A learning sequencing prediction system for ubiquitous learning based on SCORM sequencing and navigation

Abstract

This paper proposes a learning sequencing prediction system which applies the caching concept to improve the efficiency for ubiquitous learning and overcome the problems encountered in the usage of mobile devices as learning platforms. Two of the main problems are considered. First, the impermanent network environment due to insufficient coverage or wireless link failure leads to the continuous interruptions during learning process. Second, conversely the persistent connection offered by cellular phones using a telecommunication protocol, but with a relatively weak computing power and very limited network bandwidth which makes ubiquitous learning or m-learning become a time-consuming process. Moreover, learning contents are currently composed of various multimedia resources that induce long latency to display on handheld devices such as smartphones with GPRS. Recently, a lot of e-learning systems and contents have conformed to the sharable content object reference model (SCORM) since it was introduced by ADL in the late 90s. The sequencing and navigation (S&N) specification is an important part of SCORM. S&N is defined to prescribe the intended studentpsilas learning sequence by instructors. In this paper, we propose an adaptive learning sequencing prediction strategy based on the S&N specification in a ubiquitous learning environment.