Learning Mechanism Aggregation - Towards Enhanced Student E-Learning

ID: 53045 Type: Full Paper: Research
  1. Carl Christopher W. Beckford and Ezra K. Mugisa, University of the West Indies, Jamaica

Wednesday, June 27 11:15-11:45 AM

Presider:
Joshua Weidlich, Department of Technology-Enhanced Learning, University of Education, Heidelberg in Germany, Germany

Higher Education Institutions (HEIs) embrace the use of technology to enhance course delivery. As technology improves, teachers and administrators are asked to use new teaching strategies and assessments as they prepare students. Where learners do not access knowledge in the mode most suited to their form of knowledge acquisition, there may be non-optimal learning. Many theories of teaching and learning have been purported. Some of these are with respect to various learning mechanisms such as cognition, needs, intelligences and learning styles. A debate persists whether there is a need to examine learning mechanisms (styles) or whether there is any impact to learning by applying the determined learning mechanisms. The debate concludes the need for a best-fit of content matching to the type of teaching methodology to be applied. This paper posits OLECENT, an approach to provide increased learning in a batch of learners by lessening the gap between teaching and learning styles. The Learning Mechanism Aggregation Framework is posited for the aggregation of learning assessment instruments and identifying the commonalities or primitives where such learning instruments are deemed to be within same equivalence class. The Framework determines the transference of knowledge based on the determined best fit learning mechanisms but allows the service requester or learner the flexibility of receiving knowledge transference in other learning indexes within the learning instrument.

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