My thanks go to the many people at UCL who helped and encouraged me during my time within their department. In particular Janet McDonnell, my second supervisor, who provided an insightful view of my work. Also, Jane Hughes, Louise Clark and Anna Watson, for their willing support, advice, and hard work in many aspects of organising and conducting the field trials.
I am indebted to the partners of the National Support Centres for the Multimedia Integrated Conferencing for Europe project, who recruited and supported students studying remotely at the Universities of Wales, Edinburgh and Glasgow, and University of London Computer Centre (ULCC). Without their support, encouragement, and advice the initial remote trials would not have been possible. I wish to note in particular, Dave Price (U. Wales), Geoff Constable (formerly U. Wales), Graeme Wood (U. Edinburgh), Anne Marie Fleming and Jim Mullin (U. Glasgow), and Syngen Brown (ULCC).
I am grateful to the many students who participated (voluntarily or involuntarily) in the three field trials. Without them the research would not have been possible, and their tolerance, good humour, and insight added much. I hope they felt as rewarded as I did for their time and effort. I would like to thanks my colleagues at the University of Westminster who have shouldered an increased workload while I have been otherwise occupied. In particular, I wish to note the encouragement and support of a retired colleague, George McCulloch.
I am particularly indebted to my wife and best friend Clare, who has turned a blind eye to the dereliction of duty that occurred in our family over the past few years.
Finally, my biggest thanks go to my supervisor, Angela Sasse, whose positive, informed, and encouraging nature has been an inspiration throughout.
Good communication is vital for facilitating effective learning. Students' conceptualisations must be nurtured, and desktop videoconferencing (DVC) appears to offer much in its ability to support essential learning dialogue at a distance. In order to provide an understanding of the appropriate use of DVC in the teaching-learning environment, this thesis explores two key aspects:
the specification of DVC in a scalable role for supporting the teaching-learning environment, and
the development and application of an evaluation tool capable of identifying changes in learning outcomes as a result of changes in DVC quality.
Current published literature does not confront these issues directly, although it does provide pointers from related research. With these insights, a role for DVC has been specified and refined through field trial evaluations. As little is known about DVC-quality factors in such a role, attention has focussed on evaluating video channel issues as these have significant ramifications on cost and therefore deployment opportunities.
Once an effective tutorial support role for DVC had been established, it became possible to explore specific issues of DVC quality. A new discourse content analysis scheme was developed and applied to data gathered from tutor-less peer-group tutorials. This identified that objective measures of learning opportunities increased week by week, but there was no additional increase as a result of improvements in DVC video quality. It also identified that the influence of certain students within a tutor-less tutorial group was likely to be more significant than improvements in DVC video channel quality.
CONTENTS
1. INTRODUCTION
1.1 Desktop Videoconferencing
1.2 The Problem Definition
1.3 Pointers to the solution of the problem
1.4 Research Scope Overview
1.5 Contributions of the Thesis
1.6 Thesis Overview
2. LEARNING TASK SUPPORT
2.1 Introduction
2.2 Educational Theory for Learning Task Support
2.2.1 The Nature of Student Learning
2.2.2 A Model for Student Learning ?
2.2.3 From Learning to Teaching
2.3 Appropriate Use of Learning Support Tools
2.3.1 From Teaching Model to Tools and Techniques
2.3.2 A Framework to Identify Learning Task Support
2.3.3 Differentiation Between Requirements for Content Negotiation
2.3.4 Content Delivery with Limited Negotiation
2.3.5 Content Negotiation Mechanisms
2.3.6 A New Framework for Learning Task Support
2.4 Tutoring
2.4.1 Tutorial Dialogue
2.4.2 Inherent Support of the Learning Group
2.4.3 Tutor-less Groups
2.4.4 Negative Aspects of Tutorless Groups
2.4.5 Role of the Tutor
2.4.6 Group Activities and Group Size
2.4.7 Conclusions of Tutoring Issues
2.5 Learning Task Support Conclusions
3. VIDEOCONFERENCING
3.1 Introduction
3.2 Interpreting the Literature
3.3 Arguments Against the Use of Video
3.4 Justification for Video
3.5 Room-Based Videoconferencing Case Studies
3.5.1 Columbia University Network (CVN)
3.5.2 ISDN-based videoconferencing in Australian tertiary education (ATE)
3.5.3 University of Ulster (UoU)
3.5.4 University of Wales Video Network (WN)
3.5.5 Sweden-Silicon Valley Link (SSL)
3.5.6 Science University of Malaysia (SUM)
3.6 Issues in Room-based Videoconferencing Case Studies
3.6.1 Access
3.6.2 Teaching
3.6.3 Contact
3.6.4 Group Interaction
3.6.5 Practical and Technical Issues
3.7 Desktop Videoconferencing Case Studies
3.7.1 HIPERNET
3.7.2 OU Virtual Summer School
3.7.3 University of Exeter
3.7.4 ReLaTe (Remote Language Teaching)
3.7.5 Remote Office Collaboration
3.7.6 Other Case Studies
3.8 Issues in Desktop Videoconferencing Case Studies
3.9 Research into Channel Quality
3.9.1 Room-based Videoconferencing
3.9.2 Macro Analysis Findings
3.9.3 Educational-based Case Study
3.9.4 Media Influences
3.9.5 The role of the face in communication
3.10 A Discussion of Video Channel Image Quality Issues
3.10.1 Issues related to the case studies
3.10.2 Conclusions on Channel Quality Issues.
3.11 Summary of Videoconferencing Issues
4. EDUCATIONAL EVALUATION
4.1 Evaluation Methodology
4.1.1 Models of Educational Evaluation
4.1.2 Purpose of Educational Research
4.1.3 Issues with Objective-based Research
4.1.4 Issues with Subjective-based Research
4.1.5 Comprehensive Evaluation
4.1.6 Candidate Environments for Investigation
4.2 Influences to be Addressed with Educational Research
4.2.1 Auto-compensation
4.2.2 Personality
4.2.3 Perceptions
4.2.4 Context
4.2.5 Motivation
4.2.6 Control Groups
4.2.7 Additional Issues
4.3 Data Gathering Techniques
4.3.1 Summary of General Techniques
4.3.2 DVC Channel Quality Data Gathering
4.3.3 Content Analysis Data Gathering Case Studies
4.3.4 Development of a New Content Analysis Scheme for DVC
4.4 Evaluation Conclusions
5. RESEARCH PLAN
5.1 Research Proposal
5.2 Research Methodology
5.3 Scope
5.3.1 Research Environment
5.3.2 Available Tools
5.4 Deliverables
6. DVC ENVIRONMENT STUDY (Trial 1)
6.1 Purpose
6.2 Method
6.3 Outline Specification of the DVC Environment
6.4 Scope and Nature of Trial 1
6.5 The Learning Environment
6.5.1 Overview
6.5.2 Computer Aided Instruction package (CAI)
6.5.3 Weekly Questions
6.5.4 Tutorials
6.5.5 Tutorial Sound Clips
6.6 Data Gathering Methods
6.6.1 Methodology
6.6.2 CAI Time-line
6.6.3 Weekly Questions and Student Log
6.6.4 Weekly Tutor Diary
6.6.5 Test for Academic Achievement.
6.6.6 Final Student Questionnaire.
6.7 Field Trial Events
6.7.1 Preparations
6.7.2 Trial Dates
6.8 Results
6.8.1 Attendance and Participation
6.8.2 Pre Study Exercise
6.8.3 General Observations From the Tutor's Diary
6.8.4 Tutorial Issues As Seen by the Tutor
6.8.5 Questionnaire Results
6.8.6 CAI Time-line
6.9 Discussion
6.9.1 Detractors
6.9.2 Evidence of academic progress
6.9.3 CAI package
6.9.4 Tutorials
6.9.5 Web tutorial clips.
6.9.6 Pace
6.10 Summary Conclusion
7. DVC ENVIRONMENT REFINEMENT (Trial 2)
7.1 Purpose
7.2 Methods
7.3 Outline Specification of the Revised DVC Environment
7.4 Scope and Nature of Trial 2
7.5 The Learning Environment
7.5.1 Overview
7.5.2 Tutorials
7.5.3 Tutor-less section
7.5.4 Tutor-led section
7.6 Data Gathering Methods
7.6.1 Methodology
7.6.2 Trial 2 Student Questionnaire
7.6.3 Interview Questions
7.7 Field Trial Events
7.8 Results
7.8.1 Attendance and Participation
7.8.2 Detractors
7.8.3 Observations From the Tutor's Diary
7.8.4 Questionnaire 2 Analysis
7.8.5 Interviews:
7.9 Discussion
7.9.1 Participation
7.9.2 Group Issues
7.9.3 Video Channel Quality
7.10 Summary Conclusion
8. CHANNEL QUALITY ANALYSIS (Trial 3)
8.1 Purpose
8.2 Methods
8.3 Outline Specification of the Revised DVC Environment
8.4 Scope and Nature of Trial 3
8.4.1 Participation
8.4.2 DVC Quality
8.5 The Learning Environment
8.6 Data Gathering Methods
8.6.1 Overview
8.6.2 Student Questionnaire
8.6.3 Recorded Dialogue
8.7 Field Trial Events Period
8.8 Results
8.8.1 Attendance and Participation
8.8.2 Observations From the Tutor's Diary
8.8.3 Student questionnaire results
8.8.4 Discourse Content Analysis
8.9 Discussion
8.9.1 Content Trends from Video Quality Improvements
8.9.2 Scope of Findings with the Main Content Trend
8.9.3 Relationships between Subjective and Objective Quality Findings
8.9.4 Impact of Specific Tutorial Participants
8.9.5 Content Tag Occurrences and Overall Module Performance
8.9.6 Tags not related to Learning Enablers
8.9.7 Issues Relating to Subjective Findings
8.10 Conclusions
8.10.1 Overview of the Trial
8.10.2 Content Analysis Findings
8.10.3 Content Analysis Technique Review
8.11 Progress Review 258
9. LEARNING SUPPORT THROUGH DVC
9.1 General Review
9.2 An Appropriate Environment For DVC
9.2.1 Demands for Academic Dialogue
9.2.2 Intensity and Isolation
9.2.3 Peer Groups
9.2.4 Tutor's Role
9.2.5 Support For Non-academic Issues
9.2.6 CMC As An Alternative
9.3 DVC Channel Quality in Relation to Learning Support
9.3.1 Overall Image Quality
9.3.2 Image Size
9.3.3 Attention to Video Channel
9.3.4 Channel Latency and Asynchrony
9.3.5 Audio Quality
9.3.6 Room-based Videoconferencing
9.3.7 Channel Quality Conclusions
9.4 Research Methodology Review
9.4.1 Subjective Methods
9.4.2 Discourse Content Analysis
9.5 Future Research Possibilities
9.5.1 Increased Accuracy
9.5.2 Separate Components of Channel Quality
9.5.3 Channel Synchrony
9.5.4 Subject-content Demands
9.5.5 Individual Student Differences
9.5.6 Comparisons between Tutor-less and Tutor-led Participation
9.5.7 General Tutorial and Peer Group Analysis
9.5.8 Remote Tutorials compared with Live Tutorials
9.5.9 Shared Presence
9.6 Final Conclusions
10. References
Glossary
Appendices
Figures
1.1 Relationships between chapters
2.1 Laurillard's (1993) 12 activity model
2.2 A model for analysing support for inter-personal interaction 35
2.3 The nature of subject content in arts subjects
2.4 The nature of subject content in science subjects
2.5 Tools and techniques in the dialogue interaction model
5.1 Research domains appropriate to the proposed research
5.2 DVC tools in action
6.1 The tutor's view of two students participating in trial 1 176
7.1 Discussing students' answers written on the whiteboard 201
8.1 Relative size of QCIF and CIF
8.2 Using the DVC equipment
8.3 Summary grouping 'A' showing the occurrence of learning enablers with
linear trend line
8.4 Summary grouping 'A' after interpolation with linear trend line
8.5 'Group Control' (gc) Variations by Student
8.6 'Dependent Deep' (dd) Variations by Student
Tables
3.1 Application and purpose of videoconferencing case studies
4.1 Reciprocal Category Scheme
4.2 Equivalent Talk Categories
6.1 Student tutorial attendance by week for trial 1
6.2 Students weekly self-study answers shown by week for trial 1 175
7.1 Student tutorial attendance by week for trial 2
7.2 Students weekly self-study answers shown by week for trial 2 200
8.1 Student tutorial attendance by week for trial 3
8.2 Participant levels for each session for each week
8.3 Summary of Tag Categories
8.4 Average attendance and average occurrences of content categories shown
for each week 237
8.5 Summary grouping of the occurrence of learning enablers (A,B,C,D)
8.6 Summary grouping of the occurrence of learning enablers after
interpolation
8.7 Sample Selection Showing Percentage Student Dialogue by Tag
Category 246
8.8 Estimated Learning Enabler Tags Per Student
9.1 Summary of key issues in the specification of DVC
B.1 Trial 1 questionnaire results
B.2 Trial 2 questionnaire results
B.3 Trial 3 questionnaire results
C.1 Example of tag coded content
C.2 Total Content Tag Occurrences per Session
C.3 Student Learning Enabler Contributions
C.4 Percentage contribution made by a student in their tutorial
C.5 Final module mark correlated with the estimated number of
learning enablers per student