Use of predictive analytics in blended learning

Predictive and adaptive analytics using formative
assessment data in a blended learning classroom
The 2nd Active Learning Conference
University of Sussex
Brighton, United Kingdom
5 June 2018
1
Brian O’Dwyer
Embry-Riddle Aeronautical University Asia (Singapore Campus), Adjunct Faculty
CognaLearn, Executive Chairman and Commercial Founder
Duke-National University of Singapore Medical, Former Entrepreneur-in-Residence

Acknowledgements
2
▪ The presenter would like to acknowledge the contribution of Dr Timothy Wertz,
Lecturer at Yale-NUS College to the prediction of student outcomes case study.
▪ The presenter is the Commercial Founder of and has a financial interest in
CognaLearn. CognaLearn is the company that developed InteDashBoard™
www.intedashboard.com, which is TBL software developed in collaboration with
Duke-US Medical School; InteDashBoard™ is one of the technology tools
described in this presentation.
▪ A poster version of this presentation was presented at the 2018 Team-Based
Learning Collaborative Conference 2018, San Diego, United States
Copyright Brian O’Dwyer and CognaLearn Pte Ltd and www.intedashboard.com where applicable.
Slides available from odwyerb@erau.edu and may be used with attribution

Objectives
3
After this session participants should be able to:
1. Explain: who I am and why I am here
2. Define: team-based learning (“TBL”)
3. Describe: three examples of how TBL data can be
used to predict and optimize outcomes

Team-based learning (“TBL”)
4Copyright Brian O’Dwyer and CognaLearn Pte Ltd and www.intedashboard.com where applicable.
In class: apply
1. Pre-work 4. Clarify
doubts
2. Quiz 5. Team
applications
3. Team
quiz
In class: theory
Also 360° teammate evaluation

1. Pre-work

2. Individual Readiness Assurance Test
(“IRAT”)

3. Team Readiness Assurance Test
(“TRAT”) with immediate feedback

4. Clarifications

5. Applications
Canberra
Sydney
Significant problem
Same problem
Specific choice
Simultaneous report

TBL generates data
1 2 3 4 5 6 7 8 9 10 11 12
Week
Each class will typically generate
Individual test data
Team test data
Team case data

TBL data examples
11
1. Individual versus Team versus Final
2. Prediction
3. Adaption

Individual versus team: Michaelsen
Delayed feedback
(end of test):
cumulative team
score averaged 11%
higher than score of
best member
Immediate
feedback:
cumulative team
score averaged
23% higher than
score of best
member
Source: Larry Michaelsen, David Ross Boyd Professor
Emeritus of Management at the University of Oklahoma,
Founder of TBL. Personal communication.
Score of Best
Member
Team Score: Delayed
Feedback
Team Score:
Immediate Feedback
In a study of over 1,500 TEAMS…
+23%
+11%

1. Teams outperform individuals
76%
Team TestIndividual Test
BEFORE
93%
+22%
Source: Embry-Riddle Aeronautical University, Bachelor of Science in Aviation Business course in Airport Administration and Finance

2. Individuals retain team gains
76%
BEFORE
93%
95%
Individual Final
Exam AFTER

3. High-low range narrows
BEFORE
Individual Final
Exam AFTER
High: 80
Low: 54
High: 98 +23%
Low: 92 +70%

Example 2: simple adaption
Density altitude
Density altitude Density altitude
Q5 Density
Altitude

Example 3: prediction
Predict final grades in three weeks13 weekly assessments
Source: Dr. Timothy Wertz; Lecturer, Yale-NUS College

Caveats
18
▪ Repeated testing impact
▪ Partial credit impact
▪ Small sample sizes
▪ Early stage of analysis

Summary
19
▪ Team-based learning (“TBL”):
type of blended learning with lots
of formative assessment data
▪ TBL data examples suggest:
1. Final > team > individual
2. Repeat hard questions
3. Past performance -> future
performance
Learn more
Brian O’Dwyer
odwyerb@erau.edu
Team-Based Learning
Collaborative
www.teambasedlearning.org
TBL software
www.intedashboard.com

Use of predictive analytics in blended learning

More Related Content

Similar to Use of predictive analytics in blended learning (20)

Recently uploaded (20)

Use of predictive analytics in blended learning