Presented at the 2014 SLATE conference (www.slategroup.org) Faculty development is occurring increasingly online through text-based guides, just-in-time video tutorials, and social media, which is convenient for faculty looking for information on teaching or using technology. However, this makes it difficult for faculty development centers, used to traditional forms of assessments, to assess the quality and effectiveness of these programs and resources. In this session, we will share how the Faculty Development and Instructional Design Center at Northern Illinois University has used web analytics to evaluate the usage of online materials and how the results have impacted our practice.

1. Measuring Digital
Professional Development
Analytics for the Use of Web and Social Media Materials
View the slides at
facdev.niu.edu/slate14analytics

2. Presenters
Stephanie Richter
Assistant Director
srichter@niu.edu
Peter Gowen
Online Analytics Coordinator
pgowen@niu.edu

3. The Need for Web and
Social Media Analytics

4. Faculty Development and
Instructional Design Center

5. Faculty Development and
Instructional Design Center
niu.edu/facdev

6. Teaching with Blackboard
niu.edu/blackboard

7. Academic Integrity Tutorial
niu.edu/ai

8. Responsible Conduct of
Research Tutorial
niu.edu/blackboard

9. Social Media
@facdev
@NIUBlackboard
@NIUTeachOnline
facdev facdev

10. Faculty Development and
Instructional Design Center

11. Web Analytics

12. Why Web?
• Programs
• Services
• Resources
* No (or little) “cross-talk”

13. Analytics
The use of data, statistical analysis, and
explanatory and predictive models to gain
insights and act on complex issues
Bichsel, 2012, p. 6

14. On Data
• Data let you answer questions
– Are our resources being used?
– What is being used?
– How are they being used?
– Why?*
*May not be possible, or at least not easy

15. Why Google Analytics?
• Measure utilization easily
• Measure behavior (with some work)
• Key terms
– Traffic: Pageviews or Users, per given time span
– Visits/Sessions: A (default) 30-minute sliding activity
window
– Hits/Pageviews: A webpage request from a web
browser
– Visitors/Users: A unique ID set by Google per browser

16. What can GA do?
Screenshot from Google Analytics

17. What can GA do?
Traffic for Teaching w/ Bb,
Aug. 16, ‘13 – Aug. 15, ‘14
Fall 2013 Spring 2014 Summer 2014
Pageviews 140,951 128,990 39,630
Users 48,236 48,462 17,531

18. What can GA do?
Geography of Sessions to Teaching w/ Bb,
95.07%
from the US
67.04%
from Illinois
Aug. 16, ‘13 – Aug. 15, ‘14
~160 countries

19. What can GA do?
Top 5 Pages for Teaching w/ Bb
/index
/students/safeassign
/assess/safeassign
/students/index
/faq/qa/safeassign
135,207
27,635
5,994
4,408
4105
Deeper analysis:
• Traffic over time, per page
• Categorizing pages

20. What can GA do?
Top 10 Browsers
Desktop v Mobile

21. What can GA do?
Top 10 visiting schools

22. Google Analytics Resources
• Get started with Analytics
• How To Set Up Google Analytics Account
Setup 2014 (YouTube tutorial)
• Setup Checklist (for all/advanced features)

23. Social Media Analytics

24. Key Terms
• Users
– Follower: someone who sees Twitter posts in their
stream
– Like: someone who (maybe) sees your posts on
Facebook in their News Feed
– Subscriber: someone who received notifications
of new videos posted on YouTube

25. Key Terms
• Engagement
– Retweet: someone shares your Twitter post with
their followers
– Favorite: someone appreciates or approves of
your tweet
– Like: someone appreciates or approves of your
Facebook post
– Share: someone reposts your Facebook post to
their friends
– View: someone begins watching a video on
YouTube

26. Social Media Presence
@facdev
@NIUBlackboard
@NIUTeachOnline
facdev facdev

27. Followers on Twitter
800
700
600
500
400
300
200
100
0
Aug '13 Sep '13 Oct '13 Nov '13 Dec '13 Jan '14 Feb '14 Mar '14 Apr '14 May '14 June '14 July '14
@facdev @niublackboard @niuteachonline

28. Twitter Analytics – Tweet activity

29. Twitter Analytics - Followers

30. 200
195
190
185
180
175
170
165
160
155
150
Likes on Facebook
8/13 9/13 10/13 11/13 12/13 1/14 2/14 3/14 4/14
facdev

31. Facebook Insights

32. Views on YouTube
1400
1200
1000
800
600
400
200
0
8/13 9/13 10/13 11/13 12/13 1/14 2/14 3/14 4/14
facdev

33. YouTube Analytics

34. Conclusions

35. What We Have Learned
• Online resources are heavily used, at NIU and
around the world
• Top pages are a priority for updates
• Popular topics may need additional workshops
• Mobile users are a small but growing group
• Significant site traffic comes from other
universities and colleges
• Social media engagement is growing but
needs further analysis

36. Next Steps
• Look at traffic to segments of content instead
of single pages
• Identify trends in topics over time
• Compare trends to consultation trends and
workshop schedule
• Investigate user behavior more deeply
– page visits vs. searches
– page visits vs. question submission
– in-page behavior

37. Questions?
Stephanie Richter
srichter@niu.edu
Peter Gowen
pgowen@niu.edu
View the slides at
facdev.niu.edu/slate14analytics

38. References
• Bichsel, J., 2012. Analytics in higher education:
Benefits, barriers, progress, and
recommendations. Louisville, CO: EDUCAUSE
Center for Applied Research. Available from:
http://net.educause.edu/ir/library/pdf/ERS12
07/ers1207.pdf [Accessed 21 February 2014].