An exciting new potential source of data came my way this morning as some of my notification services picked up on the reports that have been published as part of MOOC Research Hub, an initiative funded by the Gates Foundation and based at Athabasca University. They bring together work from a range of different contexts and include contributions from our own Martin Weller and Martin Hawksey.
There are 22 reports in total, and it should be noted that these findings have yet to undergo peer review or final project approval. The reports aren’t numbered but I’ve given them numbers to make it a bit easier to keep track of them. Just to be clear: these are links to reports by MOOC Research Hub and any queries should be directed to them!
1. The discursive construction of MOOCs as educational opportunity and educational threat
Project Leads: Neil Selwyn, Scott Bulfin
Mainstream news media have restricted public understanding of MOOCs to a set of concerns relating to the ‘economics’ of higher education (i.e. massification, marketization and monetization), while marginalizing debate of ‘educational’ and ‘technological’ issues such as online learning and pedagogy, instructional design and student experience.
click here to download
2. Patterns of Persistence: What Engages Students in a Remedial English Writing MOOC?
Project Lead: John Whitmer, Ed.D.
Project Collaborators: Eva Schiorring, RP Group, Pat James, Mt San Jacinto College, and Steve Miley
Learners in a remedial English writing MOOC engage with the course in meaningful ways that are revealed by using clustering methods and other approaches that are different from those used to measure the learning impact of traditional for-credit courses.
click here to download
3. Learning Analytics for Smarter Psychological Interventions
Project Lead: Daniel Greene
Students’ beliefs in the malleability of their intelligence was not associated with their persistence in a MOOC.
click here to download
4. The Relationship Between the Motivational Profiles, Engagement Profiles and Persistence fo MOOC Participants
Project Lead: Bruno Poellhuber
Collaborators: Normand Roy, Ibtihel Bouchoucha, Terry Anderson
In a MOOC, participant intentions say little about their actual behaviour, but their early behaviour in the course predicts the outcomes very well, with those engaging early and frequently with the whole range of resources being much more likely to persist and succeed.
click here to download
5. Beyond and Between “Traditional” MOOCs: Agile and Just-in-Time Learning
Project Lead: Jennifer Campbell
MOOCs that are available as archives after their live sessions end continue to attract new learners who use the videos, assessments, and discussion forums in a similar manner to learners who were active in the live session.
click here to download
6. Secondary School Students and MOOC’s: A Comparison between Independent MOOC Participation and Blended Learning
Project Lead: Dilip Soman, University of Toronto and Rosemary Evans, University of Toronto Schools
Engaging with MOOC’s as components of secondary school courses, both independently and through a blended model, holds promise as a way of exposing students to university level learning in terms of student learning outcomes and persistence.
click here to download
7. MOOC Learner Motivation and Course Completion Rates
PI: Yuan Wang, Teachers College, Columbia University
Co-PI: Ryan Baker, Teachers College, Columbia University
Students who take a MOOC because they are excited about MOOCs as a new platform of learning are less likely to complete the course.
click here to download
8. Conceptualising interaction and learning in MOOCs
Project Lead: Rebecca Eynon
Hybrid qualitative and computational methodologies reveal the hidden characteristics of learner interactions in different discussion environments.
click here to download
9. MOOCs Personalization for various Learning Goals
Project Lead: Sergiy Nesterko
Among MOOC learners, individual week-to-week clickstream activity stays consistent throughout the course.
click here to download
10. Characteristics and completion rates of distributed and centralised MOOCs
Project Lead: Martin Weller
MOOC completion rates show a consistent pattern, averaging 12% with a number of factors being correlated, and learning design analysis reveals different patterns of MOOC usage of resources.
click here to download
11. UW System College Readiness Math MOOC Study
Project Lead: Robert Hoar
Participants who complete the Math MOOC gain the necessary skills to be successful in college level mathematics, regardless of their age, background, or motivation for taking the course.
click here to download
12. Social Network Formation and its Impact on Learning in MOOC-Eds
Shaun Kellogg, Sherry Booth, and Kevin Oliver
Peer interaction in MOOCs for K-12 educators, influenced in part by underlying network mechanisms, resulted in productive and meaningful exchanges driven largely by small core of highly engaged participants.
click here to download
13. Peer Assessment and Academic Achievement in a Gateway MOOC
14. Detecting and Analyzing Subpopulations within Connectivist MOOCs Project Details
15. The life cycle of a million MOOC users
Project Lead: Laura W. Perna
Although high numbers registered for the first-generation MOOCs offered at the University of Pennsylvania, only small proportions of registrants reached final course milestones.
click here to download
16. Developing data standards and technology enablers for MOOC data science
17. Writing to Learn and Learning to Write across the Disciplines: Peer-to-Peer Writing in Introductory-level MOOCs
Project Lead: Denise Comer; with Dorian Canelas
Peer-to-peer writing, through discussion forums and peer assessment, has a positive impact on student learning in introductory-level writing and chemistry MOOCs.
click here to download
18. Professional Learning through Massive Open Online Courses
Project Lead: Allison Littlejohn with Colin Milligan
This research provides insight into the way professionals learn for work in the context of a MOOC.The study identifies a mismatch between the learning intentions and learning behaviours of the MOOC participants. We should encourage professional learners – particularly those motivated by specific work challenges – to integrate MOOC activities with their work tasks, rather than focusing on completing course assignments.
click here to download
19. Enabling Resilient Massive scale Open Online Learning Communities through Models of Social Emergence
Project Lead: Carolyn Penstein Rose
Probabilistic graphical models are used to identify patterns of bond formation in MOOC discussion forums that predict patterns of attrition and suggest potential interventions that may reduce attrition over time.
click here to download
20. Understanding Massive Open Online Courses (MOOCs) as a Pathway to Employment for Low-Income Populations
Project Lead: Tawanna Dillahunt
Our quantitative study revealed that learners 25 and older with less than associate degrees (primarily non-students) reporting an inability to afford a formal education were significantly underrepresented in MOOCs but completed more courses with distinction than all other learners. Our qualitative study revealed that participants taking MOOCs based on affordability and enhanced employment opportunities presented very limited tangible evidence of employment mobility as a result of taking MOOCs.
click here to download
21. Hatch, match, and dispatch: Examining the relationship between student intent, expectations, behaviours and outcomes in six Coursera MOOCs at the University of Toronto
Project Lead: Carol Rolheiser
Based on frequent sequence analysis of clickstream data, students who ultimately receive a certificate of accomplishment in a MOOC show less diverse patterns of behavior than their colleagues who do not receive a certificate of accomplishment.
click here to download
22. Promoting a Higher-Level Learning Experience: Investigating the Capabilities, Pedagogical Role, and Validity of Automated Essay Scoring in MOOCs
Project Lead: Stephanie Corliss and Erin D. Reilly
Analyses of automated essay scoring (AES) in MOOCs revealed areas of both significant differences and modest comparability between human and AI-grading patterns, suggesting that AES may be more useful for formative assessment – as opposed to summative or high-stakes assessment–to engage students in higher-level thinking, immediate writing feedback, and critical analysis.
click here to download
Leave a Reply