Metadata for Serious Games in Learning Object Repositories

Fernández‐Manjón, Baltasar

doi:10.1109/rita.2016.2554019

Cited by 4 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we focus on the metadata models proposed to describe LGs. Indeed, LGs have specific characteristics that are not found in other learning resources [17] and therefore require the modification of LOM [18,19].…”

Section: Metadata Models For Learning Gamesmentioning

confidence: 99%

LGMD: Optimal Lightweight Metadata Model for Indexing Learning Games

Morie

Marfisi-Schottman

Goore

2020

Communications in Computer and Information Science

View full text Add to dashboard Cite

Learning Games (LGs) have proven to be effective in a large variety of academic fields and for all levels; from kindergarten to professional training. They are therefore very valuable learning resources that should be shared and reused. However, the lack of catalogues that allow teachers to find existing LGs is a significant obstacle to their use in class. It is difficult for catalogues, or any type of search engine, to index LGs because they are poorly referenced. Yet, many researches have proposed elaborate metadata models for LGs. However, all these models are extensions of LOM, a metadata model that is widely used for referencing learning resources, but that contains more than 60 fields, of which more than half are irrelevant to LGs. The gap between these models and the information that game designers are willing to provide is huge. In this paper, we analyze the LG metadata models proposed in previous research to detect the fields that are specific to LGs and the fields that are irrelevant to LGs. We then propose LGMD (Learning Games Metadata Definition), an optimal lightweight metadata model that only contains the important information for LG indexing. LGMD reduces by two thirds the number of fields compared to the previous models. We confronted this model with the information actually provided by LG editors, by analyzing 736 LG page descriptions found online. This study shows that LGMD covers all the information provided by the LG editors.

show abstract

Section: Metadata Models For Learning Gamesmentioning

confidence: 99%

LGMD: Optimal Lightweight Metadata Model for Indexing Learning Games

Morie

Marfisi-Schottman

Goore

2020

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…In order to create a LG catalogue that covers all types of levels and educational fields and that is automatically updated with new LGs, it is necessary to reduce human intervention and switch to an automatic method that will scan the pages of the LG editors' webpages to retrieve the necessary information, analyze it and format it according to an indexing standard. This is where the fist difficulty appears: LG editors do not follow standards such as LOM (Learning Object Metadata) [21][22][23], MLR (Metadata for Learning Resources) [24] or ontologybased systems [25,26] to define their games [27]. This greatly complicates the automatic indexing task, since the system cannot immediately understand the information.…”

Section: Related Workmentioning

confidence: 99%

Information Extraction Model to Improve Learning Game Metadata Indexing

Morie¹,

Marfisi-Schottman²,

Tra³

2020

ISI

View full text Add to dashboard Cite

The use of Learning Games (LGs) in schools is a success factor for students. The benefits they bring to the learning process should be widely disseminated at all levels of education. Currently, there are thousands of LGs that cover a large variety of educations fields. Despite this large choice of LGs, very few are used by teachers, due to the difficulty of finding and selecting suitable LGs. The aim of this paper is to propose an extraction model that will automatically collect the information about LGs directly from their web pages, in order to index them in a catalogue. The proposed ADEM (Automatic Description Extraction Model), browses the web pages describing LGs and does a first cleaning to remove any unnecessary information. Then a detection of description blocks, based on a certain number of criteria, identifies the regions containing the LG description text. Finally, an indexing on specific fields is performed. ADEM made it possible to automatically process 785 web pages to extract LG metadata indexing information. The results of this extraction process were validated by 20 teachers. This model therefore offers a promising starting point for better LG indexing and the creation of a complete catalogue.

show abstract

“…(14) The Zip Packager component, recover the project generated in the game engine and generate a ZIP file. (15) The response messages component takes the ZIP file path and returns a URL with the web address to download the file. (16) Finally, the response packager component returns the URL to the GUI to generate a button to download the game app.…”

Section: Tool's Architecturementioning

confidence: 99%

“…In their work, Freire and Fernández‐Manjón [15] highlight the main challenges that serious games pose for online learning object (LO) repositories. The authors consider serious games as LOs and describe strategies both to facilitate their assessment and improve their indexing, search, and retrieval from LO repositories.…”

Section: Related Workmentioning

confidence: 99%

Zeus – a tool for generating rule‐based serious games with gamification techniques

et al. 2020

View full text Add to dashboard Cite

Gamification refers to the use of rules and game design techniques to involve and motivate people to achieve their goals. This work proposes an application development architecture that can generate game applications with gamification techniques. As proof of concept, the authors introduce Zeus, a platform for developing ruled-based serious game applications with gamification techniques. Zeus aims at generating gamified applications that can meet the learning goals set by users. These goals will be reflected through both learning and game attributes that users can personally select. To assess the functionality of the authors tool, they conduct a qualitative evaluation of four rule-based serious game applications developed with Zeus to help students learn about arithmetic. They make use of the Fun Toolkit to perform this evaluation in terms of the delivery of both fun and learning experiences. Their findings are encouraging in the context of learning basic arithmetic operations with Zeus game applications.

show abstract

Metadata for Serious Games in Learning Object Repositories

Cited by 4 publications

References 10 publications

LGMD: Optimal Lightweight Metadata Model for Indexing Learning Games

LGMD: Optimal Lightweight Metadata Model for Indexing Learning Games

Information Extraction Model to Improve Learning Game Metadata Indexing

Zeus – a tool for generating rule‐based serious games with gamification techniques

Contact Info

Product

Resources

About