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Technology Enhanced Assessment

Overview of attention for book
Cover of 'Technology Enhanced Assessment'

Table of Contents

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    Book Overview
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    Chapter 1 A First Step Towards Synthesizing Rubrics and Video for the Formative Assessment of Complex Skills
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    Chapter 2 Case Study Analysis on Collaborative Problem Solving Using a Tangible Interface
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    Chapter 3 Feedback Opportunities of Comparative Judgement: An Overview of Possible Features and Acceptance at Different User Levels
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    Chapter 4 Student Teachers’ Perceptions About an E-portfolio Enriched with Learning Analytics
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    Chapter 5 Support in Assessment of Prior Learning: Personal or Online?
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    Chapter 6 The Role of Formative Assessment in a Blended Learning Course
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    Chapter 7 Self- and Automated Assessment in Programming MOOCs
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    Chapter 8 Assuring Authorship and Authentication Across the e-Assessment Process
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    Chapter 9 Today - Only TEA and no CAAffee. But Tomorrow?
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    Chapter 10 A Hybrid Engineering Process for Semi-automatic Item Generation
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    Chapter 11 Assessing Learning Gains
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    Chapter 12 Requirements for E-testing Services in the AfgREN Cloud-Based E-learning System
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    Chapter 13 A Review of Interactive Computer-Based Tasks in Large-Scale Studies: Can They Guide the Development of an Instrument to Assess Students’ Digital Competence?
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    Chapter 14 Design and Development of IMS QTI Compliant Lightweight Assessment Delivery System
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    Chapter 15 Exploring a Solution for Secured High Stake Tests on Students’ Personal Devices
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    Chapter 16 What Types of Essay Feedback Influence Implementation: Structure Alone or Structure and Content?
Attention for Chapter 1: A First Step Towards Synthesizing Rubrics and Video for the Formative Assessment of Complex Skills
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Chapter title
A First Step Towards Synthesizing Rubrics and Video for the Formative Assessment of Complex Skills
Chapter number 1
Book title
Technology Enhanced Assessment
Published by
Springer, Cham, May 2017
DOI 10.1007/978-3-319-57744-9_1
Book ISBNs
978-3-31-957743-2, 978-3-31-957744-9

Kevin Ackermans, Ellen Rusman, Saskia Brand-Gruwel, Marcus Specht

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Professor 3 23%
Student > Ph. D. Student 3 23%
Student > Master 3 23%
Researcher 1 8%
Professor > Associate Professor 1 8%
Other 0 0%
Unknown 2 15%
Readers by discipline Count As %
Social Sciences 7 54%
Computer Science 2 15%
Psychology 1 8%
Environmental Science 1 8%
Unknown 2 15%