Adaptive Learning Module for a Conversational Agent to Support MOOC Learners

Massive open online courses (MOOCs) pose a challenge for instructors when trying to provide personalised support to learners, due to large numbers of registered participants. Conversational agents can be of help to support learners when working with MOOCs. This article presents an adaptive learning...

Full description

Saved in:
Bibliographic Details
Published in:Australasian Journal of Educational Technology 2021-01, Vol.37 (2), p.24-44
Main Authors: González-Castro, Nuria, Muñoz-Merino, Pedro J, Alario-Hoyos, Carlos, Delgado Kloos, Carlos
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Assisted Instruction
Computer Science Education
Course Content
Difficulty Level
Individualized Instruction
Instructional Effectiveness
Item Response Theory
Knowledge Level
Learning Analytics
Learning Modules
MOOCs
Natural Language Processing
Online Courses
Online instruction
Open Education
Programming
Questioning Techniques
Usability
Video Technology
Visual Aids
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Massive open online courses (MOOCs) pose a challenge for instructors when trying to provide personalised support to learners, due to large numbers of registered participants. Conversational agents can be of help to support learners when working with MOOCs. This article presents an adaptive learning module for JavaPAL, a conversational agent that complements a MOOC on Java programming, helping learners review the key concepts of the MOOC. This adaptive learning module adapts the difficulty of the questions provided to learners considering their level of knowledge using item response theory (IRT) and also provides recommendations of video fragments extracted from the MOOC for when learners fail questions. The adaptive learning module for JavaPAL has been evaluated showing good usability and learnability through the system usability scale (SUS), reasonably suitable video fragments recommendations for learners, and useful visualisations generated as part of the IRT-based adaptation of questions for instructors to better understand what is happening in the course, to design exams, and to redesign the course content.
ISSN:1449-5554
1449-3098
1449-5554
DOI:10.14742/ajet.6646