Unobtrusive Observations of Learning in Digital Environments : Examining Behavior, Cognition, Emotion, Metacognition and Social Processes Using Learning Analytics.

Format:

Book

Contributor:

Kovanovic, Vitomir.

Azevedo, Roger, 1966-

Gibson, David C.

lfenthaler, Dirk.

Series:

Advances in analytics for learning and teaching

Advances in Analytics for Learning and Teaching Series

Language:

English

Subjects (All):

Internet in education--Psychological aspects.

Internet in education.

Web-based instruction--Psychological aspects.

Web-based instruction.

Genre:

Electronic books.

Physical Description:

1 online resource (x, 244 pages) : illustrations (some color).

Place of Publication:

Cham : Springer International Publishing AG, 2023.

Summary:

This book integrates foundational ideas from psychology, immersive digital learning environments supported by theories and methods of the learning sciences, particularly in pursuit of questions of cognition, behavior and emotion factors in digital learning experiences. New and emerging foundations of theory and analysis based on observation of digital traces are enhanced by data science, particularly machine learning, with extensions to deep learning, natural language processing and artificial intelligence brought into service to better understand higher-order thinking capacities such as self-regulation, collaborative problem-solving and social construction of knowledge. As a result, this edited volume presents a collection of indicators or measurements focusing on learning processes and related behavior, (meta-)cognition, emotion and motivation, as well as social processes. In addition, each section of the book includes an invited commentary from a related field, such as educational psychology, cognitive science, learning science, etc.

Contents:

Intro

Preface

Contents

About the Editors

Part I: Learning Processes

Chapter 1: Unobtrusive Observations of Learning Processes

1 Section Overview

Chapter 2: A Review of Measurements and Techniques to Study Emotion Dynamics in Learning

1 Introduction

2 The Features of Emotion Dynamics

2.1 Emotional Variability

2.2 Emotional Instability

2.3 Emotional Inertia

2.4 Emotional Cross-lags

2.5 Emotional Patterns

3 The Measurements of Emotion Dynamics

3.1 Experience Sampling Method

3.2 Emote-Aloud

3.3 Facial Expressions

3.4 Vocal Expressions

3.5 Language and Discourse

3.6 Physiological Sensors

4 The Techniques for Analyzing Emotion Dynamics

4.1 Conventional Statistical Methods

4.2 Entropy Analysis

4.3 Growth Curve Modeling

4.4 Time Series Analysis

4.5 Network Analysis

4.6 Recurrence Quantification Analysis

4.7 Sequential Pattern Mining

5 The Challenges of Studying Emotion Dynamics in Learning

5.1 Deciding What to Measure About Emotion Dynamics

5.2 Deciding How to Analyze Emotion Dynamics

5.3 Addressing Individual and Developmental Differences

5.4 Differentiating Between Short-Term and Long-Term Emotion Dynamics

6 Concluding Remarks and Directions for Future Research

References

Chapter 3: Applying Log Data Analytics to Measure Problem Solving in Simulation-Based Learning Environments

2 Background

3 Methods

3.1 Experiment 1

3.2 Experiment 2

3.3 Log Data Processing

4 Results

4.1 Problem-Solving Outcomes as Measured by Solution Quality

4.2 Problem-Solving Processes as Captured by Features Extracted from Log Data

4.3 Pause as a Generalizable Indicator of Deliberate Problem Solving

4.4 How Log Data-Based Features Were Associated with Specific Problem-Solving Practices

5 Discussion

6 Limitations

7 Conclusion

Chapter 4: Challenges in Assessments of Soft Skills: Towards Unobtrusive Approaches to Measuring Student Success

2.1 Developing Soft Skills

2.2 Leadership Skills

2.3 Challenges of Assessing Soft Skills

3 Case Study

3.1 Study Context

3.2 Extracting Unobtrusive Measures

3.3 Assessing Leadership Mastery

3.4 Assessing Systematic Progression

4 Conclusion

References

Chapter 5: Reconfiguring Measures of Motivational Constructs Using State-Revealing Trace Data

1 Introduction: Self-Regulated Learning

2 Dynamic Nature of Motivation

2.1 How to Capture Motivation

2.2 A Role for Trace Data in Motivational Studies

3 Critiques of Recent Studies

3.1 Hershkovitz and Nachmias (2008)

3.1.1 Theoretical Framework

3.1.2 Contexts

3.1.3 Data and Indicators

3.1.4 Data Analysis and Results

3.2 Cocea and Weibelzahl (2011)

3.2.1 Theoretical Framework

3.2.2 Contexts

3.2.3 Data and Indicators

3.2.4 Data Analysis and Results

3.3 Zhou and Winne (2012)

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Other Format:

Print version: Kovanovic, Vitomir Unobtrusive Observations of Learning in Digital Environments

ISBN:

9783031309922

3031309928

OCLC:

1382693953

Access Restriction:

Restricted for use by site license.