A Conceptual Framework for Evolving, Recommender Online Learning Systems

Abstract

A comprehensive conceptual framework is developed and described for evolving recommender‐driven online learning systems (ROLS). This framework describes how such systems can support students, course authors, course instructors, systems administrators, and policy makers in developing and using these ROLS. The design science information systems research approach was used to develop the framework. The ROLS framework incorporates both the cognitive and situative perspectives of the constructivist paradigm of learning. Research propositions are developed to highlight new research opportunities for these systems. The framework demonstrates how various components of an evolving ROLS can be integrated to provide potential benefits for all users.

Building and evaluating ESET: A tool for assessing the support given by an enterprise system to supply chain management

“…Currently KBDSS is designed with knowledge bases that evolve with use to support users with dynamic personalised recommendations in different situations, giving content-based and collaborative user-filtering based (filtered out and filtered in) recommendations [62,65]. The foundation of ESET was based on this recommender-driven DSS framework [62]. ESET's components ○ SCM performance strategies ○ SCM measurement metrics ○ Organisational processes that support those selected SCM strategies ○ Persons responsible for specific processes • Ability to store, retrieve, share and manage very large amounts of data [44,77,87] • To allow an application to mine and display information proactively [65,87] 4 Key solvers designed to support key objectives therefore are Users, Model/Solver base, Visualisation base, Interface, and the Data/Knowledge base (Table 2).…”

“…DSS is defined as highly interactive, computer-based information systems that use interfaces, models and solvers together with robust databases, and knowledge bases, and a knowledge engine to solve unstructured and complex problems faced by stakeholders [44]. Currently KBDSS is designed with knowledge bases that evolve with use to support users with dynamic personalised recommendations in different situations, giving content-based and collaborative user-filtering based (filtered out and filtered in) recommendations [62,65]. The foundation of ESET was based on this recommender-driven DSS framework [62].…”

Evaluating a Prototype of a Recommender‐Driven Online Learning System

“…ROLS based on REGIONLS offer moderate constructivist learning, which is a combination of cognitive and situative learning within a Web‐client‐based environment (Peiris & Gallupe, ). Cognitive learning encourages individuals to gain deep insights to content taught, while situative learning encourages and enables learners to collaborate and use the environment and persons around them to learn.…”

“…These systems incorporate intelligence to enable active learning by not only giving immediate feedback on questions answered and practice assignments done but also by monitoring and mentoring learners and providing them with automated dynamic personalized recommendations to facilitate learning. A few techniques used by ROLS for this purpose are analyzing and informing a learner about frequent mistakes made, using scaffolding and fading to provide extra support in practice, and checking knowledge gained by comparing against pre‐set learning objectives (Peiris & Gallupe, ). Also, dynamic recommendations generated by analyzing information collated from implicit (gleaned) or explicit user‐feedback given to the system augment course‐authoring tasks and help with prioritizing systems administration activities.…”

“…A ROLS based on the conceptual model REGIONLS, is defined as “an interactive, integrated and evolving intelligent computer‐based information system that is driven by assessment and recommendations and delivered via web‐based applications to fixed and mobile ICT devices in order to mainly provide personalized active learning within a moderate constructivist learning environment. It also supports multiple other stakeholders in their tasks with personalized task oriented recommendations” (building on the definition from Peiris & Gallupe, , p. 391).…”