introduction the educational system
TRANSCRIPT
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How Do Educational Settings at the Secondary Level
Impact on Learners' Use of the English Passive? –
Evidence from the Secondary-Level Corpus
of Learner English (SCooLE)
Learner Corpus Research, Bergen/Norway, 27-29 September 2013
Verena Möller
Université catholique de Louvain
Centre for English Corpus Linguistics
Universität Hildesheim
Institut für Informationswissenschaft
und Sprachtechnologie
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1. Educational Settings at the Secondary Level
2. The Passive as a Diagnostic Criterion
3. Compilation of the Corpora
• TeaMC (Teaching Materials Corpus)
• SCooLE (Secondary-Level Corpus of Learner English)
4. Evidence from the Corpora
• Procedure
• TeaMC
• SCooLE
LCR Bergen 2013 1
Overview
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Educational Settings at the Secondary Level
Baden-Württemberg
2 LCR Bergen 2013
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Educational Settings at the Secondary Level
EFL and Content and Language Integrated Learning (CLIL)
Schools without an English section
Schools with an English section
Year 12 Year 11 EFL EFL EFL+CLIL Year 10 (CLIL (CLIL available, (CLIL available Year 9 not but not and Year 8 available) chosen) chosen) Year 7
Year 6 EFL
Year 5
BUT:
"Is CLIL so beneficial, or just selective?" (Bruton 2011)
3 LCR Bergen 2013
"CLIL0" "CLIL-" "CLIL+"
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The Passive as a Diagnostic Criterion
Motivation
4 LCR Bergen 2013
Input:
CLIL materials differ from EFL materials in that they are
scientifically oriented, i. e. they resemble scientific text
Research suggests that the passive is characteristic
of scientific text
Svartvik 1966 (scientific text): 19.3 pass./1,000 w.
Wanner 2009 (research abstracts): 17.0 pass./1,000 w.
Wanner 2009 (research abstracts): 25.2 % of VPs
Holtz 2011 (research abstracts): 55.7 % of VPs
Holtz 2011 (research articles): 46.6 % of VPs
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The Passive as a Diagnostic Criterion
Motivation
5 LCR Bergen 2013
Strategies:
Less advanced learners may use synonymous
active structures
Lexis-grammar interface:
Less advanced learners may prefer passives
introduced by EFL materials as lexical chunks
before the passive is introduced
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Compilation of the TeaMC
Subcorpora/Linguistic annotation
TeaMC (980,773 words)
TeaMC (input) (568,328 words)
TeaMC (reference) (412,445 words)
Year EFL (149,015 words)
CLIL (419,313 words)
7 Geography (147,837 words)
8 History (202,596 words)
9 Politics (6,730 words)
10 Biology (62,150 words)
11 EFL
12 (412,445 words)
LCR Bergen 2013 6
POS-Tagging: • TreeTagger (cf. Schmid 1994)
• CLAWS (cf. Garside/Smith 1997)
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Compilation of the SCooLE
Text data
Discuss TWO of the following statements – choose one from set I and one from set II.
I. 1. In Germany, the education system offers equality of
opportunity to everyone, rich or poor.
2. Minority groups should make greater efforts to integrate
into the mainstream population.
3. Germany and the USA have a special relationship.
4. Privacy is a thing of the past.
Text 1: Arbeitsplatz Wechseldatenträger text1.txt rechte Maustaste "Öffnen mit" Editor Bitte das Speichern nicht vergessen! Text 2: Arbeitsplatz Wechseldatenträger text2.txt rechte Maustaste "Öffnen mit" Editor Bitte das Speichern nicht vergessen! Bitte arbeiten Sie NICHT mit Word.
II. 5. A better understanding between cultures can be created
by travelling to other countries as a tourist.
6. The death penalty should be reintroduced in Germany.
7. In order to fight teenage drinking, the legal drinking age
should be raised to 21.
8. In modern society, men and women are given equal chances.
7 LCR Bergen 2013
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Compilation of the SCooLE
Metadata on learner variables
STUDIE ZUR
ENGLISCHEN LERNERSPRACHE
BADEN-WÜRTTEMBERGISCHER
GYMNASIASTEN
I. Persönliche Angaben:
1. Teilnehmernummer: ______
2. Alter: ______ Jahre
3. Geschlecht: männlich
weiblich
4. Muttersprache (Sprache, in der
zuerst das Sprechen gelernt
wurde):
__________________________________________________
(bei mehrsprachiger Erziehung seit der Geburt
bitte alle betreffenden Sprachen angeben)
5. Sprachen, die täglich zu Hause
gesprochen werden:
__________________________________________________
(bitte alle betreffenden Sprachen angeben)
6. Gesamtdauer der Aufenthalte in
englischsprachigen Ländern:
keine
kürzer als 3 Monate
3-6 Monate
6-12 Monate
länger als 12 Monate ( ______ Jahre)
II. Fremdsprachen:
7. Erste Fremdsprache: __________________________________ seit ____ Jahren
sehr gut
eher gut
mittel- mäßig
eher schlecht
sehr schlecht
8. Zweite Fremdsprache: __________________________________ seit ____ Jahren
sehr gut
eher gut
mittel- mäßig
eher schlecht
sehr schlecht
9. Dritte Fremdsprache: __________________________________ seit ____ Jahren
sehr gut
eher gut
mittel- mäßig
eher schlecht
sehr schlecht
10. Vierte Fremdsprache __________________________________ seit ____ Jahren
sehr gut
eher gut
mittel- mäßig
eher schlecht
sehr schlecht
11. Weitere Fremdsprachen: __________________________________________________
8 LCR Bergen 2013
Informal questionnaire:
• age
• gender
• L1
• language(s) spoken at home
• cumulative duration of stays in
English-speaking countries
• other L2
• self-rated L2 competence
• school career
• educational settings attended
• spare time activities related to
the English language
• etc.
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Compilation of the SCooLE
Metadata on learner variables
9 LCR Bergen 2013
Psychometric test:
Aspects of intelligence
• overall
• verbal
• word recognition
• word fluency
• verbal reasoning
• non-verbal reasoning
• concentration
• etc.
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Compilation of the SCooLE
Metadata on learner variables
10 LCR Bergen 2013
Psychometric test:
Aspects of motivation
• orientation towards
performance and success
• perseverance and effort
• etc.
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Compilation of the SCooLE
Subcorpora
11
851 essays: > 250,000 words
LCR Bergen 2013
116,146
31,019
108,465
2,956
CLIL0 CLIL- CLIL+ information not given
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12
Compilation of the SCooLE
Challenges
Types of deviance:
• Omission of be:
e. g. *Should the death penalty reintroduced in Germany?
• Morphological and/or orthographic errors in the form of be
or related clitics:
e. g. *You arent forced to post anything in the internet.
• Morphological and/or orthographic errors in the past participle:
e. g. *[…] alcohol can just be buyed by 21 old people.
• Lexical errors:
e. g. *[…] so he is already prisoned by the police.
• Overpassivization:
e. g. *[…] the murderer can not try to murder another human
after they are released him from the prisoner.
• etc.
LCR Bergen 2013
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Compilation of the SCooLE
Annotation
Text data (electronic)
Text data + learner metadata (XML)
Learner variables (paper)
Normalisation of accents/apostrophes
VARD-based normalisation of deviances (cf. Baron/Rayson 2009)
Manual normalisation of (virtual) homophones
TreeTagger (cf. Schmid 1994) CLAWS (cf. Garside/Smith 1997)
Merging of TreeTagger and CLAWS annotations
CWB (cf. Evert/Hardie 2011)
Manual normalisation of passives
LCR Bergen 2013
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CANS 2013 14
Compilation of the SCooLE
Recall rates for beVed (pilot study)
Procedure TreeTagger CLAWS
Initial
134/149
(89.9 %)
137/149
(91.9 %)
Normalisation
of apostrophes
134/149
(89.9 %)
139/149
(93.3 %)
VARD-based
normalisation
140/149
(94.0 %)
139/149
(93.3 %)
Manual normalisation
of homophones
141/149
(94.6 %)
140/149
(94.0 %)
Manual normalisation
of passives
148/149
(99.3 %)
147/149
(98.7 %)
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I PP I PPIS1 I 'm VBP be VBM be not RB not XX not <passive constr="beved" oaldadj="no" target="yes"> <replaced meth="manual" orig="allow" type="false"> allowed VVN allow VVN allow </replaced> </passive> to TO to TO to buy VV buy VVI buy it PP it PPH1 it
TreeTagger CLAWS
15
Compilation of the SCooLE
Annotation
LCR Bergen 2013
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Evidence from the Corpora
Procedure
LCR Bergen 2013
Case 1:
TreeTagger and CLAWS agree on classification
of participle:
• be{0}Ved (be Ved with no intervening element)
• be{1}Ved (be Ved with one intervening element)
Automatic analysis of results, no manual check
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Evidence from the Corpora
Procedure
LCR Bergen 2013
Case 2:
TreeTagger and CLAWS do not agree on classification
of participle:
• be{0}Ved (be Ved with no intervening element)
• be{1}Ved (be Ved with one intervening element)
Automatic analysis of results + manual check
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Evidence from the TeaMC
Frequency: be{0}Ved and be{1}Ved per 1,000 words - Results
LCR Bergen 2013 18
0
2
4
6
8
10
12
14
16
18
EFL7-10
EFL11-12
CLILGeography
7-10
CLILHistory
7-10
CLILPolitics
7-10
CLILBiology
7-10
Relative number of passives is considerably higher in CLIL materials
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Evidence from the SCooLE
Frequency: be{0}Ved and be{1}Ved per 1,000 words - Results
LCR Bergen 2013 19
Participants in CLIL produce a higher relative number of passives
than are displayed in EFL materials for any level!
0
1
2
3
4
5
6
7
8
9
10
11
CLIL- CLIL0 CLIL+ EFL materials11-12
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Evidence from the SCooLE
Possible influence of learner variables – Intelligence (standard score)
LCR Bergen 2013 20
CLIL programmes have a tendency to sort students into
groups of (non-)participants according to their cognitive skills
90
92
94
96
98
100
102
104
106
108
110
verbal reasoning concentration overall
CLIL- CLIL0 CLIL+
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Evidence from the SCooLE
Possible influence of learner variables – Motivation (T-score)
LCR Bergen 2013 21
CLIL programmes have a tendency to sort students into
groups of (non-)participants according to their motivation
40
42
44
46
48
50
52
54
56
58
60
performance/success perseverance/effort
CLIL- CLIL0 CLIL+
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Evidence from the SCooLE
Possible influence of learner variables – Consequences
LCR Bergen 2013
Bruton 2011:
"Is CLIL so beneficial, or just selective?"
CLIL is indeed selective
Differences in the use of the English passive
may be due to factors other than educational
setting
Sound statistical analysis of data is needed
to determine the extent to which CLIL is
beneficial with respect to the English passive
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Evidence from the SCooLE
Passive ratio
LCR Bergen 2013 23
Granger 2013:
"Some verbs display strong passive attraction,
while others are characterized by passive repulsion."
Verbs with high passive ratio Verbs with low passive ratio
lemma passive ratio lemma passive ratio
oblige 68.2 % learn 4.2 %
deem 60.0 % receive 3.3 %
entitle 55.3 % attend 2.7 %
expect 53.5 % want 0.8 %
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Evidence from the SCooLE
Passive ratio (be{0}Ved and be{1}Ved)
LCR Bergen 2013 24
Lemma SCooLE TeaMC
CLIL- CLIL0 CLIL+ EFL 11-12
raise 40.0 % 36.3 % 31.7 % 18.8 %
allow 92.6 % 84.0 % 77.8 % 20.0 %
(re-)introduce 56.3 % 61.2 % 55.7 % 26.0 %
give 10.5 % 22.3 % 17.9 % 7.9 %
kill 11.4 % 17.7 % 28.7 % 22.2 %
discuss 58.8 % 18.1 % 24.2 % 7.7 %
make 5.3 % 6.0 % 8.3 % 7.0 %
treat 66.7 % 52.9 % 59.5 % 32.4 %
create 44.4 % 40.0 % 36.7 % 12.8 %
see 4.9 % 5.1 % 6.8 % 7.4 %
Most frequent passive forms (SCooLE: all subcorpora)
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Evidence from the SCooLE
Passive ratio (be{0}Ved and be{1}Ved)
LCR Bergen 2013 25
Lemma SCooLE TeaMC
CLIL- CLIL0 CLIL+ EFL 11-12
raise 40.0 % 36.3 % 31.7 % 18.8 %
allow 92.6 % 84.0 % 77.8 % 20.0 %
(re-)introduce 56.3 % 61.2 % 55.7 % 26.0 %
give 10.5 % 22.3 % 17.9 % 7.9 %
kill 11.4 % 17.7 % 28.7 % 22.2 %
discuss 58.8 % 18.1 % 24.2 % 7.7 %
make 5.3 % 6.0 % 8.3 % 7.0 %
treat 66.7 % 52.9 % 59.5 % 32.4 %
create 44.4 % 40.0 % 36.7 % 12.8 %
see 4.9 % 5.1 % 6.8 % 7.4 %
Overuse possibly triggered by prompts
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Evidence from the SCooLE
Passive ratio (be{0}Ved and be{1}Ved)
LCR Bergen 2013 26
Lemma SCooLE TeaMC
CLIL- CLIL0 CLIL+ EFL 11-12
raise 40.0 % 36.3 % 31.7 % 18.8 %
allow 92.6 % 84.0 % 77.8 % 20.0 %
(re-)introduce 56.3 % 61.2 % 55.7 % 26.0 %
give 10.5 % 22.3 % 17.9 % 7.9 %
kill 11.4 % 17.7 % 28.7 % 22.2 %
discuss 58.8 % 18.1 % 24.2 % 7.7 %
make 5.3 % 6.0 % 8.3 % 7.0 %
treat 66.7 % 52.9 % 59.5 % 32.4 %
create 44.4 % 40.0 % 36.7 % 12.8 %
see 4.9 % 5.1 % 6.8 % 7.4 %
Overuse possibly triggered by introduction
as lexical chunk in EFL materials
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Evidence from the SCooLE
Passive ratio (be{0}Ved and be{1}Ved)
LCR Bergen 2013 27
Lemma SCooLE TeaMC
CLIL- CLIL0 CLIL+ EFL 11-12
raise 40.0 % 36.3 % 31.7 % 18.8 %
allow 92.6 % 84.0 % 77.8 % 20.0 %
(re-)introduce 56.3 % 61.2 % 55.7 % 26.0 %
give 10.5 % 22.3 % 17.9 % 7.9 %
kill 11.4 % 17.7 % 28.7 % 22.2 %
discuss 58.8 % 18.1 % 24.2 % 7.7 %
make 5.3 % 6.0 % 8.3 % 7.0 %
treat 66.7 % 52.9 % 59.5 % 32.4 %
create 44.4 % 40.0 % 36.7 % 12.8 %
see 4.9 % 5.1 % 6.8 % 7.4 %
Overuse possibly triggered by transfer from German
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Evidence from the SCooLE
Passive ratio (be{0}Ved and be{1}Ved)
LCR Bergen 2013 28
Lemma SCooLE TeaMC
CLIL- CLIL0 CLIL+ EFL 11-12
raise 40.0 % 36.3 % 31.7 % 18.8 %
allow 92.6 % 84.0 % 77.8 % 20.0 %
(re-)introduce 56.3 % 61.2 % 55.7 % 26.0 %
give 10.5 % 22.3 % 17.9 % 7.9 %
kill 11.4 % 17.7 % 28.7 % 22.2 %
discuss 58.8 % 18.1 % 24.2 % 7.7 %
make 5.3 % 6.0 % 8.3 % 7.0 %
treat 66.7 % 52.9 % 59.5 % 32.4 %
create 44.4 % 40.0 % 36.7 % 12.8 %
see 4.9 % 5.1 % 6.8 % 7.4 %
Underuse possibly triggered by avoidance
in less proficient learners
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Conclusion
LCR Bergen 2013
1. Participants in CLIL programmes use a
higher relative number of passives.
This may be due to
• the type of input they are faced with in CLIL;
• learner variables.
Future work:
Statistical analysis of available data to
determine the exact influence of
educational setting.
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Conclusion
LCR Bergen 2013
2. All groups of learners overuse the passive
with respect to the passive ratio of some
verbs while underusing it with respect to
others.
This may, amongst others, be due to
• the treatment of passives by EFL materials;
• transfer from L1.
Future work:
• Analysis of the representation of passives
in EFL materials;
• analysis of corresponding verbs in L1.
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References
LCR Bergen 2013
Baron, Alistair & Rayson, Paul (2009). Automatic standardisation of texts containing spelling variation.
How much training data do you need? In: Michaela Mahlberg, Victorina González-Díaz & Catherine
Smith [Eds.]. Proceedings of the Corpus Linguistics Conference, CL 2009, Liverpool, UK, 2009.
Bruton, Anthony (2011). Is CLIL so beneficial, or just selective? Re-evaluating some of the research. In:
System, 39, 2011. 523-531.
Evert, Stefan & Hardie, Andrew (2011). Twenty-first century corpus workbench: Updating a query
architecture for the new millennium. In: Proceedings of the Corpus Linguistics 2011 Conference,
Birmingham, UK.
Garside, Roger & Smith, Nicolas (1997). A hybrid grammatical tagger: CLAWS4. In: Roger Garside,
Geoffrey Leech & Anthony McEnery [Eds.]. Corpus Annotation: Linguistic Information from Computer
Text Corpora. London: Longman. 102-121.
Granger, Sylviane (2013). The passive in learner English. Corpus insights and implications for
pedagogical grammar. In: Ishikawa, Shin Ichiro [Ed.]. Learner Corpus Studies in Asia and the World.
Vol. 1. Papers from LCSAW2013 (5-15). Kobe: School of Languages and Communication, Kobe
University.
Holtz, Mônica (2011). Lexico-grammatical properties of abstracts and research articles. A corpus-based
study of scientific discourse from multiple disciplines. Darmstadt: Technische Universität, PhD Thesis.
Tuprints.ulb.tu-darmstadt.de/2638/1/PhD-Thesis-Monica-Holtz.pdf
Horn, Wolfgang (2003). PSB-R 6-13. Prüfsystem für Schul- und Bildungsberatung für 6. bis 13. Klassen –
revidierte Fassung. Göttingen: Hogrefe.
Petermann, Franz & Winkel, Sandra (2007). FLM 7-13. Fragebogen zur Leistungsmotivation für Schüler
der 7. bis 13. Klasse. Frankfurt/Main: Harcourt.
Schmid, Helmut (1994): Probabilistic Part-of-Speech Tagging Using Decision Trees. In: Proceedings of
International Conference on New Methods in Language Processing, Manchester, UK.
Svartvik, Jan (1966). On Voice in the English Verb. The Hague/Paris: Mouton.
Wanner, Anja (2009). Deconstructing the English passive. Berlin: Mouton de Gruyter.
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32 LCR Bergen 2013
Möller, Verena (2013). How Do Educational Settings at the
Secondary Level Impact on Learners' Use of the English Passive? –
Evidence from the Secondary-Level Corpus of Learner English
(SCooLE). Paper presented at Learner Corpus Research, LCR
2013, Bergen/Norway, 27-29 September 2013.