Presentation at D2L Oklahoma Regional User Forum, October 2012. Most slides created by Alfred Essa and Jeannette Brewer

1. Norman,
Oklahoma

2. What is Analytics?
• Analytics is the use of data, statistical and
quantitative methods, and explanatory and
predictive models to allow organizations and
individuals to gain insights into and act on
complex issues.
• In colleges and universities, analytics is used to
improve operational efficiency and student
success.
Source: Educause, Oblinger: Let’s Talk Analytics
http://www.educause.edu/ero/article/lets-talk-analytics

3. What is Analytics?
• The term big data is often used interchangeably
with analytics, but the scientific community
uses big data to describe research that uses
massive amounts of data.
• The use of analytics to improve administrative
functions is often called business
intelligence; similarly, academic analytics is
used to help run the business of the higher
education institution.
Source: Educause, Oblinger: Let’s Talk Analytics
http://www.educause.edu/ero/article/lets-talk-analytics

4. What is Analytics?
• Finally, learning analytics focuses specifically on
students and their learning behaviors, gathering
data from course management and student
information systems in order to improve student
success.
• Although the labels can be confusing, overall the
term analytics refers to an approach that can be
used to explore a broad range of questions.
Source: Educause, Oblinger: Let’s Talk Analytics
http://www.educause.edu/ero/article/lets-talk-analytics

5. Advanced Analytics

6. Analytics Maturity Levels
Information Value
Insight

7. Analytics: Big Data (R2)
Multiple Levels of
Reporting
with Drill-Down
Filters
Extensive
Data Domains
Aggregates and
Trends Over Time

8. Big Data: Data Sets
• Enrollments. The enrollment data mart tracks user enrollments and withdrawals
across one or more organizations.
• Competencies. The competencies data mart tracks competencies, learning objectives,
activities and rubrics by user, department, program, institution, and system.
• User Logins. The user access data mart tracks the number of user logins/distinct
sessions over a period of time. It is a very simple way of tracking student patterns of
accessing the system.
• Content and Tool Access. The module data mart tracks content access & tool usage.
• Web Analytics. The web analytics data marts track internet statistics such as
bandwidth usage, geographical location, and browser types.
• Test and Quizzes. The quizzing data mart tracks quiz, test, and survey results,
including measuring of quiz effectiveness.
• Grades. The grades data mart tracks grades at student, course, department or school
level, including filtering by grade ranges or date ranges.

9. Tech Data
• IIS Web Analytics
• Client Access
(OS/Browser)
• SMTP
• Global/Local
Traffic Manager
Logs

10. Elemental LMS Data

11. Elemental LMS Data: Data Mining

12. Tool Usage: Overall vs. Pattern

13. Tool Specific Data: Content

14. Tool Specific Data: Quiz Overall

15. Tool Specific Data: Quiz Patterns

16. Grades Data: Org vs. Course

17. Institutional Effectiveness
Define Outcome Standards
Continuous
Make Informed Design Curricula
Improvement of Align Assessments
Improvements
Education Quality
Analyze Results
Report on Evidence

18. Curriculum Mapping: Mechanics
Institution
Program
Department
Courses

19. Curriculum Mapping: Big Data

20. Learning Outcome Evaluation

21. Learner Competency Progress
} View Overall
Proficiencies

22. Big Data: Risk Analysis

23. Analytics Maturity Levels
Information Value
Insight

24. Analytics Optimization
finding an optimal path to a desired future

25. Application Logic
Exceptional
Predict Intervene Success
At-Risk

26. Application Workflow
Understand the Problem
Interrogate Raw Data
Reach a Diagnosis
Intervene, Make a Referral
Track the Success

27. Limitations of Current Approach
• Interpretation
• Not enough information for intervention
• Interactivity
• Unable to interrogate and make sense of the particulars
• Generalizability
• Same model is used for every course at every
institution

28. Collective Intelligence
Consensus decision making

29. Predictive Domains
Multiple
Semantic Units

30. Student Success System (S3)
SSS is an Early Intervention System. It empower institutions with predictive
analytics tools for improving student success, retention, completion, and
graduation rates.
Highlights
– Course-specific predictions of student success and risk levels
– Success index that enables comparison of key success indicators
– Innovative data visualizations
– Case history and intervention management
Availability
General Availability in 2013. (Pilot project starting Oct. 2012)

31. Student Success System

32. Powerful Reporting and Analysis
Personalized Detailed analysis lets you drill
assessment down to individual classes
Intervention }
}
management
In-depth
Success
indicators
} reporting
Innovative data visualizations

33. Challenges and Remedies
Challenges for Institutions Student Success System Remedy
Inability to predict, and consequently improve student Predictive modeling identifies at-risk students based
success, retention, graduation, and completion rates on engagement, performance, and profile data
Limited resources to create personalized intervention Visualizations and statistical indicators provide
plans diagnostic insights to help design individualized
interventions
Lack of data correlating engagement with success Analyze student engagement patterns and effects on
academic success
Inability to identify isolated students Visualize social network patterns based on discussion
data, to improve social learning

34. Value to Institutions and Students
• Predictive analytics provides early identification of at-risk
students enabling instructors to identify and understand
where issues are and create appropriate resolution plans
to address the problem
• Graduation and retention rates are increased when at-risk
students are identified early on the process and supported
throughout the term with informed counter-tactics

35. Summary – Student Success System at a Glance
Institution Challenges Description of SSS
• Improving Student Success • Early Intervention System driven by
• Identifying academically at-risk, dis- advanced predictive analysis and data
engaged or isolated students visualization to identify at-risk students
• Increasing retention, completion, and and intervene to improve their retention,
graduation Rates completion, graduation and success rates.
Student Success System Value Ideal Customer Profile
• Easily identify at-risk students, and
• Institutions looking to empower
understand where the issues lie
• Design and implement individualized instructors with predictive analytics
intervention programs to improve student success.
• Improve institutional effectiveness
• Increase student success

36. Norman,
Oklahoma