engleski

A Prevalence Trend of Characteristics of Intelligent and Adaptive Hypermedia E-Learning Systems

The main aim of this research is to determine a prevalence trend of characteristics of intelligent and adaptive hypermedia e-learning systems (IAHe-LS). IAHe-LS characteristics were determined by examining published scientific papers indexed in relevant databases. We analysed 1170 papers and identified 61 systems. The description of system architecture was used as the selection criterion, which yielded 21 characteristics used to describe the systems, namely: learning style, cognitive style, adaptivity inference mechanism, granularity of learning content, pedagogical model, domain knowledge model, learner activity tracking, knowledge testing, testing previously acquired knowledge, experimental use, form of presented content, adaptivity criteria, standardisation, system interface model, teacher model, description model, and interactive tools. A prevalence of characteristics was clustered by the didactic pyramid. The learner’s characteristic of the highest prevalence is learning style and of the lowest is cognitive style. All analysed characteristics related to educational technology have increased prevalence from 2008 onwards. The teacher’s characteristic of the highest prevalence is knowledge testing, whereas the one with the lowest is teacher model. The most difficult part was to investigate the prevalence of characteristics associated with the content as in the analysed articles that part is explained poorly. However, we noticed that from 2008 onwards both identified characteristics have increase in prevalence. A Poisson regression analysis was carried out in order to determine the connection between the occurrence of characteristics of IAHe-LS and the year they occured. Although the number of occurrences for some characteristics was too little in order to conduct an analysis, it has shown that the model obtained by Poisson regression is suitable for all other characteristics.

Adaptive hypermedia e-learning systems ; Intelligent e-learning systems ; Learner’s characteristics ; Teacher’s characteristics ; Content’s characteristics ; Educational technology characteristics ; Prevalence

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