Approaches to facilitate adaptivity in ubiquitous learning environments 16 January, 2012
Dr. Kinshuk, Associate Dean, Faculty of Science and Technology, Athabasca University, Canada led the weekly discussion on 16th January 2013 via A-VIEW with his talk on “Approaches to facilitate adaptivity in ubiquitous learning environments”. He is also Professor, School of Computing and Information Systems and NSERC/ iCORE/ Xerox/ Markin Industrial Research Chair for Adaptivity and Personalization in Informatics at Athabasca University.
Adaptivity in Online Educationt
Learners typically learn on their own in online learning, and do not have the kind of support from teachers and other learners as is common in face-to-face classroom environment. Adaptivity can play a significant role in such situation by customizing learning content and activities to suit individual learners. Adaptive learning systems can be classified on a spectrum of those system that adapt to the learners automatically based on algorithms that infer about learners’ needs (adaptive systems) and those that allow learners to change certain system parameters (adaptable systems).
Adaptivity with respect to domain competence
Adaptive learning systems play a role of “filling the gap” that exists in online learning due to the absence of human experts at the time of learning. Various components of domain competence can be supported by these systems. Computer simulations and virtual reality can help in acquiring know-how, whereas adaptive hypermedia can help with know-why aspect. Integration of location awareness and temporal awareness add know-when and know-where dimensions.
Cognitive trait model
Cognitive traits or mental abilities influence how individual learners approach to learning process and effectively learn. Traits such as working memory capacity, inductive reasoning ability, information processing speed and associative learning skills can be reliably measured through monitoring various actions learners take during learning process and mapping them to various patterns found on cognitive and instructional psychology. The model created in such manner remains useful over a variety of domains and over a long period of time, since cognitive trait levels of individual learners do not change for very long periods and do not get affected by the subject domains.