Learning strategies and Learning Objects structural models : how to classify them

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Current Developments in Technology-Assisted Education (2006)


Learning strategies and Learning Objects' structural models:
how to classify them
M. Pedroni1
1 CARID Centro di Ateneo per la Ricerca, l'Innovazione Didattica e l'Istruzione a Distanza, Università
degli Studi di Ferrara, Italy

In e-learning context, teaching-learning strategies are carried out inside the different structural models of
Learning Objects. The attempt to compose a taxonomic classification of those models, which structure is
hierarchic, is possible but arbitrary, because its development is necessary founded on some parameters,
and omits, or subordinates, other parameters. More interesting is the multidimensional classification,
which places the different models on as many coordinates as the parameters, and intercross them, using
adapted graphics modalities, obtaining forms of representation free from arbitrary choices on the
parameters' importance levels. The more meaningful issue of this research on Learning Objects'
classification is the possibility to use the obtained strutcture for the choice of the didactic strategy to apply
in e-learning courses, related to learners' and learning context's characteristics and to methodological and
technological issues and compatibilities.
Keywords learning strategies, Learning Objects
1. Introduction
The technologies of online communications within the context of e-learning are used in two basic
directions: for the construction and use of Learning Objects on one side and the implementation and
fruition of interactive functions on the other, the latter being further divided into the two macro-
categories of organizational-administrative and teaching functions.
While the dividing line between organizational and learning interactions is fairly clear-cut thanks to
their different goals (with the exception of those within the learning environment connected to certain
aspects of activity tracking reporting), the same cannot be said of the dividing line between Learning
Object management functions (design and realization environments, or Learning Object Management
Systems and fruition and tracking environments on the basis of the SCORM protocol) and the functions
aimed at sustaining the synchronous and asynchronous dialogue between process actors, collaborative
activities, methods of learning assessment and process quality and, in general, all those functions that
contribute to improving the learning process.
The study carried out by CARID of the University of Ferrara [1] regarding the taxonimization of
Learning Objects gives further evidence of the existence and spread of structural models tinged by
numerous learning strategies which cross the dividing line between a unique transmission of knowledge
(transmission in which the informational flow is substantially one-way and interaction is limited to the
options offered by navigation technologies implemented in HTML protocol), and two-way transfer of
information and documents between the user and the structure, or between a community of users within
the context of a collaborative fruition of the Learning Object (this is the case of Learning Objects
afferent to Problem Solving, Simulation, Virtual Role Playing, Web Quest, and Collaborative Concept
Map models).
Consequently, the rigorously hierarchical taxonomic structure proposed by the above-mentioned study
and illustrated below, does not satisfy the needs for exhaustively indicating the relations between the
various learning strategies and different Learning Object models. On the other hand, the very act of
analysis takes into consideration awareness of this problem, making note that a precise and definite
classification of structural reference models within the context of asynchronous e-learning is not possible
for a number of reasons. On the one hand, the differences between certain models are not evident enough

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to allow for clear attribution in the analysis of learning documents, and on the other the development of
new models resulting from the evolution of online technologies, their progressive differentiation in
derived structures and their fusion or interpolation in complex document contexts, impose continuous
ontological revision in this regard.

LEARNING OBJECT STRUCTURAL MODELS
Models based on explicit transfer of content

Sequential structure models


Presentation


Story
telling

Hierarchical structure models


Tutorial


Field
trips

Grid
structure
models


Conceptual maps for navigation support


Information
maps


Information
Landscapes

Models based on interactive learning


Single
user
models


Sequential structure models


Interactive structure models: Drill & Practice

Single or collaborative use models


Explorative



Case-based
learning



Problem
solving


Play



Games



Simulations

Necessarily-collaborative
models


Generative
Learning


Web
Quest


Role-playing


MUDs and Virtual Worlds


Collaboratively constructed conceptual maps


2. Beyond the taxonomy
How then must this taxonomic schema be structured, or restructured, taking into consideration the
imprecision and flexibility of the boundaries between the various Learning Object models, and primarily
in function of the arbitrariness of the definition of a hierarchical order between classification criteria?
The proposal that emerges from CARID research is founded on the need to reconsider the concept of
taxonomy itself in light of the potential offered by communication technologies in representing
knowledge. If taxonomy is interpreted as hierarchical organization of classification criteria, this imposes
difficult choices not easy to maintain and susceptible to numerous criticisms depending on the way the
problem is approached; vice versa if the criteria used for classification is interpreted from the standpoint
of a multi-dimensional model based on the use of Cartesian space and the representation of objects and
the relations between them, the possibility exists to compose a schema that is certainly dynamic and
evolvable and certainly the object of discussion and negotiation, but not vitiated by basic choices that
preclude sharing (figure 1).

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One could hypothesize a different visualization mode in this particular context, based on the
representation of analysis parameters using a system of Cartesian axes and graphing techniques that does
not call for creating various levels of importance, and the resulting placement of the functions and tools
through which they are concretized within this system.
Resulting from this is the potential for exhaustive expression of interpolation characters between the
functions through their representation not as a point that intersects precise, unique values within the
parameters, but in the form of a line or area that connects or circumscribes sets of values.
As an example, let’s look at the identification of the Tutorial as a model for content transmission in
which the level of learning interaction may range from a minimal level of sequential connection between
topics, to an intermediate level of hierarchical representation or graph mapping of the cognitive
environment involved, up to a high level marked by forms of interpolation with interactive functions
such as learning assessment through tests and inclusion of addition content.

Fig. 1 representation in an x-y axis of Learning Object models and e-learning functions [2].

In other words, a division between Learning Objects, like that presented in the taxonomic diagram,
based on the explicit transmission of content or active role of the user or community of users [1, 3],

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reveals from even initial exploration its inelasticity or, more precisely, its inability to adequately support
evolution and interpolation between a range of instruments, structural models and learning strategies. On
the other hand, a Cartesian space defined by a system of axes corresponding to classification criteria,
such as that shown in figure 1, easily meets the problems of adaptability to modified contexts, both
through repositioning of objects within the schema, as well as through insertion of additional factors of
criteria study.
The definition of independent parameters in the classification of Learning Object structural models is
not limited to quantitative and qualitative levels of content transmission, but also includes other aspects,
in particular process management and context description.
Within the functions, the process management axis measures the incidence of process monitoring and
control actions. In Learning Objects, the development of these actions corresponds to the degree
classification and tracking protocols are implemented (LOM, AICC, SCORM).
By “context description” we mean the ability of a function, through its use, to correctly and constantly
transmit the perception of the cognitive context in which it operates. Those functions located on a high
level of this axis are therefore, for example, Learning Object models based on the interactive use and
representation of the context in tree or map form, or those interactive models which, in line with the
needs of Knowledge Management, arrange knowledge contributions in a structured manner, i.e.,
Generative Learning.


Fig. 2 conceptual map of a little number of Learning Object models [3].

3. Conclusion: the use of conceptual maps

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The representation of function classification structure becomes more complex. To an initial schema that
can be reduced in essence to parameters of content transmission and interaction – in other words a two-
dimensional schema – two other axes are added that relate to process management and context
description; the result is the impossibility of utilizing Cartesian space (whose representation, for obvious
reasons, does not go beyond three-dimensions).
But other graphic solutions for representing parameters in different and alternative forms to
orthogonal axes do exist. For example, maintaining the basic two-dimensional structure, the size or
colour of the positioned objects may be configured as parametric measures or, alternately, putting aside
completely Cartesian spaces or planes, Learning Object models and classification criteria may be
inserted as nodes in grid structures in which relationships are expressed graphically and through the use
of text labels, as defined in conceptual map theory in order to support both quantitative and qualitative
aspects (for example, how a given object, for example a Learning Object, implements the actions of
process management).
To conclude this study aimed at identifying the most effective tools for correct and thorough
representation of the whole of Learning Object learning strategies and structural models, we note how
conceptual maps through their adaptability in supporting the visualization of all E-R (entity-relation)
type schemas present themselves as optimal tools for going beyond the structural limits of the
taxonomies that exclusively involve hierarchical connections, as well as the intrinsic graphic limitations
of other representational forms, thus also permitting and facilitating collaborative modes of research,
analysis and development of this ontological context.
The comments offered to this point could be considered the prelude to a more in-depth look at the
research activity already initiated by CARID at the University of Ferrara, the purpose of which is to
construct a representational model that is both exhaustive and elastic within the context of Learning
Object learning strategies and structural models, and therefore ontological activity founded on a mode of
criteria study which is not aimed at consolidating a priori hierarchical relationships between various
classification parameters.


References
[1] M. Pedroni, E-learning e rappresentazione della conoscenza. Ferrara, Tecomproject (2006)
[2] P. Frignani, L. La Vecchia, M. Pedroni, G. Poletti, Dal forum strutturato all'ambiente di strutturazione della
conoscenza, proceedings of Conference Progettare e-learning, Macerata, Italy (2006)
[3] M. Pedroni, La rete: strumento, modello, messaggio, proceedings of Conference Saperi, competenze e nuove
tecnologie, Crotone, Italy (2006)


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