poster

Student affect during learning with a MOOC

Authors:

John Dillon,

G. Alex Ambrose,

Nirandika Wanigasekara,

Malolan Chetlur,

Prasenjit Dey,

Bikram Sengupta,

Sidney K. D'MelloAuthors Info & Claims

LAK '16: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge

Pages 528 - 529

https://doi.org/10.1145/2883851.2883960

Published: 25 April 2016 Publication History

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Abstract

This paper presents affect data collected from periodic emotion detection surveys throughout an introductory Statistics MOOC called "I Heart Stats." This is the first MOOC, to our knowledge, to capture valuable student affect data through self-reported surveys. To collect student affect, we used two self-reporting methods: (1) The Self-Assessment Manikin and (2) A discrete emotion list. We found that the most common reported MOOC emotion was Hope followed by Enjoyment and Contentment. There were substantial shifts in affective states over the course, notably with Anxiety and Pride. The most valuable result of our study is a preliminary description of the methods for collecting self-reported student affect at scale in a MOOC setting.

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Cited By

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Vanacore KLee JEgorova AOttmar E(2023)Beyond Performance AnalyticsPerspectives on Learning Analytics for Maximizing Student Outcomes10.4018/978-1-6684-9527-8.ch009(168-187)Online publication date: 24-Oct-2023
https://doi.org/10.4018/978-1-6684-9527-8.ch009
Corral JKonstantinidis SBamidis P(2021)Learning analytics, education data mining, and personalization in health professions educationDigital Innovations in Healthcare Education and Training10.1016/B978-0-12-813144-2.00009-X(137-150)Online publication date: 2021
https://doi.org/10.1016/B978-0-12-813144-2.00009-X
Okur EAslan SAlyuz NArslan Esme ABaker R(2018)Role of Socio-cultural Differences in Labeling Students’ Affective StatesArtificial Intelligence in Education10.1007/978-3-319-93843-1_27(367-380)Online publication date: 20-Jun-2018
https://doi.org/10.1007/978-3-319-93843-1_27
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Index Terms

Student affect during learning with a MOOC
1. Applied computing
  1. Education
    1. Distance learning
    2. E-learning

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Reviews

Reviewer: Stewart Mark Godwin

An interesting perspective on the massive open online courses (MOOCs) topic has the potential to change the delivery format and possible retention rates. This paper reports on research that collected the emotional feelings of students working through an introductory statistics course. The research requested students to self-report their emotions at several points during the course. The authors acknowledge the limitations of the research, as the high attrition rate of students from the course could not be reported. The data from students who withdraw would be of significant interest to all educators working in the area. However, the positive outcome from this paper is the possibility for educators to identify students who are experiencing problems and offer support before they decide to withdraw. Online Computing Reviews Service

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Information & Contributors

Information

Published In

LAK '16: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge

April 2016

567 pages

ISBN:9781450341905

DOI:10.1145/2883851

General Chairs:
Dragan Gašević
University of Edinburgh, United Kingdom
,
Grace Lynch
Society for Learning Analytics Research
,
Program Chairs:
Shane Dawson
University of South Australia
,
Hendrik Drachsler
University of the Netherlands
,
Carolyn Penstein Rosé
Carnegie Mellon University

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 April 2016

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LAK '16

LAK '16: 6th International Conference on Learning Analytics and Knowledge

April 25 - 29, 2016

Edinburgh, United Kingdom

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LAK '16 Paper Acceptance Rate 36 of 116 submissions, 31%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

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Vanacore KLee JEgorova AOttmar E(2023)Beyond Performance AnalyticsPerspectives on Learning Analytics for Maximizing Student Outcomes10.4018/978-1-6684-9527-8.ch009(168-187)Online publication date: 24-Oct-2023
https://doi.org/10.4018/978-1-6684-9527-8.ch009
Corral JKonstantinidis SBamidis P(2021)Learning analytics, education data mining, and personalization in health professions educationDigital Innovations in Healthcare Education and Training10.1016/B978-0-12-813144-2.00009-X(137-150)Online publication date: 2021
https://doi.org/10.1016/B978-0-12-813144-2.00009-X
Okur EAslan SAlyuz NArslan Esme ABaker R(2018)Role of Socio-cultural Differences in Labeling Students’ Affective StatesArtificial Intelligence in Education10.1007/978-3-319-93843-1_27(367-380)Online publication date: 20-Jun-2018
https://doi.org/10.1007/978-3-319-93843-1_27
Zhao JHuo SLi YLi DLiu XZhou JLui JWang XWolf ALiu YHu C(2017)Tide and shockProceedings of the ACM Turing 50th Celebration Conference - China10.1145/3063955.3063970(1-6)Online publication date: 12-May-2017
https://dl.acm.org/doi/10.1145/3063955.3063970

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Recommendations

Affect Sequences and Learning in Betty's Brain

The False Dichotomy between Positive and Negative Affect in Game Play

Real-time expression of affect through respiration

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