From Data to Action: Driving Innovation through Predictive Analytics (168044365)

7/29/2019 From Data to Action: Driving Innovation through Predictive Analytics (168044365)

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Analytics for Higher Education:Key Considerations around Data & Predictive Analytics for Innovation in Information Management:

Jon Phillips

Dell Managing Director - Worldwide Education



2 Copyright 2013 Dell Inc. Prepared by

2 Confidential

During a five-year period,

taxpayers spent over $9 billion to

support college students whodropped out before their

sophomore year.1

• Recruitment and retention

• Sound fiscal management

• Operational efficiency

• Efficient and reliable complianceand reporting

• Grant success tracking

• Monitoring of PK-20 data (all of education plus the workforce)

• Gainful employmentMeasurement / WorkforceReadiness

2 Confidential Global Marketing

Education Analytics for Higher Education

“Saving 1 percent in Student

Retention can save my

University’s bottom line

$1M a year”

CIO Major University

1. American Institutes for Research (AIR). http://www.air.org/news/index.cfm?fa=viewContent&content_id=988

http://www.air.org/files/AIR_Schneider_Finishing_the_First_Lap_Oct101.pdf



3 Services ConsultingConfidential

Stage 2:

Inception

Stage 3:

Refinement

Stage 4:

Consolidation

Stage 5:

Enhancement

Stage 1:

Baseline

Stage 6:

Innovation

The Value of Analytics Management Maturity

Operational Data Spread-marts Data MartsData

WarehousesEnterprise Data

WarehouseAnalyticServices

Cost Center Inform

ExecutivesEmpower Workers

Monitor Performance

Drive theBusiness

Drive the Market

BI Unaware Shadow ITMinimal

EngagementIT Executive

Sponsor Business Exec

SponsorsHolistic BIStrategy

BI Maturity

Maturity increases value & decreases cost



4 Services ConsultingConfidential

Stage 2:

Inception

Stage 3:

Refinement

Stage 4:

Consolidation

Stage 5:

Enhancement

Stage 1:

Baseline

Stage 6:

Innovation

Stages of Analytics Practice Maturity

One-off Reports Spread SheetsOperationalDashboards

Line of BusinessDashboards

BusinessProcess

Dashboards

Customer Facing

Dashboards

Operational Data Spread-marts Data MartsData

WarehousesEnterprise Data

WarehouseAnalyticServices

Cost Center Inform

ExecutivesEmpower Workers

Monitor Performance

Drive theBusiness

Drive the Market

Operational Data Stores On-line Analytical ProcessingEnterprise Data

ModelBig Data

Defined ToolsetMultiple Tools

Meta DataCumbersome Data AcquisitionLife Cycle

ManagementAutomated Data

Quality

Stove Pipe Architectures Robust & Scalable Architecture

Project Based Development 5 Year Roadmap

Steward less Minimal SkillsTactical

Stewardship

Federated

Processes

Reporting

Framework

BI Strategic

Competency

BI Unaware Shadow ITMinimal

Engagement

IT ExecutiveSponsor

Business ExecSponsors

Holistic BIStrategy

BI Centers of Excellence

Enterprise DataGovernance

Analytics Center of Excellence

Ungoverned Minimal ProcessPockets of

Governance

AnalyticalCompetency

PrescriptiveAnalytics

Raw DataData

VisualizationInformationDiscovery

DescriptiveStatistics

New Paradigm

Historical Norm

Moving forward to the New Paradigm



5 Copyright 2013 Dell Inc. Prepared by

Poll

Rate your organizational readiness & Maturity foranalytics

1. Various units work independently on different types of

analysis2. We have occasional coordination across multiple units

that perform analysis

3. Multiple units across campus share a common data

warehouse for single-source-of-truth data4. Multiple units across campus readily share analysis

techniques, artifacts and tools with each other

UK rules



6 9/13/2013 Software

Road blocks and capabilities to build at each level of maturity

Spreadsheets + lack of systematic analysis

Budget constraints + poor

data integration + poor dataquality

Slow and lengthy BIprojects + poor datainteractivity

Limited skills and executivesupport

Under-developed tools andlimited skills

What’s lacking

Predictive

analytics

Proactive reporting

Information consolidation

Casual data access and analysis

Cognitive

analytics

Pivot tables, operational

reporting

Data modeling, Database and

data warehouse design

Data quality, master data

management

BI & dashboards, KPI metric

design, data governance

Data mining, forecast

modeling, trend metrics

Social listening, sentiment

analysis, pattern recognition

Capabilities tobuild




Biggest inhibitors to realizing value of big data

30% 30%

22%

13%

5%

Leadership andorganization

Analyticscapabilities and

skills

Infrastructure andarchitecture

Investment,budget, ROI

Risk concerns(security, privacy)

4.4 M big data jobscreated globallythrough 2016. Only

1/3 will be filled

Unstructured data

hard to analyze

Raw-data-to-insights

latency (time-to

action)

Need to exploit

diverse content

RDBMS/traditional

infrastructure won’t

work

Survey data Gartner – May 2012




Petabyte

Exabyte

Zettabyte

Terabyte

The explosion of data continues to burden the data tool chain

Transactional DataTraditionally, only

transactional data was

generated and stored in

databases

• Structured

• Measured growth

Human FilesBut over time, westarted creatingunstructured data

• Likes, tweets,relationships (social)

• Sensor data (machine)

• Exponential growth

Social & Sensors

have addedexponentially

mainframe PC internet mobile machine

• Docs, Images, Video

• Multiple formats

• Fast growth



10 9/13/2013 Software

Resulting in a complex environment todayVolume/velocity/variety and disparate systems have fractured the tool chain

Structured

Text

Sensor

Social

DB ETL DP DA

DB ETL DP DA

DB ETL DP DA

DB ETL DP DA

Trained Staff

Tool-Chain

Data Type

DW

DW

DW

DW

Structured

DB ETL DP DATrained Staff

Tool-Chain

Data Type

DW

Analysis

Analysis

Analysis

Analysis

Analysis

Trained Staff

Tool-Chain

Data Type

Trained Staff

Tool-Chain

Data Type

Trained Staff

Tool-Chain

Data Type



11 9/13/2013 Software

• InnovativeInformationmanagement fosterscollaboration betweenIT and LOB

• Analyze all data toyou can break down

the data silos andadvance your business objectives

• Empower morepeople to discover opportunities byoffering self serviceaccess to data

The needed approach minimizes the complexity and enables analyzing all

data

Structured, Text, Sensor andSocial

DBETL

DP DADW

Analysis



12 9/13/2013 Software

The Big Data ecosystem

M o v i n g U p t h e S t a c k

Hardware InfrastructureServers, Storage and Networking

Information Management/ Ent. Search Architectures, policies, and process, for data lifecyclemanagement. (controlled, optimized, access, searched)

BI ToolsDashboards, Reporting

ETL and Data IntegrationBulk, Real-time

Advanced AnalyticsPredictive Modeling, Data Visualization tools

Database Management SystemOLTP, Relational, SQL, NoSQL, NewSQL, Columnar, Pig

Data WarehouseOLAP, Hive

Serviceconsiderations

Consulting

Implementation

Support

Maintenance

Training

Big Data Hosted

Services



13 9/13/2013 Software

Ideal solution approach

Analytics

Sophisticated NLP, machine learning, predictive modeling,

sentiment analysis, SNA, and visualization. NoHadoop/MapReduce programming expertise required

Analyze

unstructured,

structured and

semi-

structured datafrom a single

work-bench

SearchInteractive and intuitive. Search interface allows business

analyst to explore and exploit all data resources

Visualization

Interactive web-based authoring empowers business users

to perform analysis, visualize results and take decisions

Analytical

Producer

Analytical

Consumer



14 9/13/2013 Software

Capabilities

Natural languageprocessingDerive value from the vast

sources of unstructured text and

scanned content from archives

Entity extraction

Easily extract entities from rawunstructured text.

Machine learning

scalable machine learningprovides the ability to trainalgorithms over time for

predictive analysis and patternrecognition

Social network analysis

Ability to identify unknownrelationships in social networks.

Search

Leveraging industry' powerfulsearch platform with full-textsearch, hit highlighting, faceted

search and real-time indexingprovides insight into largevolumes of data.

Sentiment analysis

Decipher true meaning behindunstructured text.



Analytics in Higher Education

Vince Kellen, Ph.D.Senior Vice ProvostAcademic Planning, Analytics and Technologies

University of Kentucky

[email protected] September, 2013

This is a living document subject to substantial revision.

mailto:[email protected]

mailto:[email protected]



Analytic categories

Helping the student

• Personalization and interaction

• Learning analytics

• Recruiting and retention modeling

• Micro-surveys

Helping the university

• Building utilization and course/event planning

• Monitoring and predicting enrollment in classes

• University research and instructional output

• Tuition revenue projections

16



Models, models, models

Student enrollment

Student recruiting

Student statistics per term

Student demographics and

demographic details

Student accounting and student

financial aid

Student retention cohorts

Student micro-surveys

17

Student majors/minors

Student advising

Student K-Score

Transfer students

Faculty statistics per term

Building utilization

Human resources statistics

Key business process workflow



Poll

Rate your data integration maturity

1. We integrate data in nightly batches from some data sources

2. We integrate data in nightly batches from all significant data

sources

3. We can integrate data real time from some data sources

4. We can integrate data real time from all significant sources

18



20



21



Academic Health

Bar (Alerts)

• The key to the system as a tool for positive behavior is that each notification includes

relevant information and/or necessary actions.

• For low severity events, the notification may merely suggest resources that may help,

in which case the student can manually dismiss the notification.

• More severe notification types will have clear and specific actions the student can taketo clear the notification.

Healthy At RiskUnhealthy

The Blackboard system shows you have submitted only 1 of the last 5

assignments for MA109. The class average is 4.6 submitted assignments.

To clear this alert:

• Either

-Meet with your advisor

-Or meet with your professor

-Or attend an appointment at the Study or Mathskellar • And submit an assignment



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28



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Advising Hub: Home Screen



Poll

What analytic technologies do you employ (select all that apply)

1. We use distributed analytic approaches (e.g., Hadoop)

2. We use relational database analysis tools (e.g., from vendors like

Microsoft, Oracle, IBM, etc.)

3. We use high-speed in-memory methods (e.g., SAP HANA)

4. We use social media analysis tools

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WAKE UP! GET TO CLASS!

Who sets alarms for themselves?

Why not automatically set alarms for

students around their schedule?

Why not have automated wake-up

calls?

Why not suggest wake up times

based on class attendance?

Why not consider manipulation of

reminders as a form of engagement?

Can we ascertain student prospective

memory capability and personalize

based on it?

32



What would Abraham Lincoln think of a MOOC?

Abraham Lincoln

• Autodidactic

• Books, books, books

• Became a skilled military strategist

• Penchant for poetry, Shakespeare,

politics and history

My nephew

• Not an autodidact

• Good worker, smart kid, but…

• It takes a village• After a few low-security colleges

and much money borrowed

• He has found an intellectual home

33

http://blog.cutter.com/2012/09/08/educating-abraham-lincoln/

http://blog.cutter.com/2012/09/08/educating-abraham-lincoln/



Assumptions

The rewards of investments in teaching technology go

disproportionately to the most capable individuals

• The most capable learners tend to receive more benefit from

technological enhancements than less capable learners

Choosing college is a complex decision

• Cultural, social and economic factors affect the decision

• Wealth stratification plays a role (and it has for 2,300 years)

• It isn’t an entirely rational decision

Information technology (IT) does matter • IT can improve productivity

• IT can enhance short-term and mid-term competitiveness

• But, advantage from IT does not follow its procurement, but from its

marrying with business processes and organizational capital

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Small class, large class, books & MOOCS

Small class Large class Book MOOC

Student-teacher

interactionHigh Low Low Low

Student-student

interaction

High Low Low Low-mid

Quality of

instructor Low-high Low-high Moderate-high Moderate-high

Convenience Low Low High High

Overall

experience?? ?? ?? ??

35 http://www.nytimes.com/2013/04/21/opinion/sunday/grading-the-mooc-university.html

Conclusions about the quality of the experience need to take into account what the

learner is capable of and what they need



Déjà vu?

36

https://reader009.{domain}/reader009/html5/0421/5ada47533c8b7/5ada4769c94ee.jpg

MOOCs

Large

lecturesPHI 698

???



How technology can affect cost and quality

37

High

effectiveness

Low

effectiveness

Low volume High volume

Small F2F class

Broadcast class

Current MOOC approach

MOOC + PT

MOOC + PT + F2F

F2F = Face-to-face

PT = Personalization technology, adaptive

learning technology



Personalization technology

• Real-time personalized interactions• Target on-demand peer tutoring based on student’s profile

• Deliver micro-surveys and assessments to capture additional information

needed to improve personalization

• Give students academic health indicators that tell students where they can

improve in study, engagement, support, etc.

• Let students opt their parents in to this information so the family can supportthe student

• Tailor and target reminder services, avoid over messaging

• Allow for open adaptive learning• How content gets matched to students is psychologically complex

• Several theories of how humans learn give many insights• Students differ in the following abilities and attributes: visual-object, visual-

spatial, reasoning, cognitive reflection, need for sensation, need for cognition,

various verbal abilities, confidence, persistence, prospective memory, etc.

• We need an open architecture to promote rapid experimentation, testing and

sharing of what works and what doesn’t




Potential services

Reminder services

• Upcoming classes, assignments, tests, events

Predictive support

• Micro-segment prediction of academic difficulty, involvement, integration

• Non-cognitive factors, social network analysis in models

• Behavioral cues around registration, financial aid applications

• Digital footprint analysis (use of degree progress tools, academic ‘category’

involvement, walking across campus)

• Recommend friends, study groups, student groups, foster peer-interactions

Real-time analytic integration with LMS content consumption

• Detect lack of comprehension, difficulty, frustration

• Recommend peer tutoring, additional materials, tutoring services

• Call students in the middle of?

Parent portal

• With the student’s permission, let the parent see key learning performance indicators,

alerts, etc.

39



Taxonomy? Automatic metadata? Automatic

atomic metadata?

Let learners navigate an

audio/visual stream

Let the system learn what are top

terms. Let the system map terms

to concepts. Let instructional

designers lightly ‘bump’ the

taxonomy, post production

Record student engagement with

specific terms / concepts

Deliver personalized messages to

students 40



41



Class slides take a

central position.

The audio/video and

slide content is

synchronized.



The note pad allows

recording and

sharing of notes.



Personalizedrecommendations are a

guide thru the material.

Feedback on engagement

and mastery assist in

gauging understanding.

Resources and tutors arealso provided if a little

assistance is needed.



A lecture concept map helps to put the

lecture in a visual context.

The map is generated from analysis of

the text and ‘bumped’ into shape by a

course designer or instructor.

Concepts can be rated to collect

perception of usefulness and improve

future versions.

Jump to the media segment by clicking

on the tag.

Test knowledge with small quizzes – ace

the quizzes and you’re in good shape!



A one-stop-shop for searching.

Keywords from the video,

slides, trends, notes and

conversations will appear.



Key questions

• Can the audio and slides be reliably transcribed into ‘useful’ text?

• Can a concept map be derived automatically from the text

generated or easily edited by an instructor?

• How easy will it be for designers-instructors to create a quiz and

place it in the right location in the video?

• Can we personalize the recommendations to reflect prior

knowledge, student ability and individual differences in

information processing?

• Can the interface support real-time integration with analytic back-

ends (e.g., HANA)?

This is just one conceptualization.

What other interface designs exist today? How effective are they?



Poll

Have you integrated your mobile and analytic strategies? (check all

that apply)

1. We are well underway in deploying analysis tools to smartphones

and tablets

2. We are gathering mobile interaction data to understand studentand user behavior

3. We are exploring mobile deployment of analytic tools to

smartphones and tablets

4. We not ready to gather mobile interaction data into our analytic

environment



The debate

Constructivists/social learning versus reductionists/cognitive psychology

• ‘Mind as sacred space’ versus ‘brain as decomposed machine’

• Learning as a social-emotional interaction with humans versus learning as

cogno-neuro-sensory interaction with information

Social constructivist, subjectivist, humanist

• I as teacher am the one who personalizes things• Students construct their knowledge via indeterminate, emergent social

interactions

• I don’t see how IT has a significant role in this, except to support ease of

student interaction with each other and with content

Cognitive reductionist, positivist, technocrat

• An IT system can on the fly determine which content/people you the learner

may need next, learning follows principles of progression

• Students differ in the cognitive skills in ways IT can address

• IT can be a more complete feedback-driven learning environment for all

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Questions?

From Data to Action: Driving Innovation through Predictive Analytics (168044365)

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