LEARNING ANALYTICS : using talent data to improve business outcomes.

✍ Scribed by CRISTINA HALL

• Publisher: KOGAN PAGE
• Year: 2020
• Tongue: English
• Leaves: 401
• Edition: 2
• Category: Library

✦ Table of Contents

Cover
Contents
List of figures and tables
About the authors
Foreword
How to read this book
Acknowledgements
PART ONE The L&D value gap and how to close it
01 The rise of learning analytics
Why is all of this important?
Standards are coming
Data availability
Changing the way talent analytics work gets done
Providing unique insight into employee behaviour
Learning analytics rises
Notes
02 What is learning analytics?
Introduction
Learning analytics today: measure for measure, what should be measured?
Why measure learning?
Most organizations start with the simple: measure training activity and satisfaction
Efficiency, effectiveness and business outcomes: closing the learning measurement gap
The journey to learning analytics
The Four Levels of Evaluation
The Return on Investment Methodology
Impact Measurement Framework
Success Case Method
Performance-Based Evaluation
Conclusion
Notes
03 The value-centred learning organization: A new evaluation paradigm
Volume is not value
We’re already delivering value, though… right?
Delivering and demonstrating value: the Talent Development Value Framework
The Talent Development Value Framework in action
Advancing measurement maturity
Conclusion
Notes
PART TWO Establishing sound measurement practices
04 Aligning L&D’s value with the C-suite: The Four Value Drivers and Portfolio Evaluation
What the C-suite wants from L&D
Connecting L&D with the business strategy
The Four Value Drivers
Building business alignment
Translating value drivers to action: Portfolio Evaluation for L&D
Immediate benefits of portfolio alignment
Additional benefits: portfolio management
Change the conversation
Conclusion
Notes
05 Linking learning to business impact
What works?
Why does it work?
Experimental designs
Alternatives to experimental designs
Alternative designs: practical ways forward
Conclusion
Notes
06 The new leading indicators of success and how to manage them
Your training programmes may not be as good as you think they are
Scrap learning and how to reduce it
Performance improvement
Net promoter score
Manager support and how to improve it
Predictive Learning Impact Model 2.0: Causal Modelling
Conclusion
Notes
07 Developing a sustainable reporting strategy
The role of reporting in learning analytics
Getting started: design principles
Components of an effective reporting strategy
Reporting strategy development
Critical success factors
Perform gap assessment
Implementing the strategy
Special cases: dashboards and scorecards
Monitor the strategy: success indicators
Conclusion
Notes
08 Technology’s role in learning measurement
What should technology do?
Benefits and costs of learning technologies
The requirements for a new technology system in the BI space
The challenge of self-reported data
What is the ROI of technology systems?
Applying principles of business intelligence systems to L&D
Additional technologies
Conclusion
Notes
09 Benchmarks
Comparison to standards provides insights for decision-making
Benchmarking improves maturity
Why are benchmarks valuable in the L&D space?
What benchmarks are available?
Benchmarks and statistical significance
What does MTM bring to the market beyond benchmarks?
How do clients use benchmarks to support decision-making?
Conclusion
Notes
PART THREE Refine the strategy: Evolution, ongoing transformation and innovation
10 Driving alignment from strategy through execution
Measure twice, cut once
The ADDIE Model: sustaining alignment using a cyclical approach
Closing the loop
Conclusion
Notes
11 Optimizing investments in learning
Learning and development groups struggle to create value
Developing a framework
Reporting measures to the business
Working with business leaders
Continuous improvement and management approaches
Principles
Less is more
Assumptions
Moving from reporting to action
Conclusion
Notes
12 Measuring informal learning outcomes
Introduction
What is informal learning? What is social learning?
The new learning landscape
Learning from the past: e-learning lessons
Organizational ecosystem for informal learning
Traps, potholes and pitfalls of informal learning
Showing value through measurement
What should we measure to show value?
Use cases
Conclusion
Notes
13 Beyond learning analytics to talent management analytics
The future is for those who can predict it
Defining what to measure in talent management
Understanding the employee life cycle
Integrating data
Research on talent analytics
It’s not the analytics that matter: it’s how they are applied
Managing data in the analytics process
Improving analytic impact
How companies are addressing the challenge of talent analytics impact
Analytics across the talent life cycle
Conclusion
Notes
Index