FACET AND PRISM BASED MODEL FOR PEDAGOGICAL
INDEXATION OF TEXTS FOR LANGUAGE LEARNING
The Consequences of the Notion of Pedagogical Context
Mathieu Loiseau, Georges Antoniadis and Claude Ponton
LIDILEM, Universit Stendhal Grenoble 3, Grenoble, France
Keywords:
Pedagogical indexation, Computer assisted language learning, Natural language processing, Modeling, Meta-
data.
Abstract:
In this article, we discuss the problem of pedagogical indexation of texts for language learning and address it
under the scope of the notion of “pedagogical context”. This prompts us to propose a new version of a model
based on a couple formed of two entities : prisms and facets.
We first evoke the importance of material selection in the task of planing a language class in order to introduce
our point of view of Yinger’s model of planing applied to language teacher’s search of texts. This is closely
intermingled with the elaboration of the notion of pedagogical context from which our model stem. This
version though in a way similar to our first attempt provides sounder notions on which to build on.
1 PEDAGOGICAL INDEXATION
1.1 The MIRTO Project
The MIRTO project, started in 2001, stemmed from
the observation of various recurrent issues in Com-
puter Assisted Language Learning (CALL) systems:
rigidity, inability to adapt the learning sequences to
the learners and the necessary adaptation of teachers
who are not provided with means to manipulate con-
cepts pertaining to their field of expertise (language
didactics) (Antoniadis et al., 2004). The aim of Multi-
apprentissages Interactifs par des Recherches sur des
Textes et l’Oral (MIRTO) was to promote the use of
Natural Language Processing (NLP) in order to ad-
dress those problems by adding an abstraction layer
between the user and the material he/she manipulates.
Indeed, they consider that to allow teachers to formu-
late their problems in didactics relevant terms, lan-
guage should not be handled as character sequences
but as a system of forms and concepts (Antoniadis
et al., 2005). In order to do so, MIRTO proposes to
separate treatments (e.g. gap-filling exercise genera-
tion script) and the data on which they are to be ap-
plied (a text in this case).
1.2 Definition and Objectives
This made evident the need for a text base, which,
for consistency’s sake, would have to allow user’s to
perform language teaching driven queries. In other
words, a subpart of the problem was the conception
of a system that could perform pedagogical indexa-
tion of texts. In this work we defined pedagogical in-
dexation as “an indexation performed according to a
documentary language that allows users to query for
objects in order to use them for teaching” (Loiseau,
2009). Considering the aforementioned context, we
are therefore working towards pedagogical indexation
of texts for language learning.
Indeed, a study of the literature concerning the
most often used language teaching methods and a
series of interviews with some language teachers
prompted us not only to consider this problem in the
context of the future use of the text in a CALL activ-
ity, but to try to consider the problem globally: few
of the teachers we had interviewed were really com-
puter savvy, all the same, they all underlined the im-
portance of text search in their practices. We later got
confirmation of this nature of things by a larger scale
study, which established text search as a common task
in language teaching (Loiseau, 2009).
Having modified the scope of our work – with-
out completely cutting ties with MIRTO, for integra-
tion remained a perspective – into the conception of
413
Loiseau M., Antoniadis G. and Ponton C. (2010).
FACET AND PRISM BASED MODEL FOR PEDAGOGICAL INDEXATION OF TEXTS FOR LANGUAGE LEARNING - The Consequences of the Notion
of Pedagogical Context.
In Proceedings of the 5th International Conference on Software and Data Technologies, pages 413-422
DOI: 10.5220/0003015504130422
Copyright
c
SciTePress
a model for pedagogical indexation of texts for lan-
guage teaching, we started to consider the existing
means to achieve it.
1.3 Learning Resource Description
Standards
A wide array of research tackles the definition and
use of learning resource description standards. The
principal standards we analyzed were Learning Ob-
ject Metadata (LOM) (IEEE, 2002), Sharable Con-
tent Object Reference Model (SCORM) (SCORM,
2006) and some teaching oriented application pro-
files of the Dublin Core Metadata Initiative (DCMI)
(GEM, 2004; edna, 2006). As for providing a so-
lution to our problem, all the standards we stud-
ied came with the same flaws, most of which come
from the fact that these standards try to integrate in
the same model, entities of very different conceptual
level: the ressources used to set up activities (low ag-
gregation level in the LOM terminology) and the ac-
tivities themselves (higher aggregation level) (Pernin,
2006). Balatsoukas et al. take the analysis a little bit
further in pointing out that the lower the aggregation
level of the learning object the broader its spectrum
(i.e. the range of activities that can be performed with
it) (Balatsoukas et al., 2008). Indeed, in the particu-
lar case of texts (raw resources), the descriptors pro-
vided by the standards seem, at best, difficult to use:
how does one assign a “Description” (“Comments on
how this learning object is to be used” (IEEE, 2002))
when the resource potentially could be used in differ-
ent contexts.
The approach advocated by Recker & Wiley pro-
poses to treat differently what they call intrinsic
(“derivable by simply having the resource at hand”)
and extrinsic properties (which “describe the context
in which the resource is used”) (Recker & Wiley,
2001). All the same, their analysis cannot be directly
transposed to our problem, for their aim is to provide
a collaborative resource description system in which
authoritative and non-authoritative annotation coex-
ist. On the other hand our aim is, in the first place,
to provide a model that would allow a system to auto-
mate as much as possible the pedagogical indexation
of texts. User annotation is, in this context, more a
potential extension of the system than a core feature.
There was therefore at this point no clear cut direction
in which to go: the pedagogical properties seemed to
constitute extrinsic properties for the raw resources
that are texts, thus potentially discarding educational
metadata as a solution. We therefore decided to resort
to an empirical study to confirm this hypothesis and
get a grasp of teachers practices regarding text search.
2 PEDAGOGICAL CONTEXT
2.1 Empirical Study
Our empirical study took the form of a survey, which
built on a series of interview and an exploration of
the literature, part of which we have just summed up
above. Beyond the confirmation of the hypothesis of
the multiple uses texts can have in language teaching,
we aimed at obtaining a first look into the process of
text search. We meant our point of view to be as gen-
eral as can be, in the hope to extract invariants, that
would remain unaffected by variables such as the lan-
guage taught, the country in which it is taught or to
whom. The study was mostly filled online, but also
in paper form, both medium adding up to 130 testi-
monies. Beside confirming unequivocally that texts
can be used in various language teaching situations
1
,
the survey allowed us to extract a (non necessarily ex-
haustive) list of four practices that lead to texts being
used in language learning: search for a text to use
in a precise activity, writing the text, text encounter
during personal readings and texts on a syllabus (of
any form). We will focus here on the provenance that
is closest to the role of a pedagogically indexed text
base, i.e. the search for a text in order to use it in a
specific activity, which also happens to be the most
widely represented practice (concerning nearly 97%
of the teachers answering the survey).
2.2 Adaptation of Yinger’s Model
To describe the task of searching for a text for a
given activity we resorted to using Yinger’s model
of planification (Yinger, 1978) or more precisely part
of it. Yinger defines planing as a three stage pro-
cess: problem finding, problem formulation/solution
and finally implementation, evaluation, routinization
(Yinger, 1978). In our task, the problem is already
found (the teacher has an activity in mind) and the
search is supposed to provide a text to actually use
in class and thus precedes implementation. We fo-
cus here on the problem formulation/solution, which
according to Yinger is an “helicoidal” repetition of
three phases: elaboration, investigation and adapta-
tion (Yinger, 1978), which we adapt to our problem
under the labels selection, evaluation and transforma-
tion (cf. figure 1 p. 3) (Loiseau, 2009).
The dashed semi-ovoid at the bottom of figure1
contains a set of texts the teacher has access to. The
1
97,3% of the teachers who answered the question de-
clare they consider that a given text can be used with vari-
ous goals in different contexts and 94,5% of them (92% of
our the sample) declare having done so.
ICSOFT 2010 - 5th International Conference on Software and Data Technologies
414
Transformation
Evaluation
Selection
+ Evaluated properties
+ Projected properties
Figure 1: Yinger’s model adapted to text search.
intensity of the gray inside the form represent to
which extent they are pedagogically “connoted”. For
instance a text taken straight from a newspaper and
that has never been used in teaching (to the knowl-
edge of the teacher) is not connoted, whereas a text
recommended by peers or found inside a textbook has
some sort of pedagogical connotation. The aim is not
to evaluate this “connotation” or even theorize it, but
to acknowledge that the teacher can resort to sources
with different statuses.
The selection phase consists in the teacher relying
on his necessary preconceptions
2
projecting onto the
text properties linked in a way or another to the activ-
ity they are planning. An example of such a behav-
ior would be a teacher choosing an author based on
some properties they attribute to their writing: “Roald
Dahl, [...] all his short stories are packed with these
verbs [...] for emotion and gestures [...], that in French
[require] a whole phrase [...].” (testimony from our
study).
Once the text is selected based on the properties
that the teacher has attributed a priori to the text, the
text is actually in the hands of the teacher (or virtu-
ally so) for the first time in this planning sequence
and they can now attribute a new set of properties to
the text. They are no longer projected properties, they
constitute the teacher’s actual perspective on the re-
source based on the activity they want to set up with
it. This set of properties can confirm or invalidate the
ones that have been assigned during the first phase
or concern totally different aspect of the text. For in-
2
Without preconceptions this phase would consist in a
random selection of texts.
stance, it is completely imaginable that the teacher we
quoted above should confirm her hypothesis, but con-
clude that the short story can turn out to be difficult
for her learners, which brings us to the last phase: tak-
ing action upon the evaluated properties. The action
transforms the text status-wise, there are three alter-
natives:
• the text is assigned a use context corresponding
the teacher’s current search and is transformed
into actual teaching material (solid arrow in fig-
ure 1);
• the text, though considered unfit for this particular
activity, is deemed useable in another context and
can be kept for future use in a personal repository:
it is transformed into potential teaching material
(dotted arrow in figure 1);
• the text is not relevant from the teacher’s point of
view and is just discarded (not represented).
2.3 First Definition
The description of these three phases allowed us to
precise the role of a pedagogically indexed text base:
it is meant to assist the teacher in the selection phase
and possibly allow him to perform it according to
less instinctive criteria when applicable (for example
concerning the linguistic content of the text), but it
also allowed us to introduce the notion of Pedagogi-
cal Context (PC)
3
as: “set of features which describe
the teaching situation” (Loiseau, 2009). This notion
is especially useful in order to describe the process of
text search and its integration in a learning sequence
for the various iterations of the above scenario corre-
spond to a gradual definition of the PC: the material is
a component of the teaching situation (Charlier, 1989)
thus influencing it and at the same time its choice is
influence by the other components of the PC since the
search is performed for a given activity. In order to
achieve pedagogical indexation of texts for language
learning, it seems necessary to be able to take into ac-
count the PC, which means studying the link between
components of the PC and the actual properties of the
text.
3 PC AS AN INFLUENCE CAST
ON TEXT PROPERTIES
Among our objectives with our second survey was
trying to establish relations between properties of the
3
In order to avoid exceedingly numerous repetitions, we
will either refer to it using “PC” or its complete form “Ped-
agogical Context”.
FACET AND PRISM BASED MODEL FOR PEDAGOGICAL INDEXATION OF TEXTS FOR LANGUAGE
LEARNING - The Consequences of the Notion of Pedagogical Context
415
PC and properties of the text. We cross-examined:
• the activity type (gap-filling exercise – 3 types –,
comprehension activity, introduction of new no-
tions – vocabulary or syntax –) with the size of
text, the number of representative elements of a
notion (if the notion is the preterit this will be
the number of preterit conjugated verbs of the
text, and the tolerance to newness (vocabulary and
grammar-wise);
• the learners’ first language and tolerance to new-
ness ;
• the learners’ level and tolerance to newness.
The length of the text and the number of represen-
tative elements were numerical variables and asked
for each activity type. In this case, the tolerance to
newness was evaluated using two separate categori-
cal variables, one concerning new vocabulary (other
than the object of the lesson) and the other concern-
ing new grammatical structures (other than the object
of the lesson). Both variables could take their values
between “proscribed”, “tolerated” and “sought”. For
each activity type used, we asked the teachers to rate
their tolerance to newness using this scale for both
variables.
When crossed with the learners’ level and first lan-
guage, the tolerance to newness was also the object of
a closed-ended question. These questions allow the
teacher to state that the criteria is not relevant or can
decide not to answer. The other two possibilities de-
pended on the question and do not distinguish vocab-
ulary and grammar:
• first language: the more similar the mother tongue
and the learned language, [the more/the less] one
will accept unknown grammatical structures or
vocabulary;
• level: the higher the level, [the more/the less] one
will accept unknown grammatical structures or
vocabulary.
The results can be summed up by figure 2
4
.
Properties such as the length of a given text are to-
tally independent from the Pedagogical Context and
thus do not need it to be computed, but our study
showed that the activity type had an effect on text
length
5
, which means that depending on the activity
type, teachers will be looking for texts of different
4
Due to room restrictions we cannot include detailed
statistics in this paper, they are available in section 5.3
(pp. 231–245) of (Loiseau, 2009) though.
5
ANOVA: F(143) = 3, 362 ; p <, 01. Post-hoc tests are
significant when comparing “comprehension activity” with
the various forms of “gap-filling exercises” (Loiseau, 2009).
Text
Properties
Pedagogical
Context
Text length
Goals
Audience :
- Level
- L1
Activity
Decision
Number of representative
elements
Unknown vocabulary
and structures
Decision
Decision
System
text
description
Sequence for a given text
Figure 2: Influence of the pedagogical context on the attri-
bution of text properties.
lengths. A text property such as the number of repre-
sentative elements of a notion obviously depends on
the notion, which in turn is a direct consequence of the
pedagogical goals of the teachers. Likewise, the num-
ber of representative elements of a notion that is con-
sidered appropriate by the teacher will depend on the
activity type (e.g. 4 or 5 occurrences might be enough
to introduce a notion, whereas to practice it under the
form of a gap-filling exercise teachers seek an aver-
age of 11 occurrences)
6
. Finally, if the amount of un-
known vocabulary/structures is a property of the text,
it cannot be evaluated unless we link it with the au-
dience with whom the activity is going to be used. It
directly depends on the level of the students, which is
also used differently afterwards to take a decision on
whether or not to use the text: the higher the students’
level the more tolerant the teachers will be regarding
the presence of new vocabulary or structures (other
than the object of the lesson). The activity type
7
and
the proximity between the learners’ language and the
one that is taught also seem to have a significant effect
on the tolerance to “newness”
8
.
The various tests we have performed on the above
series of variables tend to show that the Pedagog-
ical Context indeed influences text properties. We
lack data to precisely characterize the relations be-
6
ANOVA: F(127) = 4, 739 ; p <, 005. Post-hoc tests are
significant when comparing “introduction of a new notion”
with “comprehension gap-filling exercise” and “introduc-
tion of a new syntactic notion” with “form aimed gap-filling
exercises” (Loiseau, 2009).
7
χ
2
(10) = 32, 2 ; p <, 001 (Loiseau, 2009).
8
81.3% of teachers taking into account their learners
first language considered that closer languages allow more
tolerance (Loiseau, 2009) and 71.4% consider that the
higher the level of the learners the more unknown vocab-
ulary/structures they will accept (Loiseau, 2009).
ICSOFT 2010 - 5th International Conference on Software and Data Technologies
416
tween text properties and the PC, but we have been
able to demonstrate their existence. The fragmen-
tary knowledge we have come to gather have yet al-
lowed us to explore examples of ways to take into ac-
count these concurrent influences that the Pedagogi-
cal Context has on text properties or on the way to
act upon them. Interestingly, they all follow the same
pattern, the properties which depend on the PC repre-
sent a sort of point of view of the text reflecting the
problem of the teacher in his search. The Pedagogical
Context, despite still representing the same entities in
the real world, has become, thanks to a switch of fo-
cus, “a paradigm casting its influence on the texts’
properties”.
4 PRISM-FACET BASED MODEL
The following model aims at taking into account the
role of the Pedagogical Context in the evaluation of
text properties, in order to propose help to the user
in his selection task. It is a second version of the
model which has been introduced in (Loiseau et al.,
2008). We will first describe this new version of the
model, before we conclude by explaining the main
differences between the two versions.
4.1 Recursive Definitions
The model is articulated around a couple of two indis-
sociable notions: prism and facet. The prism insures
that the properties are coherent in the way they are
computed: “a prism is a mechanism – computerizable
or not – associated to a property defined considering
the texts’ later exploitation in teaching, which allows
to assign a value to this property for all text depending
on a given pedagogical context”
9
.
This definition allows us to highlight the link with
pedagogical indexation: the definition of the prism
depends on the needs of the teachers. This definition
revolves around the difference between the concep-
tual level of the properties (class of properties) and
their value (after instantiation). It is the essence of the
prism which is the procedure which allows to make
the transition from the first to the latter, when apply-
ing the concept to a given object (a text).
This leads us to the formalization of the property
and like the prism depends on the property it is meant
to describe depends on its alter ego: “a text facet is a
property of the text, which was defined with a view to
its pedagogical exploitation in laguage teaching and
9
Translation of the definitions page 257 of (Loiseau,
2009).
for which an evaluation procedure can be defined and
applied to any given couple (text, PC)”
9
.
4.2 Facet and Facet-Value
Before we go on and explore the consequences of the
above definitions, we shall enter a terminological is-
sue. Like the term “property”, the word “facet” is, as
we use it, polysemic. It can, depending on the context,
designate either the concept or the attribute. For in-
stance, “parallelism” is a property (concept) which is
applicable to a certain type of object, and two planes
(for instance the ground and a shelf) can have the
property to be parallels. In the case of facets, we
might use the word to designate either the property in
its conceptual form – facet F
i
, text facet or just facet
with no other precision – or its value for a given cou-
ple (text, PC) – a given text’s facet, F
i[CP]
(T ) –.
4.3 Constant Facets
From the point of view of the task of selection, the
facet is the central entity on the conceptual level: in
the planning process, the facets represent the notions
upon which the teachers base their reasoning. A ped-
agogically indexed text base will not be able to take
into account every teacher’s individual point of view
of every facet presented to them (or not in the near
future), the usability of such a system therefore relies
on the prisms, which offer consistency through their
mechanical, systematic, nature.
Going through some of the properties represented
in figure 2 will allow us to explain further the model.
Text
T
F
author
(T)
=Андрей
Курков
P
wrdCount
P
author
F
wrdCount
(T)
=1101
Figure 3: Prism examples and values for the corresponding
facets (independent from PC).
In figure 3, we indicate two examples of facets.
The word count (F
wrdCount
), which is exactly the same
as the property in figure 2 and F
author
corresponding
to the author of the text. The diagram also presents
the values of these facet for a given text T . We intro-
duce a functional notation based on the facets, even
though strictly speaking the application that allows
the computation of the values is defined inside the
FACET AND PRISM BASED MODEL FOR PEDAGOGICAL INDEXATION OF TEXTS FOR LANGUAGE
LEARNING - The Consequences of the Notion of Pedagogical Context
417
prisms (P
wrdCount
and P
author
, here), which precise the
status of both entities:
• the prism is a tool, materializing a process;
• the facet is a concept, a text property which has a
value for every couple (text, PC).
4.4 The Pedagogical Context in the
Model
In these first examples, the Pedagogical Context does
not influence the value of the facet, which remains
constant for a given text T for any PC. The aim of
the model is to represent more complex properties. In
figure 2, the number of representative elements is an
example of such a facet. We represent it in figure 4.
In this figure, a sole prism (P
repE
t
) is shown revealing
F
repE
t
[Pretérito]
(T
1
)=0
F
repE
t
[haber* que +
inf]
(T
2
)=2
F
repE
t
[haber* que +
inf]
(T
1
)=4
F
repE
t
[Pretérito]
(T
2
)=3
Text
T
1
Text
T
2
P
repE
t
PC
2
PC
1
pretérito indefinido
haber* que + inf
Figure 4: Prism examples and corresponding facets for 4
different (text, PC) couples.
two facets for each text. Each of the two facets of T
1
and T
2
corresponds to a different Pedagogical Context
for which both text could be compared to come to a
decision. In the example of figure 4, the T
1
contains 4
occurrence of haber que structures
10
and no preterit,
while T
2
contains 2 occurrences of haber que struc-
tures and 3 occurrences of preterit. Figure 4 also rep-
resents the metaphor behind the name of prism and
facets. In this metaphor the Pedagogical Context is
a light cast on a text through a prism, thus revealing
one of its facets. Consistently with its optical counter-
part the prism divides the ray of the PC to keep only
the components (frequencies) which are necessary to
compute the value of the facet. Applied to a system
which would assist the user in its selection task, the
choice of prisms would have an expressive function:
the user would only be asked of the PC components
10
Used to express duty in Spanish: Para soar hay que
dormir (to dream, one has to sleep), Habr que resistir un
tiempo ms (One will have to go on resisting for a while).
required by the prisms selected, thus providing them
with means to describe the features of the teaching sit-
uation which are relevant for their search (figure 5).
T
PC
P
n
P
i
P
1
Figure 5: Expressive function of the prisms.
The notation introduced in figure 4 is meant to
render the difference of status that exist in the model
between the PC and the texts. This difference comes
from the function of the model, namely to provide
a framework for the implementation of a system of
pedagogical indexation of texts for language learn-
ing. When performing a given iteration of the cycle
described in 1, the PC is constant. Of course, for a
task of text search to yield a text that is actually used
in language learning, the Pedagogical Context might
evolve during the various iterations of the cycle, but
the PC will be constant inside a given selection sub-
task (for which a system is supposed to provide assis-
tance) of a given cycle. Yet, each prism is evidently
meant to be reusable from one cycle to the other and,
by definition, has to be able to compute values of its
associated facet for all PC
11
, hence the notation.
4.5 Prisms as a Mean of Selection
By definition, indexation is essentially a description
task (Bertrand et al., 1996), yet its aimed at allow-
ing users to easily spot the texts that satisfy their
needs, an objective of discrimination. In our case,
part of the discrimination task, will not be automat-
able (e.g. based on interestingness or on the ability
to give rise to a debate), the other part will mostly
rely on constraining the tolerated values of facets. We
have concluded that the better way to model that kind
11
The implementation of certain facets, such as the num-
ber of occurrences of a given type of reported speech (direct,
indirect, free indirect) would require manual intervention.
All the same a mechanism can be defined in order for a hu-
man to annotate it (making it a facet). In a system, such a
facet could be implemented on a set of texts. To make such
texts coexist in a system with not annotated texts (treating it
as a subcorpus), not applicable has to be an accepted value
of a facet for a text in a certain pedagogical context.
ICSOFT 2010 - 5th International Conference on Software and Data Technologies
418
of constraint is to integrate it inside the Pedagogical
Context and thus to take it into account in the value
of the facets. A constrained version of a facet just
add a phase to the mechanism associated to its com-
putation: after the value of the non-constrained avatar
of the facet is computed a simple test instruction can
be added, to return false if the constraint is not met
and the value computed otherwise. In the constrained
facet obtained, the expression of the constraint is part
of the Pedagogical Context. Indeed, it is relevant to
the problem of the teacher to decide, depending on
the situation they want to use the text in, to exclude
texts based on the value of facets such as its length.
We have been convinced of that when trying to
consider higher level facets. For instance, one can
imagine developing a prism which would allow to
take into account the information we have gathered in
our study regarding the activity type
12
: let F
AN
be the
facet associated to this prism. F
AN
could be a boolean
property telling whether a text is potentially suitable
for an activity. The PC components used would be
the activity type and the notion on which to work.
The treatment would rely on the facets we have called
F
wrdCount
and F
repE
t
, fixing threshold values for each
activity type (for instance a gap-filling exercise could
not be longer than n words and could not contain less
than, say, 5 occurrences of the notion. The constraint
of F
wrdCount
and F
repE
t
is directly derivable from the
PC of F
AN
, which is a clue in the direction of our so-
lution. But the decisive element is the fact that the
threshold values that could be defined based on our
study, despite lacking precision, come from teacher
declarations. They were given the possibility to con-
sider the criteria not pertinent, which means that it
is very likely that it corresponds to a conscious fea-
ture expected in the text (if not explicitly evaluated)
and thus qualify as a component of the Pedagogical
Context. We do not consider this the only solution,
but find it a consistent and practical one.
4.6 Facet vs Metadata
The notions of facet and prism allow to:
• associate the concept (facet) and its modeling,
making explicit the sense of the concept handled
by the tool (prism);
• model the influence of the Pedagogical Context on
the properties of the objects (texts).
These two characteristics distinguish facets from
metadata. According to Bourda, metadata is informa-
12
The actual implementation of such a facet would re-
quire much more experimentation: we only have declared
practices, which would lack precision.
tion on objects which can be understood by humans
and processed by software (Bourda, 2002). Both
facets and metadata are therefore meant to propose
a global point of view of an object rather than high-
light information contained in the document (for in-
stance F
repE
t
means to provide a unique value asso-
ciated to a structure, not to list all the occurrences of
the structure). This similarity in the object of both
notions is especially conspicuous for constant facets
(cf. figure 3), which could be treated with metadata.
But in the same way that constant functionals such as
f (x) → 0 are a particular case of functionals, constant
facets are only a particular case of a generic notion,
which cannot be efficiently modeled with metadata.
This can be shown with the example of F
repE
t
.
In order to implement comparable description with
metadata one would need to anticipate any possible
request made by teachers. The text “Rabbits run.”
would require a descriptor saying it contains one oc-
currence of the form “rabbits” but also one occurrence
of a form the lemma of which is “rabbit”. The text
should also be found if the teacher is looking for the
form “run”, but also if they are looking for a text con-
taining occurrence of the present simple of the verb to
run. We already have 4 descriptors indicating one oc-
currence of a given structure. But it might also be
pertinent to know that the text contains one occur-
rence of “rabbits run”, one of a form whose lemma
is “rabbit” with the verb run, one occurrence of the
form run associated to a plural subject, etc. And this
only concerns a 2 word text.
When the Pedagogical Context offers a certain va-
riety of potential values – each of which should be
associated with a value for each text – the fixedness
of metadata requires to anticipate every single one of
them, making it potentially hazardous or inefficient
as far as storage is concerned (in our example, despite
not being exhaustive, we have found 7 descriptors for
a single facet and a two word text). Facets and prisms,
by associating a property and a means to compute it
introduce flexibility and dynamicity in the description
of resources, which seem necessary to handle the no-
tion of Pedagogical Context.
5 TOWARDS IMPLEMENTATION
The example of F
repE
t
leads to considering implemen-
tation options. Indeed, in order to introduce flexibility
and to make computation of facet values possible, the
information on the text provided by F
repE
t
relies on
information of the text. The computation of values
of F
repE
t
could be handled first by performing mor-
phological analysis of the text, before using regular
FACET AND PRISM BASED MODEL FOR PEDAGOGICAL INDEXATION OF TEXTS FOR LANGUAGE
LEARNING - The Consequences of the Notion of Pedagogical Context
419
expressions on the resulting annotated version of the
text. We will refer to the information of the text added
by the first part of the process as underlying proper-
ties of the text. They are to be analyzed to provided
information on the text, namely facet values.
When implementing this sequence of treatments
in the perspective of indexing them, the addition
of underlying properties (morphological analysis for
F
repE
t
), which will be referred to as pre-processing,
should be performed once and for all, when the text
is added to the system. On the other hand, in order
to introduce the dynamicity that metadata lacks, the
computation of facet values, which we will refer to
as computation, need to be performed when the user
queries the system.
5.1 Prisms and Functions
This decomposition of the prism’s mechanism as a se-
quence of treatments decomposed into pre-processing
and computation allows us to answer the question
asked by note
11
p 6. When implementing a facet
based system, a prism mechanism can require human
pre-processing but computation needs to be fully au-
tomatable.
As far as implementing prisms, to provide evolu-
tivity and take advantage of already developed tools
(especially NLP procedures), we recommend reusing
the concept of function as defined in MIRTO (Anto-
niadis et al., 2004). According to this point of view
a prism is linked to a facet and composed of two se-
quences of functions: pre-processing and computa-
tion (cf. figure 6).
Fn
4
Fn
2
Fn
3
Fn
n
Fn
i
Fn
1
Interface
Coordination module
Language
teacher
Text
collection
Pre-processing
Computation
Views
FunctionsScripts
Prisms
Selection
Evaluation
Figure 6: Proposed general architecture for a facet based
system.
5.2 Views
In figure 6 prisms are not the only entity composed
of functions. As an extension of the indexation sys-
tem and a means for the user to interact with the sys-
tem we introduce the notion of views. Considering
the complexity of certain properties which intervene
in the process of searching for a text to use in lan-
guage teaching and the difficulty to achieve reliability
in NLP when moving away from the form, a realist
approach needs to acknowledge the amount of work
left to the user during the phase of evaluation. Among
other considerations, the fact that “100% reliability
is, and may stay in the future, an unattainable goal.
Therefore it is more realistic to stress on ‘assisted’
rather than ‘fully automated’ approaches” (Blanchard
et al., 2009) is at the origin of their “didactic trian-
gulation strategy”. Adapting it to our problem, views
come as a mean to assist language teacher in the eval-
uation phase. They are meant to allow the user to ac-
cess to some of the underlying information, in order
to help them in their evaluation, adopting a qualita-
tive point of view where prisms are quantitative. For
instance in figure 7.
Figure 7: Example of views linked to F
repE
t
for PC preterit.
A user looking for a text to have their learners
work a structural exercise on the preterit tense in En-
glish, might want a text with at least 7 occurrences of
the tense. They might want to make sure that the text
contains irregular verbs including “to be”. To discard
a text the list view would be sufficient and might be
more convenient than the highlighted view (see fig-
ure 7), which would offer to the teacher an in context
glimpse at the verbs, that might be preferable to make
sure that the resulting activity would not prove too
difficult (or easy) for the learners.
The notion of view has not been fully formalized
yet. The link with facets has to be specified further:
are some of the views completely independent from
ICSOFT 2010 - 5th International Conference on Software and Data Technologies
420
any facet (and thus prism), relying on their own pre-
processing or should they all be linked to a facet the
way the views in figure 7 are to F
repE
t
? Should the
ones that are linked to specific facets solely be linked
to them by their common pre-processing or should
they before all be linked to a prism ?
6 CONCLUSIONS
We introduced this model as a second version of a pre-
vious work (Loiseau et al., 2008). This new version is
not only justified by a concerned to make it clearer:
despite being similar in philosophy, it comes after
the theorization of the notion of Pedagogical Context.
Even though present in the first version of the model,
PC was roughly defined. The work on the notion has
allowed us to build on sounder basis the notions of
facet and prism, which have be subject to semanti-
cal alteration. The prism was in the first version a
global module of the system handling all processes
and which is now explicitly linked to a facet, thus un-
derlying the tight link between the two of them.
Despite its simplicity, prism P
wrdCount
exemplifies
this relation, the kind of approximation inherent to the
task at hand and the usefulness of NLP in the imple-
mentation of such a system. Depending on the capac-
ities of the pre-processing
13
the definition of the facet
can be altered (or the other way around). The word
count can be based on a list of separators between
which lie the words to be counted. But in this case the
French “chou-fleur” could be two words, while it ac-
tually designate a precise object (cauliflower)
14
. The
decision of which kind of treatment to use can come
from a didactic question: one wants to evaluate the
length of the text, in order to provide an idea of size
of the text, considering compounds as separate words
might not be a problem. But one might consider that
the word count should be as consistent with the lin-
guistic definition of word as possible. But what in-
terest teachers could actually be to consider as words
only non function words in order to get a better grasp
at the quantity of vocabulary necessary to understand
the text. On the other hand the choice of what the
facet actually means might come from purely practi-
cal reasons: the available word count function works
with no dictionary whatsoever and cannot distinguish
function words from others or even identify a com-
13
In this case the pre-processing actually could evaluate
the property, due to its independence from the PC.
14
’-’ should be a separator in French since it is added
when the verb and subject are inverted to form a question:
Dort-elle ? Oui, elle dort comme une masse. (Is she sleep-
ing? Yes she is sleeping like a log)
pound. In both, case the link between the concept be-
hind the facet and the prism should remain unaltered,
might it mean modifying the prism, the facet or both...
The meaning of view has also changed (the view
of this version of the model corresponds more or less
to the visualization of the former) leading to alter-
ation of the implementation. The questions raised in
the previous section by this extension to the evalua-
tion task are among the various implementation ques-
tions at hand. We are implementing a prototype of
this version of the model. It will undoubtedly raise
more questions, such as the definition of a framework
for prisms in order to make their integration and de-
velopment easier.
Such a definition could also lead us to consider the
problem of the system’s adaptation to its users up to
allowing them to create their own prisms and facets.
Indeed we have seen with F
AN
that a new prism could
with didactic added value could be implemented with
very little treatment (threshold values definition) be-
yond the grouping of two existing prisms. Careful
analysis and specification of implementation conse-
quences of the properties of prisms might constitute a
viable path toward end-user programming functional-
ities (Nardi, 1993) through the creation of compound
prisms.
REFERENCES
Antoniadis, G.,
´
Echinard, S., Kraif, O., Lebarb
´
e, T.,
Loiseau, M., & Ponton, C. (2004). NLP-based script-
ing for CALL activities. In Lemnitzer, E. H. L., edi-
tor, COLING 2004 eLearning for Computational Lin-
guistics and Computational Linguistics for eLearn-
ing, pages 18–25, Gen
`
eve. COLING. Available from:
http://hal.archives-ouvertes.fr/hal-00190373/fr/.
Antoniadis, G.,
´
Echinard, S., Kraif, O., Lebarb
´
e, T., &
Ponton, C. (2005). Mod
´
elisation de l’int
´
egration de
ressources TAL pour l’apprentissage des langues :
la plateforme MIRTO. ALSIC, 8(Num
´
ero sp
´
ecial
TALAL):65–79. Available from: http://alsic.u-
strasbg.fr/v08/antoniadis/alsic v08 04-rec4.htm.
Balatsoukas, P., Morris, A., & O’Brien, A. (2008). Learn-
ing objects update: Review and critical approach to
content aggregation. Journal of Educational Tech-
nology & Society, 11(2):119–130. Available from:
http://www.ifets.info/journals/11 2/11.pdf.
Bertrand, A., Cellier, J.-M., & Giroux, L. (1996). Ex-
pertise and strategies for the identification of the
main ideas in document indexing. Applied Cogni-
tive Psychology, 10(5):419–433. Available from:
http://www3.interscience.wiley.com/journal/21437/
abstract.
edna, edna resources - metadata application
profile [online]. (2006). Available from:
http://www.edna.edu.au/edna/webdav/site/myjahiasite
/shared/edna resources metadata 1.0.pdf.
FACET AND PRISM BASED MODEL FOR PEDAGOGICAL INDEXATION OF TEXTS FOR LANGUAGE
LEARNING - The Consequences of the Notion of Pedagogical Context
421
GEM. Listing of GEM 2.0 top-level ele-
ments [online]. (2004). Available from:
http://www.thegateway.org/about/documentation/
metadataElements/index html.
Blanchard, A., Kraif, O., & Ponton, C. (2009). Mastering
noise and silence in learner answers processing: sim-
ple techniques for analysis and diagnosis. CALICO
Journal. Available from: http://tr.im/calicoabokcp.
Bourda, Y. (2002). Des objets p
´
edagogiques aux
dossiers p
´
edagogiques (via l’indexation). Docu-
ment num
´
erique, 6(1-2):115–128. Available from:
http://www.cairn.info/revue-document-numerique-
2002-1-page-115.htm.
Charlier,
´
E. (1989). Planifier un cours, c’est prendre des
d
´
ecisions. P
´
edagogies en d
´
eveloppement. S
´
erie 5,
Nouvelles pratiques de formation. De Boeck Univer-
sit
´
e, Bruxelles ; Paris.
IEEE (2002). Final 1484.12.1 LOM draft standard
document. Technical report, IEEE LTSC WG12.
Available from: http://ltsc.ieee.org/wg12/files/
LOM 1484 12 1 v1 Final Draft.pdf.
Loiseau, M. (2009).
´
Elaboration d’un mod
`
ele pour
une base de textes index
´
ee p
´
edagogiquement pour
l’enseignement des langues. PhD thesis, Uni-
versit
´
e Stendhal Grenoble 3. Available from:
http://tel.archives-ouvertes.fr/tel-00440460/fr/.
Loiseau, M., Antoniadis, G., & Ponton, C. (2008).
Model for pedagogical indexation of texts for
language teaching. In Cordeiro, J., Shishkov,
B., Ranchordas, A., & Helfert, M., editors, IC-
SOFT (ISDM/ABF), volume ISDM/ABF, pages
212–217. INSTICC Press. Available from:
http://mathieu.loiseau.free.fr/bdtip/fichiers/articles/
icsoft-2008.pdf.
Nardi, B. A. (1993). A Small Matter of Programming: Per-
spectives On End User Computing. MIT Press, second
printing (1995) edition.
Pernin, J.-P. (2006). Normes et standards pour la concep-
tion, la production et l’exploitation des EIAH. In
Grandbastien, M. & Labat, J.-M., editors, Environ-
nements informatiques pour l’apprentissage humain,
pages 201–222. Herm
`
es et Lavoisier, Paris.
Recker, M. M. & Wiley, D. A. (2001). A non-
authoritative educational metadata ontology for
filtering and recommending learning objects. In-
teractive Learning Environments, 9(3):255–271.
Available from: http://search.ebscohost.com/ lo-
gin.aspx?direct=true&db=aph&AN=5848430&site
=ehost-live.
SCORM (2006). SCORM overview. Sp
´
ecification SCORM
2004 3rd Edition Content Aggregation Model Version
1.0, Advance Distributed Learning. Available from:
http://tr.im/scorm2004 3.
Yinger, R. J. (1978). A study of teacher planning: De-
scription and a model of preactive decision mak-
ing. East Lansing, MI. Michigan State Univer-
sity, Institute for Research on Teaching. Avail-
able from: http://www.eric.ed.gov/ERICWebPortal/
detail?accno=ED152747.
APPENDIX: ACRONYMS
CALL Computer Assisted Language Learning
DCMI Dublin Core Metadata Initiative
edna Educational Network of Australia
EIAH Environnements Informatiques pour
l’Apprentissage Humain
GEM the Getaway to Educational Material
LOM Learning Object Metadata
MIRTO Multi-apprentissages Interactifs par des
Recherches sur des Textes et l’Oral
NLP Natural Language Processing
PC Pedagogical Context
SCORM Sharable Content Object Reference
Model
TAL Traitement Automatique des Langues
ICSOFT 2010 - 5th International Conference on Software and Data Technologies
422