Information quality metrics in e-learning context

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I. Introduction

Within the widespread use of e-learning at the different levels of education, the increasing number of accessible e-learning systems, and commercialization of these systems lead to the necessity of quality evaluation of published learning materials. Moreover today information quality is considered a critical issue for education in general, and particularly for e-learning. However, quality cannot be expressed and set by a simple definition, since in itself quality is a very abstract notion. The specified context and the perspectives of users need to be taken into account when defining quality in e-learning context. It is also essential to classify suitable criteria to address quality [1].

Although quality evaluation of learning materials in e-learning systems becomes increasingly important, evaluation standards and methods for information quality in such systems did yet not reach a consensus. Besides that, criteria and methods of this evaluation have their own characteristics which differ from the used methods for usual learning materials. All that underscores the need to find clear and specified quality criteria for this information, and to develop a reliable measurement method, which allows solving this problem [2].

Based on original questionnaire data and factor analysis, in a previous work In a previous work [3], we proposed a framework to measure the quality of the information provided by e-learning systems based on original questionnaire data and factor analysis. The proposed framework could be used to reach a comprehensive indication of information quality in the context of e-learning for system designers, providers and users. The framework consisted of 14 quality attributes grouped in three quality factors: intrinsic, contextual representation and accessibility. Within the proposed quality framework we assigned a relative importance weight for each attribute within the main quality factors,

This paper is part of a wider research project aiming to define metrics to determine the quality of the content provided by distributed learning materials, for integrating intelligent agent technologies as a means of gathering information for quality evaluation.

In this paper we focus on the metrics identification and their integration in the domain model. They are necessary for allowing reasoning at the modelling level. The objective of such a measurement system is to reach the best Return on Security Investment (ROSI) for the studied IS, and so to optimise the alignment between the business of the organisation and its IS security. In this paper, we investigate what the metrics relevant for performing ISSRM and reasoning about ROSI are. Our research objective is, first, to propose a systematic manner to define what the metrics to be used in ISSRM are. Second, the application of this research method shall propose a set of metrics to be integrated in our domain model and so in our modelling language.

The rest of the

II. information quality in the context of e-learning

In a previous work we proposed an information quality framework, which was derived from the user's perspective, to measure the quality of the content provided by e-learning systems [3]. The proposed framework, shown in Fig. 1, consists of 14 quality dimensions grouped in three quality factors: intrinsic, contextual representation and accessibility.

We started the framework development process by adopting Wang & Strong's data quality framework and using it as the reference point owing to its popularity and acceptance by the information systems quality community [4]. Then we examined seventeen frameworks within the recently published literature in order to extend Wang & Strong's data quality framework and to include any undiscovered quality dimensions [3]. In order to determine the users' perspective of the relative importance of quality dimensions in the context of e-learning systems, the identified quality dimensions were arranged in a questionnaire format. Based on the collected data and factor analysis, we proposed a new quality framework to measure the quality of the content provided by e-learning systems. Moreover, linear regression was used to calculate the relative importance for each factor in the overall quality.

III. goal/question/metrics(gqm)approach

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IV. application of gqm

A. defining quality goals

B. Formulating questions

C. Identifying metrics

V. conclusion and futurework


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