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Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment

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Cover of 'Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment'

Table of Contents

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    Book Overview
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    Chapter 1 Releasing Personal Information Within Learning Analytics Systems
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    Chapter 2 Measurement Challenges of Interactive Educational Assessment
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    Chapter 3 Integrated e-Learning Paradigm in the Twenty-First Century: Management Education
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    Chapter 4 Building a Learning Experience: What Do Learners’ Online Interaction Data Imply?
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    Chapter 5 Evaluating the Usefulness of the Visualization Tool SimReal+ for Learning Mathematics: A Case Study at the Undergraduate Level
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    Chapter 6 Participation and Achievement in Enterprise MOOCs for Professional Development: Initial Findings from the openSAP University
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    Chapter 7 Online Learners’ Navigational Patterns Based on Data Mining in Terms of Learning Achievement
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    Chapter 8 Performance Analysis of a Serial Natural Language Processing Pipeline for Scaling Analytics of Academic Writing Process
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    Chapter 9 An Analysis of Open Learner Models for Supporting Learning Analytics
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    Chapter 10 Fostering Active Learning with Video Using Teacher-Set Analysis Categories
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    Chapter 11 Ontology Technique and Meaningful Learning Support Environments
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    Chapter 12 Assessing the Interaction Between Self-Regulated Learning (SRL) Profiles and Actual Learning in the Chemistry Online Blended Learning Environment (COBLE)
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    Chapter 13 Unraveling the Research on Deeper Learning: A Review of the Literature
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    Chapter 14 Awareness Tools for Teachers to Support Students’ Exploratory Learning: Challenges and Design
Overall attention for this book and its chapters
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Mentioned by

11 tweeters
1 Facebook page


2 Dimensions

Readers on

57 Mendeley
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Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment
Published by
Springer International Publishing, January 2019
DOI 10.1007/978-3-030-15130-0
978-3-03-015129-4, 978-3-03-015130-0

Demetrios Sampson, J. Michael Spector, Dirk Ifenthaler, Pedro Isaías, Stylianos Sergis

Twitter Demographics

Twitter Demographics

The data shown below were collected from the profiles of 11 tweeters who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 57 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 19%
Researcher 7 12%
Lecturer 6 11%
Student > Doctoral Student 5 9%
Professor 3 5%
Other 9 16%
Unknown 16 28%
Readers by discipline Count As %
Social Sciences 9 16%
Computer Science 9 16%
Arts and Humanities 5 9%
Linguistics 5 9%
Psychology 3 5%
Other 8 14%
Unknown 18 32%