Titlebook: Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessme; With Examples in R a Alina A. von Dav

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Computational Psychometrics: A Framework for Estimating Learners’ Knowledge, Skills and Abilities frional psychometrics as a framework for the measurement of learners’ skills, knowledge, and abilities. We discuss the changes in educational measurement that led to the need for expanding the psychometrics toolbox and describe the properties of psychometric data. We then give an example of a class of

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Virtual Performance-Based Assessmentsin order to provide evidence about their knowledge, skills, or other attributes. Examples include tasks based on interactive simulations, games, branching scenarios, and collaboration among students communicating through digital chats. They may be used for summative purposes, as in certification exa

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Knowledge Inference Models Used in Adaptive Learninge recent approaches to student modeling that infer student knowledge (i.e. what students know at any given moment during the learning experience) are discussed, as student knowledge is the most common learner characteristic widely assessed in large-scale adaptive systems. This chapter concludes with

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Concepts and Models from Psychometricsrediction and selection. Ideas emerged over that nevertheless hold value for the new psychological perspectives, contexts of use, and forms of data and analytic tools we are now seeing. In this chapter we review some fundamental models and ideas from psychometrics that are be profitably reconceived,

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Supervised Machine Learningapter, we focus on an important branch of machine learning, supervised machine learning, and introduce three widely used supervised learning methods, the Support Vector Machine, Random forest, and Gradient Boosting Machine. Python codes examples are included to show how to use these methods in pract

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Unsupervised Machine Learningth a brief background on machine learning and then a high-level discussion on the differences between supervised and unsupervised learning algorithms. We present three categories of unsupervised machine learning techniques that include clustering, outlier detection, and dimension reduction; five pre

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Advances in AI and Machine Learning for Education Researchs problem solving, communication and collaboration. In these real-world applications student data is captured with a high degree of granularity, variety of temporal scales and in a multitude of modalities. Unfortunately such complex, noisy and unstructured data limit the applicability of traditional

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