Adaptive Content Presentation in Asynchronous Learning Environments

Abstract

Abstract-In Adaptive Educational Hypermedia Systems (AEHS), we expect that the learning content presentation should be appropriately retrieved from learning object repositories, and dynamically tailored to each learner's needs. Each learner has a profile, subject to continuous change. The basic components of the learner's profile include his/her cognitive characteristics, background of knowledge, previous experience, and current emotional situation. This paper proposes the architecture of a Petri net-based workflow engine -scheduled to be implemented in a AEHS -aiming to provide a reliable and efficient platform for the execution of learning course flows in a grid environment. Dealing with the question of adaptive management of learning content, the proposed p-timed Petri net is capable of presenting learning content adapted to the learner's Learning Style and knowledge background. The proposed schema is accurately tested using a p-timed Petri net simulator. The schema may now be extended to include other components of the learner's profile.

Web-based adaptive presentation techniques to enhance learning outcomes in higher education

“…Our results agree with the findings in the literature (Bikanga-Ada, 2018;Lestari et al, 2017;Mohamad, 2012;Popenici & Kerr, 2017;Tarpin-Bernard & Habieb-Mammar, 2005;Techataweewan, 2010;Tsoulouhas et al, 2012;Wang et al, 2019) which found that adaptive hypermedia techniques have a positive effects on improving the student' learning outcomes. Nevertheless, it differs with Ross et al (2018) that showed no improvement in students' learning outcomes.…”

“…Accordingly, each student is directed to the appropriate path based on their information and knowledge needs. The reason for this is that it helps students to focus on the knowledge and experience that they need while learning as affirmed by Surjono and Maltby (2003) and Tsoulouhas et al (2012). Furthermore, the techniques have provided individual learning for each student according to his speed and self-progress.…”

“…Rowe et al (2010) presented the implementation and evaluation of an adaptive multimedia presentation system with contextual supplemental support media. Tsoulouhas et al (2012) introduced an adaptive content presentation in asynchronous learning environments. Furthermore, Sanchez-Gordon and Luján-Mora (2015) presented a proposal to enhance MOOCs' accessibility by adapting course content presentation according to student needs, preferences, and skills.…”

“…Such factors primarily include the learner's cognitive characteristics, knowledge background, educational preferences, etc. (Tsoulouhas, Georgiou, & Karakos, 2012).…”

Predicting Instructor Performance in Higher Education Using Intelligent Agent Systems

“…Classification algorithms are used for teachers and student perception. The author in [10] depicted performance of the student evaluation where the amount of stored educational data is of high. They have studied about artificial neural networks and discriminant analysis models which improved accuracy and specificity.…”