Knowledge Base

In Charness, Krampe and Mayr’s (1996) framework of factors supporting expertise/skill acquisition, knowledge base (chunk size, retrieval structures) is an integral part of the software of cognitive system. Similarly, Simon and Chase (1973) proposed that expert performance in ‘any skilled task (e.g. football, music)’ (1973: 279) was the result of vast amounts of knowledge and pattern-based retrieval acquired over many years of experience in the associated domain. Shreve suggested that knowledge is an ‘essential prerequisite to expert skill’ (2002: 155). This conception of expertise is consistent with theories of skill acquisition (Anderson, 1983; Fitts & Posner, 1967), based on the assumption that knowledge is first acquired (i.e. declarative knowledge) and then organized into procedures (i.e. procedural knowledge) for responding to encountered situations. For the development of expertise, knowledge must be acquired in such a way that it is highly connected and articulated, so that inference and reasoning are enabled, as is access to procedural actions. The resulting organization of knowledge provides a schema for thinking and cognitive activity.

Knowledge is also one of the three components of Alexander’s (2004) Model of Domain Learning. According to Alexander (2003), when learners orient to a complex, unfamiliar domain, they have limited and fragmented knowledge. They lack a cohesive and well-integrated body of domain knowledge. As individuals progress towards expertise, quantitative and qualitative changes occur in their knowledge base. Experts not only demonstrate a foundational body of domain knowledge, but that knowledge is also more cohesive and principled in structure. Similarly, Hoffman has made the observation that ‘the development of expertise involves a progression from a superficial

and literal understanding of problems (a qualitative mark of the cognition of novices), to an articulated, conceptual, and principled understanding (a qualitative mark of the cognition of experts)’ (1997: 197).

The knowledge base of a person, it is now generally assumed, is made up of different types of knowledge. The best-known examples are declarative and procedural knowledge (e.g. Sternberg, 2000), but more elaborate distinctions exist (e.g. Alexander & Judy, 1988; Alexander, 1997; Garcia & Pintrich, 1994; De Jong & Ferguson-Hessler, 1996). In Anderson et al.’s (2001) revised Taxonomy for Learning, Teaching and Assessing, four distinct types of knowledge are defined according to a taxonomy of learning outcomes: factual, conceptual, procedural and metacognitive. While the first three categories were included in the original Taxonomy by Bloom and his colleagues (1956), the metacognitive knowledge was added to the new Taxonomy. Metacognitive knowledge involves knowledge about cognition in general, as well as awareness of and knowledge about one’s own cognition (Anderson et al., 2001: 29; Pintrich, 2002). According to Krathwohl (2002: 214), metacognitive knowledge is of increasing significance as it is important for students to be made aware of their metacognitive activity, and then to use this knowledge to appropriately adapt the ways in which they think and operate.

A review of the literature on interpreting expertise revealed that no prior study has attempted to link metacognitive knowledge to the development of expertise in interpreting for trainee interpreters. Instead, previous studies have generally focused on those types/categories of knowledge that are thought to be immediately related to the execution of interpreting tasks. For instance, Gile (2009: 8–10) argues that interpreters must have (a) good passive knowledge of their passive working languages; (b) good command of their active working languages; (c) sufficient knowledge of the themes and subject-matters addressed by the speeches they interpret; and (d) both declarative and procedural knowledge about interpreting. Gile (2009: 110) further defines the interpreter’s knowledge base, which he notes is necessary for both comprehension and reformulation, as comprising knowledge of the source and target languages (linguistic knowledge) and knowledge of the world (extralinguistic knowledge). Hoffman (1997: 201) cites sources as stating that expert interpreters need to possess ‘encyclopaedic knowledge’, need to continually enrich and expand their ‘world knowledge’ (Viaggio, 1992a, b), must have a broad general knowledge (AIIC statement), must know both source and target languages and accents thoroughly, must know the source and target

cultures thoroughly, must know the topic being interpreted, must have skills in some sort of short-hand notation, must possess a comprehensive vocabulary, and must have a ‘powerful’ memory and a comprehensive general knowledge. Moser-Mercer et al. (2000: 108–109) have shown that the differences between expert interpreters and novices in terms of their knowledge base and its organization relate to four categories: factual knowledge, semantic knowledge, schematic knowledge and strategic knowledge. In terms of strategic knowledge, Moser-Mercer et al. suggest that experts tend to proceed from known to unknown information, whereas novice interpreters more often focus on the unknown and then easily get stuck. Experts thus use more global plans, whereas novices tend to use low-level microcontextual plans (2000: 109). Kurz states that professional conference interpreters have, through their training and experience, acquired sufficient expertise (defined by Kurz as ‘a combination of knowledge and better strategies’), which is reflected in the ability to process larger segments, and to adopt the right strategy quickly, or even automatically (2003: 60). Kurz observes that experts (professional conference interpreters) and novices (student interpreters) have been found to differ in terms of meaningful patterns of information, organization of knowledge, and context and access to knowledge (2003: 58–59).

It is relatively easy to see why past research has focused on some types/categories of knowledge that are thought to be immediately related to the execution of interpreting tasks, such as general knowledge, cultural knowledge, linguistic knowledge, textual knowledge, transfer knowledge and subject knowledge, while neglecting other significant types of knowledge, such as metacognitive knowledge, which can play an important role in student learning (Pintrich, 2002). This is because they have focused on the nature of expertise in interpreting rather than on the acquisition/development of expertise in interpreting. They have focused on the nature of the knowledge of expert and novice interpreters rather than on the knowledge of trainee interpreters. They have focused on what interpreters know so as to be able to interpret effectively, but not on what trainee interpreters know so as to be able to learn effectively. They have focused on the differences in the nature of the knowledge possessed by expert interpreters (professional conference interpreters) and novice interpreters (student interpreters), but not on the differences between relatively high-achieving and low-achieving trainee interpreters under the category of novice interpreters. We know from past research that the nature of the knowledge of the expert interpreter differs from that of the novice in profound ways. Yet, little is known about how expert learners and novice learners of

conference interpreting differ in their metacognitive knowledge about conference interpreting learning. Thus, an important purpose of this study is to address this gap in the literature.