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Attitude, Motivation and Behavior at MOOCs - Research Plan


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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.

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Attitude, Motivation and Behavior at MOOCs - Research Plan

  1. 1. Attitude, Motivation and Behavior at MOOCs - Research Plan Eyal Rabin The Open University of Israel Supervisors: Marco Kalz– Phd - OUN Yoram Kalman – Phd - OUI 4th GO-GN Seminar Apr. 2015
  2. 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. 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. 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. 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. 6. Method • Research stages and tools: 1. Pre-course questionnaire 2. Logging of MOOC participants` behavior 3. Post-course questionnaire
  7. 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. 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. 9. Post-course questionnaire • Demographics –SES –Gender –Education • Learning experience • Factors that promoted learning process • Barriers • Assessment learning environment
  10. 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. 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. 12. Research model Kalz et al., (Submitted). Establishing a European cross-provider data collection about open online courses. IRRODL.
  13. 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. 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. 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. 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. 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. 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. 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. 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. 21. Thank you. @eyalra