The PhD`s research plan of Eyal Rabin, dealing with "Attitude, Motivation and Behavior at MOOCs". Presented at the 4th GO-GN seminar Apr.2015 in Banff, Canada.

1. Attitude, Motivation and Behavior
at MOOCs - Research Plan
Eyal Rabin
The Open University of Israel
Eyal.rabin@gmail.com
Supervisors:
Marco Kalz– Phd - OUN
Yoram Kalman – Phd - OUI
4th GO-GN Seminar
Apr. 2015

2. Introduction
• One of the most interesting questions about MOOCs is
the question of learners` motivations, intentions and
personal goals to learn in this unique way.
• Different learners have different motivations, intentions
and personal goals that affect their learning behavior.
• Understanding people’s uniqueness
1. Enables providing personalized services and personal
learning paths.
2. Enables reaching audiences that don’t currently learn with
MOOCs.

3. MOOCs and Personalized learning process
One of the main problems of Higher education
in general and MOOCs in particular is the lack of
personalization and personalize in the learning
process.
The MOOC`s classrooms are replications of the
traditional classrooms.

4. The affect of Intentions and Motivations
on learning behavior in MOOCs.
• Respondents that answered an opening survey showed a higher
course completion rate than all other students.
• Participants who stated in an opening survey that they intend to earn
a certificate had a higher success rate.
• Completers rated higher than non-completers their possibility
(belief) of completing the course in a pre-course questionnaire.
• No differences were found between completers’ and non-completers’
motivations that are specific to the domain of the course such as
`extending current knowledge of the topic`.
(Koller et al., 2013 ; Reich, 2014; Wang & Baker, 2014)

5. MOOCs and data mining in
education
One of the major advantages of teaching MOOCs
through the internet is that it allows us to mine
and collect massive amounts of data.
MOOCs participants leave a huge digital
footprint behind them, much of which is
collected in log data.

6. Method
• Research stages and tools:
1. Pre-course questionnaire
2. Logging of MOOC participants` behavior
3. Post-course questionnaire

7. Pre-course questionnaire Categories:
• Demographics (SES, Gender, Education)
• ICT competencies
• Prior learning experience
• How many MOOCs learned and completed
• Intention & Motivation
• Environmental influences
• Outcome expectations
• Lifelong Learning Profile
• Feedback & Assessment

8. Behavioral measurements - Examples
Persistence
• # lectures that the participant had took.
• # quizzes that the participant
– Took
– Passed
Social learning/ Social participation
• Participation level in weekly forum
– # entering the forum
– # writing a post
– # replying to a post
External reward
• # badges
• certificate

9. Post-course questionnaire
• Demographics
–SES
–Gender
–Education
• Learning experience
• Factors that promoted learning process
• Barriers
• Assessment learning environment

10. Research model
Two theoretical frameworks:
1. The Reasoned Action Approach (Fishbein &
Ajzen, 2010)
2. The Self-Determination Theory (Ryan and
Deci, 2000).
Based on these theories, domain specific
questionnaires were developed (Kalz et al.,
Submitted).

11. Gaps/ Links between intention and
behavior
There are several theories that try to predict
behavior.
For example:
– Protection Motivation Theory (PMT, Rogers, 1983)
– The Prototype/Willingness Model (PWM; Gibbons,
Gerrard, & Lane, 2003)
– The Theory of Planned Behavior (TPB; Ajzen, 1991)
– Social Cognitive Theory (Bandura, 1997)
– The Implementation Intentions Model (Gollwitzer,
1999).

12. Research model
Kalz et al., (Submitted).
Establishing a European cross-provider data collection about open online courses.
IRRODL.

13. Research questions
1: Beliefs, attitudes and motivations (intentions) about learning in MOOC
– What are the personal goals of learners in MOOCs.
– How the distal and proximal variables affect the personal goals of the learner.
2: The link between intention and behavior
– Can the beliefs, attitudes and motivations (intentions) of learners in MOOCs
predict their learning behavior in MOOCs
– What are the gaps between intentions and learning behavior
3: Intentions, behavior and post evaluation
– Can the intention of the learner in MOOCs and his/her learning behavior predict
his/her post evaluation of the course and his/her intention to take more courses.

14. Pilot study- Preliminary findings
A pilot study was conducted on a MOOC that
took place at the OUI from Jan. to Feb. 2015
(period of 5 weeks).
The subject of the MOOC was “Genocide” and it
was taught in Hebrew.

15. Preliminary findings
• 1689 participants in the MOOC.
• 9.5% answered the pre-course questionnaire (n=160)
• 14.5% answered the post-course questionnaire (n=244).
• In addition, 561 participants answered a short
anonymous questioner.
Including questions about:
– Demographics questions
– ICT abilities
– Motivations to participate at the MOOC.

16. Pre-course questionnaire -
Demographic
• Gender
60% female (n=95).
40% male (n=64).
• Age
range from 19 to 80 yrs old (mean=49, SD=17).
75% between 19 to 65 yrs old.
• Education
Mostly Bachelor's and above (71.5%).
• Occupation
61.5% working for wages or self employed.
24% retired.
14.5% out of work or homemaker.

17. Behavioral measurements - Findings
The responders had a high commitment to the
course.
On average, the responders took 20 out of the 26
lectures in the MOOC.

18. An example of correlations between
intention and behavior -
Predicting Quizzes attempted
# Quizzes attempted
Intention - Goal setting .30
Intention – persistence .18
Advantages in the labor market .22
Technical skills .17
* Spearman correlations.
* All correlations are sig. at p<0.05

19. Limitations
• Selection bias
The responders have very high
commitments to the MOOC
• External validity
The course content is in the humanities,
non applied, non employment subject matter

20. Further issues
• Theoretical issues
– Metacognition and learning process
– Gap between attitude and behavior
– Checking and comparing the model in applied /work
oriented MOOCs
– Personalization and personalize learning
• Statistical comparisons
– Cross culture and cross providers investigation
– Comparing between responders to the short
questionnaire and the long questionnaire
– Finding different participants clustering

21. Thank you.
Eyal.rabin@gmail.com www.eyalrabin.com @eyalra