From the Editors
Welcome to
the January 2011 issue of the Learning Technology newsletter.
Semantic
web technologies have the potential to improve the quality of ICT applications
and services in many application domains. In the context of TEL, they are
extensively used to mainly improve the quality of searching and retrieving
learning resources. This issue introduces some papers which describe research
semantic web technologies for TEL. Celino et al., describes the PANDORA project
which is creating a training environment to coach Crisis Managers. Azevedo et al., describe the usage of ontologies in
learning object repositories and discusses how to improve keyword specification
and query expansion through ontologies. Finally, Ruiz-Calleja
et al., propose a methodology which is based on Linked Data to improve the
retrieval of educational tools. The issue also includes a section with regular
articles (i.e. articles that are not related to the special theme). Iskandar et al., outline a machine-processable
model of learning outcomes, aiming to provide a better understanding of
teaching and learning; and Moradi et al., describe a
project which aims to evaluate the effects of educational games on (the working
memory of) children under age of 7.
We
sincerely hope that this issue will help in keeping you abreast of the current research
and developments in usability aspects of TEL. In our effort to improve the
usefulness of the newsletter, this issue also includes an annex with a list of
conferences related to Learning Technology (the list is taken from ASKs
Web-Site, at http://www.ask4research.info).
We also
would like to take the opportunity to invite you to contribute your own work on
technology enhanced learning (e.g., work in progress, project reports, case
studies, and event announcements) in this newsletter, if you are involved in
research and/or implementation of any aspect of advanced learning technologies.
For more details, please refer to the author guidelines at http://www.ieeetclt.org/content/authors-guidelines.
Deadline for submission of articles: March
15, 2011
Special
theme of the next issue: Advanced Learning Technologies for Disabled
and Non-Disabled People
Articles
that are not in the area of the special theme are most welcome as well and will
be published in the regular article section!
Editors
Sabine Graf
Athabasca University, Canada
sabineg@athabascau.ca
Charalampos Karagiannidis
University of Thessaly, Greece
karagian@uth.gr
Special Theme Section: Semantic Web Technologies
for Technology Enhanced Learning
Introduction
In a
catastrophic event, human behaviour determines the efficacy
of crisis management. Timeliness of reactions and exactness of decisions are
the most relevant factors. In this context, training plays an important role to
prepare Crisis Managers.
Technology-enhanced Learning (TEL) bridges the gap between table-top exercises low-cost
preparation testing the theoretical responsiveness to a situation and real-world simulations very effective
and expensive exercises to gain valuable skills. TEL provides a near-real
training environment at affordable costs. The PANDORA project
(http://www.pandoraproject.eu/) is creating a training environment to coach
Crisis Managers.
Modelling a Crisis Knowledge Base
To
re-create crisis scenarios, PANDORA employs a Crisis Planner based on
Timeline-based Planning and Scheduling technologies [4]. This Planner creates training storyboards of
events for the trainees (e.g. news videos, phone calls or e-mails) and
reacts to trainees strategic decisions, triggering consequent events to
continue the training session.
To simulate
such scenario, a great effort is required to understand the problem specificity
and to model the relevant aspects [3]. Within the PANDORA project, we are building the
Crisis Knowledge Base (CKB) collecting and maintaining the knowledge about
crisis scenarios and training sessions. The CKB illustrated in Figure 1:
- models
the trainer knowledge about scenarios and training path alternative
options;
- provides the
Crisis Planner with events to be triggered during the training sessions;
- supplies
other PANDORA components with information about the training session;
- records each sessions events and
decisions, to create individual trainee reports at the end of the
training.
To fulfil those requirements, the CKB should model the crisis
scenarios, the training events, the trainees behaviour,
etc. This is an opportunity to exploit Semantic Web Technologies (SWT) for TEL.
The
Crisis Knowledge Base Ontologies
SWT call
for the adoption of ontologies
[11] to explicitly formalise
shared knowledge conceptualizations. Moreover, developing modular ontologies [10] allows for an improved reuse of such modelling.
We designed
two modular OWL [12] ontologies to model the CKB
knowledge:
- A Timeline-based Planning Ontology(http://swa.cefriel.it/ontologies/tplanning),
reflecting the basic conceptualization of Planning Applications based on
Timeline Representations; and
- A PANDORA Ontology(http://swa.cefriel.it/ontologies/pandora),
specifying the Crisis Management Training entities (training events,
trainees, crisis situations, etc.).
- The
Timeline-based Planning Ontology models the knowledge handled by a
Timeline-based Planner; this ontology represents a valuable foundation for
a systematic use of SWT in Planning research. The
PANDORA Ontology represents the domain-specific modelling
of crisis scenarios. Finally, our ontologies refer to pre-existing models,
like the Time Ontology (http://www.w3.org/TR/owl-time/)
or the WGS84 Geo-Positioning vocabulary (http://www.w3.org/2003/01/geo/),
thus linking the CKB to other datasets.
- Still SWT offer further opportunities: we
are extending our CKB by modelling the logical
consequences between the crisis events in rule languages like RIF [2] or the SPARQL 1.1 Entailment Regimes [5]; we are exploring existing implementations using
SPARQL [9] for inference, like SPIN [6].
Figure 1 - The
Crisis Knowledge Base.
Populating
the Crisis Knowledge Base
The CKB
stores the crisis data as RDF triples [8] described in the aforementioned ontologies. While the
basic data are provided by the trainers, who hold the experience to model such
knowledge, they cannot insert all the potentially useful information to
describe a crisis scenario (e.g. a town topology); still, such elements can be
crucial to make the training realistic.
We adopted
Linked Data principles [1] to connect our CKB to the Web of Data. Linking to GeoNames, for example, lets our CKB to directly benefit
from a geographical database containing over 10 million geographical names.
Similarly, the CKB is linked to other general-purpose datasets like Freebase (http://freebase.com/) or DBpedia
[7] (http://dbpedia.org/).
We will
also publish our CKB as Linked Data. This brings two advantages: we provide our
contribution to the Web of Data, enabling other researchers to re-use our
knowledge base; and we open the CKB to the community contribution, which can
extend our knowledge base.
Conclusions
and future works
In this
paper we presented our work towards a comprehensive Crisis Knowledge for the
Crisis Managers Training. We explained how we employ SWT to enhance this TEL
environment. Our future activities include the CKB deployment as Linked Data
and the development of a read/write REST API to let external components
interact with the CKB. Our approach will be tested and validated in the PANDORA
environment.
Acknowledgments
This
research is partially funded by the EU PANDORA project (FP7-ICT-2007-1-225387).
References
[1]
Christian Bizer, Tom Heath, Tim Berners-Lee: "Linked Data - The
Story So Far", International Journal on Semantic Web and Information
Systems, Vol.5, Nr.3, p.1-22 (2009).
[2]
Harold Boley et al.:
"RIF Core Dialect", W3C Recommendation, http://www.w3.org/TR/rif-core/ (2010).
[3]
Amedeo Cesta, Gabriella Cortellessa, Simone
Fratini, Angelo Oddi:
Developing an End-to-End Planning Application from a Timeline Representation
Framework, Proceedings of the 21st Applications of Artificial
Intelligence Conference (2009).
[4]
Amedeo Cesta, Simone Fratini: The
Timeline Representation Framework as a Planning and Scheduling Software
Development Environment, Proceedings of the 27th Workshop of the UK Planning
and Scheduling SIG (2008).
[5]
Sandro Hawke et al.: "SPARQL 1.1 Entailment
Regimes", W3C Working Draft, http://www.w3.org/TR/sparql11-entailment/ (2010).
[6]
Holger Knublauch: "The SPIN
Standard Modules Library", Specification Draft, http://spinrdf.org/spl.html, 2009.
[7]
Jens Lehmann,
Chris Bizer, Georgi Kobilarov,
Sören Auer, Christian Becker, Richard Cyganiak, Sebastian Hellmann: "DBpedia
- A Crystallization Point for the Web of Data", Journal of Web Semantics,
Vol.7, Nr.3, p.154-165 (2009).
[8]
Frank Manola, Eric Miller:
"RDF Primer", W3C Recommendation, http://www.w3.org/TR/rdf-primer/ (2004).
[9]
Eric Prud'hommeaux, Andy Seaborne: "SPARQL Query Language for
RDF", W3C Recommendation, http://www.w3.org/TR/rdf-sparql-query/ (2008).
[10]
Heiner Stuckenschmidt, Christine
Parent, Stefano Spaccapietra: "Modular Ontologies: Concepts, Theories and Techniques for Knowledge
Modularization", Lecture Notes in Computer Science, Vol.5445, p.378,
Springer (2009).
[11]
Rudi Studer, Richard Benjamins, and Dieter Fensel:
"Knowledge Engineering: Principles and Methods", Data and Knowledge
Engineering, Vol.25, p.161-197 (1998).
[12]
W3C OWL Working
Group: "OWL 2 Web Ontology Language - Document Overview", W3C
Recommendation, http://www.w3.org/TR/owl2-overview/
(2009).
Irene Celino
CEFRIEL ICT Institute, Politecnico di Milano
Milano, Italy
irene.celino@cefriel.it
Daniele DellAglio
CEFRIEL ICT Institute, Politecnico di Milano
Milano, Italy
daniele.dellaglio@cefriel.it
Riccardo De Benedictis
ISTC CNR
Roma, Italy
riccardo.debenedictis@istc.cnr.it
Sara Grilli
CEFRIEL ICT Institute, Politecnico di Milano
Milano, Italy
sara.grilli@cefriel.it
Amedeo Cesta
ISTC CNR
Roma, Italy
amedeo.cesta@istc.cnr.it
Using ontologies in Learning Objects Repositories
Introduction
Semantic
metadata for the organization of resources in repositories currently represents
a major application area of semantic technologies among Semantic Web
researchers. Under the CASPOE project we have used ontologies in the TREE
(Teaching Resources for Engineering Education) repository [1, 2] for two main reasons:
- Allow a
more detailed description of the resources, suggesting additional terms
that can be used as keywords; and
- Expand the initial query terms provided by
users, using ontologies to have related terms added to the query.
Improving keywords
specification
To avoid an
inadequate representation of the contents of the resources uploaded to the TREE
repository in the form of keywords, we considered two complementary steps. At
first, the relevant keywords are extracted from textual resources (pdf, doc or txt files) using XtraK4Me [3], which makes use of many GATE
(General Architecture for Text Engineering) components. Then, using those
keywords we submit SPARQL queries to a Sesame
(http://sourceforge.net/projects/sesame) repository where we store some
selected ontologies assigned to each community (civil engineering, informatics,
mathematics, etc).
In an ontology we have a set of concepts and relationships
between them, which we use to obtain additional possible keywords. All these
keywords are put under user consideration and he/she can modify them or add new
ones. However, that processing is just like it was described when the language
of the resource is English. It is common the use of two other languages,
Portuguese and Spanish, in the TREE repository. Thus, in that case, we use the
Google Translate 0.7 API (http://code.google.com/p/google-api-translate-java)
to translate the resources provided in other languages, and then the process
continues as explained before. Although that approach was tested with resources
written in Portuguese, Spanish and English, any of the languages supported by
the Google Translate API can be used (more than 50). Google Translate received
the best rank in a recent study, among 10 free machine translators, in the
translations of sentences in a formal style [4].
With this
kind of processing for the keywords specification we were able to obtain a
usable representation of the resources contents. Although it did not represent
a problem during our experiments, the main drawback is that the Google
Translation service may not be always available within an adequate timeframe.
Use of ontologies
for query expansion
Keyword-based
search is very common in many popular search engines on Internet. People are
used to submitting keywords to a search engine, which in turn returns a ranked
list of documents to the user. However, when a user specifies keywords in a
query in order to retrieve the desired documents, many relevant documents can be
disregarded because they do not contain the exact keywords specified. The
expansion of queries based on formal domain ontologies is used in the TREE
repository to overcome that limitation.
For the
query expansion we considered IsPartOf/hasPart relations, as well as simple taxonomy relations, up
to two levels, but also equivalence relations and instance data. One advantage
of considering query expansion techniques is that it is not necessary to change
the internal functioning of the query processing, since we just add other terms
to be considered in each query. We developed a module to expand the query terms
provided by the TREE users, but allowing the users to agree or not with the use
of the additional terms.
The
architecture of the query expansion module is detailed in Figure 1. The
fundamental element of the architecture is the domain ontologies repository, in
which all relevant knowledge is stored.
Figure 1. Architecture
of the query expansion module.
Some final remarks
CASPOE
project ends in December 2010. We found that ontological query expansion can
improve the results provided. Also, the keyword specification with the suggestion
of terms from ontologies, together with their automatic extraction, allows
better characterization of the resources. However, it is important the quality
of the ontologies applied.
In [1, 5] we described our approach to reuse
online ontologies. Different from other approaches [6] [7] [8], our does not rely on a single
ontology specifically developed for its application in the repository or a
well-validated by the scientific community in the area, which makes the quality
of the ontologies considered even more important.
References
[1] I.
Azevedo, A. Ortiz, C. V. Carvalho,
R. Seiça, and E. Carrapatoso,
"Applying and Reusing Knowledge in a Repository," in The 10th IEEE International Conference on
Advanced Learning Technologies (ICALT 2010), Sousse, Tunisia, 2010.
[2] R. Seiça,
A. Ortiz, I. Azevedo, C. V. Carvalho,
and E. Carrapatoso, "Tree - A Repository of
Learning Objects for Engineering based on open source tools," in FINTDI Conference Vigo, Spain, 2009.
[3] A. Schutz,
"XtraK4Me: Extraction of keyphrases for metadata
creation," in GATE plug-in software.
Galway, Ireland: Semantic Information Systems and Language Engineering Group (SmILE), DERI, 2008.
[4] S. Hampshire and C. P. Salvia,
"Translation and the Internet: Evaluating the Quality of Free Online Machine
Translators," Quaderns. Rev. trad.
, vol. 17, 2010.
[5] I. Azevedo, R.
Seiça, A. Ortiz, E. Carrapatoso,
and C. V. d. Carvalho, "A Semantic Approach for
Learning Objects Repositories with Knowledge Reuse," in EKAW 2010, Lecture Notes in Artificial
Intelligence, Lisbon, Portugal, 2010, pp. 580589.
[6] W. Ali and S. Khan, "Ontology
driven query expansion in data integration," in The 2008 Fourth International Conference on Semantics, Knowledge and
Grid (SKG 08), 2008, pp. 5763.
[7] Y.-F. Huang and C.-H.
Hsu, "PubMed smarter: query expansion with implicit words based on gene
ontology," Knowledge-based Systems, vol.
21, pp. 927-933, 2008.
[8] M.-C. Lee, K. H. Tsai,
and T. I. Wang, "A practical ontology query expansion algorithm for
semantic-aware learning objects retrieval," Computers and Education, vol. 50, pp. 12401257, 2008.
Isabel Azevedo
ISEP, GILT, Portugal
iazevedo@dei.isep.ipp.pt
Rui Seiça
FEUP, Portugal
ruiseica@gmail.com
Adela Ortiz
ISEP, GILT, Portugal
adela@dei.isep.ipp.pt
Eurico Carrapatoso
FEUP, Portugal
emc@fe.up.pt
Carlos Vaz de Carvalho
ISEP, GILT, Portugal
cvc@dei.isep.ipp.pt
Nowadays,
Information and Communication Technologies (ICTs) are becoming ubiquitous in
education [1]. The emergence of the Web 2.0 movement [2] and the proliferation
of Web-based applications have boosted the adoption of ICTs to support learning
scenarios. In this sense, tools not specifically designed for educational
purposes have been successfully introduced in the classroom, as in the case of
wikis or blogs [3]. Given this situation, there are many opportunities to
leverage current learning scenarios with technology, although teachers need
some support to be aware of available tools that can be employed in their
classroom.
Specifically,
there are very few search systems that guide teachers in the retrieval of
educational tool information; that is why most teachers use general purpose
search engines, such as Google, when they are looking for education-specific
software tools. General purpose search engines provide low precision since they
index a huge amount of information that is irrelevant when looking for
educational tools. Another possibility is to use domain-specific search engines;
they can collect domain knowledge and they only index software tools that can
be used in learning settings. Therefore, the results provided by this kind of
search engines are more precise and relevant for educators. However, there are
very few educational tool search engines and all of them work with isolated
data silos that need to be manually updated.
An example
of an educational tool search engine is Ontoolsearch
(http://www.gsic.uva.es/ontoolsearch), which uses Ontoolcole,
an ontology that describes a software tool taxonomy based on the educational
tasks that tools can support. Nevertheless, since Ontoolsearch
gets information from a data silo, it is unable to automatically import
information from external sources, which makes it a very-hard-to-sustain
information source. For example, the tool Microsoft Word was described in Ontoolsearch but a new version appeared; even if the
information related to this new version is published in some accessible data
sources (e.g. Wikipedia) it has to be manually published in Ontoolsearch
by the search engine administrator.
In order to
tackle this data maintenance problem the Linked Data [4] approach has been
recently proposed as a way of publishing data to facilitate the automatic
access to the information contained in external repositories. The Linked Data
methodology consists of four basic principles for publishing data and many
providers are linking their datasets according to these principles, building
the so-called Web of Data (see http://linkeddata.org/). Indeed, there are
updated descriptions of software tools in some repositories of the Web of Data,
such as DBpedia (http://dbpedia.org/). However, in the current state of the Web of Data,
descriptions of software tools from an educational point of view are scarce
(e.g. which educational tasks could be carried out using a particular tool?). Nevertheless,
and following the same Linked Data principles, it would be possible to create
datasets with education-specific information about software tools. That information
could therefore be linked with existing non-educative descriptions of those
same tools already available in third-party, potentially updated datasets of
the Web of Data. Thus, an educational tool search system using the Web of Data
could benefit from this distributed data publication and maintenance approach
so as to get a better precision in the results (education-specific information
is available) based on more easily updated data (non-educative information is
maintained by third-parties).
Figure 1 depicts
the linked-data based proposed infrastructure for the retrieval of educational
tools. The figure underlines the main actors and components needed to support
the publication, linking, updating and searching of education-specific
information about software tools. This data is
enriched by linking it to an Educational Data set that describes the
educational capabilities of the tools. Using such infrastructure, a search tool
would be able to automatically retrieve updated information about software tools.
Moreover, the system includes a publishing tool that allows teachers to create
or modify the educational descriptions of the tools; thus, it will be possible
to create a community of teachers that enrich the data about software tools
available on the Web.
Figure 1. Main
actors and components of the proposed infrastructure.
The work already done focuses on the development of the Educational
Data dataset. For this purpose the Ontoolcole
ontology is used as the data model since there is no other ontology
specifically developed to describe educational software tools. A key step in
the Educational Data source development was to define the relationships
between Ontoolcole concepts and the
conceptualizations of external data. Using these relationships, a software
agent can automatically match software tools published in external sources to
the educational concepts defined by Ontoolcole.
For
example, the relationship All the tools described in DBpedia
as Word_Processor support the task defined in Ontoolcole as Writing can be defined; after that, when
educators search for tools that allow their students to write, they will
retrieve updated information about several tools from DBpedia.
However, since DBpedia does not provide all the data
that would be desired about software tools, no results will be found when
asking more expressive questions (e.g. Tools that support the collaborative
edition of documents that can be exported as HTML). That is why current work
aims to design the Publishing Tool, where teachers could publish new
educational metadata related to software tools. For example, a teacher can
publish Google Docs supports the collaborative edition of text documents and
another teacher (or a technical user) Google Docs is able to export data in
ODT, RTF, PDF and HTML. This way, when other teacher asks the aforementioned
question, he will realize that Google Docs is a good choice.
References
[1] Sutherland, R., Robertson, S., and John,
P. (2009). Improving classroom learning with ICT. Routledge, New
York, NY, USA.
[2] O'Reilly,
T. (2005). What is Web 2.0. Design patterns and
business models for the next generation of software. URL:
http://oreilly.com/web2/archive/what-is-web-20.html, last visited December
2010.
[3] Richardson,
W. (2010). Blogs, wikis, podcasts, and other powerful web
tools for classrooms. Corwin Press, Thousand Oaks, CA,
USA, third edition.
[4] Berners-Lee,
T. Linked Data - Design Issues (2006). URL: http://www.w3.org/DesignIssues/LinkedData.html,
last visited December 2010.
Adolfo Ruiz-Calleja
GSIC-EMIC
University of Valladolid
adolfo@gsic.uva.es
Guillermo Vega-Gorgojo
GSIC-EMIC
University of Valladolid
guiveg@tel.uva.es
Juan Ignacio Asensio-Pérez
GSIC-EMIC
University of Valladolid
juaase@tel.uva.es
Miguel Luis Bote-Lorenzo
GSIC-EMIC
University of Valladolid
migbot@tel.uva.es
Eduardo Gómez
Sánchez
GSIC-EMIC
University of Valladolid
edugom@tel.uva.es
Carlos Alario
Hoyos
GSIC-EMIC
University of Valladolid
calahoy@gsic.uva.es
Regular Articles Section
Machine-processable Representation of Training Outcomes
Introduction
Modelling
a domain, a process, or data is a common way of understanding it. The purpose
of modelling is simplification, so that the domain is
easier to understand. Often, models are mathematical because they are
predictable and repeatable. There are many teaching and learning theories such
as behaviourism, cognitivisim,
constructivism, and cybernetics. Modelling and
validating these theories is problematic because of their inherent aspect of
ambiguity and lack of repeatability. This paper constructed a model of a major
aspect of teaching and learning that is machine‑processable.
This provides repeatable, realistic, less ambiguous,
and deterministic results for testing and validating. A machine‑processable
representation may be expect to be able to validate
such models to better understand teaching and learning situations.
Competency Model
The field
of educational psychology has long been sensitive to the desirability of
establishing learning objectives for instruction [1]. These learning objectives are
variously called behavioural objectives,
instructional objectives, performance objectives, or intended learning
outcomes. Intended learning outcomes (ILOs) guide the learner and guide the teacher.
The rationale is that learners will use ILOs to identify the skills and
knowledge they must master, while teachers will use ILOs to create learning
environments that support the learning activities entailed [2]. Instructional design may be taken
as that process which designs teaching and learning activities in support of
intended learning outcomes.
Figure
1: Competence conceptual model (modified from [3])
A development of current ideas
surrounding competences suggests a conceptual model of ITO augmented by
contextual factors, as illustrated in Figure 1. Such augmented ITOs are called
competences in this paper. While an ITO may be reasonably constrained by an
agreed ontology of capability terms and an agreed subject matter topics list,
context is in principle limitless and dependent upon particulars (if not
peculiarities) of the target students, teachers, locations, times, tools,
required mastery levels, available services, etc [4].
Competence
analysis is often referred to as pre-requisite analysis, and can be used to
diagnose failures in learning by identifying the pre-requisites that learners
failed to master. A competence structure depicts these pre-requisites in an
ordered hierarchical relationship. The lowest skills in the structure are
typically learned before the higher-level ones, up to the highest level ITO.
The lower-level skills are pre-requisite to the higher-level skills. The
structure represents what is expected to be a general pattern to be followed by
the student: making sure that relevant lower-order skills are mastered before
learning related higher-order skills.
Implementing the Competency Model in the Design
of Training Outcomes
The
conceptual model of an ITO describes a statement of a capability, and a
statement of the subject matter to which the capability applies. Subject matter
refers to what the learners are expected to know and capability describes what
the learners are expected to be able to do in relation to the subject matter [5]. This description of an ITO represents
what the learner is to be able to do and whose achievement is capable of
verification when learning has been accomplished. Figure 2 represents some
rowing ITOs based on the competence model.
Figure
2: Example conceptual model of training outcomes
The
simplest competence structure consists of a pair of procedural skills, one
subordinate to the other. The competence structure describes what the learner
must be able to do before something else can be learned. The learning relation
is identified by the following sentence: A learner
must be able to X in order to be able to Y, where X and Y are ITOs. For
example, in order to achieve C0 (athletes are able to perform automatically
rowing), athletes should achieve C0.1 (athletes are able to perform
automatically catch), C0.2 (athletes are able to perform automatically drive),
and C1 (athletes are able to articulate rowing). In order to achieve C0.1
(athletes are able to perform automatically catch), athletes should be able to
demonstrate both C0.1.1 (athletes are able to perform automatically grip
handles) and C0.1.2 (athletes are able to perform automatically positioning
shins).
Figure 2
also illustrates that the achievement of C0.1 (athletes are able to perform
automatically catch) supports athletes in proceeding to C0.2 (athletes are able
to perform automatically drive). Psychomotor skills are characteristically
procedural, where the achievement of a higher-level skill involves the assembly
of a set of lower-level skills into a sequence.
Figure 2
shows an effective mapping of ITOs using the competency model.
Future Implementation
Semantic
technologies aim at giving information a well-defined meaning and better
enabling humans and machines to work together [6] through ontologies. Ontologies
provide a controlled vocabulary of concepts, where each concept comes with
explicitly defined and machine-processable semantics [7]. We suggest that future work could
represent ILOs, ITOs, and statements of competence in the form of semantic
networks. When transformed into ontologies such networks will maximize
reusability and enhance their compatibility with other systems and
environments.
Future work
could use the network to suggest training materials for the athletes. The
system could suggest appropriate training material to the athletes depending
upon their position in the network and their desire to achieve certain training
outcomes. The system could integrate the athletes current competence level,
required ITOs by the coaches, desired outcomes of the athletes, and the context
of the training activities to provide more personalised
training materials recommendations while at the same time taking into account
the context of the athletes such as tools and resources.
Conclusion
Learning
and training outcomes are at the heart of teaching and learning activities.
This paper suggests machine-processable
representations of training outcomes and statements of competence at a level of
semantic and ontological content well beyond current representations such as
RDCEO [8]and HRXML [9]. The syntax and notation of
competences are defined explicitly so that they can be interpreted,
instantiated, and automated by a machine. This allows the testing and
validation of teaching and learning models which incorporate intended learning
or training outcomes, skills, educational objectives, or competence statements.
References
1. Krathwohl, D.R., A
revision of Bloom's taxonomy: An overview. Theory Into
Practice, 2002. 41(4): p. 212 - 218.
2. Kemp,
J.E., G.R. Morrison, and S.M. Ross, Designing
Effective Instruction. 1998, Upper Saddle River, N.J.: Merrill.
3. Sitthisak,
O., L. Gilbert, and H. Davis, An
evaluation of pedagogically informed parameterised
questions for self assessment. Learning, Media and
Technology, 2008. 33(3): p.
235-248.
4. Gilbert,
L., Repurpose, re-use: Reconsider. IEEE Learning Technology Newsletter Special Issue
On Learning Objects and Their Supporting Technologies for Next Generation
Learning, 2009. 11(4).
5. Näsström,
G. and W. Henriksson, Alignment of standards and assessment: A theoretical and empirical
study of methods for alignment. Electronic Journal of
Research in Educational Psychology, 2008. 6(3): p. 24.
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7. Gašević,
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Ontology-based annotation of learning object
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2008 [cited 2010 December]; Available from: http://www.e-framework.org/Default.aspx?tabid=797.
School of Health Sciences
Health Campus
Universiti Sains Malaysia (USM)
Kelantan, Malaysia
yhpi07r@ecs.soton.ac.uk
Lester Gilbert
The Learning
Societies Lab
School of
Electronics and Computer Science University of Southampton, UK
lg3@ecs.soton.ac.uk
Gary B Wills
The Learning
Societies Lab
School of
Electronics and Computer Science University of Southampton, UK
gbw@ecs.soton.ac.uk
This is a
project conducted at the Machine Intelligence and Robotics group at the school
of Electrical and Computer Engineering, University of Tehran. The goal of this
project is to evaluate the effects of games, specifically educational games, on
children under age of 7. In the first phase of the project, the working memory
of the children has been considered for evaluation.
In this
study, two games from PBSkids.org
were e selected based on a character called Caillou.
Since the children already know Caillou from a
cartoon series based on his character, they can connect easier to his games.
The games are called matching and follow the stars games. The first one
requires the children to memorize images, and recall them later with or without
zero, 4 and 12 seconds delays. The original game has been changed to include
these delays, and also more complex images have been added to it (Figure 1.)
The 2nd game requires the children to memorize sequences of images,
and recall them later.
Figure
1: The normal level of the matching game: The target image on
the left consists of two leaves while there are six sets of two leaves on the
right, from which the player should select the correct ones.
The follow
the stars game consists of 4 stars (Figure 2) that blink in sequence. Each
star creates a unique sound, while it blinks, to create an auditory memory for
itself. The player should remember the sequence of blinking stars, and click on
them in the correct order.
A tracking
program has been developed which logs the user clicks for analysis. From this
log, the speed of response, the changes in the speed of response, the
correctness of responses, and other useful information can be determined, which
can be used to model and assess the players.
To evaluate
the impact of the games on working memory, a group of 35 pre-school students
were selected, and were divided into test and control groups of 17 and 18
students, respectively. The students were between 6 to 7 years old who went
through pretest using CANTAB
Delayed Matching to Sample (DMS) and SSP (Spatial Span) tests. Furthermore, the
students intellectual abilities were measured using Raven Progressive
Matrices. The students used each game approximately for three minutes a day, 3
days a week for two months. It is important to mention that the students in
each group were attending the preschool every other day, odd and even days of
the week. Consequently there was no chance for interaction between the two
groups.
Figure 2: follow the stars game with 4 stars which
would blink in sequence and for each; there is a unique sound effect.
After the
intervention period, in which each student used each game for 60 minutes
approximately, the DMS and SSP tests were conducted again, on both groups of
students, to compare with the original test results and measure the changes in
these two working memory measures. Currently, a team of psychology researchers
are analyzing the result to determine the effects of playing these games on working
memory of these children. The preliminary results show slight improvement in a
few measures in DMS and SSP.
It is
interesting to mention that both matching and follow the stars games are
very similar to DMS and SSP tests, respectively. We further changed the
matching game to make it further similar to DMS test. The original version only
shows the target image and the collection of candidate images simultaneously.
The revised version includes zero, four, and twelve seconds delay between the
target image and the collection of candidate images. The mentioned similarity
between the games and CANTAB would allow us to investigate the possibility of
using games as assessment tools.
Another
feature we included in our study is the use of the mounted web cams on the
notebook stations that the players used. Each camera captures the face of the
player using the notebook. The captured stream would be used to determine if
the player is really playing or if he is distracted. A program has been
developed to detect the face in the incoming stream (figure 3.) If the face is
detected and the players eyes are toward the computer, then it is assumed that
the player is actually playing. Otherwise, if the player is not facing the
computer, the data from that specific point in time is marked for deletion,
till the point that the player faces the computer again. By using this
approach, the logged data can be cleaned for more accurate processing. We are
currently analyzing the effectiveness of this approach in determining unreliable
logged data.
(a)
(b)
Figure 3: The face detection program determines
whether the player looks at the screen or not. (a) shows
a box around the face when the face is toward the monitor while (b) shows the
case that the player is not attending to the game, and his face is turned away.
In the next
phase, we will be using data mining methodologies to use games for assessment
and user modeling. This approach can also be used in special education, as a
tool for assessing abilities and designing interventions for improving
attention and working memory in children with Down syndrome, attention
deficit-hyper activity disorder, and learning disabilities.
Acknowledgement:
The authors
like to thank Solaha preschool for their support in conducting
this research. This research is funded by the Iranian National Game
Organization.
H. Moradi
Machine
Intelligence and Robotics Group & CIPCE
University of
Tehran
Iran
moradih@ut.ac.ir
M. Assadpour
Machine
Intelligence and Robotics Group & CIPCE
University of
Tehran
Iran
asadpour@ut.ac.ir
M. Tehranidoost
Tehran University
of Medical Sciences
The Institute for
Cognitive Science Studies
Iran
tehranid@sina.tums.ac.ir
B. Ghaderi
Machine
Intelligence and Robotics Group
University of
Tehran
Iran
ba.ghaderi@gmail.com
Z. Najafi
The Institute for
Cognitive Science Studies
Iran
zm.najafi@gmail.com
K. Khalvati
Department of
Computer Science
University of
British Columbia,
Canada
kooshakh@cs.ubc.ca
P. Mousavi
School of
Electrical and Computer Engineering
University of
Tehran
Iran
p.mousavi@ece.ut.ac.ir
A.A. Bagherzadeh
School of
Electrical and Computer Engineering
University of
Tehran
Iran
amir.ali.bagherzade@gmail.com
Afshin Dehghan
School of
Electrical and Computer Engineering
University of
Tehran
Iran
afshin.dn@gmail.com
L. Kashani
Psychology
Department
University of
Tehran
Iran
lkashanimoradi@yahoo.com
Conferences
|
Conference
Title
|
Date
|
Venue
|
Submission
Date
|
|
CSEIT 2011 2nd
Annual International Conference on Computer Science Education: Innovation and
Technology
|
5 - 6
December 2011
|
Hilton
Phuket Arcadia Resort & Spa, Thailand
|
11
October 2011
|
|
SE 2011 2nd
Annual International Conference on Software Engineering
|
5 - 6
December 2011
|
Hilton
Phuket Arcadia Resort & Spa, Thailand
|
11
October 2011
|
|
ICELF 2011 International
Conference for eLearning Futures 2011
|
30
November -
2 December 2011
|
Auckland,
New Zealand
|
1 June
2011
|
|
ICCE 2011 The 19th
International Conference on Computers in Education
|
28
November -
2 December 2011
|
Chiang
Mai, Thailand
|
13 May
2011
|
|
Media
& Learning 2010
|
25 - 26
November 2011
|
Flemish
Ministry of Education Headquarters, Brussels
|
N/A
|
|
C&C
2011 8th ACM Conference on Creativity & Cognition
|
3 - 6
November 2011
|
Atlanta,
Georgia, USA
|
25
March 2011
|
|
mLearn 2011 10th
World Conference on Mobile and Contextual Learning
|
18 - 21
October 2011
|
Beijing,
China
|
5 May
2011
|
|
MobiWIS 2011 The
8th International Conference on Mobile Web Information Systems
|
19 - 21
September 2011
|
Niagara
Falls, Ontario, Canada
|
15
March 2011
|
|
Edutainment 2011 The 6th International
Conference on E-learning and Games
|
7 - 9
September 2011
|
Taipei,
Taiwan
|
1 April
2011
|
|
CIT 2011 The 11th
IEEE International Conference on Computer and Information Technology
|
31
August -
2 September 2011
|
Ayia Napa,
Cyprus
|
1
February 2011
|
|
DEXA 2011 22nd
International Conference on Database and Expert Systems Applications
|
29
August -
2 September 2011
|
Toulouse,
France
|
25
March 2011
|
|
CollabTech 2011 6th
International Conference on Collaboration Technologies
|
29 - 31
August 2011
|
Tokyo, Japan
|
14
March 2011
|
|
ICSNC 2011 The
6th International Conference on Systems and Networks Communications
|
23 - 28
August 2011
|
Barcelona,
Spain
|
20 May
2011
|
|
27th
Annual Conference on Distance Teaching and Learning
|
3 - 5
August 2011
|
Madison,
Wisconsin, USA
|
Closed
|
|
ASONAM 2011 The 2011
International Conference on Advances in Social Network Analysis and Mining
|
25 - 27
July 2011
|
Kaohsiung,
Taiwan
|
1 March
2011
|
|
KES IIMSS
2011 The 4th International Symposium on Intelligent Interactive
Multimedia Systems and Services
|
20 - 22
July 2011
|
University
of Piraeus, Piraeus, Greece
|
Closed
|
|
eL2011 The IADIS
International Conference on e-Learning 2011, part of the IADIS Multi
Conference on Computer Science and Information Systems (MCCSIS 2011)
|
20 - 23
July 2011
|
Rome,
Italy
|
24
January 2011
|
|
T4E 2011 3rd
IEEE International Conference on Technology for Education
|
14 - 16
July 2011
|
Chennai,
India
|
27
February 2011
|
|
CATE 2011 The 14th
IASTED International Conference on Computers and Advanced Technology in
Education
|
11 - 13
July 2011
|
Cambridge,
United Kingdom
|
15
February 2011
|
|
UMAP 2011 19th International
Conference on User Modeling, Adaptation, and Personalization
|
11 - 15
July 2011
|
Girona, Spain
|
Closed
|
|
MME 2011 1st
IEEE Workshop on Multimedia in Edutainment in conjunction with the IEEE
International Conference in Multimedia and Expo (ICME2011)
|
11 - 15
July 2011
|
Barcelona,
Spain
|
20
February 2011
|
|
ICE 2011 7th
International Conference on Education
|
7 - 9
July 2011
|
Samos
Island, Greece
|
3 April
2011
|
|
ICALT 2011 The
11th IEEE International Conference on Advanced Learning Technologies
|
6 - 8
July 2011
|
Athens,
Georgia, USA
|
Closed
|
|
CISIS 2011 5th
International Conference on Complex, Intelligent, and Software Intensive
Systems
|
30 June
-
2 July 2011
|
Korean
Bible University (KBU), Seoul, Korea
|
Closed
|
|
AIED 2011 15th
International Conference on Artificial Intelligence in Education
|
27 June
-
1 July 2011
|
University
of Canterbury, Christchurch, New Zealand
|
Closed
|
|
ED-MEDIA
2011 World Conference on Educational Multimedia, Hypermedia &
Telecommunications
|
27 June
-
1 July 2011
|
Lisbon,
Portugal
|
Closed
|
|
ICWE 2011 11th
International Conference on Web Engineering
|
20 - 24
June 2011
|
Paphos,
Cyprus
|
26
April 2011
|
|
EDEN 2011 European
Distance and E-Learning Network Annual Conference on Learning and
Sustainability The New Ecosystem of Innovation and Knowledge
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19 - 22
June 2011
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Dublin,
Ireland
|
28
January 2011
|
|
BIS 2011 14th
International Conference on Business Information Systems
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15 - 17
June 2011
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Poznan,
Poland
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Closed
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|
ICELW 2011 The
International Conference on E-learning in the Workplace
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8 - 10
June 2011
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Faculty
House, Columbia University New York
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Closed
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Hypertext 2011 22nd
ACM Conference on Hypertext and Hypermedia
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6 - 9
June 2011
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Eindhoven,
the Netherlands
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29
January 2011
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|
EMCIS 2011 8th
European, Mediterranean and Middle Eastern Conference on Information Systems
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30 - 31
May 2011
|
Athens
Greece
|
10
February 2011
|
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Ce-Learning
2011 Workshop on Collaboration and e-Learning 2011
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23 - 27
May 2011
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The
Sheraton University City Hotel Philadelphia, PA, USA
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Closed
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CTS 2011 International
Conference on Collaboration Technologies and Systems 2011
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23 - 27
May 2011
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The
Sheraton University City Hotel Philadelphia, Pennsylvania, USA
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Closed
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SWEL '11 @ FLAIRS-24 International
Workshop on Ontologies and Semantic Web for E-Learning in conjunction with
the 24th International FLAIRS Conference
|
18 - 20
May 2011
|
Palm
Beach, Florida, USA
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Closed
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CSEDU 2011 3rd
International Conference on Computer Supported Education
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6 - 9
May 2011
|
Noordwijkerhout, The
Netherlands
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Closed
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ICTA 2011 3rd
International Conference on Information and Communication Technology and
Accessibility
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5 - 7
May 2011
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Gammarth,
Tunis, Tunisia
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Closed
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ICONTE 2011 2nd International
Conference on New Trends in Education and Their Implications
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27 - 29
April 2011
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Antalya,
Turkey
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31
January 2011
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CSERC 2011 Computer
Science Education Research Conference
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7 - 8
April 2011
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Heerlen,
the Netherlands
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Closed
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CICE 2011 Canada
International Conference on Education
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4 - 7
April 2011
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Toronto,
Canada
|
Closed
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dataTEL 2011 1st
workshop on Data Sets for Technology Enhanced Learning at the 2nd STELLAR
Alpine Rendez-Vous
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30 - 31
March 2011
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La Clusaz, France
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Closed
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W4A 2011 8th
International Cross-Disciplinary Conference on Web Accessibility
"Crowdsourcing the Cloud: An Inclusive Web by All and For All?"
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28 - 29
March 2011
|
Hyderabad,
Andhra Pradesh, India
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Closed
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|
PerEL 2011 7th
IEEE International Workshop on PervasivE Learning,
Life, and Leisure
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25
March 2011
|
Seattle,
WA, USA
|
Closed
|
|
ACM SAC
2011 ACM Symposium On Applied Computing, Track on Intelligent,
Interactive and Innovative Learning environments
|
21 - 25
March 2011
|
Tunghai
University, TaiChung, Taiwan
|
Closed
|
|
WT @ SAC
2011 ACM Symposium on Applied Computing Track on Web Technologies
|
21 - 25
March 2011
|
Tunghai
University, TaiChung, Taiwan
|
Closed
|
|
AICT 2011 The
Seventh Advanced International Conference on Telecommunications
|
20 - 25
March 2011
|
St.
Maarten, The Netherlands Antilles
|
Closed
|
|
TELDAP 2011 Taiwan
e-Learning and Digital Archives Program International Conference
|
16 - 19
March 2011
|
Academia
Sinica, Taipei, Taiwan
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Closed
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ML 2011 IADIS
International Conference Mobile Learning 2011
|
10 - 12
March 2011
|
Avila,
Spain
|
Closed
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SITE 2011 22nd
International Conference of the Society for Information Technology and Teacher
Education
|
7 - 11
March 2011
|
Nashville,
Tennessee, USA
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Closed
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LAK 2011 1st
International Conference on Learning Analytics and Knowledge 2011
|
27
February -
1 March 2011
|
Banff,
AB, Canada
|
Closed
|
|
eL&mL 2011 The
Third International Conference on Mobile, Hybrid, and On-line Learning
|
23 - 28
February 2011
|
Gosier,
Guadeloupe, France
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Closed
|
|
eKNOW 2011 The
Third International Conference on Information, Process, and Knowledge
Management
|
23 - 28
February 2011
|
Gosier,
Guadeloupe, France
|
Closed
|
|
IEEE CogSIMA
2011 IEEE Conference on Cognitive Methods in Situation Awareness and
Decision Support
|
22 - 24
February 2011
|
Miami
Beach, Florida, USA
|
Closed
|
|
WM2011 6th
Conference on Professional Knowledge Management: From Knowledge to Action
|
21 - 23
February 2011
|
Innsbruck,
Austria
|
Closed
|
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IUI 2011 International
Conference on Intelligent User Interfaces
|
13 - 16
February 2011
|
Palo
Alto, California, USA
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Closed
|
|
VISSW 2011 3rd
International Workshop on Visual Interfaces to the Social and Semantic Web In
conjunction with the ACM Conference on Intelligent User Interfaces
|
13
February 2011
|
Stanford
University, Palo Alto, California, US
|
Closed
|
|
CaRR 2011 IUI
2011 Workshop on Context-awareness in Retrieval and Recommendation
|
13
February 2011
|
Palo
Alto, California, USA
|
Closed
|
