Formative Assessment, Learning Data Analytics and Gamification

In ICT Education
 
 
Academic Press
  • 1. Auflage
  • |
  • erschienen am 10. Mai 2016
  • |
  • 382 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-0-12-803667-9 (ISBN)
 

Formative Assessment, Learning Data Analytics and Gamification: An ICT Education discusses the challenges associated with assessing student progress given the explosion of e-learning environments, such as MOOCs and online courses that incorporate activities such as design and modeling. This book shows educators how to effectively garner intelligent data from online educational environments that combine assessment and gamification.

This data, when used effectively, can have a positive impact on learning environments and be used for building learner profiles, community building, and as a tactic to create a collaborative team. Using numerous illustrative examples and theoretical and practical results, leading international experts discuss application of automatic techniques for e-assessment of learning activities, methods to collect, analyze, and correctly visualize learning data in educational environments, applications, benefits and challenges of using gamification techniques in academic contexts, and solutions and strategies for increasing student participation and performance.


  • Discusses application of automatic techniques for e-assessment of learning activities
  • Presents strategies to provide immediate and useful feedback on students' activities
  • Provides methods to collect, analyze, and correctly visualize learning data in educational environments
  • Explains the applications, benefits, and challenges of using gamification techniques in academic contexts
  • Offers solutions to increase students' participation and performance while lowering drop-out rates and retention levels


Dr. Santi Caballé is an Associate Professor of Computer Science at the Open University of Catalonia (UOC). His research interests are Software Engineering and Web-applications for collaborative Work. He has presented at over 100 well-established international conferences and workshops such as the 6th IEEE International Conference on Intelligent Networking and Collaborative Systems and the Fourth International Workshop on Adaptive Learning via Interactive, Collaborative and Emotional Approaches (ALICE 2014) As part of his work in collaborative learning and computational intelligence he has edited nine books and dozens of journal articles.
  • Englisch
  • San Francisco
  • |
  • USA
Elsevier Science
  • 17,61 MB
978-0-12-803667-9 (9780128036679)
0128036672 (0128036672)
weitere Ausgaben werden ermittelt
  • 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 Moodle
  • 3. 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 System
  • 4.1. 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 Acquisition
  • 5.2. 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 students
  • 5. 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
  • 3.2.1. Mathematical confidence
  • 3.2.2. Mathematical learning
  • Analysis plan
  • 4. Main Findings and Discussion
  • 4.1. Mathematical Confidence
  • 4.2. Mathematical Learning
  • 4.3. Exploring the Relationship Between Confidence and Mathematical Learning: Student's Profile
  • 5. Conclusions and Future Lines
  • Acknowledgments
  • References
  • Chapter 6: Teaching and Learning Methods at the Open University of Japan: Transition From the Broadcasting Model to a Mor...
  • 1. The Open University of Japan
  • 2. First Online Courses at OUJ
  • 2.1. Teacher-Oriented Factors
  • 2.2. Student-Oriented Factors
  • 2.3. Institutional/Administrative Factors
  • 3. Possible Solutions to the Obstacles
  • 3.1. Computer-Based Formative Assessment
  • 3.2. Tutor System
  • 3.3. Peer Assessment
  • 4. Conclusions
  • References
  • Part 2: Learning Analytics
  • Chapter 7: An Assessment Analytics Framework (AAF) for Enhancing Students Progress
  • 1. Introduction
  • 2. Motivation
  • 3. Methodology
  • 4. Results
  • 4.1. Induction Phase: Literature Review of Assessment Analytics Approaches
  • 4.2. Concept Mapping Phase: Classification of Studies and Main Concepts
  • 4.3. The Assessment Analytics Framework
  • 4.3.1. The role of the context
  • 4.3.2. The "input" block: what, why, who, when, and where is being tracked and assessed?
  • 4.3.3. The "process" block: how the collected data are analyzed and interpreted?
  • 4.3.4. The "output" block: what, why, who, when, and where is the outcome of the assessment process?
  • 4.3.5. The "feedback" block: what, why, when, and where is delivered to close the loop effectively?
  • 4.4. Deduction phase: validation of the AAF
  • 5. Discussion and Conclusions
  • References
  • Chapter 8: Automating Learner Feedback in an eTextbook for Data Structures and Algorithms Courses
  • 1. Introduction
  • 2. Monitoring the Student Learning Process: What We Learn From Surveys and Learning Analytics
  • 2.1. Monitoring Levels of Student Engagement
  • 2.1.1. Case study: Recursion
  • 2.1.2. Case study: Recursion exercises
  • 2.1.3. Case study: Algorithm analysis
  • 2.1.4. Case study: Student use of exercises for study
  • 2.2. Student Perceptions of OpenDSA
  • 2.2.1. Student ranking of OpenDSA exercises
  • 2.2.2. Student self-efficacy regarding learning DSA material
  • 2.2.3. Effects of OpenDSA exercises on student performance
  • 3. Gamification: How Feedback Motivates Student Behavior
  • 3.1. Introduction
  • 3.2. Related Work
  • 3.3. OpenDSA Gamification Elements
  • 3.3.1. Proficiency for embedded slideshows
  • 3.3.2. Proficiency for embedded exercises
  • 3.3.3. Proficiency for modules
  • 3.3.4. Table of contents indicators
  • 3.3.5. OpenDSA gradebook
  • 3.4. Student Perceptions of OpenDSA Gamification Elements
  • 3.4.1. Student perceptions on visual feedback indicators
  • 3.4.2. Effects of visual feedback indicators on student behavior
  • 3.5. Impact of Proficiency Indicators on Student Time Spent on Slideshows
  • 3.6. Students Use of the Gradebook
  • 3.7. Discussion
  • 4. Conclusion and Future Work
  • Acknowledgments
  • References
  • Chapter 9: Creating University Analytical Information Systems: A Grand Challenge for Information Systems Research
  • 1. Introduction
  • 2. Business Analytical IS
  • 3. Universities Analytical IS
  • 3.1. Other Analytical Projects in the Context of Higher Education
  • 3.2. Weaknesses of Analytical IS in Universities
  • 4. Towards a Global University Analytical IS
  • 5. Is This a Grand Challenge?
  • 5.1. A Good Long-Range Research Goal?
  • 5.2. A Grand Challenge?
  • 6. Conclusions
  • Acknowledgments
  • References
  • Chapter 10: Methodology of Predictive Modeling of Students Behavior in Virtual Learning Environment
  • 1. Introduction
  • 2. Related Work
  • 3. Methodology of Predictive Modeling
  • 3.1. Business Understanding
  • 3.2. Data Understanding
  • 3.3. Data Preparation
  • 3.3.1. Data Cleaning
  • 3.3.2. Session Identification and Path Completion
  • 3.3.3. Definition of Variables
  • 3.3.4. Reduction of Cases
  • 3.4. Modeling
  • 3.5. Model Determination
  • 3.6. Parameter Estimation
  • 3.7. Logit Estimation
  • 3.8. Probability Estimation
  • 3.9. Probability Visualization
  • 3.10. Evaluation
  • 4. Discussion and Conclusions
  • Acknowledgment
  • References
  • Chapter 11: A Review of Emotion-Aware Systems for e-Learning in Virtual Environments
  • 1. Introduction
  • 2. Methodological Study
  • 2.1. Search and Retrieval
  • 2.2. Analysis and Selection
  • 2.3. Review
  • 2.4. Discussion and Report
  • 2.5. Conclusions and Refine
  • 3. Affective Learning
  • 3.1. Neurophysiology
  • 3.2. Education
  • 3.3. Social Psychology
  • 3.4. Social-Emotional Learning
  • 4. Affective Learning Tools to Collect Emotional Information
  • 4.1. Psychological (Self-Reporting) Tools
  • 4.2. Bio-Physiological Tools
  • 4.3. Motor-Behavioral Tools
  • 5. Affective Feedback Strategies
  • 5.1. Learning Technologies Applied for Affective Learning
  • 5.1.1. Learning analytics
  • 5.1.2. Serious games for education and storytelling
  • 5.1.3. Collaborative complex learning resource
  • 5.1.4. Massive open online courses
  • 5.1.5. Mobile learning
  • 6. Discussion
  • 6.1. Models and Methods for Affective Learning
  • 6.2. Affective Learning Tools to Collect Emotional Information
  • 6.3. Affective Feedback Strategies
  • 6.4. Learning Technologies Applied for Affective Learning
  • 7. Findings and Next Goals
  • 8. Conclusions and Future Work
  • Acknowledgments
  • References
  • Part 3: Gamification
  • Chapter 12: LudifyME: An Adaptive Learning Model Based on Gamification
  • 1. Introduction
  • 2. Learning and Technology
  • 2.1. Active Learning
  • 2.2. Technology Enhanced Learning
  • 3. Learning and Gamification
  • 3.1. Games, Gamification and More
  • 3.2. Gamified Learning
  • 4. LudifyME Model
  • 4.1. Objectives and Main Features
  • 4.2. Description
  • 4.3. Components of LudifyME System
  • 4.3.1. Tools
  • 4.3.2. Learning Networks or Itineraries
  • 5. Case Study: PLMan
  • 5.1. Description
  • 5.2. PLMan and LudifyME
  • 5.3. Progressive Prediction System
  • 5.4. Experiments and Results
  • 6. Lessons Learned and Conclusions
  • References
  • Chapter 13: ?PAX: Experiences on Designing a Gamified Platform for Serious Gaming
  • 1. Introduction
  • 2. Background
  • 2.1. On the Basis of Gamification
  • 2.2. Platforms Based on Gamification
  • 3. ?PAX Conceptual Design
  • 3.1. System Requirements
  • 3.2. Gamification Design
  • 3.3. Website and Interface Design
  • 4. ?PAX Architectural Design
  • 4.1. Services Definition
  • 4.2. Database Definition
  • 4.3. Security
  • 4.3.1. User authentication
  • 4.3.2. Integrity and trustworthiness
  • 5. Conclusions
  • 5.1. Technical Development
  • 5.2. Academic Impact
  • 5.3. Future Work
  • Acknowledgments
  • References
  • Chapter 14: An Attrition Model for MOOCs: Evaluating the Learning Strategies of Gamification
  • 1. Introduction
  • 2. Related Work
  • 2.1. About AMOES (Attrition Model for Open Learning Environment Setting)
  • 2.2. About Gamification Strategies
  • 3. Research Methodology
  • 4. Design of Experiments
  • 5. Experimentation Findings
  • 5.1. Gamified MOOC: Learner Behavior Analytics
  • 5.2. Comparison of Traditional MOOC and Gamified MOOC: Learner Behavior Analytics and Aspects of Attrition
  • 5.3. Comparison of Traditional MOOC and Gamified MOOC: Dropout Analysis
  • 6. Conclusions and Future Work
  • References
  • Chapter 15: Conversational Agents as Learning Facilitators: Experiences With a Mobile Multimodal Dialogue System Architecture
  • 1. Introduction
  • 2. State of the Art
  • 3. Our Framework to Develop Educative Multimodal Conversational Interfaces for Mobile Devices
  • 4. The Geranium Pedagogical System
  • 5. Conversational Metabot Providing Academic Information
  • 6. Conclusions
  • References
  • Author Index
  • Subject Index
  • Back Cover

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