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8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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Diving Deep into Data:
Motivations, Perceptions, and
Learning in Minnesota MOOCs
D. Christo pher Brooks, Ph.D.
J .D. Walk er, Ph .D.
EDUCAUSE Learning Initiative
Annual Meeting
4 February 2014
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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The Day of the MOOC
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MOOCs at Minnesota: Background
• Offered 5 MOOCs in summer 2013:
– Statistical Molecular Thermodynamics
– Social Epidemiology
– Health Informatics – Global Food Systems/Sustainability
– Canine Theriogenology
• Total nominal enrollment: 71,029
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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Researching MOOCs Anyway:
Design and Methods
• Research questions: – Time & effort
– Student characteristics
– Student outcomes
• Data sources:
– Time & effort diaries
– Faculty interviews
– Pre and post surveys – Coursera data
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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Instructor and TA Self-Reported Effort in Hours
Planning Executing Total
Statistical Molecular
Thermodynamics
Sustainability & Food
Systems
Interprofessional Health
Care Informatics
Social Epidemiology
Canine Theriogenology
for Dog Enthusiasts
Unidentified & TAs
TOTAL
AVERAGE
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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Instructor and TA Self-Reported Effort in Hours
Planning Executing Total
Statistical Molecular
Thermodynamics 207.50
Sustainability & Food
Systems272.50
Interprofessional Health
Care Informatics 263.00
Social Epidemiology 114.50
Canine Theriogenology
for Dog Enthusiasts 19.92
Unidentified & TAs 234.95
TOTAL 1,112.37
AVERAGE 222.47
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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Instructor and TA Self-Reported Effort in Hours
Planning Executing
Statistical Molecular
Thermodynamics 207.50 205.50
Sustainability & Food
Systems272.50 157.50
Interprofessional Health
Care Informatics 263.00 56.00
Social Epidemiology 114.50 74.50
Canine Theriogenology
for Dog Enthusiasts 19.92 52.35
Unidentified & TAs 234.95 242.00
TOTAL 1,112.37 787.85
AVERAGE 222.47 157.57
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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Instructor and TA Self-Reported Effort in Hours
Planning Executing Total
Statistical Molecular
Thermodynamics 207.50 205.50 413.00
Sustainability & Food
Systems272.50 157.50 430.00
Interprofessional Health
Care Informatics 263.00 56.00 319.00
Social Epidemiology 114.50 74.50 189.00
Canine Theriogenology
for Dog Enthusiasts 19.92 52.35 72.27
Unidentified & TAs 234.95 242.00 476.95
TOTAL 1,112.37 787.85 1,900.22
AVERAGE 222.47 157.57 380.04
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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The Faculty Experience: Overview
PersonalSatisfaction
Despiteconsiderableeffort
Student &ProfessionalConnections
BroadReachDespite lowcompletion ratesProfessionalSatisfaction
Keeping
ThingsFresh
Despite thepublic debate
Think
aboutTeaching
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The Faculty Experience:
The MOOC Idea
GlobalOutreach
Social
Justice
EdutainmentCommunity
Building
Best
InteractiveTextbook
Th F l E i
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The Faculty Experience:
Beyond the MOOC
BIGIDEAS
Student-Centered
Discussion& Activities
BriefLecture
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The Faculty Experience:
Effort and Evaluation
PeerReview
Not abook
Not anarticle
Not adiscovery
Not apatent
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The Faculty Experience:
International Audience
Culture
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University-relatedreasons
(3.929)
Access-relatedreasons
(1.475)
Professional
reasons(1.312)
Enjoyment-relatedreasons
(1.029)
Reasons for Enrolling: Individual
Questions and Factor Analysis
1. This subject is relevant to my academic field of study
2. This class teaches skills that will help my job/career
3. Because this course is offered by a prestigious
university
4. I think taking this course will be fun and enjoyable
5. I am not geographically close to educational
institutions
6. Traditional courses are too expensive
7. I was interested in taking a course with this professor
8. This course is offered by the University of Minnesota
9. General interest in the topic
10. To help me decide whether to take further
college/university classes
11. To make professional connections
12. To obtain a badge or certification that will be useful to
me professionally
• Total variance explained: 64.55%• (Eigenvalues in parentheses)
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Reasons for Enrolling:
Overall Mean Ratings
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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Reasons for Enrolling
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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Reasons for Enrolling
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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Reasons for Enrolling
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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• Strivers: outside US, male, younger, non-
native English, work/study in same field,
professional reasons
• Grazers: inside US, older, native English,complete less, enjoyment reasons
Two Populations of Students
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Predicting Success: Models of Student- versus Instructor-Defined Completion
Intent Self-Reported 50% Com pleted Total Points
R e a
s o n s f o r
E n r o l l i n g
University
Professional
Access
Enjoyment
D
e m o g r a p h i c s English
Proficiency
Location: USA
Age
Sex
O
b s t a c l e s t o
C o m p l e t i o n
Tech Unfamiliar
Connection Problems
Computer Problems
Time Zone Issues
Family IssuesWork Issues
Intent
Self-Reported
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Predicting Success: Models of Student- versus Instructor-Defined Completion
Intent Self-Reported 50% Com pleted Total Points
R e a
s o n s f o r
E n r o l l i n g
University +
Professional +
Access +
Enjoyment +
D
e m o g r a p h i c s English
Proficiency+
Location: USA 0
Age –
Sex 0
O
b s t a c l e s t o
C o m p l e t i o n
Tech Unfamiliar
Connection Problems
Computer Problems
Time Zone Issues
Family IssuesWork Issues
Intent
Self-Reported
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Predicting Success: Models of Student- versus Instructor-Defined Completion
Intent Self-Reported 50% Com pleted Total Points
R e a
s o n s f o r
E n r o l l i n g
University +
Professional +
Access +
Enjoyment +
D
e m o g r a p h i c s English
Proficiency+
Location: USA 0
Age –
Sex 0
O b s t a c l e s t o
C o m p l e t i o n
Tech Unfamiliar
Connection Problems
Computer Problems
Time Zone Issues
Family IssuesWork Issues
Intent
Self-Reported
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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Predicting Success: Models of Student- versus Instructor-Defined Completion
Intent Self-Reported 50% Com pleted Total Points
R e a
s o n s f o r
E n r o l l i n g
University + +
Professional + 0
Access + 0
Enjoyment + 0
D
e m o g r a p h i c s English
Proficiency+ -
Location: USA 0 +
Age – 0
Sex 0 0
O b s t a c l e s t o
C o m p l e t i o n
Tech Unfamiliar 0
Connection Problems +
Computer Problems 0
Time Zone Issues 0
Family Issues 0Work Issues 0
Intent 0
Self-Reported
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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Predicting Success: Models of Student- versus Instructor-Defined Completion
Intent Self-Reported 50% Com pleted Total Points
R e a
s o n s f o r
E n r o l l i n g
University + +
Professional + 0
Access + 0
Enjoyment + 0
D
e m o g r a p h i c s English
Proficiency+ -
Location: USA 0 +
Age – 0
Sex 0 0
O b s t a c l e s t o
C o m p l e t i o n
Tech Unfamiliar 0
Connection Problems +
Computer Problems 0
Time Zone Issues 0
Family Issues 0Work Issues 0
Intent 0
Self-Reported
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Predicting Success: Models of Student- versus Instructor-Defined Completion
Intent Self-Reported 50% Com pleted Total Points
R e a
s o n s f o r
E n r o l l i n g
University + +
Professional + 0
Access + 0
Enjoyment + 0
D
e m o g r a p h i c s English
Proficiency+ -
Location: USA 0 +
Age – 0
Sex 0 0
O b s t a c l e s t o
C o m p l e t i o n
Tech Unfamiliar 0
Connection Problems +
Computer Problems 0
Time Zone Issues 0
Family Issues 0Work Issues 0
Intent 0
Self-Reported
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Predicting Success: Models of Student- versus Instructor-Defined Completion
Intent Self-Reported 50% Com pleted Total Points
R e a s o n s f o r
E n r o l l i n g
University + + +
Professional + 0 0
Access + 0 0
Enjoyment + 0 0
D
e m o g r a p h i c s English
Proficiency+ - 0
Location: USA 0 + -
Age – 0 -
Sex 0 0 0
O b s t a c l e s t o
C o m p l e t i o n
Tech Unfamiliar 0 0
Connection Problems + 0
Computer Problems 0 0
Time Zone Issues 0 0
Family Issues 0 -Work Issues 0 -
Intent 0 +
Self-Reported
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Predicting Success: Models of Student- versus Instructor-Defined Completion
Intent Self-Reported 50% Com pleted Total Points
R e a s o n s f o r
E n r o l l i n g
University + + +
Professional + 0 0
Access + 0 0
Enjoyment + 0 0
D
e m o g r a p h i c s English
Proficiency+ - 0
Location: USA 0 + -
Age – 0 -
Sex 0 0 0
O b s t a c l e s t o
C o m p l e t i o n
Tech Unfamiliar 0 0
Connection Problems + 0
Computer Problems 0 0
Time Zone Issues 0 0
Family Issues 0 -Work Issues 0 -
Intent 0 +
Self-Reported
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Predicting Success: Models of Student- versus Instructor-Defined Completion
Intent Self-Reported 50% Com pleted Total Points
R e a s o n s f o r
E n r o l l i n g
University + + +
Professional + 0 0
Access + 0 0
Enjoyment + 0 0
D
e m o g r a p h i c s English
Proficiency+ - 0
Location: USA 0 + -
Age – 0 -
Sex 0 0 0
O b s t a c l e s t o
C o m p l e t i o n
Tech Unfamiliar 0 0
Connection Problems + 0
Computer Problems 0 0
Time Zone Issues 0 0
Family Issues 0 -Work Issues 0 -
Intent 0 +
Self-Reported
P di i S M d l f S d I D fi d C l i
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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Predicting Success: Models of Student- versus Instructor-Defined Completion
Intent Self-Reported 50% Com pleted Total Points
R e a s o n s f o r
E n r o l l i n g
University + + + 0
Professional + 0 0 0
Access + 0 0 0
Enjoyment + 0 0 0
D
e m o g r a p h i c s English
Proficiency+ - 0 +
Location: USA 0 + - 0
Age – 0 - 0
Sex 0 0 0 -
O b s t a c l e s t o
C o m p l e t i o n
Tech Unfamiliar 0 0 -
Connection Problems + 0 -
Computer Problems 0 0 0
Time Zone Issues 0 0 0
Family Issues 0 - -Work Issues 0 - -
Intent 0 + +
Self-Reported +
P di ti S M d l f St d t I t t D fi d C l ti
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Predicting Success: Models of Student- versus Instructor-Defined Completion
Intent Self-Reported 50% Com pleted Total Points
R e a s o n s f o r
E n r o l l i n g
University + + + 0
Professional + 0 0 0
Access + 0 0 0
Enjoyment + 0 0 0
D
e m o g r a p h i c s English
Proficiency+ - 0 +
Location: USA 0 + - 0
Age – 0 - 0
Sex 0 0 0 -
O b s t a c l e s t o
C o m p l e t i o n
Tech Unfamiliar 0 0 -
Connection Problems + 0 -
Computer Problems 0 0 0
Time Zone Issues 0 0 0
Family Issues 0 - -Work Issues 0 - -
Intent 0 + +
Self-Reported +
P di ti S M d l f St d t I t t D fi d C l ti
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Predicting Success: Models of Student- versus Instructor-Defined Completion
Intent Self-Reported 50% Com pleted Total Points
R e a s o n s f o r
E n r o l l i n g
University + + + 0
Professional + 0 0 0
Access + 0 0 0
Enjoyment + 0 0 0
D
e m o g r a p h i c s English
Proficiency+ - 0 +
Location: USA 0 + - 0
Age – 0 - 0
Sex 0 0 0 -
O b s t a c l e s t o
C o m p l e t i o n
Tech Unfamiliar 0 0 -
Connection Problems + 0 -
Computer Problems 0 0 0
Time Zone Issues 0 0 0
Family Issues 0 - -Work Issues 0 - -
Intent 0 + +
Self-Reported +
P di ti S M d l f St d t I t t D fi d C l ti
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Predicting Success: Models of Student- versus Instructor-Defined Completion
Intent Self-Reported 50% Com pleted Total Points
R e a s o n s f o r
E n r o l l i n g
University + + + 0
Professional + 0 0 0
Access + 0 0 0
Enjoyment + 0 0 0
D
e m o g r a p h i c s English
Proficiency+ - 0 +
Location: USA 0 + - 0
Age – 0 - 0
Sex 0 0 0 -
O b s t a c l e s t o
C o m p l e t i o n
Tech Unfamiliar 0 0 -
Connection Problems + 0 -
Computer Problems 0 0 0
Time Zone Issues 0 0 0
Family Issues 0 - -Work Issues 0 - -
Intent 0 + +
Self-Reported +
Predicting Success: Models of Student versus Instructor Defined Completion
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Predicting Success: Models of Student- versus Instructor-Defined Completion
Intent Self-Reported 50% Com pleted Total Points
R e a s o n s f o r
E n r o l l i n g
University + + + 0
Professional + 0 0 0
Access + 0 0 0
Enjoyment + 0 0 0
D
e m o g r a p h i c s English
Proficiency+ - 0 +
Location: USA 0 + - 0
Age – 0 - 0
Sex 0 0 0 -
O b s t a c l e s t o
C o m p l e t i o n
Tech Unfamiliar 0 0 -
Connection Problems + 0 -
Computer Problems 0 0 0
Time Zone Issues 0 0 0
Family Issues 0 - -Work Issues 0 - -
Intent 0 + +
Self-Reported +
Predicting Success: Models of Student versus Instructor Defined Completion
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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Predicting Success: Models of Student- versus Instructor-Defined Completion
Intent Self-Reported 50% Com pleted Total Points
R e a s o n s f o r
E n r o l l i n g
University + + + 0
Professional + 0 0 0
Access + 0 0 0
Enjoyment + 0 0 0
D
e m o g r a p h i c s English
Proficiency+ - 0 +
Location: USA 0 + - 0
Age – 0 - 0
Sex 0 0 0 -
O b s t a c l e s t o
C o m p l e t i o n
Tech Unfamiliar 0 0 -
Connection Problems + 0 -
Computer Problems 0 0 0
Time Zone Issues 0 0 0
Family Issues 0 - -Work Issues 0 - -
Intent 0 + +
Self-Reported +
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• Computer science MOOC, fall 2013, ProfessorJoseph Konstan
– Compared hybrid course (face-to-face plus MOOC) and fully
online course (MOOC only)
– Pre- and post-course knowledge test
• Results:
– students can learn in a MOOC!
– and learning gains are evenly distributed
– but they learn more with a face-to-face component
• More details at ACM Learning@Scale conference,
March 4-5, Atlanta. http://learningatscale.acm.org/
Learning in MOOCs:
A Hybrid Model
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Questions?
8/13/2019 Diving Deep into Data: Motivations, Perceptions, and Learning in Minnesota MOOCs (204564982)
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Diving Deep into Data: MOOCs at
Minnesota
Thanks!
http://z.umn.edu/research
D. Christopher Brooks, Ph.D. ([email protected])
J.D. Walker, Ph.D. ( [email protected])
