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Smarter Business School enabled by Cloud and Big Data

Gilles Pellerin – Daniel Evans– Florent Schilling Enterprise Architect, Chief Innovation Officer, LMS Project Manager

IBM Paris, EMLYON Lyon – FRANCE gilles.pellerin@fr.ibm.com, evans@eml-executive.com, schilling@em-lyon.com

ICACON 2015 - BUDAPEST

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I. INTRODUCTION

II. SMARTER BUSINESS SCHOOL : A NEW MODEL FOR EDUCATION

III. TOWARDS A NEW LEARNING MODEL

IV. FROM TOOLS TO CAPABILITIES

V. NOW AND TOMORROW

VI. CONCLUSION & Q&A

Smarter Business School enabled by Cloud and Big Data AGENDA

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EMLYON Business school http://www.em-lyon.com/fr

IBM GLOBAL SERVICES – IBM GLOBAL TECHNOLOGY SERVICES – IBM RESEARCH

Daniel S. Evans, Ph.D.

Chief Innovation Officer Leader, Team InDiGo!

Introduction Smarter Business School : One team One Goal

Florent SCHILLING LMS Expert

Project Leader

Gilles PELLERIN Industry Solution Architect leader

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Introduction EMLYON OBJECTIVES

X 3,5 of students in 5 years, double in France

3 500 students on pop up campus for 2022

3 international territories : Africa/Marocco - 2016,

Asia-China - 2017, India/Africa - 2018

+72% Turnover in 5 years

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“The illiterate of the 21st century will not be

those who cannot read and write, but those

who cannot learn, unlearn,and relearn” Alvin

Toffler

Smarter Business School : a new Model for Education Large scale and personalization

(1) Dernière frontière avant le monde –Bernard Belletante 2014 Ed : L’instant qui suit

One GOAL improve learning value chain by moving from « knowledge – assimilation – diploma » to « competencies – action – collective sense » (1)

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Financial viability of education models The new student profile and lifelong

learning New entrants (corporate, financial,…) Student mobility Open Data Open Research Open Education Knowledge in networks Degrees vs. Reputation metrics

Towards A New Learning Model Critical changes in the Education Space

Class of 1 to 100,000 Seizing opportunities rather then

defend old behaviours Opportunities to easily link research

and education through data Access to vast wealth of knowledge

(once a barrier to entry) and learning resources

Learning on the fly and emergence of self-directed learners

Knowledge transfer to knowledge curation

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Data and application

protection

Proprietary systems

Use restrictions

Rewarded for research

Adaptive

Globally distributed

applications and data

SaaS

Rewarded for collaborating

Defensive/Protectionist

Fear of change

Passive learning

Teaching is learning

Sharing content (CC)

Learning responsibility

Teaching to Coaching

Collaboration

Inte

rde

pe

nd

en

ce

– H

um

an

Pe

rfo

rma

nce

(G

ilbe

rt)

Towards A New Learning Model Some changes required to drive a new value creation system

Competence Capabilities Motivation

Information Resources/Systems Rewards

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Changing behaviors : generic tactics and framing Towards A New Learning Model

TAM – Usefulness and Ease of Use

Urgency but stepwise change over time

Capabilities development to maintain

employability (future prof) -- Motivation

Burn bridges to avoid going back to old

behaviours – rules/reward mech.

Rely on critical supporters – not on militants

Balanced communication (+/-) (Hovland)

Influence through consultation (Yukl)

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Focus on ease of adoption / use through peer

based learning and workshops

Critical policy changes – teaching to learning

hours, reward mechanisms

Comm: Focus on quality of pedagogy and not

technology

Limited budgets to encourage innovation

Big Bang rollout but with features released

over time

Learning Lab to accompany

Required decrease in teaching hours

Numerous scoping sessions

Changing behaviors : what has worked ? Towards A New Learning Model

Sociodynamic mapping of allies to identify key

allies (critical) to create a general movement of

faculty – use allies to convince opponents (CLIP)

Education (12-20 hours per employee) (WIIFM)

Accompaniment in R3 Review, Refresh, Revise

(CarpeDiem processes) and Indigo!

Creation of new faculty classifications

MOOC trials as (quick wins)

Resources to facilitate sharing of learning

resources (financial, human, technical)

Availability of micro-resources to focus on

learning design (rare!)

Required use of LMS for all courses

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Value creation in this Learning Innovation Ecosystems needs to rely on continuous technology

evolutions

Innovation and co-creation has to be leveraged by openness of the ecosystem

Evolution of value creation models requires technology evolution to sustain new opportunities

Capabilities granularity should be aligned / compatible with the magnitude of innovation

challenges that can be characterized by the extent to which they require changes to the current

approach to problem solving.

Testing and assessing quickly innovations has to be facilitated by platforms like BlueMix. This is a

enabler for breaking the negative loop

“I don’t do that because the system can’t do it – The system doesn’t do that

because nobody is asking for it”

From Tools To Capabilities Interdependance of Human and Technological Capabilities

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The solution is relying on a multiple SaaS integration. Each Cloud solution offers a very wide range

of capabilities available through Web UI or mobile applications but also many APIs to support the

execution of existing of future use cases.

This offers and also allow to imagine significant levels of personalization and composition.

Long term partnership with IBM Research - Watson for Education - is covering aspects like :

SMART INSTRUCTIONAL CONTENT : The ability to deeply analyze the content materials not just for key words, but

for readability, learning style matching, complexity, interactivity and standards compliance

LONGITUDINAL STUDENT DATA : This means that while we must analyze data on attendance, behavior, course

enrollment, academic performance, demographic indicators and so on to derive student similarity measures, we should also capture

and analyze activity data involving a student such as content interaction patterns, social and work-related interactions with other

learners and educators etc., because such “data-in-motion” often provide rich insights into learning/cognitive styles and preferences

HUMAN ENGAGEMENT : We need to engage both the teacher and the student in the use of data, prediction and

intervention in order to reach the outcomes we desire. Technology that sits on a shelf or distracts from the core task of teaching and

learning will not be used nor will it be helpful. This implies that the system must provide actionable insights – insights with enough

specificity to enable action in a time frame that can affect the desired outcome, and insight that fits into the normal workflow of a

teacher, an administrator, or a student.

From Tools To Capabilities Sustainability of the solution

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From Tools To Capabilities Solution overview

API oriented : each Cloud

components is offering openess and

flexibility through APIs

BLUEMIX development platform

enables creation of new apps and

services using Watson services for

example

Multiple entry points solution with

role base services access enabling

personalization High Level Architecture Overview (simplified)

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From Tools To Capabilities Cloud provides flexibility and speed of implementation

- Pop up campus can be stup quickly

- DRP is supported by another SoftLayer DC

- Softlayer Datacenter distribution enables

redundancy and localisation and workload

management

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Now And Tomorrow On going subjects and forecast

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Conclusion & Q&A