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E-raamat: Learning Analytics: Using Talent Data to Improve Business Outcomes

  • Formaat: 400 pages
  • Ilmumisaeg: 03-Apr-2020
  • Kirjastus: Kogan Page Ltd
  • Keel: eng
  • ISBN-13: 9781789663013
  • Formaat - PDF+DRM
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  • Formaat: 400 pages
  • Ilmumisaeg: 03-Apr-2020
  • Kirjastus: Kogan Page Ltd
  • Keel: eng
  • ISBN-13: 9781789663013

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Effective evaluation and measurement of learning and development initiatives is critical to maximise the impact of training, identify gaps for improvement and ensure that efforts are aligned to the business' needs. Learning Analytics outlines how analytical approaches can respond to these challenges, the types and benefits of technological solutions and how to ask the right questions of organizational data in order to build a learning organization that boosts performance and competitive advantage.
Drawing upon case studies from organizations who have applied such approaches such as The Gap, Hilton Worldwide University and Seagate Technology, Learning Analytics will enable those involved in learning and development to make the business case for their activities and deliver an evidence-based service to their organizations. Alongside updated chapters on learning technology tools and moving beyond learning analytics to talent management analytics, this second edition also features new content on measuring informal learning, increasing data literacy, and framing L&D's contributions through a portfolio evaluation approach.



Utilize organizational data and analytics to make better decisions about managing the learning and development of your workforce.

Arvustused

"Learning Analytics reaffirms that evaluation principles when standardized and enabled by technology can provide insights about which programs are helping achieve business goals and which need improvement. This edition also brings a new methodology to the table that helps L&D organizations align and deeply connect with the business strategy." -- Kimo Kippen * Founder Aloha Learning Advisors and former Chief Learning Officer at Hilton Worldwide * "This book is a fantastic roadmap, guidebook and treasure trove of research, enabling L&D leaders and practitioners alike to solve that most obvious yet difficult of challenges, clearly linking L&D programs to all areas of talent management in a cohesive, strategic way to positively impact performance and productivity." -- Jeff Higgins * Founder and CEO, HCMI * "If you need to evaluate your investments in talent, or use HR analytics to inform your business decisions, then this book is a godsend." -- Doug Gray, Ph.D. * CEO and Founder of Action Learning Associates and former President of ATD Nashville *

List of figures and tables
xii
About the authors xviii
Foreword xix
How to read this hook xxv
Acknowledgements xxvii
PART ONE The L&D value gap and how to close it
01 The rise of learning analytics
3(18)
Why is all of this important?
6(1)
Standards are coming
7(1)
Data availability
8(1)
Changing the way talent analytics work gets done
9(3)
Providing unique insight into employee behaviour
12(1)
Learning analytics rises
13(6)
Notes
19(2)
02 What is learning analytics?
21(28)
Introduction
21(2)
Learning analytics today: measure for measure, what should be measured?
23(2)
Why measure learning?
25(1)
Most organizations start with the simple: measure training activity and satisfaction
26(2)
Efficiency, effectiveness and business outcomes: closing the learning measurement gap
28(1)
The journey to learning analytics
29(1)
The Four Levels of Evaluation
30(1)
The Return on Investment Methodology
31(2)
Impact Measurement Framework
33(1)
Success Case Method
34(2)
Performance-Based Evaluation
36(8)
Conclusion
44(2)
Notes
46(3)
03 The value-centred learning organization: A new evaluation paradigm
49(22)
Volume is not value
49(1)
We're already delivering value, though right?
50(2)
Delivering and demonstrating value: the Talent Development Value Framework
52(9)
The Talent Development Value Framework in action
61(2)
Advancing measurement maturity
63(2)
Conclusion
65(1)
Notes
66(5)
PART TWO Establishing sound measurement practices
04 Aligning L&D's value with the C-suite: The Four Value Drivers and Portfolio Evaluation
71(22)
What the C-suite wants from L&D
71(2)
Connecting L&D with the business strategy
73(1)
The Four Value Drivers
74(2)
Building business alignment
76(2)
Translating value drivers to action: Portfolio Evaluation for L&D
78(5)
Immediate benefits of portfolio alignment
83(2)
Additional benefits: portfolio management
85(5)
Change the conversation
90(1)
Conclusion
91(1)
Notes
91(2)
05 Linking learning to business impact
93(25)
What works?
93(2)
Why does it work?
95(4)
Experimental designs
99(4)
Alternatives to experimental designs
103(1)
Alternative designs: practical ways forward
103(11)
Conclusion
114(2)
Notes
116(2)
06 The new leading indicators of success and how to manage them
118(38)
Your training programmes may not be as good as you think they are
118(3)
Scrap learning and how to reduce it
121(5)
Performance improvement
126(3)
Net promoter score
129(2)
Manager support and how to improve it
131(9)
Predictive Learning Impact Model 2.0: Causal Modelling
140(14)
Conclusion
154(1)
Notes
155(1)
07 Developing a sustainable reporting strategy
156(36)
The role of reporting in learning analytics
156(3)
Getting started: design principles
159(2)
Components of an effective reporting strategy
161(9)
Reporting strategy development
170(2)
Critical success factors
172(3)
Perform gap assessment
175(6)
Implementing the strategy
181(2)
Special cases: dashboards and scorecards
183(5)
Monitor the strategy: success indicators
188(1)
Conclusion
189(1)
Notes
190(2)
08 Technology's role in learning measurement
192(29)
What should technology do?
193(1)
Benefits and costs of learning technologies
194(6)
The requirements for a new technology system in the BI space
200(6)
The challenge of self-reported data
206(3)
What is the ROI of technology systems?
209(1)
Applying principles of business intelligence systems to L&D
210(5)
Additional technologies
215(3)
Conclusion
218(1)
Notes
219(2)
09 Benchmarks
221(22)
Comparison to standards provides insights for decision-making
221(1)
Benchmarking improves maturity
222(2)
Why are benchmarks valuable in the L&D space?
224(1)
What benchmarks are available?
225(3)
Benchmarks and statistical significance
228(6)
What does MTM bring to the market beyond benchmarks?
234(1)
How do clients use benchmarks to support decision-making?
235(2)
Conclusion
237(1)
Notes
237(6)
PART THREE Refine the strategy: Evolution, ongoing transformation and innovation
10 Driving alignment from strategy through execution
243(28)
Measure twice, cut once
243(8)
The ADDIE Model: sustaining alignment using a cyclical approach
251(15)
Closing the loop
266(2)
Conclusion
268(1)
Notes
269(2)
11 Optimizing investments in learning
271(26)
Learning and development groups struggle to create value
271(2)
Developing a framework
273(2)
Reporting measures to the business
275(10)
Working with business leaders
285(1)
Continuous improvement and management approaches
285(3)
Principles
288(1)
Less is more
289(2)
Assumptions
291(1)
Moving from reporting to action
291(4)
Conclusion
295(1)
Notes
295(2)
12 Measuring informal learning outcomes
297(27)
Introduction
297(2)
What is informal learning? What is social learning?
299(1)
The new learning landscape
300(4)
Learning from the past: e-learning lessons
304(2)
Organizational ecosystem for informal learning
306(2)
Traps, potholes and pitfalls of informal learning
308(3)
Showing value through measurement
311(1)
What should we measure to show value?
312(5)
Use cases
317(3)
Conclusion
320(1)
Notes
321(3)
13 Beyond learning analytics to talent management analytics
324(33)
The future is for those who can predict it
324(1)
Defining what to measure in talent management
325(3)
Understanding the employee life cycle
328(3)
Integrating data
331(1)
Research on talent analytics
331(6)
It's not the analytics that matter: it's how they are applied
337(2)
Managing data in the analytics process
339(2)
Improving analytic impact
341(3)
How companies are addressing the challenge of talent analytics impact
344(6)
Analytics across the talent life cycle
350(7)
Conclusion 357(1)
Notes 358(2)
Index 360
John R. Mattox II is the Head of Talent Research at Explorance. He helps clients develop measurement strategies to improve business performance by measuring the impact of training. Prior to this, Mattox was a Principal Consultant with Gartner and led training evaluation teams at KPMG, PwC and Arthur Andersen.

Peggy Parskey is a part-time managing consultant at Explorance. Based in Connecticut, she also runs her own consulting firm, Parskey Consulting, which provides organizational improvement initiatives to Fortune 500 firms.

Cristina Hall is Vice President of Strategy at Explorance and is based in Chicago, Illinois. Before this, she worked at Gartner for over 10 years, where she was most recently director of product strategy.