Competency-based personalization for massive online learning

Gilbert Paquette,Michel Léonard,Olga Mariño,Delia Rogozan

doi:10.1186/s40561-015-0013-z

Gilbert Paquette, Michel Léonard + Show 2 more

Open Access

https://doi.org/10.1186/s40561-015-0013-z

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Abstract

This paper investigates the problem of personalization in massive open online courses (MOOC) based on a target competency profile and a learning scenario model built for the course. To use such a profile for adaptive learning and resource recommendation, we need to be able to compare competencies to help match the competencies of learners with those involved in other learning scenario components (actors, activities, resources). We present a method for computing relations between competencies based on a structured competency model. We use this method to define recommendation agents added to a MOOC learning scenario. This approach for competency comparison has been implemented within an experimental platform called TELOS. We propose to integrate these functionalities to a MOOC platform such as Open-edX. We present a personalization process and we discuss the tools needed to implement the process.

Highlights

This paper investigates the problem of personalization in massive open online courses (MOOC) based on a target competency profile and a learning scenario model built for the course
In section “Competency referencing of learning scenario components”, we present the competency model used for recommendation and adaptation and a simple scenario example which is used to illustrate the main concepts involved in our proposal
In section “Recommendations based on competency comparison”, we present the definition of rule-based advisory agents, where the competency relations defined in section “Competency comparison” are used into the rules’ conditions

Summary

Introduction

This paper investigates the problem of personalization in massive open online courses (MOOC) based on a target competency profile and a learning scenario model built for the course. To use such a profile for adaptive learning and resource recommendation, we need to be able to compare competencies to help match the competencies of learners with those involved in other learning scenario components (actors, activities, resources). We present a method for computing relations between competencies based on a structured competency model We use this method to define recommendation agents added to a MOOC learning scenario. 2011) where knowledge about users and context of use enables the personalization of Web resources, including learning scenarios

Objectives

Conclusion