JS Gero (ed.), Artificial Intelligence in Design’02, 2002 Kluwer Academic Publishers, Dordrecht. Printed in the Netherlands.

USING PROTOCOL ANALYSIS TO INVESTIGATE COLLECTIVE LEARNING IN DESIGN

ZHICHAO WU AND ALEX DUFFY University of Strathclyde UK

Abstract. It is postulated that in a collaborative design environment, when agents interact with each other, they may learn from each other. In this paper, the phenomena of collective learning in team working are investigated using protocol analysis. Through such an investigation, a model of collective learning in design has been extended from an existing model of learning in design (Sim, 2000).

1. Introduction

Design and learning are two interlinked activities (Persidis and Duffy, 1991). Designers learn during the design process (Persidis and Duffy, 1991; Sim, 2000). However, most previous research in learning in design is confined to investigating individual learning, that is, how an individual agent learns (Persidis and Duffy, 1991; Duffy and Duffy, 1996; Sim, 2000; Gero, 1990; Smithers and Troxell, 1990; Grecu and Brown, 1996a, 1996b; Reich and Fenves, 1989). In a design environment like team or distributed working, agents interact with and from each other. If the phenomena of collective learning in design exist, what is the nature of it? That is, what can be learnt collectively? When will collective learning occur? How do agents learn collectively? This paper reports an investigation into these phenomena.

The meaning of “collective” is defined in the New Oxford Dictionary of English as “belonging or relating to all the members of a group” (Pearsall, 1998). It is suggested that there are different forms that make collective learning unique from individual learning: • Combining knowledge: The knowledge in different agents can be

combined and new knowledge can be extracted and learned from the combination.

• Common Learning: All agents in a team or a group can learn the same thing at the same time.

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that learning is an adaptive change in behaviour caused by experience. In psychology, learning was defined from six perspectives as a result of an (Marton and Booth): Learning as increasing one’s knowledge; Learning as memorising and reproducing; Learning as applying; Learning as understanding; learning as seeing something as a different way; and Learning as changing a person. In the view of organisational study, learning is understood in two levels: operational and conceptual. Learning address (Kim, 1993): (1) the acquisition of skill or know-how, which implies the physical ability to produce some action, and (2) the acquisition of know-why, which implies the ability to articulate a conceptual understanding of an experience. To be able to carry out effective action, both know-how and know-why are needed. In the artificial intelligence community, Simon (1983) states that:

“Learning denotes changes in the system that are adaptive in the sense that they enable the system to do the same task or tasks drawn from the same population more efficiently and more effectively the next time”.

In the area of engineering design, Persidis and Duffy (1991) argued that designers learn when they encounter knowledge that is sufficiently different from their present state of knowledge. Learning in design is understood as the change of the human’s state of knowledge that can have a direct influence on the ability of a human to solve a problem. In this paper, learning is defined as a process of gaining and storing knowledge through interaction with other agents.

4. Collective Learning in Design

Collective learning in design is considered in this work in the context of collaborative design. Collective learning is not a new concept. In his work in organisational study, Neergaard stated collective learning is such kind of learning, where a group of individuals has learnt collectively through their interaction (Neergaard, 1994). In this paper collective learning is described as agents in a team learn collectively through their interactions. Agents in

ANALYSIS TO INVESTIGATE COLLECTIVE LEARNING IN DESIGN 5

assumed that there is a team design and collective learning loop, see Figure 2.

Figure 1. Learning from one another

Key:

Figure 2 A loop of team design and collective learning

Based on the model of learning in design (Sim, 2000), the assumption made for collective learning in design includes: There may have input knowledge and output knowledge for team design activities and collective learning activities. There may have knowledge transformers that link input knowledge and output knowledge, which may imply an epidemic link

Designing

Collective Learning

Learning

Team Design

Learnt Knowledge

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TABLE 1. Types of design activities (Sim, 2000)

Classification Activities and transcript codes

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TABLE 2. The design activities identified in protocol of team working (√ )

Types of Design Activities

The design activities identified in the protocol

Design definition activities

Abstracting, Association (√), Decomposition, Generating, Detailing (√), Defining, Standardising, Structuring/Integrating, Synthesis

Design evaluation activities

Analysis (√), Modelling, Evaluation (√), Decision making (√), Testing /Experimenting

Design management Activities

Constraining (√), Information gathering (√), Identifying (√), Exploring (√), Searching (√), Resolving, Selecting (√), Prioritising, Planning (√), Scheduling (√)

6. Elements of Collective Learning in Design

6.1 DESIGN ACTIVITIES

6.1.1 Design goals Team design goals in many design activities seem to be verbalised at the beginning of the design activity. The keywords to identify the team design goals are like “Gi: We need to design a lid...”, “Gi: We need to design something to seal the tube…”, “Ma: I still think we need something to stop the flow….”, etc. Team design goals can be inferred from these keywords. The goals within these design activities are “design a lid”, “design something to seal the tube”, and “design something to stop the flow”. Team design goals can also be inferred from the questions verbalised by the participants. For example, the questions are like “Gi: Where are we going to get a printer from…?”, “Pa: So what are they expecting us to do then? …”, “Gi: Is there any need for a tank? …”, “Gi: Right, what about the mounting of the tank then? Do you think it should be on the gantry or moving with it?”. The team design goals within those team design activities are “get a printer”, “clarify their design tasks”, “decide the location to mount the tank”. The team design goals can also be inferred from the other elements of team design activities, like input knowledge or output knowledge. For example in Table 3, Gi provides input knowledge, the tank should be sealed along the edges and the silicon gel can be used, Pa provides the input knowledge, a small gasket may be used, Da provides the input knowledge, the tank should be sealed across the three compartments, and Ma provide the input knowledge, if the tank is cut flat the silicon stuff can be used. From those

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knowledge and through sharing the team learns his design idea and the output knowledge in that design activity is the understanding of Pa’s idea of mounting the tank.

TABLE 4. Example of only one agent providing input knowledge for a team design activity

6.2 COLLECTIVE LEARNING

Collective learning can be identified through the keywords or phrases or sentences. For example, the key phrase in “Gi: We have to use cartridge because we are using the cradle. Because Ge (an agent outside the team) said so….” indicates that Gi learns the team’s design requirement from Ge. There are other examples like “Gi: … Can we not have one of those remote control syringe things that Ry (an agent outside the team) was talking about?…”, where Gi learns about the remote control from Ry. In another example, the format of question is used, like “…Pa: Where do we put a grub screw at the side to keep it tight? Da: With a plastic sleeve around it, it would not have too much definition…”. In this example, it can be inferred that Pa can learn from Da how the screw can be kept tight. There are some other examples in the protocol where collective learning occurs through asking questions, such as “Gi: Is there anyway that we can incorporate the valve that operate and that is also run by the processor in the computer? So that when that is s o

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remember you’ve got a Z direction. That whole bit there (pointing) is moved back …” from which it can be inferred that Ma can learn from his own knowledge and being reminded by Gi that his design of the location of the gantry is infeasible due to his forgetting to take consideration of the Z direction.

6.2.1 Input knowledge In the protocol analysis, the input knowledge for collective learning can be verbalised after the keywords or phrases of design goal, or after the questions. For example, in Table 13, the input knowledge from Da and Ma is followed by the key phrases “we’re talk about size here” and key words “volume” “stability” which implies that the design goal of the volume and the stability of the tank. In another example in Table 11 the input knowledge from Ma and Gi is provided after Pa’s question “what are they expecting us to do then”.

6.2.2 Output knowledge In collective learning, output knowledge of one agent can be the input knowledge from another agent, or can be acquired/derived from the input knowledge from itself and other agents, or from other agents’ input knowledge without its own input knowledge. For example, in Table 10, the output knowledge of Ma is acquired from Gi. The knowledge is only transferred from one agent to another agent. In Table 12, Gi’s output knowledge, that her option to design the top of the cartridge is not feasible, can be derived from her own knowledge and Da’s input knowledge. In Table 11, the output knowledge of Pa, that their design still depends on Gerry’s programming, can be derived from Gi and Ma’s input knowledge. In these two examples, new knowledge is created through deriving the input knowledge from itself and other agents, or just from other agents.

6.2.3 Knowledge transformers Knowledge transformers serve as a tool to change the input knowledge

into output knowledge. It is difficult to identify the knowledge transformers in collective learning which link input and output knowledge from different agents. Through the protocol analysis three types of knowledge transformers are identified: Acquisition/Sharing, Derivation (Reformulation), and Requesting/Replying. When an agent acquires new knowledge from one or more of other agents, the knowledge transformer is Acquisition/Sharing, and when new knowledge is derived from other agents or from both the agent’s own and other agents’ knowledge, the knowledge transformer is derivation.

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Beside these two knowledge transformers, another knowledge transformer, Requesting/Replying, is used where the agent request knowledge from one or more other agents, in the form of question, and the knowledge is acquired through other agents’ replies. Requesting/ Replying can be understood as a special knowledge transformer of acquisition/sharing, or derivation (reformulation), depending on whether new knowledge is derived from other agents. One example of acquisition acting as knowledge is like the design activities in Table 5, where Ma acquired the knowledge from Gi directly. In the design activity in Table 11, derivation is identified as knowledge transformer, where Pa’s knowledge that making a model still depends on Gerry’s work is derived from the input knowledge of both Gi and Ma. The design activity in Table 6 can be used to illustrate requesting/replying acting as knowledge transformer in the collective learning activity, where Ma learns from Da that the cartridge and tank can be replaced at once, and what the size of the tank could be in consideration of the useful life of the printer head through his explanation of his design idea.

TABLE 5. An example of acquisition acting as a knowledge transformer

6.2.4 Learning triggers Collective learning triggers can be divided into two subcategories: temporal triggers that reflect the links between team design and collective learning in the aspect of time, and rational triggers that are the different reasons that trigger collective learning. Temporal triggers are identified, which is the same result as Sim’s analysis based upon a single agent: Provisional triggers, In-situ triggers and Retrospective triggers. In some team design activities, one agent can learn a piece of knowledge from others before their team design and share that knowledge with the team members.

… Ma: That’s the thing we could design almost anything but it’s not going to get made

downstairs because they can’t make much out of plastic, especially that size. Gi: It’s pretty minimal - it’s like cut a bit of plastic, drill three holes in it and put

some silicon gel on the top. Ma: fh yes, I know I mean anything like complicated . …

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the potential problem in the design can also trigger the agent to learn. For example, in the design activity in Table 8, the failure of the design that members of other teams may not know the tank may be put in the top of the cartridge, triggers Gi to learn from Pa that they may clip something on the side. The success in one agent’s design idea can also trigger another agent or other agents to learn. For example, in Table 13, the design idea from Pa triggers Ma to learn. Conflict may occur in a team-working environment (Klein and Lu, 1989; Klein, 1991). When disagreement occurs among the agents, it may trigger collective learning. For example, in Table 9, Gi has the idea that they don’t have to seal the tank but with the disagreement of Da and Pa and their explanation of the results without sealing the tank Gi’s idea has been changed and Gi can learn a new piece of knowledge that the tank has to be sealed. Collective learning can also be triggered through the request of knowledge in one agent. One agent may request knowledge from other agents, or want other agents to confirm or disconfirm a design idea, or want other design agents to explain their ideas, etc. In the form of asking questions, the agent can learn from other agents triggered by their reply. The collective learning activities in Table 10 and Table 13 are triggered by requests for knowledge.

TABLE 8. The failure of the design triggered collective learning

Gi: But they might design it not knowing - that’s the problem as well - not knowing that they’re putting a big tank at the top. All that we need to worry about is they’re mounting something on the gantry arm and we were possibly going to mount something on the gantry arm.

Pa: (drawing) Something, even that and just clip something onto the side like that and I mean that’s the dimensions of it so that’s just about all you would need for one big build,

for maybe for two or...

TABLE 9. The disagreement of other agents trigger one agent's collective learning

Gi: That doesn’t have to be sealed (pointing to rawing) because as long as that water level’s there you’re not gonna get any water in it. They’re not gonna get any air in it rather. As long as it’s down to like there, know what I mean, as long as that bit’s covered.

Da: Even there, if it’s running through a sponge then you’re not gonna get any air through it anyway.

Pa: Don’t want it contaminated with dust as well, you know you want to keep it quite ... Gi: Yes, it would definitely have to be a sealed...

6.2.5 Learning goals

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It is difficult to identify collective learning goals, as the individuals did not verbalise such words as “the goal I learn from him/her is …”, etc. Similarly with the identification of the team design goal, the goal of collective learning could be identified through keywords or phrases or key sentences, and it could also be inferred from the team design goals, input knowledge and output knowledge of the team design activities. For example, in the team design activity in Table 9 the collective learning goal of Gi, that whether the tank needed to be sealed or not, is inferred from the keywords “Gi: That doesn’t have to be sealed…”, and the input knowledge of both Da and Pa. In another example in Table 6 Ma’s learning goal to understand Da’s design idea is identified through Ma’s question “What do you mean have just a thing up here (pointing to gadget) or what?” and Da’s answer to his question.

7. Four Modes of Collective Learning

Through protocol analysis, four modes of collective learning have been classified by modes refer to the relationships between the agents’ interactions: In the first mode, the input knowledge for collective learning comes from one of the other agents. For example, in Table 10, Ma learns from Gi that two valves can be used as filtering system. The second mode is that the agent learns through input knowledge from more than one of the other agents. In this mode, there is more than one of other agents providing input knowledge for the agent’s learning activity. In Table 11, Pa learns from both Ma and Gi in that they may make a prototype model for their design. The third mode is that an agent learns through both input knowledge from itself and others. For example, in Table 12, Gi learns that her own design idea of the top of the cartridge is infeasible, derived from her own knowledge and being reminded by Da. In the final mode, one agent learns the knowledge from another agent and then shares that knowledge with other agents. For example, in Table 13, Ma learns the knowledge from Pa that the tank can be designed to be detachable and then share this knowledge with Da. In this learning, it can be understood that Da can learn from Ma directly and can learn from Pa indirectly through Ma.

TABLE 10. First mode of collective learning: an agent learns through input knowledge from another agent

Ma: I think we should just keep it pretty simple because the tank that’s here at the moment is just … (Gillian smiles) and it’s got no filters or pumps or anything.

Gi: No, well it’s got two one-way valves in the system to prevent stuff getting passed through.

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Ma: Yes, we can put two valves on this.

TABLE 11. Second mode of collective learning: the agent learns through input knowledge from more than one of the other agents

Pa: So what are they expecting us to do then? Ma: I don’t know. Gi: Make a model as best we can. It’s only gonna be like a kind of prototype. It’s not like

to be sold. Ma: Or we can just design anything then make a model out of whatever, wood or

something. Gi: We just have to make a representation. Pa: It would be good if we had a prototype. Again we are dependent on Gerry coming up

with the goods. Ma: I think we should just work out a way of doing it without Gerry because to wait on him

when he hasn’t done the programme in like three weeks, then we’re in big trouble. Pa: I think we need to go down and speak to him today again.

ANALYSIS TO INVESTIGATE COLLECTIVE LEARNING IN DESIGN 17

Figure 3 Four modes of collective learning in design

8. Forms of Collective Learning

Within each mode there can be different forms of collective learning. To date, three forms of collective learning have been identified: • Mutual learning, • Common learning, and • Combining knowledge.

8.1 MUTUAL LEARNING

Through collective learning the knowledge states of agents can evolve mutually. The mutual learning process can include four phases (see Figure 4): the agent1 learns a deign idea from agent2, and then produces a new design idea. Agent2 learns that idea and produces another new idea. Agent1 and agent2 can interact with other agents and mutually evolve their knowledge. In the protocol, mutual evolution has been observed. For example, in the protocol in Table 14, Da and Gi evolved their idea mutually. Da had a vague idea to design a cap fitted into the top of the cartridge, and that idea was clarified reminded by Gi’s idea, and in turn Gi learned that design idea.

8.2 COMMON LEARNING

All the agents in the team may learn the same piece of knowledge at the same time or at different times, namely common learning. In the protocol, the common learning activity can be identified using key words like “yeah”, and the context of the communication. For example, in Table 15, Gi learned from Gerry that they have to use cartridge because they are using the cradle,

Mode 1: one to many Mode 3: many to one plus itself

Mode 2: many to one Mode 4: combination of different modes

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and she shared this piece of knowledge to the rest of the team members. It can be inferred that Da and Ma learned this piece of knowledge through their verbalisation of the key words “yeah”. It can also be inferred that the rest of the members learned this knowledge from the context of the protocol, for in the following communication, they started to discuss how to design the cartridge. Common learning can also be identified by the verbalisation. For example, the verbalisation, “Gi: Gerry told us what the maximum volume was for that kind of build…”, indicates that all the team members can learn the same piece of knowledge, the maximum volume of the flow, from Gerry.

Figure 4 Mutual knowledge evolutions

TABLE 14. An example of mutual learning

Da: But I reckon if you, instead of using these caps, if you just have (drawing)… Gi: Yeah Da: That just fits in the top - I’m trying to think of an example. You know the kind of

thing I’m talking about. Gi: Yeah, like the steradent tops? Da: Yes, is that the stuff they use for dentures? Gi: Yeah. Da: Yes, that idea.

TABLE 15. An example of common learning

Gi: Right, we have to use cartridge because we are using the cradle, because Gerry said so. So we have to design a little bit to go on the top of this about this topic.

Da: Yeah. It is not just the tube into the ink recess or whatever it is called. Ma: Yeah

…

Learning

Design

Learning

Design

1

2

3

4

Agent 1 Agent 2 Agentm

Agentn

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that link the input knowledge and output knowledge in team design activity and collective learning activity. In Sim’s model of learning in design (Sim, 2000), the mapping relationship between the type of the knowledge transformers and the type of design activities was observed. Such a mapping relationship was also postulated to exist in collective learning. The mapping result is shown in Table 18. It is found that the transformer acquisition/sharing is more likely used in the team design activities like detailing, decision-making, analysis, exploring, identifying, information gathering, and planning. The transformer Derivation (Reformulation) was more likely used in the design activities like decision making, analysis, exploring, identifying, and selecting, and the transformer Requesting/ Replying was more like used in the design activity like exploring.

TABLE 18. Mapping between knowledge transformers of collective learning and categories of team design activities inferred in the protocol analysis

Knowledge transformer Design definition activities

Design evaluation activities

Design management activities

Acquisition/Sharing Detailing Decision making, analysis

Exploring, identifying, information gathering, planning

Derivations (Reformulation)/ Randomisation

Decision making, Analysis

Exploring, Identifying, Selecting

Requesting/Replying Exploring

9.2 THE TEMPORAL LINK

In section 6.2.4 three types of temporal triggers in collective learning are identified: retrospective, in-situ, and provisional trigger. Collective learning can occur before, during, or after the team design activities. Similar to individual learning, there is temporal link between team design activities and collective learning (see Figure 5).

9.3 THE TELEOLOGICAL LINK

In section 6.2.5 and 6.1.1, the goal of collective learning and the goal of the team working were identified. In this section, the relationship between them is investigated.

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Learning and memory are interconnected – what we already have in our memory affects what we learn and what we learn affects our memory (Kim, 1993). To correspond to collective learning, the term of collective memory is used, which is defined as the sum of the knowledge in agents in a design team and the knowledge resources that shared by all the agents in the team. Collective memory plays the role of storage of knowledge in collaborative design

The modes of collective learning decide the mode of input knowledge: whether only one or more than one agent provides input knowledge for another agent’s learning activity, or whether the agent’s own knowledge, together with other agents’ input knowledge, are provided in its learning activity, or the output knowledge of one agent’s collective learning activity serves as input knowledge for another agent’s design activity through sharing. The input knowledge is stored in individual memories. Agents learn from one another through interaction. There are inextricable links between team working and collective learning. Three types of links between team design activities and collective learning activities are identified: the epistemic link, teleological link and temporal link. There are reasons for one agent learns from other agents, called rational triggers, which trigger collective learning. What is learnt collectively may be shared with other agents in the actual or future design activities, such as the design Table 13 where the knowledge learned from Pa is used in current design and it is possible that what is learnt can be used in the future design, which provides evidence for a team design and collective learning loop. The model is developed based upon the protocol analysis of team design and an existing model of learning in design (Sim, 2000). Such a model can to some degree describe the phenomena of collective learning in design and in the future work the model will be further formalized and evaluated.

11. Conclusion

To be able to use computers to support design, the nature of design should be understood before hand. Learning in design has been investigated since the 1980’s and models and theories have been developed. While most of the research is focused on individual learning in design, no comprehensive model of collective learning in design has been found prior to this paper. In current literature, there is some work related to collective learning in organisational theory and computer science, and some work in design that reflects some aspects of collective learning in design. However, a comprehensive model of collective learning is not presented in their work.

ANALYSIS TO INVESTIGATE COLLECTIVE LEARNING IN DESIGN 23

In this paper, the phenomena of collective learning in design have been investigated using protocol analysis. The elements of team design and collective learning, the links between team design and collective learning, modes of collective learning, and different forms of collective learning have been analyzed, and a model of collective learning in design has been proposed. There are different forms of collective learning, namely mutual learning, common learning and combining knowledge, which make it unique from individual learning. In future work, the proposed model of collective learning in design will be formalized and evaluated, and the investigation of collective learning will focus on how a design team as a whole learns and how collective learning and individual learning relate to each other. Computational supported means for collective learning will also be investigated.

Acknowledgements

We are indebted to Ms Christin Rankin who spent many hours to transcribe the tape for us and to the fifth-year students in DMEM, University of Strathclyde who were co-operative in the recording. Special thanks also go to the anonymous reviewers of this paper who had insightful comment on it.

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Individual Memory

Individual Memory

Or

Team design and agenti’s collective learning interaction

CLtr: Da (∑agenti) → Kt (∑agenti)

CLti: Da (∑agenti) | | Kt (∑agenti)

CLtp: Da (∑agenti) ← Kt (∑agenti)

Collective Memory

∑∑ O (agenti)

Knowledge of knowledge transformer, Kt (∑agenti)

Mapping between Da (T) and

Kt (∑agenti)

Other shared knowledge Epistemic link

Da (T) :: Kt (∑agenti)

Collective Learning triggers Rational trigger in collective

learning CLtw

Temporal triggers in collective learning CLt

CLtr

CLti

CLtp Temporal Link

Teleological link Ltw (agenti) Lg (agenti) Dg

(T) Dg (T) Lg (agenti)

∑ Ik(agenti) O (agenti)

Collective learning

mode

Figure 6. A model of collective learning in design