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Correction to: Big data management skills: accurate measurement.

Research and practice in technology enhanced learning·2019
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Big data management skills: accurate measurement.

Elspeth McKay1, Marlina Binti Mohamad2

  • 11RMIT University, School of Business Information Technology and Logistics, GPO Box 2476, Melbourne, Victoria 3000 Australia.

Research and Practice in Technology Enhanced Learning
|January 1, 2019
PubMed
Summary

Assessing digital skills for big data is crucial. This study introduces a unidimensional scale to effectively measure digital skill acquisition and individual differences in development.

Keywords:
Big data management skillsCognitive performance measurementDigital skill developmentHuman-computer interactionInstructional designItem response theoryRasch model

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Area of Science:

  • Computer Science
  • Data Science
  • Educational Measurement

Background:

  • Big data is transforming business, health, and governance, necessitating new approaches to data management and analysis.
  • The increasing volume of archived data raises global issues regarding retrieval, application, and ethical considerations.
  • A critical challenge is ensuring professionals possess adequate digital skills to manage and interpret big data effectively.

Purpose of the Study:

  • To present an effective and efficient method for identifying digital skill acquisition in individuals working with big data.
  • To demonstrate the evolution from traditional statistical measures to a more comprehensive unidimensional scale for assessing proficiency.
  • To provide a tool for understanding individual differences in digital skill development.

Main Methods:

  • Transitioned from traditional statistical measures for programmer proficiency to a unidimensional scale.
  • Employed a methodology that allows for the comprehension of human performance and test item performance relative to each other.
  • Developed a comprehensive scale for measuring digital skill acquisition.

Main Results:

  • The proposed unidimensional scale offers a more comprehensive approach to measuring digital skills compared to traditional methods.
  • The methodology effectively assesses human performance and test item performance in the context of big data skills.
  • Demonstrated a clear progression in measuring digital skill proficiency.

Conclusions:

  • The developed methodology provides an effective tool for assessing digital skill acquisition in the big data field.
  • This approach aids in understanding individual differences in digital skill development.
  • Highlights the importance of robust assessment tools for the evolving demands of big data professionals.