Affinity-based learning object retrieval in an e-learning environment using evolutionary learner profile

V R Raghuveer ,B K Tripathy

doi:10.34105/j.kmel.2016.08.012

Abstract

With the abundance of learning objects (LOs) available across the web, there arises a demand for retrieving the LOs that exactly suit the learners’ requirements. In order to achieve this, the learner profile (LP) should exactly mimic the subject specific requirements of the learner as well as evolve over the learning cycle. Moreover, each LO should project itself in such a way that the learning management system is able to find it as a suitable candidate for a specific learner requirement. This paper proposes a novel method that fetches appropriate LOs for the learner by mapping his/her learner profile with those of the LOs. The LOs thus retrieved are then re-ranked according to their affinity towards the particular learner’s requirements and then presented to the learner. The experimental results demonstrated the effectiveness of the proposed method in retrieving appropriate learning content for learners.

Highlights

With the advancement of web 2.0 and information and communication technologies (ICTs), e-learning has become an accessible mode of education for many people around the world
The misses on preference category highlights the lack of variety on the learning objects (LOs) of our system due to the limited number of objects used for the study
The problem with the retrieval of precise LOs based on a single, common, generic learner profile was resolved by modelling the profile in such a way that it reflects the domain specific requirements of the learner

Summary

Introduction

With the advancement of web 2.0 and information and communication technologies (ICTs), e-learning has become an accessible mode of education for many people around the world. The IEEE and Instructional Management Systems (IMS) standardized the usage of learner profile attributes though their Public and Private Information (PAPI) and Learner Information Package (LIP) standards respectively. These standards have explicitly defined the profile attributes in such a way that they can be used uniformly across LMSs. These standards have explicitly defined the profile attributes in such a way that they can be used uniformly across LMSs The attributes of these LP standards mainly fall under the following categories including learner identification, skills, and preferences. The IEEE LOM standard categorizes the LO metadata attributes under nine different classes including general, lifecycle, metametadata, technical, educational, rights, relation, annotation, and classification, which better describes the object’s nature and its connections with other objects

Methods

Results

Conclusion