Description

Book Synopsis

The abundance of data and the rise of new quantitative and statistical techniques have created a promising area: data analytics. This combination of a culture of data-driven decision making and techniques to include domain knowledge allows organizations to exploit big data analytics in their evaluation and decision processes. Also, in education and learning, big data analytics is being used to enhance the learning process, to evaluate efficiency, to improve feedback, and to enrich the learning experience.

As every step a student takes in the online world can be traced, analyzed, and used, there are plenty of opportunities to improve the learning process of students. First, data analytics techniques can be used to enhance the student' s learning process by providing real-time feedback, or by enriching the learning experience. Second, data analytics can be used to support the instructor or teacher. Using data analytics, the instructor can better trace, and take targeted actions

Table of Contents

Introduction: Big Data Analytics in a Learning Environment. I. Data Analytics to Improve the Learning Process. Improved Student Feedback with Process and Data Analytics. Toward Data for Development: A Model on Learning Communities as a Platform for Growing Data Use. The Impact of Fraudulent Behavior on the Usefulness of Learning Analytics Applications: The Case of Question and Answer Sharing with Medium-Stakes Online Quizzing in Higher Education. II. Data Analytics to Measure Performance. Disentangling Faculty Efficiency from Students’ Effort. Using Data Analytics to Benchmark Schools: The Case of Portugal. The Use of Educational Data Mining Procedures to Assess Students’ Performance in a Bayesian Framework. Using Statistical Analytics to Study School Performance through Administrative Datasets. III. Policy Relevance and the Challenges Ahead. The Governance of Big Data in Higher Education. Evidence-Based Education and Its Implications for Research and Data Analytics with an Application to the Overeducation Literature.

Data Analytics Applications in Education

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Order before 4pm today for delivery by Sat 17 Jan 2026.

A Hardback by Kristof De Witte

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    View other formats and editions of Data Analytics Applications in Education by

    Publisher: Taylor & Francis Inc
    Publication Date: 1/27/2017 12:09:00 AM
    ISBN13: 9781498769273, 978-1498769273
    ISBN10: 1498769276

    Description

    Book Synopsis

    The abundance of data and the rise of new quantitative and statistical techniques have created a promising area: data analytics. This combination of a culture of data-driven decision making and techniques to include domain knowledge allows organizations to exploit big data analytics in their evaluation and decision processes. Also, in education and learning, big data analytics is being used to enhance the learning process, to evaluate efficiency, to improve feedback, and to enrich the learning experience.

    As every step a student takes in the online world can be traced, analyzed, and used, there are plenty of opportunities to improve the learning process of students. First, data analytics techniques can be used to enhance the student' s learning process by providing real-time feedback, or by enriching the learning experience. Second, data analytics can be used to support the instructor or teacher. Using data analytics, the instructor can better trace, and take targeted actions

    Table of Contents

    Introduction: Big Data Analytics in a Learning Environment. I. Data Analytics to Improve the Learning Process. Improved Student Feedback with Process and Data Analytics. Toward Data for Development: A Model on Learning Communities as a Platform for Growing Data Use. The Impact of Fraudulent Behavior on the Usefulness of Learning Analytics Applications: The Case of Question and Answer Sharing with Medium-Stakes Online Quizzing in Higher Education. II. Data Analytics to Measure Performance. Disentangling Faculty Efficiency from Students’ Effort. Using Data Analytics to Benchmark Schools: The Case of Portugal. The Use of Educational Data Mining Procedures to Assess Students’ Performance in a Bayesian Framework. Using Statistical Analytics to Study School Performance through Administrative Datasets. III. Policy Relevance and the Challenges Ahead. The Governance of Big Data in Higher Education. Evidence-Based Education and Its Implications for Research and Data Analytics with an Application to the Overeducation Literature.

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