A Grid-Aware Implementation for Providing Effective Feedback to On-Line Learning Groups

Caballé, Santi; Paniagua, Claudi; Xhafa, Fatos; Daradoumis, Τhanasis

doi:10.1007/11575863_47

Cited by 13 publications

(14 citation statements)

References 7 publications

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“…In previous research [28], [29], [10] we showed that extracting and structuring LMS log data is a prerequisite for later key processes such as the analysis of interactions, assessment of group activity, or the provision of awareness and feedback involved in CSCL. With regarding to BSCW, the computational complexity of extracting and structuring BSCW log files is a costly process as the amount of data tends to be very large and needs computational power beyond of a single processor (see Fig.…”

Section: The Problem Of Processing Log Filesmentioning

confidence: 99%

A massive data processing approach for effective trustworthiness in online learning groups

Miguel

Caballé

Xhafa

et al. 2014

Concurrency and Computation

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Aquesta és la versió revisada per parells del següent article:Miguel, J., Caballé, S., Xhafa, F., Prieto, J. (2015) A massive data processing approach for effective trustworthiness in online learning groups. " Concurrency and computation. Practice and experience". Vol. 27, num. 8. p.1988Vol. 27, num. 8. p. -2003 Although computer-supported collaborative learning has been widely adopted in many educational institutions over the last decade, there exist still drawbacks that limit its potential. Among these limitations, we investigate on information security vulnerabilities in learning activities, which may be developed in on-line collaborative learning contexts. Although security advanced methodologies and technologies are deployed in learning management systems, many security vulnerabilities are still not satisfactorily solved. To overcome these deficiencies, we first propose the guidelines of a holistic security model in on-line collaborative learning through an effective trustworthiness approach. However, as learners' trustworthiness analysis involves large amount of data generated along learning activities, processing this information is computationally costly, especially if required in real-time. As the main contribution of this paper, we eventually propose a parallel processing approach, which can considerably decrease the time of data processing, thus allowing for building relevant trustworthiness models to support learning activities even in real-time.

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Section: The Problem Of Processing Log Filesmentioning

confidence: 99%

A massive data processing approach for effective trustworthiness in online learning groups

Miguel

Caballé

Xhafa

et al. 2014

Concurrency and Computation

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“…The aim is to greatly increase the application's performance by parallelizing the clients' requests and thus making it possible to provide costly functionality, such as the constant presentation of complex knowledge in real time. Our experience Caballé et al, 2005) in dealing with distributed environments makes us confident of being successful.…”

Section: Excerpt Of Selected Students' Commentsmentioning

confidence: 95%

“…The goal is to make it possible to both prepare information to facilitate its later processing and analysis and allow it to be efficiently addressed in a distributed environment where available (such as in a grid environment). Detailed information about how to structure and parallelize the processing of event log files can be found at Xhafa, Caballé, Daradoumis, and Zhou (2004) and Caballé, Paniagua, Xhafa, and Daradoumis (2005). This forms the second step of the process of embedding information and knowledge into collaborative learning applications (see Figure 2).…”

Section: Help Is Qualitativementioning

confidence: 99%

A Generic Platform for the Systematic Construction of Knowledge-based Collaborative Learning Applications

Caballé

Daradoumis

Xhafa

Architecture Solutions for E-Learning Systems

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“…In order to facilitate this step, CSCL applications may structure this information as log files in a way that takes advantage of the parallelism in order to process several files (e.g. all the groups in a classroom) at the same time and thus dramatically reduce the overall computational time to process them [10], [11]. As a result, it is possible for these applications to process a large volume of collaboration activity data and make the extracted information available even in real time.…”

Section: Efficient Processing Of the Informationmentioning

confidence: 99%

“…There is a fair deal of research focused on the analysis of online group interaction. A detailed description of an integrated approach we followed to analyze the collaborative learning interaction can be found in [11]. As a consequence of this analysis, knowledge is generated providing meta-cognition about the state and evolution of interaction, which enhances awareness about the efficiency of group activity, group behavior and the individual attitudes of its members in shared workspaces.…”

Section: Data Analysis and Extraction Of Knowledgementioning

confidence: 99%

Efficient Embedding of Information and Knowledge into CSCL Applications

Caballé¹,

Daradoumis²,

Xhafa³

Technologies for E-Learning and Digital Entertainment

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Abstract. This study aims to explore two crucial aspects of collaborative work and learning: the importance of enabling CSCL applications, on the one hand, to capture and structure the information generated by group activity and, on the other hand, to extract the relevant knowledge in order to provide learners and tutors with efficient awareness and support as regards collaboration. To this end, we first identify and define the main types of information generated in online group activity and then propose a process for efficiently embedding this information and the knowledge extracted from it into CSCL applications for awareness and feedback purposes. The conceptual model proposed finally gave rise to the design and implementation of a CSCL generic platform, called the Collaborative Learning Purpose Library (CLPL), which serves as a basis for the systematic development of collaborative learning applications and for providing full support to the mentioned process of knowledge management.

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A Grid-Aware Implementation for Providing Effective Feedback to On-Line Learning Groups

Cited by 13 publications

References 7 publications

A massive data processing approach for effective trustworthiness in online learning groups

A massive data processing approach for effective trustworthiness in online learning groups

A Generic Platform for the Systematic Construction of Knowledge-based Collaborative Learning Applications

Efficient Embedding of Information and Knowledge into CSCL Applications

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