From traces to indicators for serious games: case study concerning a new way to introduce product lifecycle management

Pernelle, Philippe; Marty, Jean‐Charles; Carron, Thibault

doi:10.1504/ijlt.2013.055670

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…In this context, several indicators can be built to observe and operate social and cognitive processes implemented [22]. Indeed, exploiting traces allows building several types of indicators [23]. This experimentation enters into the curriculum of studies in the first year at the ESTS (Ecole Supéri-eure de Technologie de Salé, Superior School of Technology of Sale) of the Mohammed V University in Rabat.…”

Section: Methodsmentioning

confidence: 99%

“…In our case, data collection was limited to the extraction of information from the Moodle database, log files and the development of quantitative indicators mentioned before, but also to safeguard the jobs posted by learners at the end of their learning processes. In this context, exploiting traces allows us to build several types of indicators [23]:…”

Section: Data Collection and Analysismentioning

confidence: 99%

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How to Assist Tutors to Rebuild Groups Within an ITS by Exploiting Traces. Case of a Closed Forum.

Salihoun¹,

Guerouate²,

Berbiche³

et al. 2017

Int. J. Emerg. Technol. Learn.

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Abstract-Computer Supported Collaborative Learning (CSCL) is a new mode of teaching and one of the popular approaches for learning process. It allows virtual interactions between groups by providing tools such as: chat, internal email and discussion forums. One of the major problems caused by this learning process is the neglect and isolation of learners in groups, and usually is the cause of a heterogeneous group through social, cognitive or emotional ways. The method used is based on the exploitation of traces left on the online learning platform by learners and groups. The data collected from the environment can be observed and exploited in order to build social and cognitive indicators. Our approach is to design a model which assists the tutor to rebuild groups who are not homogeneous in order to prevent their isolation and abandonment. Our model offers the tutor the opportunity to rebuild the groups in an automatic way and based on the characteristics of quantitative indicators of all learners. Our work allowed us to test our algorithm from a functional and technical point of view and also identifies real variables from a collaborative online learning. It also allowed us to evaluate six different indicators proposed for this experiment, showing that they may assist the tutor to rebuild many groups again. The results show us that after the rebuilding groups, there has been a lot of participation in the forum and a considerable number of shares and documents deposited to the forum for each group. This high frequency of interaction between learners, lead them to a fruitful collaboration, and a good quality work at the end.

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Section: Methodsmentioning

confidence: 99%

Section: Data Collection and Analysismentioning

confidence: 99%

How to Assist Tutors to Rebuild Groups Within an ITS by Exploiting Traces. Case of a Closed Forum.

Salihoun¹,

Guerouate²,

Berbiche³

et al. 2017

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

show abstract

“…In this context, several indicators can be built to observe and operate participative, interactive and affective processes implemented. Indeed, exploiting traces allows building several types of indicators [25] [26].…”

Section: Methodsmentioning

confidence: 99%

A three-dimensional model that helps tutors to monitor groups during a collaborative online work

Salihoun¹,

Guerouate²,

Sbihi³

2017

IJET

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Abstract-The E-Learning, model, based on learner's activity, can provide proof for competence assessment. Such models are the foundation for learning and teaching support, such as adaptation, assessment, competence analysis, recommendations and so on. This paper describes a method for analysing the learning processes that takes place in a computer-supported collaborative learning (CSCL) environment. Our approach is based on tracking the interactions between learners within groups and on a three-dimensional model that includes a participative, an interactive and an affective dimension. The exploitation of these traces can serve three main purposes: the evaluation of the quality of the process, the monitoring of student's performance in real time and the assessment of collaborative learning performances. In this study, the importance of a good climate of trust and affection in promoting teamwork is presented. In addition, a set of indicators is obtained by tracking course learner's behaviour within the learning environment. The findings of an empirical study of 14 work-based teams are discussed. The results can benefit to the tutor by providing him the opportunity to rebuild the working groups based on the stable and variable characteristics of learners.Keyword-E-learning, Traces, Indicators, Sociograms, Online collaborative work I. INTRODUCTION Usually, courses in online learning platforms are structured in various phases. In each phase, learners are required to engage in collaborative activities aimed at producing the work requested by their tutors. In order to do so, they will need to discuss, collaborate, negotiate meanings and finally reach an agreement in order to reify a shared product that is the expression of the group's achievements. However, tutors often struggle to track learners/groups progress or to detect learners/groups experiencing difficulties [1]. In order to have a deep understanding of the learning process, tutors are required to go beyond the surface level of interactions in order to understand the social and the cognitive processes involved. As computer conferencing systems usually record and maintain a history of the events occurring during the learning process, a wealth of information that can be analyzed is available.Several research studies over the last decades are leaning towards the track analysis in online learning environments. The main objective of this research is to guide tutors to interpret the learner's activities in elearning situations, by exploiting and analyzing the traces and providing knowledge on the activities, which we call learning indicators. These indicators help to understand and monitor learning in order to help tutors and offer them the opportunity to rebuild the working groups accusing difficulties based on the stable and variable characteristics of learners.Unfortunately, in our last study [1], some learners did not produce nothing due to an absence of a cohesion in the group. These include the problems of the affective dimension of learners, convergence proble...

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“…Tous ces travaux sont potentiellement applicables au Serious Game, ainsi que le montre Learning Adventure, un jeu multi-joueurs collaboratif utilisé pour la formation professionnelle (Pernelle, Marty, & Carron, 2013). Le suivi des utilisateurs dans ce jeu repose sur un Système à Base de Traces construit en utilisant le modèle de Settouti (Settouti, 2011), dans lequel le jeu enregistre des traces « basiques » (utilisation du chat, avancement dans le scénario…) qui peuvent ensuite être combinées à loisir par l'analyste pour créer des « indicateurs ».…”

Section: Approches Et Outils Pour Le Suivi Des Utilisateurs D'un Eiahunclassified

Utilisation de plateformes génériques de mesure analytique pour l’évaluation de Serious Games : une expérimentation

Djaouti¹

2014

stice

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User tracking is one of the keys to evaluate the effectiveness of serious games. Two main approaches are available to perform such user tracking : the first one is to insert the Serious Game inside a LMS through SCORM or IMS-LD, while the second one is to build a custom analysis tool for each Serious Game. In this article, we introduce a third approach to track serious games users : the use of generic metrics & analytics platforms. This article presents an experiment where two serious games have been assessed using two of these platforms : Google Analytics and Playtomic. We will first detail the context of this project, before analysing the data that has been collected. Thanks to the results of this experiment, we will then start a discussion about the advantages and the limits of these generic metrics & analytics platforms for serious games assessment, compared to the two others available approaches.

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From traces to indicators for serious games: case study concerning a new way to introduce product lifecycle management

Cited by 4 publications

References 23 publications

How to Assist Tutors to Rebuild Groups Within an ITS by Exploiting Traces. Case of a Closed Forum.

How to Assist Tutors to Rebuild Groups Within an ITS by Exploiting Traces. Case of a Closed Forum.

A three-dimensional model that helps tutors to monitor groups during a collaborative online work

Utilisation de plateformes génériques de mesure analytique pour l’évaluation de Serious Games : une expérimentation

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