This presentations introduces results of a recent study that has been conducted based on data from five MOOCs across several providers

Kalz, Marco

Kreijns, Karel

DSpace at Open Universiteit Nederland

The predictive value of 
motivation, education, and support 
on the number of MOOCs enrolled
Karel Kreijns & Marco Kalz
Open Universiteit Nederland/Welten-instituut
What are MOOCs?
EAPRIL 2016
Pagina 2
Who deliver MOOCs?
EAPRIL 2016
Pagina 3
Research Question
To what extent was MOOC enrollment influenced by employed MOOC 
takers’ educational level, the support they received, and their 
motivation to enroll in MOOCs. 
EAPRIL 2016
Pagina 4
Participants
• Participants were employed MOOC takers (N=935; M = 463,           
F = 472) in five MOOCs
• Employed:
– Employed for wages (N = 741)
– Self-employed (N = 194)
• Five MOOCs
– Business intelligence (N = 224)
– Hands on ICT (N = 156)
– Test anxiety (N = 185)
– Bended Learning (N = 24)
– Entrepreneurship (N = 246)
EAPRIL 2016
Pagina 5
Participants
EAPRIL 2016
Pagina 6
Independent Variable: Education
• Education (International Standard Classification of Education 1997):
– Pre-primary education (N = 2)
– Primary education or first stage of basic education (N = 3)
– Lower secondary or second stage of basic education (N = 15)
– (upper) secondary education (N = 64)
– Post-secondary non-tertiary education (N = 36)
– First stage of tertiary education (N= 200)
– Second stage of tertiary education (N = 615)
• Variable→ ‘2nd tertiary education’ (dichotomous: false (N = 320) or 
true (N = 615))
EAPRIL 2016
Pagina 7
Independent Variable: Support
• Support:
– Receiving encouragement
– Compensation for costs
– Compensation for time
• However, 
– 277 of the 935 employed MOOC takers did not answer
– Combinations of support had low numbers. For example, the
number of MOOC takers receiving encouragement but no cost or 
time compensation was 10
• Variable→ ‘support’ (dichotomous: false (N = 449) or true (N = 209))
EAPRIL 2016
Pagina 8
Independent Variable: Motivation
EAPRIL 2016
Pagina 9
Independent Variable: Motivation
EAPRIL 2016
Pagina 10
Independent Variable: Motivation
EAPRIL 2016
Pagina 11
Independent Variable: Motivation
EAPRIL 2016
Pagina 12
Independent Variable: Motivation
EAPRIL 2016
Pagina 13
Independent Variable: Motivation
EAPRIL 2016
Pagina 14
Independent Variable: Motivation
EAPRIL 2016
Pagina 15
Independent Variable: Motivation
EAPRIL 2016
Pagina 16
Independent Variable: Motivation
EAPRIL 2016
Pagina 17
Dependent Variable: Number of Enrolled MOOCs
EAPRIL 2016
Pagina 18
Variable→ ‘number of enrolled MOOCs’ (count variable: zero-inflated Poisson)
excess of zero counts
Research Model
EAPRIL 2016
Pagina 19
Analyses
• We performed a number of analyses:
– With regard to motivation:
• Rasch analyses to determine undimensionality, removal of DIF and
local dependencies, and to determine the person locations of the
latent class indicators [RUMM2030 & Winsteps]
• Confirmatory analyses to determine the construct validity of the five 
latent class indicators to form a measure for motivation [MPLUS]
• Latent class analyses to determine the number of latent classes 
[MPLUS]
– With regard to the research model:
• Analyses on the finite mixture model [MPLUS]
– Multinomial logistic regressions
– Zero-inflated Poisson regressions
EAPRIL 2016
Pagina 20
Findings
• Regarding the motivation classes:
– Gender had no effect on the probabilities to be in a certain
motivation class
– Education affected the probabilities to be in a certain class: 
MOOC takers who received 2nd tertiary education tend to fall in 
the low-motivated profile
– Support had no effect on the probabilities to be in a certain
motivation class
EAPRIL 2016
Pagina 21
Findings
• Regarding to the number of enrolled MOOCs:
– Education had a positive interaction effect with the moderate-
motivated profile
– Support had no interaction effect with any motivation class
– All motivation classes (i.e., the high-, moderate-, and the low-
motivated profiles) directly effected the number of enrolled
MOOCs. The high-motivated profile had the biggest impact 
followed by the low-motivated profile. The moderate-motivated
profile had the lowest impact.
– Whereas the moderate-motivated profile affected the number of 
enrolled MOOCs the least directly, it had an interaction effect with
education which possibly could compensate the loss of the direct 
effect (this has to be checked however)
EAPRIL 2016
Pagina 22
Findings
– The moderate- and low-motivated profiles were predominantly
and equally responsible for the excess of zero counts whereas the
high-motivated profile was not. However, the result was significant 
only for the moderate-motovated profile
EAPRIL 2016
Pagina 23
Conclusion
• As a high-motivated profile is characterized by a high identified
regulation, MOOC providers should take care that they offer MOOCs
that fit the professional development needs of the MOOC takers. But 
at the same time the high-motivated profile tend to have MOOC 
takers who did not received 2nd tertiary education. Therefore, MOOC 
providers shoud take care that when develping MOOCs this group of 
MOOC takers is taken into account in terms of pre-knowledge, level 
of difficulty, etcetera.
EAPRIL 2016
Pagina 24
ORD 2016
Pagina 25
Padlet
• Question 1:
– Should MOOC providers have an important role to remove 
social inequality with respect to education?
URL: http://padlet.com/pad_eapril_2016/Question11
EAPRIL 2016
Pagina 26


The predictive value of motivation, education and support on the number of MOOCs enrolled

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Open University of the Netherlands Research Portal

The predictive value of motivation, education, and support on the number of MOOCs enrolledKarel Kreijns & Marco KalzOpen Universiteit Nederland/Welten-instituutWhat are MOOCs?EAPRIL 2016Pagina 2Who deliver MOOCs?EAPRIL 2016Pagina 3Research QuestionTo what extent was MOOC enrollment influenced by employed MOOC takers’ educational level, the support they received, and their motivation to enroll in MOOCs. EAPRIL 2016Pagina 4Participants• Participants were employed MOOC takers (N=935; M = 463,           F = 472) in five MOOCs• Employed:– Employed for wages (N = 741)– Self-employed (N = 194)• Five MOOCs– Business intelligence (N = 224)– Hands on ICT (N = 156)– Test anxiety (N = 185)– Bended Learning (N = 24)– Entrepreneurship (N = 246)EAPRIL 2016Pagina 5ParticipantsEAPRIL 2016Pagina 6Independent Variable: Education• Education (International Standard Classification of Education 1997):– Pre-primary education (N = 2)– Primary education or first stage of basic education (N = 3)– Lower secondary or second stage of basic education (N = 15)– (upper) secondary education (N = 64)– Post-secondary non-tertiary education (N = 36)– First stage of tertiary education (N= 200)– Second stage of tertiary education (N = 615)• Variable→ ‘2nd tertiary education’ (dichotomous: false (N = 320) or true (N = 615))EAPRIL 2016Pagina 7Independent Variable: Support• Support:– Receiving encouragement– Compensation for costs– Compensation for time• However, – 277 of the 935 employed MOOC takers did not answer– Combinations of support had low numbers. For example, thenumber of MOOC takers receiving encouragement but no cost or time compensation was 10• Variable→ ‘support’ (dichotomous: false (N = 449) or true (N = 209))EAPRIL 2016Pagina 8Independent Variable: MotivationEAPRIL 2016Pagina 9Independent Variable: MotivationEAPRIL 2016Pagina 10Independent Variable: MotivationEAPRIL 2016Pagina 11Independent Variable: MotivationEAPRIL 2016Pagina 12Independent Variable: MotivationEAPRIL 2016Pagina 13Independent Variable: MotivationEAPRIL 2016Pagina 14Independent Variable: MotivationEAPRIL 2016Pagina 15Independent Variable: MotivationEAPRIL 2016Pagina 16Independent Variable: MotivationEAPRIL 2016Pagina 17Dependent Variable: Number of Enrolled MOOCsEAPRIL 2016Pagina 18Variable→ ‘number of enrolled MOOCs’ (count variable: zero-inflated Poisson)excess of zero countsResearch ModelEAPRIL 2016Pagina 19Analyses• We performed a number of analyses:– With regard to motivation:• Rasch analyses to determine undimensionality, removal of DIF andlocal dependencies, and to determine the person locations of thelatent class indicators [RUMM2030 & Winsteps]• Confirmatory analyses to determine the construct validity of the five latent class indicators to form a measure for motivation [MPLUS]• Latent class analyses to determine the number of latent classes [MPLUS]– With regard to the research model:• Analyses on the finite mixture model [MPLUS]– Multinomial logistic regressions– Zero-inflated Poisson regressionsEAPRIL 2016Pagina 20Findings• Regarding the motivation classes:– Gender had no effect on the probabilities to be in a certainmotivation class– Education affected the probabilities to be in a certain class: MOOC takers who received 2nd tertiary education tend to fall in the low-motivated profile– Support had no effect on the probabilities to be in a certainmotivation classEAPRIL 2016Pagina 21Findings• Regarding to the number of enrolled MOOCs:– Education had a positive interaction effect with the moderate-motivated profile– Support had no interaction effect with any motivation class– All motivation classes (i.e., the high-, moderate-, and the low-motivated profiles) directly effected the number of enrolledMOOCs. The high-motivated profile had the biggest impact followed by the low-motivated profile. The moderate-motivatedprofile had the lowest impact.– Whereas the moderate-motivated profile affected the number of enrolled MOOCs the least directly, it had an interaction effect witheducation which possibly could compensate the loss of the direct effect (this has to be checked however)EAPRIL 2016Pagina 22Findings– The moderate- and low-motivated profiles were predominantlyand equally responsible for the excess of zero counts whereas thehigh-motivated profile was not. However, the result was significant only for the moderate-motovated profileEAPRIL 2016Pagina 23Conclusion• As a high-motivated profile is characterized by a high identifiedregulation, MOOC providers should take care that they offer MOOCsthat fit the professional development needs of the MOOC takers. But at the same time the high-motivated profile tend to have MOOC takers who did not received 2nd tertiary education. Therefore, MOOC providers shoud take care that when develping MOOCs this group of MOOC takers is taken into account in terms of pre-knowledge, level of difficulty, etcetera.EAPRIL 2016Pagina 24ORD 2016Pagina 25Padlet• Question 1:– Should MOOC providers have an important role to remove social inequality with respect to education?URL: http://padlet.com/pad_eapril_2016/Question11EAPRIL 2016Pagina 26

Open Universiteit www.ou.nl The predictive value of motivation, education and supporton the number of MOOCs enrolledCitation for published version (APA):Kreijns, K., & Kalz, M. (2016). The predictive value of motivation, education and support on the number ofMOOCs enrolled. Poster session presented at EAPRIL 2016 Conference, Porto, Portugal.Document status and date:Published: 01/11/2016Document Version:Peer reviewed versionDocument license:CC BYPlease check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences betweenthe submitted version and the official published version of record. People interested in the research are advised to contact the author for thefinal version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers.Link to publicationGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain• You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, pleasefollow below link for the End User Agreement:https://www.ou.nl/taverne-agreementTake down policyIf you believe that this document breaches copyright please contact us at:pure-support@ou.nlproviding details and we will investigate your claim.Downloaded from https://research.ou.nl/ on date: 13 Nov. 2019The predictive value of motivation, education, and support on the number of MOOCs enrolledKarel Kreijns & Marco KalzOpen Universiteit Nederland/Welten-instituutWhat are MOOCs?EAPRIL 2016Pagina 2Who deliver MOOCs?EAPRIL 2016Pagina 3Research QuestionTo what extent was MOOC enrollment influenced by employed MOOC takers’ educational level, the support they received, and their motivation to enroll in MOOCs. EAPRIL 2016Pagina 4Participants• Participants were employed MOOC takers (N=935; M = 463,           F = 472) in five MOOCs• Employed:– Employed for wages (N = 741)– Self-employed (N = 194)• Five MOOCs– Business intelligence (N = 224)– Hands on ICT (N = 156)– Test anxiety (N = 185)– Bended Learning (N = 24)– Entrepreneurship (N = 246)EAPRIL 2016Pagina 5ParticipantsEAPRIL 2016Pagina 6Independent Variable: Education• Education (International Standard Classification of Education 1997):– Pre-primary education (N = 2)– Primary education or first stage of basic education (N = 3)– Lower secondary or second stage of basic education (N = 15)– (upper) secondary education (N = 64)– Post-secondary non-tertiary education (N = 36)– First stage of tertiary education (N= 200)– Second stage of tertiary education (N = 615)• Variable→ ‘2nd tertiary education’ (dichotomous: false (N = 320) or true (N = 615))EAPRIL 2016Pagina 7Independent Variable: Support• Support:– Receiving encouragement– Compensation for costs– Compensation for time• However, – 277 of the 935 employed MOOC takers did not answer– Combinations of support had low numbers. For example, thenumber of MOOC takers receiving encouragement but no cost or time compensation was 10• Variable→ ‘support’ (dichotomous: false (N = 449) or true (N = 209))EAPRIL 2016Pagina 8Independent Variable: MotivationEAPRIL 2016Pagina 9Independent Variable: MotivationEAPRIL 2016Pagina 10Independent Variable: MotivationEAPRIL 2016Pagina 11Independent Variable: MotivationEAPRIL 2016Pagina 12Independent Variable: MotivationEAPRIL 2016Pagina 13Independent Variable: MotivationEAPRIL 2016Pagina 14Independent Variable: MotivationEAPRIL 2016Pagina 15Independent Variable: MotivationEAPRIL 2016Pagina 16Independent Variable: MotivationEAPRIL 2016Pagina 17Dependent Variable: Number of Enrolled MOOCsEAPRIL 2016Pagina 18Variable→ ‘number of enrolled MOOCs’ (count variable: zero-inflated Poisson)excess of zero countsResearch ModelEAPRIL 2016Pagina 19Analyses• We performed a number of analyses:– With regard to motivation:• Rasch analyses to determine undimensionality, removal of DIF andlocal dependencies, and to determine the person locations of thelatent class indicators [RUMM2030 & Winsteps]• Confirmatory analyses to determine the construct validity of the five latent class indicators to form a measure for motivation [MPLUS]• Latent class analyses to determine the number of latent classes [MPLUS]– With regard to the research model:• Analyses on the finite mixture model [MPLUS]– Multinomial logistic regressions– Zero-inflated Poisson regressionsEAPRIL 2016Pagina 20Findings• Regarding the motivation classes:– Gender had no effect on the probabilities to be in a certainmotivation class– Education affected the probabilities to be in a certain class: MOOC takers who received 2nd tertiary education tend to fall in the low-motivated profile– Support had no effect on the probabilities to be in a certainmotivation classEAPRIL 2016Pagina 21Findings• Regarding to the number of enrolled MOOCs:– Education had a positive interaction effect with the moderate-motivated profile– Support had no interaction effect with any motivation class– All motivation classes (i.e., the high-, moderate-, and the low-motivated profiles) directly effected the number of enrolledMOOCs. The high-motivated profile had the biggest impact followed by the low-motivated profile. The moderate-motivatedprofile had the lowest impact.– Whereas the moderate-motivated profile affected the number of enrolled MOOCs the least directly, it had an interaction effect witheducation which possibly could compensate the loss of the direct effect (this has to be checked however)EAPRIL 2016Pagina 22Findings– The moderate- and low-motivated profiles were predominantlyand equally responsible for the excess of zero counts whereas thehigh-motivated profile was not. However, the result was significant only for the moderate-motovated profileEAPRIL 2016Pagina 23Conclusion• As a high-motivated profile is characterized by a high identifiedregulation, MOOC providers should take care that they offer MOOCsthat fit the professional development needs of the MOOC takers. But at the same time the high-motivated profile tend to have MOOC takers who did not received 2nd tertiary education. Therefore, MOOC providers shoud take care that when develping MOOCs this group of MOOC takers is taken into account in terms of pre-knowledge, level of difficulty, etcetera.EAPRIL 2016Pagina 24ORD 2016Pagina 25Padlet• Question 1:– Should MOOC providers have an important role to remove social inequality with respect to education?URL: http://padlet.com/pad_eapril_2016/Question11EAPRIL 2016Pagina 26

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The predictive value of motivation, education and support on the number of MOOCs enrolled

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