|
Abstract:
|
Nowadays, with the growing importance of the credit-based learning in current
educational environment, strong academic advising system is an essential ingredient of learner
success, supporting personalized advices aimed at effective and efficient learning. In that context,
within the scope of this paper, an intelligent academic advising system approach is introduced
focusing on integrating technology-enhanced learning methodologies into a pedagogy-driven and
service-oriented architecture based on semantic technology. Specifically, a knowledge-based
framework is conceptually introduced, assisting learners in identifying and assessing academic
alternatives for their life goals as well as making meaningful educational plans that are effectively
compatible with those goals. In the proposed framework, the learning data warehouse plays a key
part with information about learners’ behavior and navigation so that intelligent algorithms can be
applied and patterns can be obtained as the basis for course advising. Moreover, a data integration
prototype is studied and developed as a resource discovery tool to map, convert and harvest
advising related information from structured and semi-structured learning repositories. Thus, the
described framework emphasizes its application within an open adaptive credit-based learning,
providing abilities for accessing and managing, in an integrated manner, the adaptive interaction,
adaptive course delivery as well as adaptive content discovery and assembly. |