An introduction to Apereo, and comments on learning analytics - and the importance of recent learning analytics work undertaken by the Xerte community

1. Learning
Analytics
Xerte Conference - June 2018
Ian Dolphin
Apereo Foundation
ian.dolphin@apereo.org
IanDolphinviaFlickrAttribution2.0Generic (CCBY-NC-ND2.0)
Apereo Foundation
More about why this sign was created at http://www.intrastructures.net/Intrastructures/Actions_-_The_next_big_thing_2.html

2. Introduce Apereo
Learning Analytics Activity
How Xerte is Contributing

3. What is Apereo?

4. What is Open Source?

5. “… software for which the original
source code is made freely available and
may be redistributed and modified …”
https://opensource.org/osd-annotated
Under an approved Open Source
Initiative license
https://opensource.org/licenses

6. Why open source for
education?

7. WhyOpenSource?
Control
Cost
Suitability
*’Software and Collaboration in Higher Education: A Study of Open Source Software’
http://www.sr.ithaka.org/wp-content/uploads/2015/08/4.8.1.pdf

8. ‘Control’Unpicking
Support options
Forced upgrade paths?
Innovation & sustainability
Portability for hosted software
Guarantor of standards

9. What is Apereo?

10. – The Apereo Foundation is a non-profit [501(c)(3)]
registered in New Jersey
Mission
“…collaborate to foster, develop, and sustain
open technologies and innovation to
support learning, teaching, and research."

11. Membership Organization
Legal: New Jersey 501.c.3
Elected Board of Directors
Global
WhatisApereo?
Partnership LAMP North America
Partnership ESUP-Portail in France

12. WhatisApereo?
Reduce the overhead of collaboration
Manage collective intellectual property
Provide limited shared services

13. Educational Institutions
Apereo + ESUP + LAMP = c 170 members
Commercial Affiliates
Apereo = 10
£ $ € Software Communities
AKA “Projects”
Apereo Foundation
Shared Services

14. SharedServices Events
Incubation
Recognition Programs
Communities of interest
Tech Infrastructure
Intellectual Property
£ $ € Accounting etc
FARM: Funding and Resource Management

15. The Next Two Years: Six Key ThemesStrategy
Strategic review: https://bit.ly/2HLIpK8
1 Membership and fundraising
2 Partnerships
3 Communications, outreach & engagement
4 Development opportunities & recognition
5 Software community health measures
6 Foundation services - review

16. WhatisApereo?
Bedework
CAS
Karuta
Opencast
Open Academic Environment
Sakai
UniTime
uPortal
Xerte
EQUELLA
EDExchange
POET
Shuhari (OpenDashboard - Open Learner Warehouse - Student Success Plan)
Tsugi
ELMS:LN
uPortal Home
Notifications Backbone
OnTask

17. Content Authoring
Calendaring
Timetabling
Learning Materials Repository
VLE
ePortfolio
Single Sign On
Lecture Capture and Distribution
Portal
Transcript Exchange
Learning Analytics Platform
Collaboration
Tool Interoperability

18. WhatisApereo?
Bedework
CAS
Karuta
Opencast
Open Academic Environment
Sakai
UniTime
uPortal
Xerte
EQUELLA
EDExchange
POET
Shuhari (OpenDashboard - Open Learner Warehouse - Student Success Plan)
Tsugi
ELMS:LN
uPortal Home
Notifications Backbone
OnTask

19. Two non-obvious things:
Openness and Analytics
Xerte and Analytics

20. Learning Analytics Definition
“Measurement, collection, analysis and
reporting of data about learners and their
contexts, for purposes of understanding
and optimizing learning and the
environments in which it occurs” (1)
(1) Learning and Academic Analytics, Siemens, G., 5 August 2011, http://www.learninganalytics.net/?p=131

21. Learning Analytics Purposes
Current:
Retention
Student success
Student satisfaction
Future: Sequencing content & tools?

22. https://www.timeshighereducation.com/features/will-learning-analytics-empower-or-entrap-students-and-academics

25. Collect & Store
Intervention:
Action/Advising
Communication:
Dashboards/Early Alert
Analysis
Typical Learning Analytics Workflow

26. Learning Analytics Data Sources
Student Information Systems
Virtual Learning Environments
Attendance Monitoring Systems
Library Management Systems
Media Players
Learning Materials
??????????

27. Collect & Store
Intervention:
Action/Advising
Communication:
Dashboards/Early Alert
Analysis
Collect & Store
Intervention:
Action/Advising
Communication:
Dashboards/Early Alert
Big Data
Techniques
Analysis
Predictive Analytics

28. Predictive Analytics
Applying techniques associated with big data to
data produced in course of learning.
Analysing historical aggregate data to identify
potential failure/success.
big data
noun COMPUTING
1. extremely large data sets that may be analysed computationally to reveal patterns,
trends, and associations, especially relating to human behaviour and interactions.

29. Big Data: What Could Go Wrong?
People write algorithms …
… or people write the algorithms that
write algorithms …
It’s not difficult to find horror stories …

30. https://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/
Google predicted where flu outbreaks would take place by what symptoms
are searched accurately… but then changed their suggested search input to
include “flu symptoms” … 140% error on peak of 2013 flu season

31. https://www.theguardian.com/technology/2016/jun/30/tesla-autopilot-death-self-driving-car-elon-musk
Tesla algorithms failed to identify white truck against white sky

32. https://blogs.wsj.com/digits/2015/07/01/google-mistakenly-tags-black-people-as-gorillas-showing-limits-of-algorithms/
Not intentional racism … but algorithms have unintended effects and consequences

33. https://www.independent.co.uk/news/uk/home-news/met-police-facial-recognition-success-south-wales-trial-home-office-false-positive-a8345036.html

35. Uninvent or Influence?
The genie is out of the bottle.
There is early evidence of LA success.
Do we have the right to ignore this evidence?

36. Collect & Store
Intervention:
Action/Advising
Communication:
Dashboards/Early Alert
Analysis
Collect & Store
Intervention:
Action/Advising
Communication:
Dashboards/Early Alert
Big Data
Techniques
Analysis
Predictive Analytics

37. Spark
Hadoop
Collect & Store
Intervention:
Action/Advising
Communication:
Dashboards/Early Alert
Big Data
Techniques
Analysis
Communication:
Dashboards/Early Alert
Open Dashboard
Intervention:
Action/Advising
Student Success Plan
Big Data
Techniques
Collect & Store
Open LRW
Predictive Analytics
https://www.apereo.org/projects/shuhari
Uses historical aggregate data

38. Collect & Store
Intervention:
Action/Advising
Communication:
Dashboards/Early Alert
Analysis
Collect & Store
Intervention:
Action/Advising
Communication:
Dashboards/Early Alert
Rules
Based
Analysis
Rules Based
Doesn’t use historical aggregate data

39. Django
Pandas
ORM
Collect & Store
Intervention:
Action/Advising
Communication:
Dashboards/Early Alert
Rules
Based
Analysis
Communication
*and* Intervention:
OnTask Learning
Feedback at scale - via email -
framed as dialogue
Rules Based
https://www.ontasklearning.org/

40. Openness as safeguard
Lessons of Early LA Work

41. Eight-fold Path to Analytics Success
Open Purpose
Open Ethical Framework
Open and Inclusive Governance
Open Source Software
Open Platform
Open Standards
Open Algorithms
Open Consent - and Consent Management
IanDolphinviaFlickrAttribution2.0Generic (CCBY-NC-ND2.0)

42. Learning Analytics Data Sources
Student Information Systems
Virtual Learning Environments
Attendance Monitoring Systems
Library Management Systems
Media Players
Learning Materials
??????????
Accidental or Designed Data?

43. What you’re going to hear about today matters…

44. Because if we don’t exert influence…

45. https://www.facebook.com/authorchrisriddell/posts/1695060460546959

46. ian.dolphin@apereo.org