Adaptive Learning Guidance System (ALGS) (1911.06812v1)

Abstract: This poster presents the conceptual framework of the Adaptive Learning Guidance System ALGS. The system aims to propose a model for adaptive learning environments where two major concerns arising from past studies are being addressed; the marginal role of the teacher, and the need for a big data approach. Most past studies marginalized the teacher role in adaptive learning system, particularly the online ones. The most notable quality about ALGS is empowering the teacher with the capability of having input in all stages. This is where the hybrid recommendation system plays a crucial role in the 3-stage ALGS architecture. The second issue addressed is the need for big data to enhance the system functionality. The more the data collected by the system, the more efficient its adaptation functionality which makes it difficult for a first-time-run system and (or) a first-time user. Accordingly, collaborative filtering is used at first until adequate data about the user interaction are collected. ALGS architecture consists of a user, content, and 3-stage adaptation models.