Information quality assessment in e-learning systems.

Abstract

E-learning systems provide a promising solution as an information exchanging channel. Improved technology could mean faster and easier access to information but does not necessarily ensure the quality of this information. Therefore it is essential to develop valid and reliable methods of quality measurement and carry out careful information quality evaluations. Information quality frameworks are developed to measure the quality of information systems, generally from the designers¿ viewpoint. The recent proliferation of e-services, and e-learning particularly, raises the need for a new quality framework in the context of e-learning systems. The main contribution of this thesis is to propose a new information quality framework, with 14 information quality attributes grouped in three quality dimensions: intrinsic, contextual representation and accessibility. We report results based on original questionnaire data and factor analysis. Moreover, we validate the proposed framework using an empirical approach. We report our validation results on the basis of data collected from an original questionnaire and structural equation modeling (SEM) analysis, confirmatory factor analysis (CFA) in particular. However, it is difficult to measure information quality in an e-learning context because the concept of information quality is complex and it is expected that the measurements will be multidimensional in nature. Reliable measures need to be obtained in a systematic way, whilst considering the purpose of the measurement. Therefore, we start by adopting a Goal Question Metrics (GQM) approach to develop a set of quality metrics for the identified quality attributes within the proposed framework. We then define an assessment model and measurement scheme, based on a multi element analysis technique. The obtained results can be considered to be promising and positive, and revealed that the framework and assessment scheme could give good predictions for information quality within e-learning context. This research generates novel contributions as it proposes a solution to the problems raised from the absence of consensus regarding evaluation standards and methods for measuring information quality within an e-learning context. Also, it anticipates the feasibility of taking advantage of web mining techniques to automate the retrieval process of the information required for quality measurement. This assessment model is useful to e-learning systems designers, providers and users as it gives a comprehensive indication of the quality of information in such systems, and also facilitates the evaluation, allows comparisons and analysis of information quality.