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Learning Analytics

Book Description

Drawing on the latest research, this practical guide shows how to utilize organizational data to make better decisions about managing the learning and development of your workforce.

Table of Contents

  1. Cover
  2. Praise for Learning Analytics
  3. Title Page
  4. Dedication
  5. Contents
  6. Foreword
  7. Acknowledgements
  8. 01    Why now? The occasion for learning analytics
    1. Data availability
    2. Changing the way talent analytics work gets done
    3. Providing unique insight into employee behaviour
    4. The learning analytics opportunity
    5. Endnotes
  9. 02    What is learning analytics?
    1. Introduction
    2. Learning analytics today: measure for measure, what should be measured?
    3. Why measure learning?
    4. Most organizations start with the simple: measure training adoption and satisfaction
    5. Efficiency, effectiveness and business outcomes: closing the learning measurement gap
    6. The journey to learning analytics
    7. The Four Levels of Evaluation
    8. The Return on Investment training methodology
    9. Impact Measurement Framework
    10. Success Case Method
    11. Performance-based evaluation
    12. Conclusion
    13. Endnotes
  10. 03    Technology’s role in learning measurement
    1. What should technology do?
    2. Benefits and costs of learning technologies
    3. What are the requirements for any new technology system in the business intelligence space?
    4. What is the ROI of technology systems?
    5. Applying principles of business intelligence systems to learning and development
    6. Conclusion
    7. Endnotes
  11. 04    Linking learning to business impact
    1. What works?
    2. Why does it work?
    3. Experimental designs
    4. Alternatives to experimental designs
    5. Alternative designs
    6. The end of the null hypothesis – almost
    7. Conclusion
    8. Endnotes
  12. 05    Scrap learning: the new leading indicator of success
    1. Your training programmes are not as good as you think they are
    2. Running L&D like a business
    3. Reporting on scrap learning
    4. How can scrap be reduced?
    5. Scrap and manager engagement
    6. Conclusion
    7. Endnotes
  13. 06    Aligning L&D to business goals through needs assessment
    1. Measure twice, cut once
    2. How is alignment achieved?
    3. The ADDIE model: linear vs cyclical business alignment
    4. Unpacking the ‘Analyse’ stage of business alignment
    5. How can evaluation results inform the Analyse phase?
    6. What about tests?
    7. Using competency assessments to find skill gaps
    8. Conclusion
    9. Endnotes
  14. 07    Benchmarks
    1. A journey of a thousand miles begins with one step
    2. Benchmarking improves maturity
    3. Why are benchmarks valuable in the L&D space?
    4. What benchmarks are available?
    5. Benchmarks and statistical significance
    6. What does MTM bring to the market beyond benchmarks?
    7. How do clients use benchmarks to support decision making?
    8. Conclusion
    9. Endnotes
  15. 08    Optimizing investments in learning
    1. Learning and development groups struggle to create value
    2. Developing a framework
    3. Reporting measures to the business
    4. Working with business leaders
    5. Continuous improvement and management approaches
    6. Principles
    7. Less is more
    8. Assumptions
    9. Conclusion
    10. Endnotes
  16. 09    Beyond learning analytics to talent management analytics
    1. The future is for those who can predict it
    2. Defining what to measure in talent management
    3. Understanding the employee lifecycle
    4. Integrating data
    5. Research on talent analytics
    6. It’s not the analytics that matter; it’s how they are applied
    7. Managing data in the analytics process
    8. Improving analytic impact
    9. How companies are addressing the challenge of talent analytics impact
    10. Analytics across the talent lifecycle
    11. Conclusion
    12. Endnotes
  17. Index
  18. Copyright