Predictive analytics has been a hot topic recently as there have been many controversial questions asked if it will negatively impact students with a discouraging prediction. The power of predictive analytics in education isn’t determining a student’s future in advance. It’s helping shape positive outcomes while there is still time to act. With large class sizes and growing advisor to student ratios, identifying students in need of help can be a difficult challenge. Instructors can see current grades or whether students complete assignments on time, but this limited view does not capture the students who might be likely to struggle later in the semester even though they are doing fine now. Nicole will share about how institutions can forecast student success and struggles in their learning and how you can run a cutting-edge way of leveraging data with timely interventions offers a potentially powerful mechanism of students identification at the point and time of failure, before it is too late, and offering them strategies to overcome failures.

1. Delivering Student
Retention & Success
with Predictive
Analytics
Nicole Wall
Senior Consultant (Analytics and Learn)
International Consulting Services, Blackboard

2. Blackboard Analytics | 2Blackboard Analytics | 2
Blackboard Analytics
Product portfolio
Blackboard Intelligence
• Analytics for Learn – LMS data
• Student Management – SIS data
• Finance, HR, Advancement – ERP data
Blackboard Predict
• Predictive analytics and early alerts for success
• Provides data for faculty and advisors about students
• Formerly Blue Canary
Cognitive Analytics
• Predictive analytics
• Leverages IBM Watson, and multiple data sources
• Natural Language Processing
Past view Current view Future view
Past view Current view Future view
Past view Future viewCurrent view

3. Blackboard Analytics | 3Blackboard Analytics | 3
Proactive Advisement
Tactical
A Data warehouse vs Blackboard Predict
Summary
Blackboard
Predict
Identify Risk Behaviors* (descriptive)
Warehouse
Strategic
Instructional Design
Teaching & Learning
Student Risk Forecasts (predictive)
Graduation & Retention
* At-risk students can identified by sorting and comparing performance and activity data

4. Blackboard Analytics | 4
Blackboard Predict

5. Blackboard Analytics | 5Blackboard Analytics | 5
What if you could predict the students likely to struggle?

6. Blackboard Analytics | 6Blackboard Analytics | 6
What if you could predict the students likely to struggle?

7. Blackboard Analytics | 77
Solving the to
student:advisor
‘scale’ problem
Reaching the right
students at the
right time
Reducing
friction
Empowering
instructors and tutors
to intervene
and support
Blackboard Predict
leverages data and
advanced analytics to
identify students success
metrics and increase the
efficiency
and scale of intervention.
What is Blackboard Predict?

8. Blackboard Analytics | 88
How does Blackboard Predict work?
Problem
Which students are
not succeeding
/engaging?
Ingest
Aggregate the data
from multiple sources
Modeling
Turn the data
into valuable info
(answer the question)
Distribute
Put the valuable info
in the hands of those
who can act on it

9. Blackboard Analytics | 9
Random Forest
The machine learn algorithm “Random
Forrest” is used to make predictions. The
result is a set of decision trees created using
a random subset of features, which results in
each tree having its own path for predicting
the outcome. For any given prediction, the
data is passed through each tree, the
resulting predictions are averaged to
determine the final prediction for the case.

10. Blackboard Analytics | 1010
A robust data set, personalized to your institution
Class Level
Transfer
Course Information
LMS/VLE Engagement
Course Experience
& Performance
Social, Demographic
Economic and Profile
Academic History
Financial Aid
Attendance

11. Blackboard Analytics | 11Blackboard Analytics | 11
Changes over the semester
The model reflects the changing importance of data
Week 0 Week 7
.00 .02 .04 .06 .08 .10 .12 .14 .16
Current GPA
Course add weeks prior
Transfer credits
EFC
Percent of classes passed
Ethnicity
Class load
Academic year
Course modality
.00 .10 .20 .30 .40
Earned over attempted
Number of page views
Earned over possible
Percent of classes passed
Days since last page view
Posts to faculty
Example
Week 0 Week 7Week 4 Week 10 Week 13Week 1
Historic grades

12. Blackboard Analytics | 12
0
2
4
6
8
10
12
14
16
0 0-20 20-40 40-60 60-80 80-100
Importance of Feature (#/15)
Percentage Course Complete (Wks)
Cumulative Grade Point Average
Course size
Weeks until first log in
Course Grade Percentage
Course Grade Rank
Relative Importance of Features

13. Blackboard Analytics | 1313
Blackboard Predict arms each stakeholder
with actionable insights
STUDENTSADVISORS/TUTORS ACADEMICS
Information can also be displayed in a schools’ existing institutional system

14. Blackboard Analytics | 14
Roadmap

15. Blackboard Analytics | 15Blackboard Analytics | 15
Blackboard Predict: Roadmap at a glance
• Shared advisor notes: Enable
advisors to document their
communications, interventions
and student follow-ups.
• Batch email communication:
Help advisors scale
interventions by emailing
entire groups
• Additional predictive model:
Creation of a new model to
predict overall retention risk for
the next semester or term
• Accessibility Improvements
• Improved student profiles
Highlight the most relevant
additional SIS information in
student profiles
• Enhanced risk analytics
improve interventions by
exposing additional insights
that influence academic and
retention risk.
• Automated alerting
Provide alerts for advisors
when certain thresholds are
crossed
• Predictive modeling for
student risk scoring
• LMS integrated Instructor view
of student risk
• Consolidated advisor view of
student risk
• Identify student segments for
outreach
• Student access to grade
projection and activity
• International support
• APIs for 3rd party integration
Dates and information subject to change without notice

16. Blackboard Analytics | 16Blackboard Analytics | 16
Shared advisor notes
In Development (Q3 2017)
Dates and information subject to change without notice
• Record history of communications
with students, interventions, and
follow-ups
• Ensures visibility into previous
conversations for future advisors

17. Blackboard Analytics | 17Blackboard Analytics | 17
Batch email communications
In Development (Q4 2017)
Dates and information subject to change without notice
• Advisors can select multiple
individual students or entire groups
of students by using built-in filters
• Customize subject and message

18. Blackboard Analytics | 18Blackboard Analytics | 18
New predictive model aimed at retention risk
In Development (Q4 2017)
Goal: Identify which students are least likely
to return the following term
A new model to help identify those students at
most risk for not re-enrolling in the next term
for proactive outreach and intervention
Users will be able to:
• Triage, sort and filter students by
retention risk
• Insight into additional factors known to
influence retention risk
• Ability to dig deeper into academic
performance of high retention risk
students, including the existing course
based predictions
Dates and information subject to change without notice

19. Blackboard Analytics | 19Blackboard Analytics | 19
Invitation to Participate in Predictive Analytics
Research
Blackboard is exploring additional predictive models, and the following question in
particular: “What is the probability a student will return the following semester or
year?”
What is expected from me if I participate?
• We expect active participation in analysis, access to institution data sets, including:
• Discussions with our data science team about institution learnings and insights
about persistence factors and specific variables that affect retention
• Access to additional student/institution data for model research
What benefits do I have if my institution participates?
• Research Results. All results of Blackboard’s research and data science analysis will
be shared with your institution. This includes insight into the data model created,
relevancy of the different factors, and benchmarks vs. other institutions. These
results will be informative for an institution’s understanding and intervention efforts.
• Promotion and Publicity. Research findings will be disseminated and will credit the
participating institution as a contributing research partner.
Reach out to Nicole.Wall@blackboard.com

20. Blackboard Analytics | 20Blackboard Analytics | 20
Blackboard Predict: future initiatives
Driving effective action through:
• SMS communication
Advisor <-> student communications via text
messaging
• Remote advising
Enable advisors to schedule synchronous remote
Collaborate sessions with students
• Student “nudges”
Automated mobile nudges when certain
thresholds have been exceeded or milestones
missed.
• In-app Notifications
Allow advisors and instructors to send a mobile
notification to students.
Turning data into insights through:
• Student sentiments
Enable students to self-report sentiment
and satisfaction about majors, finances,
extracurricular responsibilities, etc with advisors
• Additional analytics for advisors and instructors
Help advisors and instructors get a fuller picture
of students for more tailored communications
and interventions.
• Surface timely data to students
Provide additional resources, insights and
messaging to students within Predict
• Intervention tracking
Better enable reporting on the efficacy of
particular interventions
Dates and information subject to change without notice

21. Blackboard Analytics | 21
Implementation Approach

22. Blackboard Analytics | 22Blackboard Analytics | 22
Get up and running in less than 90 days
Discovery Data pipeline Modeling Visualizations Implement
Total of 3-4 months end-to-end
1 – 2 weeks
• Question
• Data/tech
3 – 4 weeks
• Ingestion
1 – 2 weeks
• Features
• Iteration
1 – 2 weeks
• Embedded
• Stand alone
2 weeks
• Question
• Data/tech

23. Blackboard Analytics | 23Blackboard Analytics | 23
Who will be involved
Implementation
Project
Management
Student Info
System Expert
LMS Support
Adoption
Technology
Support Staff
Student
Support Staff
Faculty