The mini-study (Cutting 2009) described for this chapter is a pilot for part of a larger project about the spoken interaction of international students in UK universities. It examined video-recordings of five tutorials in TESOL Masters degree courses. Table 10.1 shows the tutorial composition. Although the tutori- als were all about three hours long, only the parts which were not in lecture mode were analysed.
The research question was ‘Do lecturer linguistic features correlate with student talking time?’ The academic discourse community in-group code out- lined by Cutting (2000) was used as the linguistic features model. Thus the lexis included technical terms (e.g. ‘task-based learning’, ‘schemata’), general nouns (e.g. ‘they can recognize the whole thing’), general verbs (e.g. ‘what happened?’), col- loquial words (e.g. ‘This is a bit dodgy for health and safety’) and short names, and the grammar consisted of deixis (e.g. ‘That’s not what they advertised’, ‘Whose is this?’), indefinite pronouns (e.g. ‘Everybody ready?’), discourse mark- ers (e.g. ‘Right, let’s move on then’), fillers and hedges (e.g. ‘They can sort of work out’, ‘we try er very hard’) and backchannels (e.g. ‘Aha aha’, ‘Mhm’). The fact that in this project, some syntactical, interactional and disfluency features were considered to be spoken grammar, and other disfluency features (overlaps and interruptions) were called structural features, shows that there is no one way to conceive and arrange these elements. The density of lecturer linguistic features was measured as the percentage of features out of all lecturer words. Student talking time was measured in seconds.
Table 10.1 Tutorial length and content
Tutorial Duration Words Content
1hr 30mins 4,076 student presentations, plenary
discussion
2hrs 47mins 8,250 lecturer interactive lecture, plenary
discussion
2hrs 8,825 student presentations, plenary
discussion
2hrs 30mins 16,303 student posters, small-group
discussion
20mins 1,040 student presentations, plenary
Quantitative analysis showed differences between features and tutorials but since the study had such a small database, the findings are not generaliz- able. The lecturers seemed to use more of the grammatical features than the lexical (see Table 10.2). This is hardly surprising as pronouns and interactional word chunks are more frequent than technical terms and vague informal lexis in most spoken interaction, because of the low lexical density in semi-planned spoken discourse. This result is possibly also a reflection of the decision to include interactional word chunks in the ‘grammar’ category.
There was a higher density of lecturer linguistic features in tutorial 1 than in tutorials 3 and 4. This might be because tutorial 1 lecturer had a more relaxed manner than the others. Tutorial 1 had less student talking time than tutorials 3 and 4 (see Table 10.3). In the case of tutorial 4, it could be that the poster and small group discussions gave students more opportunities to talk. However tutorial 3 had the same format as tutorial 1, so it is not clear whether the peda- gogical technique influenced the amount of student talking. It appears that the more lecturers used the in-group code features under examination, the less the students participated. A more extensive study could test whether this negative correlation is a widespread tendency.
A qualitative impression of the data seemed to support this new hypothesis. When there was a clustering of the lecturer linguistic features, student partici- pation appeared to be hampered. In excerpt (7), it could be that the lecturer’s indefinite pronoun, technical word and colloquial verb, possibly unknown
Table 10.2 Percentage of lecturer linguistic features in each tutorial
0 2 4 6 8 10 12 14 1 2 3 4 5 tutorials % features/all wds Lexical Grammatical
Continuum Companion to Discourse Analysis
to the non-English-native-speaker students, explain the low student talking time.
Excerpt (7)
LF3 So, can you give me something under the heading of micro-skills? (3) What was it? Eh, you two have a very good satisfying agreement. What would you put your success down to? (2) How did you start your conversation?
S4 Eh, self-introduction.
LF3 Aha
S4 And eh
LF3 OK.
Note the long pauses when the lecturer is waiting for the students to answer, and the fact that she has to re-phrase her question three times; the student’s responses are almost mono-syllabic. In excerpt (8) it may be that the lecturer’s general nouns and verbs are responsible for the short student turns
Excerpt (8)
LF2 = What sort of things that really actually happen (0.5) with eh, adjectives, when do people use a lot of adjectives? =
S3 = Mm =
LF2 = You know, you have to try and think //
S3 // Descriptions 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 % in seconds Lecturer Students Table 10.3 Percentage of lecturer and student talking time in each tutorial
LF2 Yeah. Yes. But but when will you use adjectives?
S2 Relevant contexts
LF2 What kind of, sorry?
S2 Relevant contexts.
LF2 Yeah. What what would they be, you know? S3 (explains to OM2) She means in real life. LF2 Yeah. What would //
S2 // Describe people.
The lecturer’s recasting, pause, filler and repetitions suggest that she might be struggling to make herself understood. In the third example from this study, the lecturer’s question contains a general noun, a filler, a pause and repetitions:
Excerpt (9)
LM1 Eh, are those (0.5) are those texts going to have all of those
activities or are those activities going to run across, some with one
text and some with another text, and or what, what’s your thinking there? (3)
S1 Eh. (6)
LM1 It’s OK to say “ I haven’t thought about that so far.” // OK? All // (laugh)
It is, in all probability, not the general noun or disfluency features that flum- mox the students however, but the complexity of the question’s grammar, with its ‘or’, its ‘and or’, and its three-questions-in-one structure. The nine-second pause is hardly broken by the student’s ‘Eh’.
In summary, while it is undeniable that certain teaching formats, strategies and techniques increase student talking time, it could be that lecturer vague language explains the student silences. Further research might show that other ways of measuring student participation give different results, or that other variables such as lecturer speech acts, PP or CP maxim observance, influence student participation more than lecturer language does.
Conclusion
New directions in this field are constantly emerging. Corpus linguistics is likely to incorporate more the other approaches to language analysis and look at language in its socio-functional context. Future studies are likely to explore further the multimodal aspects of spoken discourse, following on after studies of linguists such as Adolphs and Carter (2007) who video-recorded lecturers in tutorials, electronically tracing their head and hand movements, so as to see how active listenership is demonstrated by head-nods matched with verbal
Continuum Companion to Discourse Analysis
backchannels. With the development of electronic means of communication, there will be a growing number of studies of CMC conversations, and the com- parison with face-to-face conversations will show clearer the similarities and differences. In addition, there will be a growth in spoken discourse studies of languages other than English.
The increasing number of spoken corpora means that language learning coursebook writers can incorporate naturally occurring data into their mate- rials, and use the findings from studies in their task design. There are books that do this already: Exploring Spoken English (Carter and McCarthy 1997: 7) and
Touchstone (McCarthy et al. 2005). However, caution is recommended. Some
learners do not want to sound native-speaker-like; others have a negative atti- tude to the target language culture; for some, vague informal language has negative associations in their mother tongue. As Beebe (1988: 63) pointed out, second language learners ‘may find that the reward of being fluent in the target language is not worth the cost in lost identification and solidarity with their own native language group’. EFL teachers may prefer just to raise awareness, explain native-speaker social associations and provide choices, so that learners can opt in or opt out (Cutting 2005: 174).
Transcription Key
= indicates overlap,
// indicates interruption,
(3), (8) etc. indicate the number of seconds’ pause
Key Readings
Adolphs, S. (2008), Corpus and Context: Investigating Pragmatic Functions in Spoken Language. Amsterdam: John Benjamins.
Anderson, W. and Corbett, J. (2009), Exploring English with Online Corpora. Basingstoke: Palgrave Macmillan.
Cameron, D. (2001), Working with Spoken Discourse. London: Sage.
Carter, R. and McCarthy, M. (1997), Exploring Spoken English. Cambridge: Cambridge University Press.
Cutting, J. (ed.) (2007), Vague Language Explored. Basingstoke: Palgrave.
McCarthy, M. (1998), Spoken Language and Applied Linguistics. Cambridge: Cambridge University Press.
Thornbury, S. and Slade, D. (2006), Conversation: From Description to Pedagogy. Cambridge: Cambridge University Press.