Includes indexes.

Online resource; title from PDF title page (ScienceDirecr, viewed June 22, 2016).

Front Cover; Formative Assessment, Learning Data Analytics and Gamification: In ICT Education; Copyright; Dedication; Contents; List of Contributors; Foreword; References; Preface; Final Words; Part 1: Formative e-Assessment; Chapter 1: Collaborative e-Assessment as a Strategy for Scaffolding Self-Regulated Learning in Higher Education; 1. Introduction; Research Questions; 2. Framework; 2.1. Self-Regulated Learning; 2.2. Alternative Assessment and Formative Feedback; 2.2.1. Self-assessment; 2.2.2. Peer assessment; 2.2.3. Co-assessment; 2.3. Self-Regulated Learning and Formative Assessment.

2.4. e-Assessment With Moodle3. The Study; 3.1. Context; 3.2. The Co-Assessment Activity; 4. Methodology; 5. Results and Discussion; 5.1. Students' Questionnaire; 5.2. Content Analysis of the Written Reflections; 5.3. Qualification Outcomes; 6. Conclusions; Acknowledgments; Annex 1. Instructions for the Co-Assessment Task on the Moodle Workshop; References; Chapter 2: Towards an Adaptive e-Assessment System Based on Trustworthiness; 1. Introduction; 2. State of the Art; 2.1. Fundamental Concepts; 2.2. Trust-Based Adaptability; 2.3. Adaptive e-Assessment; 3. Adaptive Trust-Based Model.

4. General Adaptive e-Assessment System4.1. Evidential Module; 4.2. Adaptive Module; 5. Adaptive Trust-Based e-Assessment System; 6. Simulation of a Trust-Based Adaptive Assessment System; 7. Discussion and Challenges; 8. Conclusions and Future Work; Acknowledgments; References; Chapter 3: e-Assessment for Skill Acquisition in Online Engineering Education: Challenges and Opportunities; 1. Introduction; 2. e-Assessment; 3. Formative e-Assessment; 4. e-Assessment Models, Systems, and Tools; 4.1. e-Assessment Systems; 5. Challenges and Opportunities in Online Engineering Education.

5.1. Skill Acquisition5.2. Feedback; 5.3. Formative Skill Assessment; 6. Conclusions; References; Chapter 4: Evaluation Model for e-Assessment; 1. Introduction; 2. The SURE Model; Step 1. Definition of key goals; Step 2. Definition of sub goals; Step 3. Confirmation of evaluation goals; Step 4. Creation of checklist; Step 5. Acceptance of checklist; Step 6. Data collection; Step 7. Data processing; Step 8. The evaluation report; 3. Theoretical Foundation for Data Processing; 4. Examples for the SURE Model; Example 3. The satisfied students; Example 4. The unsatisfied students.

Example 5. The gambling students5. A Tool for e-Assessment With the SURE Model; 6. Conclusion; References; Further Reading; Chapter 5: Confidence and Learning: Affective and Cognitive Aspects in Online Mathematics With Automatic Feedback; 1. Introduction; 1.1. Scenario Under Study; 1.1.1. Teaching methodology; 1.1.2. Students; 1.1.3. The teacher; 2. Theoretical Framework; 2.1. Mathematical Confidence; 2.2. Mathematical Learning; 2.3. The Relationship Between Mathematical Confidence and Mathematical Performance; 3. Research Methodology; 3.1. Participants; 3.2. Data Collection and Analysis.

Includes bibliographical references and indexes.

Discussing the challenges associated with assessing student progress given the explosion of e-learning environments, this book shows educators how to effectively garner intelligent data from online educational environments that combine assessment and gamification. -- Edited summary from book.