On-line learning performance and computer anxiety measure for unemployed adult novices using a grey relation entropy method

On-line learning is an asynchronous computer-based learning mode that allows learners to learn anytime and anywhere in their own environment using information and communication technology. It can be considered as a way to bridge the digital gap. How a computer novice performs in such virtual and asynchronous learning environments is an interesting issue in human-computer interaction research. This paper presents the results of a study to investigate on-line learning performance and computer anxiety for unemployed adult novices. In this study, we propose a novel idea that integrates the concept of Shannon entropy into a grey relational analysis model. The proposed method was used to measure human information behavior in relation to on-line learning performance and computer anxiety. A total of 115 unemployed adults voluntarily participated in the experimental study. All experimental subjects were divided into groups according to individual differences in gender, age ranges, educational levels, and learning performances. Taking advantage of the grey relation entropy operation, we derived the perceptive correlations among different classified groups in terms of the accessibility of on-line learning and computer anxiety scales, respectively. Through the empirical study, certain on-line learning characteristics were also identified.