McGraw Hill Education is a 125 year old education company with 5,000 employees that provides products for K-12, higher education, and professional learning. The presentation discusses how McGraw Hill is evolving from traditional textbooks to utilizing small data and adaptive learning platforms to personalize education based on individual student needs. Small data, defined as targeted analytics rather than massive datasets, allows for strategic use of learning data to support students and instructors by guiding students through curated learning paths and providing insights.

1. ©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution.
Learning Science Platforms

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Small Data in a Big Data World
01
Small Data in a Big Data World02
MHE Adaptive Solutions03
04 Learning Science Platforms Group
Overview of McGraw Hill Education

3. 3
A leading education built over 125 years ago
~5,000
employees
125 year
history
K-12, Higher Ed
& Professional
businesses

4. ©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution.
Gathering, collecting and storing the
vast amounts of data we produce.

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What is Big and Small Data?
Source Diya Soubra Data
Science Central 2012

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Heliocentric model vs Geocentricism

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Small Data in a Big Data World
Small Data
Low Volumes
Batch Velocities
Structured VarietiesBig Data
Into Petabyte Volumes
Real-time Velocities
Multi-structured Varie

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The evolution of a learning science company
Textbooks
E-books
Adaptive software
12

13. Everyone Learns Differently
Everyone does not learn, remember and understand the same things,
the same level or at the same speed

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Small Data vs. Big Data
Targeted analytics – not “BIG DATA” – are
transforming education today
What is Small Data?
– Strategic use of learning data with a direct
correlation to performance
– Assignment scores, time on task, progress in
an adaptive learning environment
Data and analysis in service of the learner and
instructor
Small Data can begin with adaptive learning and
end with analytics and insights

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How can small data support learning?
 Longitudinal and shared identity
 Learning/Knowledge structures/maps

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Specific Data About learning Outcomes
 Data aware and data
generating learning
objects
 Pedagogically useful
insights

17. Small Data Allows Learners to Navigate
Their Own Learning Path Through a
Complex Knowledge Space

18. Flipped Teaching = More Student Centered
Lectures are done at home
Activity Focused Classroom
Actively Engaged in learning
Efficient
Teacher/Student interaction
Collaborative learning
More Time for assessment and activity
More Time for Questions
Deeper Understanding of Concepts
Traditional Teaching = More Teacher Centered (lecture)
Lectures are done in class
Bookwork at home
Students passively watch the demonstration
Students are guided rather than navigating
Flipped & Traditional

19. Instructor led
videos outside
of class
Curated
Videos
The Flipped Classroom
Engage students
Apply course
concepts
Learn from
classmates
Peer-Peer Learning
Instructor is there
to help & support
problem solving
Higher level learning
in the class
Baseline
Knowledge
outside of class

20. In Class Activities
Team Challenges
real-life situations
Discussions & Debate

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There are two parts to answering the question.
1) The actual question
2) Their confidence level in answering the question

22. Title
LO
Chap
20
Chap
10
Chap 1
LO LO
1
LO 2 LO
89
1 LearnSmart Title
X Chapters / Title
50 to 100+ LO’s per
chapter
2 to 5 probes per
LO
5,000 (+/-)
probes per title
Small Data Takes the Student To their
Most Appropriate Learning Path

23. LO LO
•LO’s are prioritized
•core - 1
•builds on core - 2
•conceptual - 3
•Learning Objectives are
related
to each other
•Probes are related
•Different types of probes
Fill in
Blank
T/F M/C RankingM/S
LO
1 2 3
Everyone Learns Differently

24. •Each probe has a certain difficulty and
predictive value
•Context of the probe
•Question type
•Statistically T/F is ‘easier’ than Fill-in-the-blank
LO
T/F M/C RankingM/S
2
Everyone Learns Differently

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inside the
system? Small data harnessed in
service of the learner and
faculty
 Pedagogy employed
 Properly constructed and
applied administrative data
ensures context, access
and feedback
 Catalog or metadata
ensures that data can be
found and dynamic
learning can take place
 Learning data forms the
basis of insights and
personalized learning plans

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Setting aside the hype, what possibilities does “Small Data” give to the
dialogical learning space?
 Personalized learning experiences
 Increased student engagement
 Data-driven decision-making
 Better outcomes: grades, retention,
completion
 Reduce remediation time, improve
placement accuracy
 Enhanced faculty satisfaction
Possibilities of Small Data

28. Small Data Solutions
Adaptive learning methodology fundamentals:

29. Proposed Solution
Continual feedback provided by study technology helps students learn better

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Small Data in a Big Data World
Data flows without commercial boundaries
Data belongs to the students, faculty and institution
Configuration is in the hands of the user
–Better user experience for students and instructors
Technology seamlessly fades into the background

31. TexThank you!