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A Model for Multi-disciplinary Collaboration

Chris Groeneboer, Denise Stockley, Tom Calvert*,,

School of Computing Science, Faculty of Education, School of Computing Science

Simon Fraser University

Burnaby, B.C. Canada V5A 1S6

604-291-3257, 604-291-3847, 604-291-3969


* This work was done collaboratively, and order of authorship is not a reflection of contribution


In this paper, a model is presented for multi-disciplinary collaboration. The problem context is examined, then the theoretical motivation for the model. A description of the model is interwoven with discussion of the major issues impacting collaboration including evaluation of the online collaborative experience. We conclude with an exploration of some of the implications of the model and future directions.


This paper presents a model for multi-disciplinary collaboration for the design and development of a world wide web application. This application, Virtual-U, was designed to provide a web-based learning environment for the design and delivery of online courses. Four multi-disciplinary teams (design, development, field test support, and evaluation) worked both independently and collaboratively to design Virtual-U. This paper presents our model for multi-disciplinary collaboration based on our experiences with Virtual-U.


The context for the problem is the design and development of Virtual-U, a web-based networked learning environment customized for the design, delivery, and enhancement of post-secondary education and industry-based learning. One of the main goals of the project is to design a flexible framework to support pedagogies based on principles of active learning, collaboration, multiple perspectives, and knowledge building, and to support varied content areas and instructional formats. The framework consists of tools to support core activities including course design, individual and group learning activities, knowledge structuring, class management, and evaluation. Some of the tools our multi-disciplinary team has developed to date include:

Vgroups Conferencing System: Gives instructors the ability to easily set up collaborative groups and define structures, tasks and objectives. Any user can learn to moderate conferences and to create subconferences.

Course Structuring Tool: Enables instructors to create complete courses online without programming knowledge. Templates prompt the instructor for relevant information such as weekly overviews, required readings, grading standards and group conferencing assignments. The tool automatically places the course syllabus on the WWW for access by all students enrolled in the course.

Gradebook: Manages the database of students' grades for each course delivered with Virtual-U. The gradebook displays text and graphical representations of student performance including personal grades and distribution of marks for evaluated activities.

System Administration Tools: Assist the system administrator in installing and maintaining Virtual-U. These include functions such as creating and maintaining accounts, defining access privileges and establishing courses on the system.

Upload Features: Allow instructors and learners to send multimedia files from their local machines to the Virtual-U server. Learners electronically submit their assignments, and the instructors organize and comment on these submissions.

Virtual-U is currently being field tested at 15 universities and industry-based organizations across Canada to deliver courses from a variety of fields. Thus the design and development of Virtual-U is reflexive in nature in that a multi-disciplinary team is collaborating on the design and development of an environment to support multi-disciplinary collaboration. This multi-disciplinary team consists of individuals from cognitive psychology, communications, computer science, education, engineering, linguistics, mathematics, and psychology.

Theoretical Perspective

A social constructivist view provided the theoretical motivation for the model. According to this view, cognitive and social factors interact in the construction of knowledge. Implicit in the notion of construction is structure, organization, orderly arrangement, design. It is argued that, from this theoretical basis, knowledge construction is a collaborative design problem.

This means that learning environments need to support people in designing knowledge collaboratively. Knowledge construction (or design) involves the creation of explanations for our experiences in the world. The outcome of a knowledge building activity is a representation of our understanding of the experience. The representation may take many forms such as a theoretical model, essay, chart, drawing, painting, poetry, etc. Collaborative learning environments thus need to facilitate people in creating representations together (and individually). Given that one of the goals of Virtual-U is to support multiple perspectives, collaborative creation of multiple representations needs to be supported and maintained.

The Multi-Disciplinary Model

The structure and process of the Virtual-U project is presented as an example of the multi-disciplinary collaboration model in practice. (Figures 1 and 2.) The project is comprised of educators, HCI (human-computer interface) specialists, engineers, communications specialists, computing scientists, cognitive scientists, database designers, instructional designers, linguists, developers, instructors, learners, and researchers. The structure consists of a set of multi-disciplinary teams, each with a different focus. These include a design team, development team, field test support team, and evaluation team. (See Figure 1.)

The design team sets requirements, conducts user interface testing, and creates a design specification which the development team implements. The software is tested initially in-house and then goes out into the field. The field test team supports users in the field, collects data from the field for evaluation research, and bridges between the users and the project teams. The evaluation team analyzes the field data.

The project consists of multi-disciplinary teams, and each team consists of multi-disciplinary researchers. The person who takes the lead on a team is a specialist in the focus area, but, because we believe in the importance and power of multiple perspectives on a problem, teams are structured so that each relevant perspective has at least one advocate. For example, in creating a design specification, a computing scientist leads a team consisting of an educator, a cognitive scientist, an HCI specialist, and a specialist in the particular problem area.

The goal of the project is the design, development, and testing of Virtual-U. We view design and development of Virtual-U as an evolutionary process (Figure 2). This process forms a loop of activities in that the results of the evaluation research, user feedback, user testing (e.g., human-computer interface experiments), technology watch, and related research results inform the design of the next phase of Virtual-U. The development team also gives feedback to the design team, on the complexity of implementing certain features, for example.

Four data sets are generated in field testing (interview responses, questionnaire responses, log files tracking usage of the software, and transcripts of online discourse) which the evaluation team then analyzes. An educator interviews instructors about their experiences online including instructional design, their role online, and their intentions for the course. A multi-disciplinary team creates the questionnaires which are available to respondents online. A mathematics specialist working with a cognitive scientist created a tool for processing log file usage data.

The transcripts are analyzed from several perspectives; team members doing discourse analysis include a communication specialist, a linguist, a psychologist, and an educator. Results of the analysis of these four data sets and integrated and cross-referenced. The integration is necessary to explore problems such as how the instructional design effects the online learning experience. Goals of the evaluation team include developing an understanding of what is happening online and applying what is learned about online learning experiences to the design of the next phase of Virtual-U.

Feedback from users must be critically analyzed because it is often the case that what people are complaining about is a symptom of an underlying problem, and it is the underlying problem that needs to be addressed in a new design. New technologies need to be analyzed for suitability for integration with Virtual-U software. The design team must then integrate evaluation results, user feedback, newly available technologies, results of user testing, research results in areas relevant to online learning environments, and recommendations from developers about what can realistically be implemented. Because resources are limited, priorities have to be set and decisions made about what will be implemented in the short term and what will be delayed for future versions.

Issues to be Considered

Based on our experience with the Virtual-U project, we have identified certain necessary conditions for productive collaboration in that context. These conditions include effective group dynamics, effective communication, balanced decision-making power, balanced group and individual work, and attention to prevention of burnout.

The importance of group dynamics is often underestimated. If the group dynamics is not working, the effect can be devastating. In the best case the project will not achieve its potential. Many of the issues involved in establishing effective group dynamics are commonsense, but are worth repeating. If people are respectful of each other, open-minded, and motivated to think critically, an environment is created in which people feel safe to explore problems from multiple perspectives. The flow of critical and creative ideas is facilitated under these conditions.

Productive collaboration also requires effective communication. One of the problems that often arises in multi-disciplinary communication is that the same word may have different meanings in different disciplines. Many times it is difficult to identify the ambiguous term. For example, it was suggested at a planning session that we do a 'survey,' and the team separated to do just that. The next session highlighted the lack of ambiguity of the term as the computer scientists and engineers brought a survey of the literature and the social scientists brought a questionnaire/survey. This miscommunication decreased over time as the team recognized the need to establish a common language. This resulted in a talk-aloud type activity at the end of each session in order for everyone to have a shared understanding.

Another issue that impacts collaboration is decision-making power. How are decisions to be made within the group? Who sets priorities? What are the factors affecting power? Is it the discipline a person represents, or individual personalities, or role on the project? Or does everyone have an equal vote? A delicate balance is required; people need power to make decisions or have input into decisions affecting their work. We originally tried a process in which the whole group took part in all decisions. It was very time consuming, and progress was painstaking. The project has evolved into the multi-disciplinary teams model described. A new problem was created, though, in that the teams were not effectively communicating. One solution to this problem was to have a few of the same people on multiple teams to 'bridge' the teams. It has helped to have a few people who know what is going on with each of teams.

Currently, each of the teams breaks into smaller focus groups to study problems and make recommendations to the larger group when that seems warranted. It is often difficult, though, to decide how to break up a problem into subproblems and how to integrate solutions. A delicate balance is also required for the issue of attribution. Does everything coming out of the group belong to the group? What if one person brought the idea to the table? Usually ideas on the table are modified through group examination and exploration. The outcomes in that case are, in a real sense, shared objects. What is the policy in other cases?

Another critical issue in (but not endemic to) multi-disciplinary collaboration is burnout. In times of budget cuts, understaffing, and limited resources, growing project task lists seems to be added to the already-stretched task lists of existing staff. Projects need to be realistic about what can be done given the scope of the problem, available resources, and people available to work on the problem.

Multi-disciplinary collaboration requires time and effort to establish and maintain effective group dynamics, effective communication, and balanced decision-making power. However, the results are well-worth the effort. Multi-disciplinary team members bring multiple perspectives to problems which often leads to creative solutions and end-products (in our case, Virtual-U).


(c) Chris Groeneboer, Denise Stockley, Tom Calvert


The author(s) assign to ASCILITE and educational and non-profit institutions a non-exclusive licence to use this document for personal use and in courses of instruction provided that the article is used in full and this copyright statement is reproduced. The author(s) also grant a non-exclusive licence to ASCILITE to publish this document in full on the World Wide Web and on CD-ROM and in printed form with the ASCILITE 97 conference papers, and for the documents to be published on mirrors on the World Wide Web. Any other usage is prohibited without the express permission of the authors.


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