. However, these
systems (or
online educational platforms,

virtua
l learning environments
, etc.
) provide

nothing more than a
means t
o
shorten dist
ances between students
and their teachers
,

and
suitable tools to
enable the former

to
practise,
interact and thereby learn. While this
situation is
relatively easy to achieve

f
or

small groups of students and their
corresponding teacher/

tutor, as the general demand for
certain
L2 learning
increases, it becomes steadily more difficult to maintain such ratio
s.

Another solution is required.

Computers have been seen as tools which c
ould play an important role in
L2 learning since they first appeared (Levy, 1997).
However, typical
Computer Assisted Language Learning (henceforth, CALL) systems are
quite limited and their impact on L2 learning has been small. To address
this problem,
Ar
tificial Intelligence techniques have been added over the
years to lead to a wide range of systems (henceforth, ICALL systems, e.g.,
Amaral &
Meurers
, 2011;
Wood, 2008;
Bailin, 1995; Chanier, 1994;
Gamper & Knapp, 2001; Holland et al., 1999)
, although
with

limited
success
.

These systems typically focus on the most formal
or
organizational linguistic aspects,

attempting to

replace a human native
speaker

to correct erroneous student comprehension and/or production.
Furthermore, where knowledge or

domain model
ling is undertaken, it is
mostly
done in a very ad hoc way, where the results of one system cannot
be easily transferred to subsequent ones.

Most systems
used for L2 learning
present three problems: the
oversimplification and reduction of the vastness and
complexity of the

183

learning domain to a few formal linguistic aspects (studied in closed and
decontextualized activities), the lack of underlying pedagogic principles,
and the complexity of automatic language parsing and speech recognition.
To overcome thes
e shortcomings
a theoretical framework which combines
individual Cognitive Constructivism and collaborative Social
Constructivism has been designed by the authors for implementation in
ICALL systems
(
Bárcena & Read [2004; 2009],
Read et al. [2002a; 2002b;
2004; 2005; 2006])
. It simulates the way an experienced language teacher
would interact with his/her students. The framework
attempts to
capture
and
model the
relevant

pedagogic, linguistic and technological
elements

for the effective development of L2 com
petence
. One of the goals were

th
at any ICALL system

developed around this framework
would

structure
the complex network of
communicative language
competences
3

(linguistic,

pragmatic and sociolinguistic) and processes (reception, production and
interaction
)

within the

L2
learning
process
in
a
causal quantitative way
,
adapting
such
process to the progress made by a given student.

The
authors
argue that the essentially qualitative terms used in the

Common European Framework of Reference for Languages: Learning,
Teaching, Assessment

(h
enceforth, CEFR; Council of Europe, 2001). The
CEF
R need to be specified in a more quantitative way (so that th
ey can be
included in a computational

model). Here, the
mo
delling granularity
is
crucial,
because

when someone’s global L2 competence is examined, what
is really found is not a unitary
measure

of ability but heterogeneous
degrees of capabilities that encompass the different modalities and
functions, which go far
beyond the traditional level of formality.
Furthermore, while the exact underlying cognitive and neuropsychological
aspects involved in verbal communication acts are still largely a matter for
academic debate, certain
external

aspects (general context, dom
ain, spatial
scenario, subject matter, status and mutual relationships, etc.) are directly
observable and, therefore, it is possible to interpret them in quantitative
terms
that enable them to be modelled in I
CALL system
s. This is
something that
has not be
en tackled up to now.


2. Modelling Second Language Learning




3

Refer to the Common European Framework of Reference for Languages: Learning, Teaching, Assessment (Council of Europe,
2001) for a precise int
erpretation of the concepts related to language use and learning used in this article.



184

Early research undert
aken by the authors produced ad
hoc models, which
were needed for each different ICALL system.
Su
ch an approach lead to
rather superficial
mod
els that became obsolete every
time a

new system
was
to be
designed. Hence, it was evident that a deeper modelling activity
was required to produce a

conceptual L2 learning framework that could be
used

and extended for future applications
.
As
such
,

the

framework was
developed
(Read et a
l. [2002a; 2002b; 2004; 2005; 2006])

as

a high level
abstraction of
L2 learning

and

subsequently,
the

relation

between the key
elements

argued to play a role in
this process

was
specified

(Bárcena &
Read [2004; 2009])
.

This
relation
is presented
in figure

1

and its
functioning can be summarized as follows.

Initially, individual learning is
undertaken through the performance of
simple closed

L2
activities,
organised in a notional
-
functional way
(van Ed and Alexander, 1975;
Wilkins, 1976)

and
involvin
g suita
ble tasks which include
reading
comprehension, pronunciation practice, new vocabulary learning, etc.
Once there is evidence that prototypical conceptual learning starts to take
place, collaboration becomes possible (for the same notion or concept)
through
the performance of more complex activities (typically involving
several associated tasks). For collaboration to occur, the students working
together must be capable of reaching mutual understanding. Such
understanding requires communication in the
L2
betwe
en the activity
participants which, in turn, requires communicative strategies to be
adopted (with the
implicit
intervention of what is known as
existential
competence
, namely, the learner's personality features, motivations,
attitudes, beliefs, etc. that
influence his/her learning progress). The
application of these strategies
,

therefore
,

permits collaboration to take
place, reinforce previous individual learning, and trigger further individual
study.

Over the last few years,
there has been an extension

to

this relation. S
ince
mobile devices are an ever

more common tool for accessing online
education systems,
the

framework
has been extended to include the
characteristics of ubiquitous learning as defined by Chen et al. (2002) and
Curtis et al. (2002).
Since

these characteristics are neither cognitive nor
collaborative, but have repercussions in both the student and
group
models,
a third model, a ubiquity model is needed that fits into the
framework as
can be seen in
figure

1
.



185


Figu
re 1
. Relation between individual, collaborative and ubiquitous learning

T
he authors
argue

that a student’s
second language competence

improves
in relation to the way

s/he
moves between individual and collaborative
learning
processes.

Here, s
tudent and gro
up models
are
updated
accordingly as progress is made,
taking into account the
aspects of
information and device
ubiquity present. T
he framework can
,

therefore,
adapt the materials and activities to the current learning context. The
underlying student and
group mo
dels established
and the ub
iquity model
needed to extend the framework

are described
subsequently, once the L2
domain is characterised and its models presented
.


3. Characterising the L2 Domain

The fundamental problem when attempting to model the L
2 domain is the

complexity of natural language and the ways in which its use
(and
learning)
can be specified. A significant breakthrough in this area came
about with the publication of the
CEFR

(
Council of Europe, 2001). The
CEFR offers
, albeit in an essen
tially qualitative fashion,
a way to structure
the knowledge and skills required in second language learning. It
is not
surprising that it
has been adopted
in the majority of European L2 learning
contexts, and even the
prestigious

L2 courses that different

European
countries
are
now
certified
in terms of the CEFR.
It
has been widely
accepted
(Morrow, 2004)
because it provides a notional
-
functional
classificati
on of language use and learning

and it is the first general
attempt to produce a taxonomy of the el
ements that intervene in language
use and learning, enabling comparable syllabi to be created for all
European languages.
It captures
the widespread functional and
communicative perspective of human language

and
follows an action
oriented approach, which t
akes into account cognitive, volitional and

186

emotional resources as well as the abilities specific to a learner both as an
individual and as a social agent.

However, in
order to use the CEFR as the linguistic reference for the L2
domain model
,

the authors
have

extracted eight
fundamental
c
onc
epts

which enable a comprehensive and insightful

quantitative representation to
be established

for the purpose of
implementing

I
-
CALL applications
,
namely:


1.

Language proficiency levels
.
The

CEFR
defines six
language
proficiency levels
, as a general classification of a given student’s L2
ability
:

from
Breakthrough

or
A1
, the lowest level of generative language
prof
iciency which can be identified, through to
Mastery or
C2
.

2.

Communicative language competence
. The most fun
damental
distinction made in the CEFR is between
communicative language
competences

and
activities
. The former are the set of the learning resources
(knowledge, skills, etc.), and the latter are what the learner can do with
them.

3.

Competence descriptors
. O
ne of the key CEFR concepts used in
the framework is that of
can
-
dos
, i.e., the language competence descriptors
at a given common reference level (A1, A2, etc.). The CEFR offers a
number of tables of illustrative descriptors which have been expanded and
ad
apted
for this
domain
model

4.

Contextualized language activities
.
P
rototypical activity

is used
to refer generically to activities according to the direction of
communication:
reception
,
production
,
interaction

and
mediation
. When a
prototypical activity is seen in the context of one of the four spheres of
reality that are distinguished in professional English, it is referred to as a
contextualized language activity
.

5.

Communicative language processes
. There are defined in the
CEFR to be the chain of neuropsychological and (and physiological)
events involved in the reception and production of speech and writing. For
the sake of computational modelling, these processes have been reduced to
what is typically used in the literature

as the “four basic linguistic skills”:
reading, writing, listening, and speaking. While the communicative
language activities are contextualized in the system according to the
spheres of reality, these processes entail
modality
, i.e., their oral/written
input/output nature.


187

6.

Domains/spheres of reality
. The external context of language
use in the CEFR can be seen to be divided into domains and situations.
Four domains are distinguished: educational, occupational, public and
personal. In this framework, howe
ver, the term
domain

has been
substituted by
spheres of reality

because it is polysemic here since it has
rather different meanings in sublanguage theory and also in AI. For the
professional speaker, four spheres of reality have been identified in the
fram
ework
: private (the sphere of intimate relationships), personal (the
sphere of personal acquaintances), public (the sphere of general social
relationships with other citizens), and occupational (the sphere of
relationships in
specialized working

environmen
ts).

7.

Situations.
The notions covered in the
materials

are also
distributed across various
situations
,
locations

and
text types
, which
constitute the external context of use of the language together with the
spheres of reality. The CEFR distinguishes situa
tions and, within these,
locations, institutions, persons, objects, events, operations and texts. In
order to simplify such descriptive parameters, the three categories with
most impact on professional English were selected, namely locations (with
attentio
n to the type of related leisure/work activities), social roles
(persons, with attention to mutual personal and/or work relationships) and
text
-
types (texts).

8.

Texts
.
T
hey provide samples of a particular type of notion and
they embody vocabulary, grammatic
al forms and functions that are to be
studied in relation to such a notion. Furthermore, language contents are
organized in
topics
, and linked to the texts (there are between three to six
topics for every text). It is essential to note that texts in the “C
EFR sense”
can in fact be written texts (e.g., e
-
mail, chats), audio sequences (e.g.,
radio recordings, phone conversations), video fragments (e.g., scenes both
within and outside of the company) and images (e.g., plans, photographs).

As well as the CEFR
inspired concepts, two additional
pedagogic features

are included in the
L2 domain models
, namely: a spiral approach and the
use of scaffolding. Firstly,
a

common problem that any language teacher
will be

familiar with is that of

a student who appears to h
ave
learnt
a given
language structure
but

later
produces errors when trying to use it. This is
an example of an internalisation problem, where short term learning is not
transferred to long term storage for
future use. This can be a big problem
for any L2
learner, since such

“knowledge holes” may eventually lead to a
“structural collapse” of the learning process
. Hence
, the
spiral approach

188

has been incorporated into the framework.
R
ather than following a
structural
approach to material presentation, where
t
opics
are
tightly
organised in sequences, the language difficulty is only partially graded and
topics are reviewed “in a spiral fashion”, where the same topic is revisited
now and again with an increasing level of complexity (Martin, 1978).
Secondly,
sc
affolding

has been seen empirically as a learning facilitator. It
is a didactic mechanism which provides support to the student when
difficulties are detected by adding “supportive devices”, and gradually
removes it as evidence of comprehension appears, a
clear metaphor of the
way in which buildings are actually scaffolded as they are built
(McLoughlin and Marshall, 2000
;
Bárcena and Read, 2004).


4. Knowledge Meta
-
model and Models

Once

a chara
cterisation of the L2 domain has

been produced for the
framewo
rk
,

it is necessary to define the
knowledge models
(which
have
evolved over a period of years as different ICALL systems have been
designed and explored
)
. Emphasis is given to the re
usability

of
information (
one of the most persistent
criticisms of AI syst
ems is the
large effort required to capture the knowledge domain which cannot be
reused subsequently in other systems
)
.
As described in this section, t
he
framework currently has
one meta
-
model
4

and
a set of
knowledge models
that can be split into two types
: domain and student/

group models.

As has been noted above,
the CEFR’s essentially qualitative nature makes
it impossible to directly use in any language learning system
, so
quantitative model
s that subsume and expand

the CEFR

have

been
developed. A central underlying structure for

these models is the so called
meta
-
model

that
enables a representation of the necessary linguistic
conceptual and
functional knowledge, competences and

skills in the
L2
domain to be structured and interre
lated. The meta
-
model takes the form of
a 3D space that characterizes language lea
rning (illustrated in figure 2
), in
terms of the increasing
language proficiency levels

that are achieved by
the
units (dimension 1), the
communicative language processes
tha
t are
developed by the activation of
conceptual units
5

(dimension 2), and the
contextualized language activities

that can be performed by such
activation (dimension 3). In such a fine grained activity
-
based



4

A model that explains or underlies a set of other models.

5

A fundamental competence item, formed by the intersection of the three dimensions. These items can
be seen

represented graphically by the dots in figure 3.7.


189

characterization of
communicative language

compet
ence assessment, a
distinction is made according to the learning phase. This reflects the
degree of attention being applied by the student on the application of a
particular conceptual unit, which is a determining factor to assess the level
of assimilation

of such unit.

This
meta
-
model is divided into two parts: the
individual and collaborative learning zones (shown as A and B). The
intersection of each dimension does not define a small set of values that
portrays learning, as if it were a “pure” stereotype

student model (Kay,
2000), but contributes to an overall communicative language competence
state, following a multidimensional stereotype model. This space is
initially empty and then gradually populated by points corresponding to
conceptual units, which
are related across the learning dimensions, as the
students learn and apply them in different activities and processes.

This meta
-
model provides a fine
-
grained representation of L2 learning,
since at any given time, a student
is not completely “at a partic
ular
proficiency level”, in the sense of having all the points within the 3D space
at the same level, but has the points distributed over adjacent levels,
depending on past experience and the state of consolidation of the
conceptual units in different comm
unicative language processes and
contextualized language activities. The uniform progression within the
space ensures that knowledge holes or gaps do not appear and therefore
prevents structural collapse of a student’s communicative language
competence (Re
ad et al., 2002a and 2002b). It also ensures balanced




190


Figure
2
. The meta
-
model

learning

between the different dimensions that make it up: communicative
language processes and contextualized language activities, e.g., a learner
of English who has very good vocabulary (because he reads endlessly in
this language) will not be left to increase h
is lexical repository and reading
at the expense of neglecting his listening or speaking skills, so that he
progresses effectively in his learning.

Now that the underlying meta
-
model has been detailed
,

it is possible to
move on to detail the actual knowled
ge models that use it in the
framework.
The
L2
domain
is represented in two
model
s: one

represent
s

the linguistic and
didactic information
, and the other,
the cognitive and
collaborative information about the students and how they work to learn.
The
problem domain

is made up of four mo
dels: conceptual, linguistic
knowledge, collaborative template and content
.

They can be described as
follows:



M1:

the
conceptual model

defines the knowledge domain for learning
professional English.

In this model,
the can
-
dos are classified for the
activities within the spheres of reality and the communicative language
processes at the different language proficiency levels.



M2
: the
linguistic knowledge model

relates each can
-
do to the grammar,
semantics, and discou
rse topics that a student needs to learn in order to “be
competent” in the particular can
-
do.



M3
: the
collaborative template model
defines the structure of the
activities and tasks that the students undertake in groups, together with
references to the too
ls and resources they use.



M4
: the
content model

contains the actual linguistic (and related)
knowledge that the students learn when working with the system. Different

191

types of didactic materials are stored to offer students a wide range of
learning optio
ns and practice depending, among other things, upon
previous study sessions and results.

The
student model

(shown in figure
3
) represents the state of
communicative language competence together with the profile and history
of a student’s individual and co
llaborative activities.

This model can be seen to be divided into four parts:

1.

The personal details of the student
. This information defines the
way in which a student prefers to work with the system: in a system driven
fashion or as a mixed initiative pr
ocess. In the former, the system leads the
student through the learning process. In the latter, the student is granted a
certain degree of study flexibility in terms of the selection and type of
materials.

2.

The individual learning activities undertaken wit
h the system
.
This is a log that prevents unnecessary repetition by recording the list of
activities that a student has done. This information is important not only
for current system functioning, but also for future analyses of effective
ways (or “study p
aths”) in which students work.



Figure
3
. The information represented in the student model

3.

The collaborative activities undertaken by a student
. This
takes the form of references to instances of group models, which record
the roles, tasks, etc., undertaken, and the results obtained (as will be seen

192

below, groups are adaptively formed and only last for the life of an
activity).

4.

The English commun
icative language competence
characterisation
. This is an instantiation of the information contained in
the 3D meta
-
model.

A

further
model is required both to structure and to coordinate the way in
which the students work together: the
group model
. This mod
el stores
collaborative data and represents the details of the set of students working
on particular tasks within an activity, detailing interactions, mistakes, etc.
This model (shown in figure
4
) is a single register that characterizes the
collaborative a
ctivity of the group. The data in the model comes partially
from the questions a student
-
monitor
(a student in the group with a
supervising

role)
has to answer when the group is evaluated, and partially
from the activity log.

The information contained in
this model is required since adaptive group
formation requires a history of previous student group activities when
assigning roles and membership. Furthermore, the log of these group
models permits the learning process undertaken by each student to be
anal
ysed so that the overall didactic properties of the framework can be
evaluated. The student and group models have different functional roles in
the system. A student model is instantiated when a student enters the
system for the first time, and exists unti
l the student is removed
(administratively speaking) from the system. A group model in contrast,
once formed, is active and available only during a particular collaborative
activity. Once the activity has finished, the group is dispersed, the group
data is

logged as part of the “history of the learning process”, and the
group model deactivated.



193


Figure
4
. The information represented in the group model

Finally, a
ubiquity model

was developed that incorporated the content,
device an
d language learning context characteristics that are necessary for
representing the various degrees of ubiquity which a second language
learner might encounter (as can be seen in figure 5). It is important that the
content is reliable, accurate and structu
red for users at different knowledge
and expertise levels. Content must be fully understandable and navigable,
ideally not depend upon one particular type of software or hardware, and
be suitable for different types of devices (e.g., screen resolutions, co
lours,
sizes, etc.). In essence, it must be completely user
-
centred, and not device
-
centred. Regarding the device, the potential for providing different degrees
of ubiquity in a learning environment requires the student to be provided
with the content or m
eans to develop certain activities at any place, any
time, and of course, undertake them with no problems. Communication
(student
-
student, student
-
teacher) should always be set up so that a system
or platform works correctly and allows the concurrency of s
everal users
for collaborative purposes. Therefore, connectivity issues must be assured
so that they do not restrict or disrupt learning, because this could have a
negative effect on student progression. Even though the available technical
infrastructure i
s a key factor, this term cannot be included directly as an
indicator inside the model, since it is device aspects and content
interoperability issues that measure the efficiency of the technical
infrastructure.


194

As well as information regarding the content

and device, this model
represents the degree of ubiquity present for given language learning
activities, devices and situations in terms of a
communicative language
learning environment
. This environment specifies the constraints which

enable language activities to be carried out following



Figure 5. The information represented in the ubiquity model

the CEFR, and has been refined by the authors from previous work (Read
et
al., 2006). It defines the specific way in which a particular handheld
device can be used in a given real world context to undertake language
learning using this framework.

5. Framework Applications and Conclusions

The framework presented in this article a
nd its knowledge models have
been
iteratively
refined by use in different ICALL applications
. I
n each
case, test study

results
hav
e shown improved learning
(
with the relevant

system
) when compared to
individual study
in

e
-
Learning

groups.
Specifically, four
systems are
representative of the main milestones in this
research
: I
-
PETER I, I
-
PETER II, COPPER and I
-
AGENT.

I
-
PETER I
(Intelligent Personalised English Tutoring EnviRonment; Read et al.,
2002
a
) wa
s the first system to use
this framew
o
rk

in its current version at
the time
. This
system contains no collaborative learning and
enables error
diagnosis
of individual learning activities
to be undertaken using a

195

Bayesian network, to reflect how teachers actually undertake this type of
process i
n the classroom. The results of this diagnosis process enable a
finer
-
grained control

of material selection than is normally possible, giving
rise to a course structure that is continuously adapted to individual student
needs.

I
-
PETER II takes off where I
-
PETER

I finishes

(Read et al., 2004
)
,
using

the same
Bayesian diagnosis
process for a larger domain (a Business
English course; 56 networks were used)
for individual learning together
with
peer and tutor corrected collaborative activities. For the first
time, the
activities were structured to combine differential knowledge use
(
mechanical reproduction + non
-
attentive application)
.

COPPER
(
Collaborative Oral and written language adaPtive Production
EnviRonment
; Read et al., 20
05
) combines
individual and
collaborative
learning
.

This is the first system to use the full framework
as
detailed in
this article. L
anguage
use
is
conceptualised in this system
as one of several
cognitive competences that are mobilised and modified when individuals
communicate.
H
igh

granularity expert
-
centric Bayesian networks with
multidimensional stereotypes

are used to update
student activities semi
-
automatically.
A
n adaptive group formation algorithm dynamically
generates communicative groups based upon the linguistic capabilitie
s of
available students, and a collection of collaborative activity templates. The
results of a student’s activity within a group are evaluated by a student
monitor, with more advanced linguistic competences, thereby sidestepping
the dif
ficulties present w
hen using natural language processing

techniques
to automatically analyse non
-
restricted linguistic production. Students
therefore initially work individually on certain linguistic concepts, and
subsequently participate in authentic collaborative communica
tive
activities, where their linguistic competences can develop
approximately
as they would in “
real foreign language immersio
n experiences”
.

The work
produced in two COPPER test groups in
a

final task undertaken in
a
pilot
study, was both quantitatively a
nd qualitatively superior to that in the first
one: it was more intelligible, more idiomatic, had less mistakes, and was
more adequate for the communicative scenarios that had been created for
the activities. Such an improvement was less noticeable in the
control
group.

Currently, the system I
-
AGENT (
Intelligent Adaptive Generic ENglish
Tutor
; Bárcena & Read, 2009
) is being finished, which is the first system
to
substitute the use of Bayesian networks for reasoning techniques taken
from the field of Web Se
mantics
, although the framework presented here

196

is used in its entirety
.

It is also different to previous systems in that
it
integrates collaborative online work via Moodle and f
ace
-
to
-
face
classroom lessons

(i.e., it uses a blended learning approach).

In this article
the models that make up
an innovative framework for use in
ICALL systems

have been
presented. It
has been
argued that such a
specification
is needed to facilitate the design and development of
general
purpose
ICALL systems
that can be used
by L2 teachers and students
.

Future work is
needed
to focus on how to incorporate the modelling of
neuropsychological aspects of language use and learning contained in the
CEFR (developing concepts such as existential competence, the mental
contexts of the

interlocutors, intentions, lines of thought, expectations,
states of mind, memory effects, etc.), and their incorporation in both the
individual and collaborative parts of the framework. For example, the
study of the optimum way in which roles can be chan
ged within a
community of students (as a result of these aspects) when undertaking
different activities in new groups may also prove to be a fruitful line for
future research in the field of collaborative learning.

Regarding subsequent evaluation of the f
ramework, a large scale test will
be necessary to study how students actually improve their overall
communicative professional English competence. A much larger
experiment (with more students at different communicative language
competence states, an extens
ive sample of activities and groups, etc.) will
facilitate a more controlled and contextualized study of the system. In
order to evaluate each of its features, parallel experimental groups will be
needed that work with controlled versions of the system in
order to allow a
given feature to be tested in isolation. This work is important because, as
well as enabling linguistic improvement to be evaluated, it will also permit
the robustness and scalability of the computational implementation to be
tested.


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