An integrated approach for an academic advising system in adaptive credit-based learning environment

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.