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Learning analytics : measurement innovations to support employee development / John R. Mattox II and Mark Van Buren ; with insights from Jean Martin.

By: Mattox, John R., II, 1971- [author.].
Contributor(s): Buren, Mark van [author.].
Material type: materialTypeLabelBookPublisher: London ; Philadelphia, PA : Kogan Page, 2016Copyright date: ©2016Description: xvii, 237 pages : illustrations ; 23 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9780749476304 (paperback).Subject(s): Employees -- Training of | Organizational learning | Personnel managementDDC classification: 658.312404
Contents:
Why now? The occasion for learning analytics? -- Data availability -- Changing the way talent analytics work gets done -- Providing unique insight into employee behaviour -- The learning analytics opportunity -- What is learning analytics? -- What is learning analytics? -- Learning analytics today: measure for measure, what should be measured? -- Why measure learning? -- Most organizations start with the simple: measure training adoption 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 training methodology -- Impact Measurement Framework -- Success Case Method -- Performance-based evaluation -- Technology's role in learning measurement -- What should technology do? -- Benefits and costs of learning technologies -- What are the requirements for any new technology system in the business intelligence space? -- What is the ROI of technology systems? -- Applying principles of business intelligence systems to learning and development -- Linking learning to business impact -- What works? -- Why does it work? -- Experimental designs -- Alternatives to experimental designs -- Alternative designs -- The end of the null hypothesis - almost -- Scrap learning: the new leading indicator of success -- Your training programmes are not as good as you think they are -- Running L&D like a business -- Reporting on scrap learning -- How can scrap be reduced? -- Scrap and manager engagement -- Aligning L&D to business goals through needs assessment -- Measure twice, cut once -- How is alignment achieved? -- The ADDIE model: linear vs. cyclical business alignment -- Unpacking the Analyse stage of business alignment -- How can evaluation results inform the Analyse phase? -- What about tests? -- Needs assessment in action -- Using competency assessments to find skill gaps -- Benchmarks -- A journey of a thousand miles begins with one step -- 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? -- 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 -- 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 lifecycle -- 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 lifecycle.
Summary: The potential to improve education due to the large amounts of data on learning and learners is unprecedented and has created an information gap in understanding what to do with all the raw data. Providing a framework for understanding how to work with learning analytics, authors John R. Mattox II and Jean Martin show L&D and HR practitioners the power that effective analytics has on building an organization and the impact this power has on performance, talent management, and competitive advantage. Martin and Mattox focus on aligning training with business needs and answering the questions Is training effective? and How can it improved or made more effective? Beginning with an explanation of what learning analytics is and the business need for it, they move on to applying business intelligence principles, linking learning to impact, connecting training content with business needs, optimizing investments in learning, and placing learning development within the larger scope of talent management. Chapters include case studies from Hilton Hotels, Shell Oil, and American Express to highlight best practice and to provide examples of how companies apply various methodologies across a range of industries-- Provided by publisher.Summary: Faced with organizations that are more dispersed, a workforce that is more diverse and the pressure to reduce costs, CEOs and CFOs are increasingly asking what the return on investment is from training and development programmes. Learning Analytics provides a framework for understanding how to work with learning analytics at an advanced level. It focuses on the questions that training evaluation is intended to answer: is training effective and how can it be improved? It discusses the field of learning analytics, outlining how and why analytics can be useful, and takes the reader through examples of approaches to answering these questions and looks at the valuable role that technology has to play. Even where technological solutions are employed, the HR or learning and development practitioner needs to understand what questions they should be asking of their data to ensure alignment between training and business needs. Learning Analytics enables both senior L&D and HR professionals as well as CEOs and CFOs to see the transformational power that effective analytics has for building a learning organization, and the impacts that this has on performance, talent management, and competitive advantage. It helps learning and development professionals to make the business case for their activities, demonstrating what is truly adding value and where budgets should be spent, and to deliver a credible service to their business by providing metrics based on which sound business decisions can be made-- Provided by publisher.
Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
General Lending MTU Bishopstown Library Lending 658.312404 (Browse shelf(Opens below)) 1 Available 00161867
General Lending MTU Bishopstown Library Lending 658.312404 (Browse shelf(Opens below)) 1 Available 00161870
Total holds: 0

Enhanced descriptions from Syndetics:

Faced with organizations that are more dispersed, a workforce that is more diverse and the pressure to reduce costs, CEOs and CFOs are increasingly asking what the return on investment is from training and development programmes. Learning Analytics provides a framework for understanding how to work with learning analytics at an advanced level. It focuses on the questions that training evaluation is intended to answer: is training effective and how can it be improved? It discusses the field of learning analytics, outlining how and why analytics can be useful, and takes the reader through examples of approaches to answering these questions and looks at the valuable role that technology has to play. Even where technological solutions are employed, the HR or learning and development practitioner needs to understand what questions they should be asking of their data to ensure alignment between training and business needs.Learning Analytics enables both senior L&D and HR professionals as well as CEOs and CFOs to see the transformational power that effective analytics has for building a learning organization, and the impacts that this has on performance, talent management, and competitive advantage. It helps learning and development professionals to make the business case for their activities, demonstrating what is truly adding value and where budgets should be spent, and to deliver a credible service to their business by providing metrics based on which sound business decisions can be made.

Includes bibliographical references and index.

Why now? The occasion for learning analytics? -- Data availability -- Changing the way talent analytics work gets done -- Providing unique insight into employee behaviour -- The learning analytics opportunity -- What is learning analytics? -- What is learning analytics? -- Learning analytics today: measure for measure, what should be measured? -- Why measure learning? -- Most organizations start with the simple: measure training adoption 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 training methodology -- Impact Measurement Framework -- Success Case Method -- Performance-based evaluation -- Technology's role in learning measurement -- What should technology do? -- Benefits and costs of learning technologies -- What are the requirements for any new technology system in the business intelligence space? -- What is the ROI of technology systems? -- Applying principles of business intelligence systems to learning and development -- Linking learning to business impact -- What works? -- Why does it work? -- Experimental designs -- Alternatives to experimental designs -- Alternative designs -- The end of the null hypothesis - almost -- Scrap learning: the new leading indicator of success -- Your training programmes are not as good as you think they are -- Running L&D like a business -- Reporting on scrap learning -- How can scrap be reduced? -- Scrap and manager engagement -- Aligning L&D to business goals through needs assessment -- Measure twice, cut once -- How is alignment achieved? -- The ADDIE model: linear vs. cyclical business alignment -- Unpacking the Analyse stage of business alignment -- How can evaluation results inform the Analyse phase? -- What about tests? -- Needs assessment in action -- Using competency assessments to find skill gaps -- Benchmarks -- A journey of a thousand miles begins with one step -- 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? -- 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 -- 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 lifecycle -- 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 lifecycle.

The potential to improve education due to the large amounts of data on learning and learners is unprecedented and has created an information gap in understanding what to do with all the raw data. Providing a framework for understanding how to work with learning analytics, authors John R. Mattox II and Jean Martin show L&D and HR practitioners the power that effective analytics has on building an organization and the impact this power has on performance, talent management, and competitive advantage. Martin and Mattox focus on aligning training with business needs and answering the questions Is training effective? and How can it improved or made more effective? Beginning with an explanation of what learning analytics is and the business need for it, they move on to applying business intelligence principles, linking learning to impact, connecting training content with business needs, optimizing investments in learning, and placing learning development within the larger scope of talent management. Chapters include case studies from Hilton Hotels, Shell Oil, and American Express to highlight best practice and to provide examples of how companies apply various methodologies across a range of industries-- Provided by publisher.

Faced with organizations that are more dispersed, a workforce that is more diverse and the pressure to reduce costs, CEOs and CFOs are increasingly asking what the return on investment is from training and development programmes. Learning Analytics provides a framework for understanding how to work with learning analytics at an advanced level. It focuses on the questions that training evaluation is intended to answer: is training effective and how can it be improved? It discusses the field of learning analytics, outlining how and why analytics can be useful, and takes the reader through examples of approaches to answering these questions and looks at the valuable role that technology has to play. Even where technological solutions are employed, the HR or learning and development practitioner needs to understand what questions they should be asking of their data to ensure alignment between training and business needs. Learning Analytics enables both senior L&D and HR professionals as well as CEOs and CFOs to see the transformational power that effective analytics has for building a learning organization, and the impacts that this has on performance, talent management, and competitive advantage. It helps learning and development professionals to make the business case for their activities, demonstrating what is truly adding value and where budgets should be spent, and to deliver a credible service to their business by providing metrics based on which sound business decisions can be made-- Provided by publisher.

CIT Module MGMT 7077 - Core reading.

Table of contents provided by Syndetics

  • Foreword (p. xi)
  • Acknowledgements (p. xviii)
  • 01 Why now? The occasion for learning analytics (p. 1)
  • Data availability (p. 1)
  • Changing the way talent analytics work gets done (p. 2)
  • Providing unique insight into employee behaviour (p. 5)
  • The learning analytics opportunity (p. 7)
  • Endnotes (p. 11)
  • 02 What is learning analytics? (p. 13)
  • Introduction (p. 13)
  • Learning analytics today: measure for measure, what should be measured? (p. 15)
  • Why measure learning? (p. 16)
  • Most organizations start with the simple: measure training adoption and satisfaction (p. 18)
  • Efficiency, effectiveness and business outcomes: closing the learning measurement gap (p. 20)
  • The journey to learning analytics (p. 21)
  • The Four Levels of Evaluation (p. 23)
  • The Return on Investment training methodology (p. 25)
  • Impact Measurement Framework (p. 26)
  • Success Case Method (p. 27)
  • Performance-based evaluation (p. 29)
  • Conclusion (p. 39)
  • Endnotes (p. 42)
  • 03 Technology's role in learning measurement (p. 45)
  • What should technology do? (p. 46)
  • Benefits and costs of learning technologies (p. 48)
  • What are the requirements for any new technology system in the business intelligence space? (p. 54)
  • What is the ROI of technology systems? (p. 61)
  • Applying principles of business intelligence systems to learning and development (p. 62)
  • Conclusion (p. 69)
  • Endnotes (p. 69)
  • 04 Linking learning to business impact (p. 71)
  • What works? (p. 71)
  • Why does it work? (p. 74)
  • Experimental designs (p. 78)
  • Alternatives to experimental designs (p. 81)
  • Alternative designs (p. 82)
  • The end of the null hypothesis - almost (p. 89)
  • Conclusion (p. 96)
  • Endnotes (p. 97)
  • 05 Scrap learning: the new leading indicator of success (p. 99)
  • Your training programmes are not as good as you think they are (p. 99)
  • Running L&D like a business (p. 107)
  • Reporting on scrap learning (p. 108)
  • How can scrap be reduced? (p. 109)
  • Scrap and manager engagement (p. 112)
  • Conclusion (p. 121)
  • Endnotes (p. 122)
  • 06 Aligning L&D to business goals through needs assessment (p. 123)
  • Measure twice, cut once (p. 123)
  • How is alignment achieved? (p. 124)
  • The ADDIE model: linear vs cyclical business alignment (p. 127)
  • Unpacking the 'Analyse' stage of business alignment (p. 130)
  • How can evaluation results inform the Analyse phase? (p. 133)
  • What about tests? (p. 138)
  • Using competency assessments to find skill gaps (p. 142)
  • Conclusion (p. 147)
  • Endnotes (p. 148)
  • 07 Benchmarks (p. 151)
  • A journey of a thousand miles begins with one step (p. 151)
  • Benchmarking improves maturity (p. 153)
  • Why are benchmarks valuable in the L&D space? (p. 155)
  • What benchmarks are available? (p. 156)
  • Benchmarks and statistical significance (p. 160)
  • What does MTM bring to the market beyond benchmarks? (p. 167)
  • How do clients use benchmarks to support decision making? (p. 168)
  • Conclusion (p. 169)
  • Endnotes (p. 169)
  • 08 Optimizing investments in learning (p. 171)
  • Learning and development groups struggle to create value (p. 171)
  • Developing a framework (p. 173)
  • Reporting measures to the business (p. 176)
  • Working with business leaders (p. 183)
  • Continuous improvement and management approaches (p. 183)
  • Principles (p. 187)
  • Less is more (p. 188)
  • Assumptions (p. 189)
  • Conclusion (p. 194)
  • Endnotes (p. 194)
  • 09 Beyond learning analytics to talent management analytics (p. 197)
  • The future is for those who can predict it (p. 197)
  • Defining what to measure in talent management (p. 198)
  • Understanding the employee lifecycle (p. 202)
  • Integrating data (p. 205)
  • Research on talent analytics (p. 205)
  • It's not the analytics that matter; it's how they are applied (p. 212)
  • Managing data in the analytics process (p. 214)
  • Improving analytic impact (p. 217)
  • How companies are addressing the challenge of talent analytics impact (p. 219)
  • Analytics across the talent lifecycle (p. 226)
  • Conclusion (p. 228)
  • Endnotes (p. 228)
  • Index (p. 231)

Author notes provided by Syndetics

John R. Mattox II is a senior measurement consultant with CEB, a best practice insight and technology company. Prior to this, John was director of research at KnowledgeAdvisors and led training evaluation teams at several Fortune 500 companies. Mark Van Buren is an HR Practice Leader at CEB. He has worked with hundreds of organizations to respond to a clear shift in employee preferences for learning and an increase in employee-driven, technology-enabled development. Jean Martin is CEB's talent solutions architect. She leads insight and product development in talent management and regularly presents to executive teams at top Fortune 500 companies. Her commentary has appeared in publications such as Harvard Business Review, The Economist, and Forbes.

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