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Centre for Applied Linguistics Sophie Reissner-Roubicek and Sue Wharton PAD & ELLTA 12 October 2011 EAP for statistics students: Issues in teaching, and.

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Presentation on theme: "Centre for Applied Linguistics Sophie Reissner-Roubicek and Sue Wharton PAD & ELLTA 12 October 2011 EAP for statistics students: Issues in teaching, and."— Presentation transcript:

1 Centre for Applied Linguistics Sophie Reissner-Roubicek and Sue Wharton PAD & ELLTA 12 October 2011 EAP for statistics students: Issues in teaching, and findings from a learner corpus

2 Centre for Applied Linguistics ESAP for MORSE students Plan: to provide 10 hours per week of “English support” to the target student group Who were the target group? Expectations

3 Centre for Applied Linguistics Meeting their needs What were their needs? – According to Year 1 Head Tutor: Pragmatic and sociocultural competence - beyond the boundaries of the classroom – According to Stats HoD, Deputy HoD and CAL: Academic literacy - EAP and ESAP – Background research: Journal of Statistics Education, etc. – Self-report Pairwork survey question design task

4 Centre for Applied Linguistics Materials development International Office Student Survey Article on use of statistics in AL BAWE corpus Texts from Journal of Statistics Education “Personal statements” database

5 Centre for Applied Linguistics What worked for them Having time to ask questions about, discuss and practice things that came up – idiom, register, modality, pronunciation etc Finding themselves in The Zone Deconstructing the “constructive criticism” section of Stats Lab reports Etc.

6 Centre for Applied Linguistics The learner corpus Plan: collect texts written by undergraduate statistics students Which text to choose? How to collect them? How to make them into a corpus?

7 Centre for Applied Linguistics Choosing texts Discussion with statistics department teachers Identification of ‘Stats Lab’ assignment where students are asked to: – Provide analyses of data in written form – Discuss the significance of these analyses – Justify their decisions – Constructively criticise their own analyses – Etc.

8 Centre for Applied Linguistics Collecting the corpus An information session for students A university consent form The help of a research assistant Co-operation from the Stats department

9 Centre for Applied Linguistics Making texts into a corpus Transcription into.txt documents Choosing tags Recording contextual information Resulted in a corpus of 40 texts, words in total. Shortest text: 523 words. Longest text: 1977 words. Sophie Reissner-Roubicek and Sue Wharton, University of Warwick 12 October 2011

10 Centre for Applied Linguistics An example from a transcribed text: The graph clearly shows a positive correlation, but no strong. First, there is an outlier, which is unrepresentative. Obviously it is not a reliable data for our further prediction. In order to reduce the error of our prediction which generated by the outlier, we consider to ignore it in our later calculation. Second, more than 2/3 of data are lied between. The Data is not well distributed symmetrically for x values and hence increasing the error of our prediction. We choose Simpler liner Regression Model to describe this group of data and use the best fit line to predict future values.. Now we are able to use this equation to estimate the missing life expectancy value for Chile: >

11 Centre for Applied Linguistics An example of contextual information Student Number 1 Nationality […] A Levels […] Week 5 Lab leader 1 Assignment 1 Group 3 Mark x/60 49

12 Centre for Applied Linguistics Choosing a task to focus on Study an OECD table on total health expenditure per capita and life expectancy at birth for member nations. Plot a graph of life expectancy vs. total health expenditure Discuss what the graph shows Use the graph to estimate a life expectancy for Chile. Sophie Reissner-Roubicek and Sue Wharton, University of Warwick 12 October 2011

13 Centre for Applied Linguistics Why focus on data description? Relatively under-researched, but frequently set in science/technology/mathematical subjects BAWE: – categorises data description assignments under the genre family label ‘Exercise’ – categorises assignments produced for a Statistics course under the discipline of Mathematics – Of the 34 Mathematics assignments in BAWE, 15 are ‘Exercises’.

14 Centre for Applied Linguistics An analytical focus: Stance A writer’s opinion or attitude towards a proposition that their sentence expresses Allows writer positioning vis a vis not only the information expressed but also the construed readership Consensus in EAP that it is challenging for NNES

15 Centre for Applied Linguistics Looking for types of stance Bottom up coding using Nvivo 8 Searching for apparent stance types in individual texts Deciding to focus on certain content categories

16 Centre for Applied Linguistics Sophie Reissner-Roubicek and Sue Wharton, University of Warwick 12 October 2011

17 Centre for Applied Linguistics Types of stance: final categories Bare assertion: 119 instances over 39 texts Hedged assertion: 81 instances over 32 texts Vague assertion: 44 instances over 23 texts Boosted assertion: 15 instances over 14 texts Assertion with inclusive ‘we’: 14 instances over 12 texts

18 Centre for Applied Linguistics Repertoire for expressing stance types Bare assertion: frequent language choices is, (57 times including lemma forms). E.g. ‘there is an outlier’, ‘there are a few outliers’. show, (29 times including lemma forms). E.g. ‘the scatter plot above shows that….’ have (13 times). E.g. ‘it has the weak positive correlation’. No other choices appear more than 5 times.

19 Centre for Applied Linguistics Hedged assertion: frequent language choices May (11 instances): ‘life expectancy value for Chile may be near 73.3 years’. Could (5 instances). indicate as an alternative to show (6 times). Estimate (16 times) (but it is in the task brief) possible (4 instances) relatively (also 4) maybe ( 1 instance) might (2 instances).

20 Centre for Applied Linguistics Vague assertion: frequent language choices about, (14 times) between (7), most (7), half (5), more (4) over(4). An apparent preference for overquantifying rather than underquantifying – most, more and over account for 15 occurrences between them. No examples of few, less, fewer, or under. Below appears once.

21 Centre for Applied Linguistics Inclusive we – frequent language choices A strong association with the metaphorical use of the verb see – 9 of the instances are some variation on the phrase ‘we can see’.

22 Centre for Applied Linguistics Pedagogic implications of this The choice of stance type may well be systematic and based on insider knowledge; how can we exploit this? The repertoire for each stance type is very narrow – how can we broaden it?

23 Centre for Applied Linguistics Generally: Raising awareness among Stats teaching staff – Includes lecturers, tutors, and “supervisors” Developing student writing through a consciousness-raising approach – Corpus-based activities designed to promote “noticing” Specifically: To expand the students’ stance repertoire Moving outwards: To extend the analysis to the ‘constructive criticism’ section To look at tutor feedback in the corpus section Goals for include :


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