Learning Technology


publication of


IEEE Computer Society’s


Technical Committee on Learning Technology (TCLT)


Volume 13 Issue 1

ISSN 1438-0625

January 2011



From the Editors …... 1

Special Theme Section: Semantic Web Technologies for Technology Enhanced Learning. 2

Ontologies, rules and linked data to support Crisis Managers Training. 3

Using ontologies in Learning Objects Repositories. 7

A Linked-Data based infrastructure for the retrieval of educational tools. 10

Regular Articles Section. 13

Machine-processable Representation of Training Outcomes. 14

The Effects of Computer Games on Working Memory on Preschool Children: A case study  18

Conferences. 22




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 ASK’s Web-Site, at

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

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!




Sabine Graf

Athabasca University, Canada


Charalampos Karagiannidis

University of Thessaly, Greece


Special Theme Section: Semantic Web Technologies for Technology Enhanced Learning

Ontologies, rules and linked data to support Crisis Managers Training


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 ( 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:

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:

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 ( or DBpedia [7] (

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.


This research is partially funded by the EU PANDORA project (FP7-ICT-2007-1-225387).


[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, (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, (2010).

[6]               Holger Knublauch: "The SPIN Standard Modules Library", Specification Draft,, 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, (2004).

[9]               Eric Prud'hommeaux, Andy Seaborne: "SPARQL Query Language for RDF", W3C Recommendation, (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, (2009).



Irene Celino

CEFRIEL – ICT Institute, Politecnico di Milano

Milano, Italy


Daniele Dell’Aglio

CEFRIEL – ICT Institute, Politecnico di Milano

Milano, Italy


Riccardo De Benedictis


Roma, Italy


Sara Grilli

CEFRIEL – ICT Institute, Politecnico di Milano

Milano, Italy


Amedeo Cesta


Roma, Italy


Using ontologies in Learning Objects Repositories


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:

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 ( 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 ( 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.


[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. 580–589.

[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. 57–63.

[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. 1240–1257, 2008.


Isabel Azevedo

ISEP, GILT, Portugal


Rui Seiça

FEUP, Portugal


Adela Ortiz

ISEP, GILT, Portugal


Eurico Carrapatoso

FEUP, Portugal


Carlos Vaz de Carvalho

ISEP, GILT, Portugal


A Linked-Data based infrastructure for the retrieval of educational tools

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 (, 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 Indeed, there are updated descriptions of software tools in some repositories of the Web of Data, such as DBpedia ( 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.


[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:, 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:, last visited December 2010.


Adolfo Ruiz-Calleja


University of Valladolid


Guillermo Vega-Gorgojo


University of Valladolid


Juan Ignacio Asensio-Pérez


University of Valladolid


Miguel Luis Bote-Lorenzo


University of Valladolid


Eduardo Gómez Sánchez


University of Valladolid


Carlos Alario Hoyos


University of Valladolid

Regular Articles Section

Machine-processable Representation of Training Outcomes


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.


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.


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.

6.         Berners-Lee, T., J. Hendler, and O. Lassila, The semantic web. Scientific American, 2001. 284(5): p. 28-37.

7.         Gašević, D., J. Jovanović, and V. Devedžić, Ontology-based annotation of learning object content. Interactive Learning Environments, 2007. 15(1): p. 1 - 26.

8.         IMS GLC. IMS Reusable Definition of Competency or Educational Objective Specification. [cited 2010 December]; Available from:

9.         e-Framework Partners. Personal competency profile information service using HR XML competency. 2008 [cited 2010 December]; Available from:


School of Health Sciences

Health Campus

Universiti Sains Malaysia (USM)

Kelantan, Malaysia


Lester Gilbert

The Learning Societies Lab

School of Electronics and Computer Science University of Southampton, UK


Gary B Wills

The Learning Societies Lab

School of Electronics and Computer Science University of Southampton, UK



The Effects of Computer Games on Working Memory on Preschool Children: A case study

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[1] 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. 

Description: C:\Users\hadi\Desktop\leaves.jpg

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[2] 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.

Description: C:\Users\hadi\Desktop\stars1.jpg

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 player’s 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.

Description: C:\Users\hadi\Documents\research\ITS\Caillou project\face recognition\face1_sketchpdf.png Description: C:\Users\hadi\Documents\research\ITS\Caillou project\face recognition\face_away_detected_sketch.png

(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[3].

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.


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



M. Assadpour

Machine Intelligence and Robotics Group & CIPCE

University of Tehran



M. Tehranidoost

Tehran University of Medical Sciences

The Institute for Cognitive Science Studies



B. Ghaderi

Machine Intelligence and Robotics Group

University of Tehran



Z. Najafi

The Institute for Cognitive Science Studies



K. Khalvati

Department of Computer Science

University of British Columbia,



P. Mousavi

School of Electrical and Computer Engineering

University of Tehran



A.A. Bagherzadeh

School of Electrical and Computer Engineering

University of Tehran



Afshin Dehghan

School of Electrical and Computer Engineering

University of Tehran



L. Kashani

Psychology Department

University of Tehran





Conference Title



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


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


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


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


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


CISIS 2011 5th International Conference on Complex, Intelligent, and Software Intensive Systems

30 June -
2 July 2011

Korean Bible University (KBU), Seoul, Korea


AIED 2011 15th International Conference on Artificial Intelligence in Education

27 June -
1 July 2011

University of Canterbury, Christchurch, New Zealand


ED-MEDIA 2011 World Conference on Educational Multimedia, Hypermedia & Telecommunications

27 June -
1 July 2011

Lisbon, Portugal


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

19 - 22 June 2011

Dublin, Ireland

28 January 2011

BIS 2011 14th International Conference on Business Information Systems

15 - 17 June 2011

Poznan, Poland


ICELW 2011 The International Conference on E-learning in the Workplace

8 - 10 June 2011

Faculty House, Columbia University New York


Hypertext 2011 22nd ACM Conference on Hypertext and Hypermedia

6 - 9 June 2011

Eindhoven, the Netherlands

29 January 2011

EMCIS 2011 8th European, Mediterranean and Middle Eastern Conference on Information Systems

30 - 31 May 2011

Athens Greece

10 February 2011

Ce-Learning 2011 Workshop on Collaboration and e-Learning 2011

23 - 27 May 2011

The Sheraton University City Hotel Philadelphia, PA, USA


CTS 2011 International Conference on Collaboration Technologies and Systems 2011

23 - 27 May 2011

The Sheraton University City Hotel Philadelphia, Pennsylvania, USA


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


CSEDU 2011 3rd International Conference on Computer Supported Education

6 - 9 May 2011

Noordwijkerhout, The Netherlands


ICTA 2011 3rd International Conference on Information and Communication Technology and Accessibility

5 - 7 May 2011

Gammarth, Tunis, Tunisia


ICONTE 2011 2nd International Conference on New Trends in Education and Their Implications

27 - 29 April 2011

Antalya, Turkey

31 January 2011

CSERC 2011 Computer Science Education Research Conference

7 - 8 April 2011

Heerlen, the Netherlands


CICE 2011 Canada International Conference on Education

4 - 7 April 2011

Toronto, Canada


dataTEL 2011 1st workshop on Data Sets for Technology Enhanced Learning at the 2nd STELLAR Alpine Rendez-Vous

30 - 31 March 2011

La Clusaz, France


W4A 2011 8th International Cross-Disciplinary Conference on Web Accessibility "Crowdsourcing the Cloud: An Inclusive Web by All and For All?"

28 - 29 March 2011

Hyderabad, Andhra Pradesh, India


PerEL 2011 7th IEEE International Workshop on PervasivE Learning, Life, and Leisure

25 March 2011

Seattle, WA, USA


ACM SAC 2011 ACM Symposium On Applied Computing, Track on Intelligent, Interactive and Innovative Learning environments

21 - 25 March 2011

Tunghai University, TaiChung, Taiwan


WT @ SAC 2011 ACM Symposium on Applied Computing Track on Web Technologies

21 - 25 March 2011

Tunghai University, TaiChung, Taiwan


AICT 2011 The Seventh Advanced International Conference on Telecommunications

20 - 25 March 2011

St. Maarten, The Netherlands Antilles


TELDAP 2011 Taiwan e-Learning and Digital Archives Program International Conference

16 - 19 March 2011

Academia Sinica, Taipei, Taiwan


ML 2011 IADIS International Conference Mobile Learning 2011

10 - 12 March 2011

Avila, Spain


SITE 2011 22nd International Conference of the Society for Information Technology and Teacher Education

7 - 11 March 2011

Nashville, Tennessee, USA


LAK 2011 1st International Conference on Learning Analytics and Knowledge 2011

27 February -
1 March 2011

Banff, AB, Canada


eL&mL 2011 The Third International Conference on Mobile, Hybrid, and On-line Learning

23 - 28 February 2011

Gosier, Guadeloupe, France


eKNOW 2011 The Third International Conference on Information, Process, and Knowledge Management

23 - 28 February 2011

Gosier, Guadeloupe, France


IEEE CogSIMA 2011 IEEE Conference on Cognitive Methods in Situation Awareness and Decision Support

22 - 24 February 2011

Miami Beach, Florida, USA


WM2011 6th Conference on Professional Knowledge Management: From Knowledge to Action

21 - 23 February 2011

Innsbruck, Austria


IUI 2011 International Conference on Intelligent User Interfaces

13 - 16 February 2011

Palo Alto, California, USA


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


CaRR 2011 IUI 2011 Workshop on Context-awareness in Retrieval and Recommendation

13 February 2011

Palo Alto, California, USA





[3] The images are changed to preserve the confidentiality of the child.