The Open UniversitySkip to content
 

Investigating Variation in Learning Processes in a FutureLearn MOOC

Rizvi, Saman; Rienties, Bart; Rogaten, Jekaterina and Kizilcec, René F. (2019). Investigating Variation in Learning Processes in a FutureLearn MOOC. Journal of Computing in Higher Education (Early Access).

Full text available as:
[img]
Preview
PDF (Version of Record) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview
DOI (Digital Object Identifier) Link: https://doi.org/10.1007/s12528-019-09231-0
Google Scholar: Look up in Google Scholar

Abstract

Studies on engagement and learning design in Massive Open Online Courses (MOOCs) have laid the groundwork for understanding how people learn in this relatively new type of informal learning environment. To advance our understanding of how people learn in MOOCs, we investigate the intersection between learning design and the temporal process of engagement in the course. This study investigates the detailed processes of engagement using educational process mining (EPM) in a FutureLearn science course (N = 2086 learners) and applying an established taxonomy of learning design to classify learning activities. The analyses were performed on three groups of learners categorised based upon their clicking behaviour. The process-mining results show at least one dominant pathway in each of the three groups, though multiple popular additional pathways were identified within each group. All three groups remained interested and engaged in the various learning and assessment activities. The findings from this study suggest that in the analysis of voluminous MOOC data there is value in first clustering learners and then investigating detailed progressions within each cluster that take the order and type of learning activities into account. The approach is promising because it provides insight into variation in behavioural sequences based on learners’ intentions for earning a course certificate. These insights can inform the targeting of analytics-based interventions to support learners and inform MOOC designers about adapting learning activities to different groups of learners based on their goals.

Item Type: Journal Item
Copyright Holders: 2019 The Authors
ISSN: 1042-1726
Project Funding Details:
Funded Project NameProject IDFunding Body
Open World LearningDS-2014-077The Leverhulme Trust
Keywords: MOOCs; Educational Process Mining; Learning Design
Academic Unit/School: Faculty of Wellbeing, Education and Language Studies (WELS)
Research Group: Centre for Research in Education and Educational Technology (CREET)
Item ID: 61295
Depositing User: Saman Rizvi
Date Deposited: 20 May 2019 08:18
Last Modified: 15 Jul 2019 23:33
URI: http://oro.open.ac.uk/id/eprint/61295
Share this page:

Metrics

Altmetrics from Altmetric

Citations from Dimensions

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU