C OLLABORATIVE LEARNING PRACTICES

Q3a: How are the concept building processes of novice data modelling students influenced by the discursive practices of the classroom environment in which they take place?

Q3b: How do novice data modellers handle the coexistence of, and

interdependencies between, different discourse types when engaged in collaborative problem solving activities in a computer science classroom?

Learning can be described as a discursive practice that is situated in a social context.

These are two of the main presuppositions that form the theoretical foundation for the research presented in this thesis. These claims are, however, closely related to each other. By discursive practices is meant any activity that in some way includes, or depends upon, the socially situated formation and mediation of meaning (see chapter 3). “Contextual features such as where and when people act, the specific contents of problems and tasks, and other elements of situated action, have all been shown to

serve as resources through which people make tasks meaningful.” (Schoultz et al., 2001: p214).

Adopting a socio-cultural perspective on knowledge as the ability to participate in cultural practices, learning thus emerges as an outcome of discursive interaction.

Discourse is not restricted to verbal interaction, but includes a number of other semiotic processes. One of these is the reference to previous experience manifested as encounters (Wickman & Östman, 2002). The students described in paper 4 demonstrate such implicit reference to previous discursive experience when they base their elaborations on familiarity with the vernacular terms adopted from the world of banking. Similarly, the students in paper 1 also utilize discursive knowledge based on previous experience when they look for terms to denote their abstract relational phenomena, or when they carry a vernacular term’s meaning content into the entity of the data model labelled by the same term. In this manner, the encounters of the participants are omnipresent in any discursive interaction through their shared contextual background. This enables them to communicate without having to explicitly define and explain every term or concept that is introduced in the conversation. It is this shared cultural frame that enables natural language use to function, in spite of the ambiguity or impreciseness of most utterances (Wittgenstein, 1958). This flexibility of language use and the contextual background is not shared by the computer. As emphasized in paper 2, this is an important reason why students experience problems when modelling the world for implementation on a computer.

Similarly, students tend to take for granted the common knowledge within the group, while they in reality do not always have compatible perceptions of what an entity or class is supposed to represent (paper 1).

Given, for example, a data model that contains a class or entity type called

“brother”. This class or entity would have quite different connotations to a Norwegian teenager compared to those of an African-American (see discussion in section 2.1).

Creating attributes or linking this class or entity to other classes or entities would accordingly be done differently depending on the frame of reference.

Claiming that learning is a discursive activity thus implies that it is founded on exactly such a cultural historical framework, which in turn implies that all learning is situated in social practices and therefore needs to be interpreted within the context

where it takes place. This means that learning is largely helped by, or even dependent on, the participants having similar linguistic backgrounds.

The slightly different conceptual understandings held by professional members of a technical domain like banking is a central topic discussed by Bürkle et al. (1995).

In the first phase of their study, no measures were taken to allow the developers and customers to interact and negotiate the meanings of the constructs to be modelled and their labels, in order to establish common knowledge. This led to problems of communication that actually caused the project to fail. Providing arenas for the establishment and continuous negotiation of common knowledge, however, proved to be surprisingly efficient. Herbsleb et al. (1995) similarly found that expert modellers asked each other frequent questions to clarify what was meant by some element of the model. One would expect the students to make a similar effort to establish common knowledge. The university students did this to a certain extent (paper 4), while the high school students hardly did so (paper 1).

The importance of the discursive activity as resource for learning is further emphasized by the findings presented in paper 3. Language is a social construction, and the meanings of terms are defined by the way these terms are used in social practice of a particular language game. The social development of semiotic relationships between expression and content normally takes place over an extended period of time, and is subject to gradual change over generations. A word commonly used for something today, may have carried different connotations, or may not have been a part of the everyday discourse a couple of decades ago. The activity of data modelling, however, implies a constantly ongoing formation of new semiotic relationships (paper 4), and thus also new language games (paper 2). The meaning of terms transformed from vernacular lexis to the technical discourse of a particular data model is defined explicitly or implicitly through the discursive interaction between the participants of the modelling activity. Simultaneously, the students are introduced to the technical language game of data modelling – be it with ER, UML or other methodologies. Making sense of the different scientific concepts introduced as parts of these new (to the students) language games is, as demonstrated in paper 3, a social process. In fact, the students belonging to a particular group seem to be collaboratively constructing their own locally functioning language game of meanings and relationships between the scientific concepts they are introduced to.

This simultaneous development of spontaneous and scientific concept types is to a large extent accomplished by collective exploration of various semiotic systems through discursive interaction, an activity that demands of the data modelling practitioner (or student, in my case) that he or she distinguishes between different language games (Wittgenstein, 1958). Successful participation in this cultural practice requires metalinguistic knowledge in order to separate the new artificially constructed signs representing a simplified version of a part of the world from the closely related signs from natural language representing more or less closely related meanings (paper 1).

I have described learning from a socio-cultural perspective as becoming able to participate in a cultural practice. Following this view, scientific concept building implies enculturation into the community of scientific language users. Different scientific disciplines have their own linguistic subcultures with particular concepts and customs for language use. The research presented in this thesis has shown that concept building in computer science classrooms isn’t only a matter of enculturation into existing predefined linguistic practices. It appears that the students as members of the classroom community also form their own locally functioning linguistic practices endemic to the group. These collectively formed discursive practices of each classroom seem to out-compete the formal definitions provided by textbooks or teacher controlled instruction. This pattern is analogous to the one described by Bürkle et al. (1995) in which they observed differences in perception between members of different work units, even for seemingly standard concepts from the banking domain. In the data modelling classrooms of my study, such discursive processes take place both on the level of academic computer science discourse and on the level of talking about the problem domain and its representations in the data model that is being collaboratively constructed (papers 1 and 4). New language games are thus socially negotiated on several levels in these learning environments (paper 2).