Abstract
In this chapter we articulate what is computational psychometrics, why we need a volume focused on it, and how this book contributes to the expansion of psychometric toolbox to include methodologies from machine learning and data science in order to address the complexities of big data collected from virtual learning and assessment systems. We also discuss here the structure of the edited volume, how each chapter contributes to enhancing the psychometrics science and our recommendations for further readings.
The R or Python codes can be found at the GitHub repository of this book: https://github.com/jgbrainstorm/computational_psychometrics
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von Davier, A.A., Mislevy, R.J., Hao, J. (2021). Introduction to Computational Psychometrics: Towards a Principled Integration of Data Science and Machine Learning Techniques into Psychometrics. In: von Davier, A.A., Mislevy, R.J., Hao, J. (eds) Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessment. Methodology of Educational Measurement and Assessment. Springer, Cham. https://doi.org/10.1007/978-3-030-74394-9_1
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