The Future of Higher Education: Harnessing Software to Enhance Learning Outcomes & Support Teaching

David Levin President and CEOStephen Laster Chief Digital OfficerAlfred Essa VP, Analytics and R&D

September 1, 2016

The Future of Higher Education: Harnessing Software to Enhance

Learning Outcomes & Support Teaching

© 2 0 1 6 M c G r a w - H i l l E d u c a t i o n

Discussion topics

3 Implications for Teaching & Learning

2

01 The Future of Higher Education

Future of Higher Education

1 Our Journey from Print to Digital


From Print to Digital: 128-year Journey

K-12, Higher Ed & Professionalbusinesses

~4,800employees

3FROM PRINT TO DIGITAL© 2 0 1 6 M c G r a w - H i l l E d u c a t i o n

Higher Ed print to digital transition

MHE DIGITAL VS. PRINT BILLINGS MIX %

H1 2015 H1 2016


5

Mastery Learning

Using data and formative assessment to support Educators in driving to Mastery Learning

5TEACHING & LEARNING© 2 0 1 6 M c G r a w - H i l l E d u c a t i o n

MHE Adaptive leverages significant investment in technology

Growth via investment . . .

200engineers,

etc.

2012DPG

Formed

2016

495engineers,

etc.

$

$180M Investment per Year in Digital

Platforms

And selective tech acquisitions

MHE Adaptive 6FROM PRINT TO DIGITAL© 2 0 1 6 M c G r a w - H i l l E d u c a t i o n

Learners who have used MHE Adaptive

~5,500,000 ~10,000,000,000Student

interactions

Adaptive platforms leverage MHE reach & scale

Introduction of SmartBook

May 2013 1,500+

adaptive products available

Now

Authors trained to use MHE Adaptive

~4,000


“Pre-determined” factors drive change in all scenarios

Pressure for ROI

increases

Focus on student

outcomes

Data-driven learning

delivers results

Micro-credentialing

on the rise

Pressure to remove

frictions in system

Learning remains a

social activity

New politicsemerge

K-8 ages &stages

Privacy & compliance

demands grow

Technology impact grows

Content & delivery channels disrupted

Education “wants to be

free”

8FUTURE OF HIGHER EDUCATION© 2 0 1 6 M c G r a w - H i l l E d u c a t i o n

Three key “uncertainties”

D2C significant or

not

Consumer pays or free

Data is constrained or

unfettered

Crowd-sourcin

g significant or

notTeachers

are barriers

or enablers

of change

K-12 digital

adoption: patchy

vs. tipping point

Education

ecosystem

Note: 1. “Formal education: ages & stages or lifelong and flexible”; and 2. “Future of accreditation” were also identified as uncertainties but were not clustered above

Consumer

Institution

ConvergeFragment Competitive

Oligopoly

The key uncertainties

Buying DecisionTech & Ed Standards Eco-System/Marketplace

9FUTURE OF HIGHER EDUCATION© 2 0 1 6 M c G r a w - H i l l E d u c a t i o n

“Uncertainties” frame 3 scenarios

UncertaintyRenewal from

Within

Invaders Unbundle Education

Platform Predominan

ce

Buyer decision

Institution Consumer InstitutionTechnology & education standards Converge Converge Fragment

Eco-System/

Marketplace Competitive Comp > Olig Oligopoly

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FUTURE OF HIGHER EDUCATION© 2 0 1 6 M c G r a w - H i l l E d u c a t i o n

The three scenarios

Big Tech Platforms

Invaders Unbundle EducationRenewal From

Within

Institutions innovate

Lower prices

Academic reformOpen platforms /

standards

Entrepreneurs provide choiceMultiple price

pointsMicro-credentialing

Survival of fittest

Big tech firms dominate

Lower costs

Data-drivenIntegrated platforms

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FUTURE OF HIGHER EDUCATION



Key developments are driving better learning outcomes

Improving LearningOutcomes

DigitalCompetencybased

Personalized

Print Seat-time One-size-fits-all

©2016 McGraw-Hill Education. Confidential and proprietary. Not for redistribution. 12

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TEACHING & LEARNING© 2 0 1 6 M c G r a w - H i l l E d u c a t i o n

The Route to Mastery: Learners learn in very different ways, in terms of sequence, pace, study habits and more

Cohort #1 achieved

mastery quickly and can move

on to other things

Cohort #2 has taken a little longer

but have achieved masteryCohort #3

never finished. Frustration? Lack of time?

Pre-requisites?

Other factors?

This learner needed over 9

hours to master the objective, but used the

time efficiently. The lack of

horizontal lines shows good,

steady progress

Source: Progress to a single learning objective: MHE 2016

13


Power intelligent learning, instruction, authoring: Drive improved outcomes

FocusedInstruction

Content

14


Learning science drives our technology, informs our design

15

Meta-Cognitiv

e Theory 1

The Theory

of Delibera

te Practice

2 The

Theory of Fun for

Game Design 3

Ebbinghaus Forgetting Curve 4

Learners learn best when they know what they don’t know

Understanding where we are weakest helps us to focus our practice

To truly learn something, learners need to commit it to long-term memory

Learners are most engaged when challenged, but not too challenged

1.Flavell, J. H. "Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. “American Pyschologist (1979) 34,906 -911. Print.2. Ericsson, K. Anders, Krampe, Ralf Th., Clemens Tesch - Romer. “The Role of Deliberate Practice in the Acquisition of Expert Performance”. Psychological Review Vol. 100 No. 3 (1993) 363-406. Print3. Ebbinghaus, Herman, Trans. Clara E. Bussenius and Henry A. Ruger Memory: A Contribution to Experimental Psychology, Eastford, CT: Martino Fine Books, 2 – 11. Print 4. Koster, Raph. A Theory of Fun for Game Design. Scottsdale, AZ: Paraglyph Press, Inc., 2005. Print.

15


http://psycnet.apa.org/index.cfm?fa=buy.optionToBuy&id=1980-09388-001

https://graphics8.nytimes.com/images/blogs/freakonomics/pdf/DeliberatePractice(PsychologicalReview).pdf

The art of Learning Science

Improving LearningOutcomes

Standards based approach allows easy extension and customization

McGraw-Hill will continue to invest in building and supporting research based content

Algorithms ensure most appropriate content is served at any moment

Data supports both Instructor, Student and Institution

16


SmartBook highlighting

17


Heat maps generate data

Authors see real-time data on the effectiveness of their content so they can

continuously improve it.

18


Editing with big data: Notice how the definition of “eardrum” has been clarified and we can document a 5% improvement in results and a 46% reduction in time.BEFORE

Data enables authors to refine their content: a virtuous cycle of improvement

AFTER

19


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Finding: Patterns emerged that suggest that certain study habits are more effective and possibly predict performance

HIGH PERFORMERS… LOW PERFORMERS…

…studied in chunks of time larger than 15 minutes and avoided studying late into the night

…when they encountered challenges, took time to look through the learning resources before returning to the probes

…were more likely to be generally aware of what they did and didn’t know

…utilized the other sections of the course such as the Reports, Library, and Recharge

…were more likely to begin the course towards the end of the week

…those learners who did not complete the course typically had low levels of accuracy and abandoned the course after less than an hour

… completed less than one-quarter of the course (all learners who made it past 25% of the course ultimately passed)

…were less likely to take the awareness questions seriously

The data provided through the system can be used to improve learner study habits, help them better use their time efficiently and to

inform remediation.

CONCLUSION

What is Learning Science Research?

Exists at the apex of data science and learning theory.

Maximize the impact of our technology & solutions for improving learning outcomes. MHE computer scientists, educational researchers, and statisticians in partnership with leading academics.

GOAL

LEARNING

SCIENCE RESEARC

H COUNCILTHE RESEARC

H

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Current areas of inquiryAlgorithms: Next generation adaptive algorithms based on the best in ALEKS and Learn Smart. Extend cognitive models to include non-cognitive factors such as motivation, confidence, affect, learning strategies, and meta-cognition.

Efficacy Research: Design and implement efficacy research studies which establish the efficacy of MHE’s products and solutions. Rigorous studies would include Randomized Control Trials (RCT) but would also incorporate new statistical techniques such as Propensity Score Matching. Formative Assessment Quality: Apply statistical techniques such as Item Analysis and Item Response Theory (IRT) to evaluate and improve the quality of our formative assessment item banks.

Insights: Advanced interactive visualizations which provide actionable insights to learners and instructors. Key areas include insights into achievement of learning outcomes, enterprise insights which display achievement across multiple course sections, and accreditation reports.

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Learning Research in Action | Retention Analytics

Problem: A significant number of students abandon their Connect courses before the semester ends. These at-risk students can be difficult for instructors to identify on their own.

Solution: A predictive model that pre-emptively identifies at-risk students.

Classifier Predictio

ns

1 0 1

Notebook

Connect DB

F1 F2 F3 F4 F5 F6

Section Data

1 F1 F2 F3 F4 F5 F60 F1 F2 F3 F4

F5 F61Training data

F1 F2 F3 F4 F5 F61 F1 F2 F3 F4

F5 F60 F1 F2 F3 F4 F5 F61

Testing data

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Problem: Which assessments items are effective? How well do they discriminate among learners of different abilities?

Solution: An automated service which examines item quality and flags problematic ones for review and evaluation.

Learning Research in Action | Assessment Quality


24


Problem: Randomized Control Trials (RCTs) are the gold standard in establishing efficacy. However, RCTs are expensive, difficult to design and conduct, controversial, and often impractical in the education domain

Solution: Apply a relatively new statistical technique, Propensity Score Matching (PSM), which when designed properly and with large data sets, can achieve similar statistical power and validity as a RCT. At MHE, an important area of research investigation is to use new statistical approaches such as causal modeling and observational studies.

How do we establish that our products and solutions are effective in improving learning

outcomes?

Learning Research in Action | Efficacy Studies


25


Improving outcomes whilst enhancing affordability

Traditional Option: Purchase the textbook alone on Amazon for $201

New Option: Purchase Connect for $90, which comes with an adaptive reading experience, an e-book (available to print if the student so chooses), AND the option to purchase a full-color, loose-leaf version of the text for $25. The loose leaf ships to the student free of charge.

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Adaptive delivers for all Higher Education stakeholders

Deeper relationship and feedback with faculty

Direct relationship and feedback from student

Improved teaching outcomes

Enhanced learning feedback loop better focuses instruction

Instructor-student workflow more efficient

Improved student retention rates

Real-time measurable learning outcomes

Real time quality improvement feedback

Better outcomes Better content and

editing

Improved learning outcomes

Adaptive and personalized learning

Lower prices

STUDENTS FACULTY

INSTITUTIONS

AUTHORS

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Thank you

Q&A