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Table of Contents
Intro
Foreword: Higher Education Computer Science in a Post-pandemic World
Preface
Contents
Part I Approaches to Learning
1 Changing Minds: Multitasking During Lectures
1.1 Introduction
1.2 Information Foraging Theory and Multitasking Check Para Numbering Here
1.3 Multitasking Is Multidisciplinary
1.3.1 Multitasking and the Brain
1.3.2 Action-Based Learning
1.3.3 Gen Z and Boredom
1.3.4 Cognitive Systems and Control
1.3.5 Confirmation Bias and Supertaskers
1.3.6 Academic Writing: A Bridge Too Far
1.4 Debating the Banning of Laptops During Lectures
1.4.1 Note-Taking
1.5 Smartphone Dependencies
1.6 The Survey
1.6.1 Intrinsic Questions
1.6.2 Extrinsic Questions
1.6.3 Employability
1.7 Conclusion
References
2 Active Learning in Large Lectures
2.1 Introduction
2.2 Challenges and Motivation
2.3 Active Learning
2.4 Techniques and Practices
2.5 Outcomes and Reflection
2.6 Summary and Conclusion
References
3 The Flipped Classroom
3.1 Introduction
3.2 The Module
3.3 Motivation and Justification of the Flipped Classroom Approach
3.4 Establishing the Flipped Classroom Approach
3.5 Weekly Learning Content Folder
3.6 Weekly Tutorial Tasks Folder
3.7 What the Results Show-the Data
3.8 Module Mark
3.9 Attendance Versus Attainment
3.10 VLE Engagement
3.11 VLE Engagement Versus Marks Data
3.12 Benefits of the Flipped Classroom Approach
3.13 Issues with Flipped Classroom Approach
3.14 Personal Reflections
3.15 Conclusions
References
4 Applying Cognitive Theory to the Teaching of Programming: Metaphors, Robots and Problem-Based Learning
4.1 Introduction
4.2 A Cognitive Perspective
4.3 A Potential Way Forward
4.4 Problem-Based Learning (PBL)
4.5 Conclusion
References
5 Distance Learning: Lessons Learned from a UK Masters Programme
5.1 Introduction
5.2 Approaches to Learning
5.3 Lessons Learned
5.4 Recommendations and Further Developments
5.5 Conclusions
References
6 Academic Integrity for Computer Science Instructors
6.1 Background
6.2 Introduction
6.3 Academic Integrity in Computer Science
6.4 Teaching Academic Integrity Principles
6.5 Assessing with Academic Integrity
6.6 Detecting Breaches of Academic Integrity
6.7 Conclusions
References
7 Contextualisation in Data Science
7.1 Introduction
7.2 The Challenges of Data Science Education
7.3 Contextualisation in Data Science
7.4 Is Contextualisation Aligned with the Educational Framework?
7.5 Applying Contextualisation at the Curricular Level
7.6 Examples of Contextualisation
7.7 Digital Learning
7.8 Virtual Reality in Data Science Education
7.9 Conclusion
References
Part II Teaching Examples and Practice
8 Using Graphics to Inspire Failing Students
8.1 Introduction
8.2 The Learning Objectives of Our CS1 and CS2 Modules
8.2.1 CS1
Foreword: Higher Education Computer Science in a Post-pandemic World
Preface
Contents
Part I Approaches to Learning
1 Changing Minds: Multitasking During Lectures
1.1 Introduction
1.2 Information Foraging Theory and Multitasking Check Para Numbering Here
1.3 Multitasking Is Multidisciplinary
1.3.1 Multitasking and the Brain
1.3.2 Action-Based Learning
1.3.3 Gen Z and Boredom
1.3.4 Cognitive Systems and Control
1.3.5 Confirmation Bias and Supertaskers
1.3.6 Academic Writing: A Bridge Too Far
1.4 Debating the Banning of Laptops During Lectures
1.4.1 Note-Taking
1.5 Smartphone Dependencies
1.6 The Survey
1.6.1 Intrinsic Questions
1.6.2 Extrinsic Questions
1.6.3 Employability
1.7 Conclusion
References
2 Active Learning in Large Lectures
2.1 Introduction
2.2 Challenges and Motivation
2.3 Active Learning
2.4 Techniques and Practices
2.5 Outcomes and Reflection
2.6 Summary and Conclusion
References
3 The Flipped Classroom
3.1 Introduction
3.2 The Module
3.3 Motivation and Justification of the Flipped Classroom Approach
3.4 Establishing the Flipped Classroom Approach
3.5 Weekly Learning Content Folder
3.6 Weekly Tutorial Tasks Folder
3.7 What the Results Show-the Data
3.8 Module Mark
3.9 Attendance Versus Attainment
3.10 VLE Engagement
3.11 VLE Engagement Versus Marks Data
3.12 Benefits of the Flipped Classroom Approach
3.13 Issues with Flipped Classroom Approach
3.14 Personal Reflections
3.15 Conclusions
References
4 Applying Cognitive Theory to the Teaching of Programming: Metaphors, Robots and Problem-Based Learning
4.1 Introduction
4.2 A Cognitive Perspective
4.3 A Potential Way Forward
4.4 Problem-Based Learning (PBL)
4.5 Conclusion
References
5 Distance Learning: Lessons Learned from a UK Masters Programme
5.1 Introduction
5.2 Approaches to Learning
5.3 Lessons Learned
5.4 Recommendations and Further Developments
5.5 Conclusions
References
6 Academic Integrity for Computer Science Instructors
6.1 Background
6.2 Introduction
6.3 Academic Integrity in Computer Science
6.4 Teaching Academic Integrity Principles
6.5 Assessing with Academic Integrity
6.6 Detecting Breaches of Academic Integrity
6.7 Conclusions
References
7 Contextualisation in Data Science
7.1 Introduction
7.2 The Challenges of Data Science Education
7.3 Contextualisation in Data Science
7.4 Is Contextualisation Aligned with the Educational Framework?
7.5 Applying Contextualisation at the Curricular Level
7.6 Examples of Contextualisation
7.7 Digital Learning
7.8 Virtual Reality in Data Science Education
7.9 Conclusion
References
Part II Teaching Examples and Practice
8 Using Graphics to Inspire Failing Students
8.1 Introduction
8.2 The Learning Objectives of Our CS1 and CS2 Modules
8.2.1 CS1