research-article

On the Impact of Personality in Massive Open Online Learning

Authors:

Guanliang Chen,

Dan Davis,

Claudia Hauff,

Geert-Jan HoubenAuthors Info & Claims

UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

Pages 121 - 130

https://doi.org/10.1145/2930238.2930240

Published: 13 July 2016 Publication History

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Abstract

Massive Open Online Courses (MOOCs) have gained considerable momentum since their inception in 2011. They are, however, plagued by two issues that threaten their future: learner engagement and learner retention. MOOCs regularly attract tens of thousands of learners, though only a very small percentage complete them successfully. In the traditional classroom setting, it has been established that personality impacts different aspects of learning. It is an open question to what extent this finding translates to MOOCs: do learners' personalities impact their learning & learning behaviour in the MOOC setting? In this paper, we explore this question and analyse the personality profiles and learning traces of hundreds of learners that have taken a EX101x Data Analysis MOOC on the edX platform. We find learners' personality traits to only weakly correlate with learning as captured through the data traces learners leave on edX.

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Reviews

Reviewer: Stewart Mark Godwin

This paper presents two research questions that revolve around how a student's personality reflects their academic success. The authors collected data from a massive open online course (MOOC) environment and used various social media systems in their analysis. The major difficulty identified is the diverse range of learners who enrolled in MOOC courses. However, the cohort in this research was divided into two groups, based on their prior content knowledge, for the data analysis stage. There are some minor issues with the content of this research. For example, the paper begins with the statement that MOOCs began in 2011; however, the originators developed this concept in 2008. Since about 2012, two accepted approaches to this type of educational delivery have been developed, both of which would be known to the authors. This distinction would have contextualized the research. The findings from this paper confirm the poor completion rates of this type of educational delivery, and support the notion that a student's conscientiousness is a strong predictor of academic success. There is acknowledgement that this research is exploratory and a foundation for future investigation. This paper would be of interest to academics in the area of MOOC delivery systems. Online Computing Reviews Service

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Published In

UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

July 2016

366 pages

ISBN:9781450343688

DOI:10.1145/2930238

General Chairs:
Julita Vassileva
University of Saskatchewan, Canada
,
James Blustein
Dalhousie University, Canada
,
Program Chairs:
Lora Aroyo
VU Amsterdam, the Netherlands
,
Sidney D'Mello
University of Notre Dame, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 13 July 2016

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The Extension School of the Delft University of Technology
The Leiden-Delft-Erasmus Centre for Education and Learning

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UMAP '16

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UMAP '16: User Modeling, Adaptation and Personalization Conference

July 13 - 17, 2016

Nova Scotia, Halifax, Canada

Acceptance Rates

UMAP '16 Paper Acceptance Rate 21 of 123 submissions, 17%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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UMAP '25

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33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

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https://doi.org/10.1080/10447318.2024.2332031
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García-Molina SAlario-Hoyos CMoreno-Marcos PMuñoz-Merino PEstévez-Ayres IDelgado Kloos C(2020)An Algorithm and a Tool for the Automatic Grading of MOOC Learners from Their Contributions in the Discussion ForumApplied Sciences10.3390/app1101009511:1(95)Online publication date: 24-Dec-2020
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