Download - Diving into Data: Trends and Patterns in MOOC Analytics

Diving into DataTrends and Patterns from MOOC Analytics

Online Learning Consortium Conference

Orlando, Florida

October, 2014

Chery Takkunen, PhD-School of Education

Jen Rosato, MA -Department of Computer Science/Information Systems

The College of St. Scholasticawww.css.edu

SoTL Commons Conference- 2014- Georgia

http://www.css.edu

http://www.css.edu

College of St. Scholastica, www.css.eduCollege of St. Scholastica, www.css.edu

Goals of the SessionQuestions

Background

Course Design

Data

Implications

College of St. Scholastica, www.css.edu

The College of St. Scholastica

Location

College

Growth Strategy 10% Gr


Background

CS + EDU= Unique Partnership

Computer Science Education

Professional Development Workshops

Experience in Online Teaching and Learning

Grants= TAG, Google CS4HS, Local/Regional*New- 2015- National Science Foundation

*New- Certificate in Computer Science Education


CS4HS is an annual grant program promoting computer science education worldwide by connecting educators to the skills and resources they need to teach computer science & computational thinking concepts in fun and relevant ways. Traditionally, these have been in-person workshops.


MOOC defined *

A Massive Open Online Course (MOOC; English pronunciation: /muːk/) is an online course aimed at unlimited participation and open access via the web. In addition to traditional course materials such as videos, readings, and problem sets, MOOCs provide interactive user format that help build a community for students, professors, and teaching assistants (TAs). MOOCs are a recent development in distance education.

Wikipedia.org: http://en.wikipedia.org/wiki/Massive_open_online_course

http://en.wikipedia.org/wiki/Help:IPA_for_English


http:mfeldstein.comemerging_student_patterns_in_moocs_graphical_view/

Phil Hill


Conceptual FrameworkCommunity of Inquiry

The Community of Inquiry model. Garrison, R., Anderson, T, Archer, W. and Rourke, L et al. (2007).


Teacher Professional Development

1) Long-term and intensive2) Clear outcomes3) Collaboration and community4) Use of online tools for effective PD5) Five core features:

a) content and pedagogyb) consistency with reforms c) coherence with educational goalsd) active learning- reflection and inquirye) aligned with standards


Applying Principles to CS4HS Course

MOOC-like DesignLMS- Course BuilderParticipation LevelsBuilding Community

Professional Learning Communities (PLC) Mentors

Google HangoutsGoogle Hangout on Air with guest speakers

Discussion forumsNarrated presentations


Participant Levels

Casual Participant

vs.

Certificate Completer


App Inventor

Dave WolberProfessor, Computer ScienceUniversity of San Francisco

http://www.appinventor.org/

http://www.appinventor.org/


Course

Unit Page Types:1. Objectives2. CS Unplugged*3. Hangout On Air4. App Inventor Tutorial, Part 1*5. App Inventor Tutorial, Part 2*6. Pedagogy*7. Group Hangouts8. Discussion9. Additional Resources

*Included activities (formative assessments)


Analytics Data...the discovery and communication of meaningful patterns in data

GA - Google AnalyticsGCB - Google Course Builder

GG - Google Groups

http://en.wikipedia.org/wiki/Analytics


Participants

We were planning on 50

Over 400 participants

Over 40 states

Over 40 countries


Google Analytics (GA)

Other: Canada, Puerto Rico, Tunisia

90 % of visits from the US

*Kristen Donahue, Kassandra Quick & Alvaro Hernandez-Feris


GCB: Registration Data


http:mfeldstein.comemerging_student_patterns_in_moocs_graphical_view/

Phil Hill


GCB: Student Progress


GA: Student Pageviews

What questions does this raise? What else would you

like to know? How would you investigate?


GCB: Completion Rates


GCB: Assessment Progress


GA: Page Types




GG: Discussion Post Data


GG: Thread Interactions

Average

Total

Replies

3.9 67

Views 32.5 552




GG: Thread Interactions


Summary of Trends

Completion rates follow the typical MOOC behavior (w/higher rates in ours)Combine analytics and registration data for a more complete pictureCompletion rates increase later in course (students are more invested)Assessment participation follows completion ratesDiscussion participation follows completion ratesSome content appears to be more engaging than others

(pedagogy & guest speakers vs unplugged lessons)


Continuous Improvement

How have we used the data from summer 2013?

Multiple certificate levelsOnline office hours & Community manager (contact for those not in a PLC)Paid close attention to the Unit 2 drop off

Unit 2 lighter than followingMentors checked in with all participants in their PLC groupsMonitored forum more closely

Page types Kept a separate pedagogy page in unitsQuizly questions AI tutorial pages


Recommendations

Analytics Recommendations:Know what data you can get from where

(GA vs LMS and other tools)Learn what the data meansPilot the analytics before the courseSet time limits for collecting data

General Recommendations:Be intentional Give teachers a range of optionsMatch intent with support levelsPlan design around learner behaviorProvide opportunities to informally interact

w/content & each otherRequire discussion - tempers the superposter

phenomenon

Download - Diving into Data: Trends and Patterns in MOOC Analytics

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