Transcript A Tentative Study on the Effects of Task Features on L2

A Tentative Study on the Effects
of Task Features on L2 Writing
Performance: Topic and Genre
Effects on Lexical Richness
Ma Rui Tianjin Normal University
Email: [email protected]
Motivation for the research
 Tasks play a central role in current SLA research and
language pedagogy, thus trigger the exploration of their
contextualization in language assessment.
 Sequencing and generalizing tasks in the scope of
language testing have always been problematic. In fact,
identification of valid sequencing criteria is ‘one of the
oldest unsolved problems’ (Brown 1989: 42).
 Task feature analysis is more initial and essential for task
sequence potential.
 most of the studies focused on speaking rather than
writing as productive language skills
The role of task features in writing
 Task features (or variables) in writing tests are ‘those
elements that must be manipulated and controlled to
give every test taker the opportunity to produce his or
her best performance’ (Hamp-Lyons 1990: 73).
 To describe whether and how some of the features affect
language performance by manipulating task variables
 In standardized writing tests like IELTS , topic variable is
the only manipulated feature within the same genre that
makes difference between tasks, while different genres
might affect writing performance differently
Lexical richness
According to Read, there are generally four
approaches to measure lexical richness
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lexical variation (LV)
lexical sophistication (LS)
lexical density (LD)
number of errors
Measures of lexical variation
 Type/token ratio (TTR)
 Standardised type/token ratio (STTR)
Type/token ratio (TTR)
num berof different words ( word type)
TTR 
 100%
total num berof words in the text (token)

Standardised type/token ratio (STTR)
In order to avoid unreliability, a modified
method is used—standardised type/token
ratio.
STTR is computed in every n words, then
a running average is computed, which
means that you get an average TTR
based on consecutive n-word chunks of
text
Measures of lexical sophistication
 The Lexical Frequency Profile (LFP)
 P_Lex
P_lex
‘P_Lex is a computer programme to
assess lexical difficulty of the texts. It is
specially designed to assess the lexical
richness of texts’ (P_Lex Instruction: 1).
P_Lex takes word frequency as the basic
theory for classification.
 ‘Difficult’ in this context means any word
which is not found in the high frequency
list in P_Lex dictionary files.
The study
Research Questions
Do topics in descriptive writing affect
performance in terms of lexical richness?
Do topics in argumentative writing affect
performance in terms of lexical richness?
Does genre affect performance in terms of
word richness?
The study
Subjects
79 L1 Chinese mainland learners who
took the four-week EAP course conducted
by the Lancaster University Linguistics
Department in 2004
Language proficiency: 5.5-6.5 in IELTS
The study
Data collection
All data involved in this study comes from
LANCAWE corpus, which is an ongoing
project at the moment.
The study
Design of the study
independent variables: topic & genre
dependent variables: lexical richness
test-retest method was adopted to check
the reliability of the research (T2 & T3)
The study
Methods and procedures
1. Task characteristics analysis
topic variable was elicited as the only
difference among the three tests
Topics of tasks in the 3 tests
The study
 Methods and procedures
2. Statistic measurement
oneway ANOVA, paired samples T-test,
and correlation analysis have been
conducted in the study.
Results and findings
For STTR, The results got in T2 were always
opposite to the results in T3.
Results and findings
 There are indications that measure of variation
by means of TTR or STTR is unsuitable for
proficiency with 3000 words above (Vermeer
2000). The subjects in this study is supposed to
be beyond this level, which renders STTR
measures less suitable for this study. Vermeer
suggested that more effective measures of
lexical richness might be based not on the
distribution of or the relation between the types
and tokens, but on the degree of difficulty of the
words used. Inspired by Vermeer’s conclusion,
we are more confident of the validity of using
P_Lex as a measure of lexical richness.
Results and findings
Taking research findings from P_Lex, it seems
clear to answer the research questions:
 Topic does not significantly affect lexical
richness in descriptive writing
 There is a significant difference between topics
on lexical richness in argumentative writing.
 Lexical richness in descriptive writing is clearly
distinct from that in argumentative writing. The
effect of genre difference is statistically
significant.
Conclusions
For the insight of these findings in terms of
task design, we might tentatively say that
in convergent tasks, topic factor does not
cause any difference on performance
according to lexical richness. However, in
divergent tasks, we must be cautious of
the selection of different topics as they can
elicit difference on performance.
Limitations of the study
Future research
References
 Brown, J. D. (1989) "Criterion-referenced test
reliability", University of Hawaii Working Papers
in English as a Second Language, 8(1), 79-113.
 Hamp-Lyons, L. (1991) "Basis concepts". In
Assessing second language writing in academic
contexts(Ed, Hamp-Lyons, L.): Norwood, NJ:
Ablex.
 Read, J. (2000) Assessing vocabulary:
Cambridge: Cambridge University Press.
 Vermeer, A. (2000) "Coming to grips with lexical
richness in spontaneous speech data",
Language Testing, 17(1), 65-83.