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https://doi.org/10.1177/02676583211050522
Second Language Research
2022, Vol. 38(4) 965 –983
© The Author(s) 2021
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DOI: 10.1177/02676583211050522
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CEDEL2: Design, compilation
and web interface of an
online corpus for L2 Spanish
acquisition research
Cristóbal Lozano
Universidad de Granada, Spain
Abstract
This article presents and reviews a new methodological resource for research in second language
acquisition (SLA), CEDEL2 (Corpus Escrito del Español L2 ‘L2 Spanish Written Corpus’), and its
free online search-engine interface (cedel2.learnercorpora.com). CEDEL2 is a multi-first-language
corpus (Spanish, English, German, Dutch, Portuguese, Italian, French, Greek, Russian, Japanese,
Chinese, and Arabic) of L2 Spanish learners at all proficiency levels. It additionally contains
several native control subcorpora (English, Portuguese, Greek, Japanese, and Arabic). Its latest
release (version 2) holds material from around 4,400 speakers, which amounts to over 1,100,000
words. CEDEL2 follows strict corpus-design criteria (Sinclair, 2005) and L2 corpus-design
recommendations (Tracy-Ventura and Paquot, 2021), and all subcorpora are equally designed to
be fully contrastable, as recommended by Contrastive Interlanguage Analysis (Granger, 2015).
Thanks to its design and web interface, CEDEL2 allows for complex searches which can be further
narrowed down according to its SLA-motivated variables, e.g. first language (L1), proficiency
level, self-reported proficiency level, age of onset to the L2, length of exposure to the L2, length
of residence in a Spanish-speaking country, knowledge of other foreign languages, type of task,
etc. These CEDEL2 features allow L2 researchers to address SLA questions and hypotheses.
Keywords
L2 acquisition research, L2 corpora, L2 Spanish corpus, learner corpora, second language
acquisition (SLA)
I Introduction: Learner corpora and SLA
In learner corpus research (LCR), learner corpora are defined as systematic collections
of authentic and contextualized written/spoken language produced by second language
Corresponding author:
Cristóbal Lozano, Universidad de Granada, Facultad de Filosofía y Letras, Campus de Cartuja, Granada,
18071, Spain.
Email: cristoballozano@ugr.es
1050522SLR0010.1177/02676583211050522Second Language ResearchLozano
research-article2021
Review Article
966 Second Language Research 38(4)
(L2) learners (Callies and Paquot, 2015b: 1) and assembled according to explicit design
criteria (Granger, 2009: 14). They ‘can contribute to SLA [second language acquisition]
theory by providing a better description of interlanguage . . . and a better understanding
of the factors that influence it’ (Granger, 2008: 259). CEDEL2 contributes to SLA by (1)
incorporating these ‘factors’ into its corpus design via metadata, (2) adhering to specific
design principles (Sinclair, 2005), and (2) following LCR recommendations (Tracy-
Ventura and Paquot, 2021), which state that L2 corpora should:
1. focus on L2s other than English;
2. include learners at all proficiency levels, with varied L1s, from different ages,
and from different learning backgrounds and settings;
3. promote cross-linguistic comparisons;
4. include more learner and task variables (metadata);
5. include varied tasks, some of which promote hypothesis-testing research;
6. consider different perspectives on what a ‘control’ corpus is;
7. be freely available to the research community (Open Science).
SLA researchers have traditionally favoured experimental and controlled data to test SLA
hypotheses (Mackey and Gass, 2016). However, learner corpora are on the increase
(Granger, 2012; Mendikoetxea, 2014; Myles, 2015) and recent corpus studies have started
to test SLA hypotheses too (Lozano, 2021b). Large L2 corpora: (1) can offer a wide empiri-
cal base to test SLA theories (Mendikoetxea, 2014); (2) are ecologically valid since they
sample unconstrained language, i.e. learners can choose their own wording (Granger, 2008);
(3) contain highly contextualized language that allow researchers to go beyond the lexical/
sentential level and explore discursive/pragmatic aspects (Myles, 2015); and (4) can be
interrogated to find patterns leading to the formulation of hypotheses (hypothesis-finding)
and also to test hypotheses against the corpus data (hypothesis-testing) (Mendikoetxea,
2014; Myles, 2005). In this context, we will discuss CEDEL2, which ‘represents a laudable
attempt to bring learner corpus research and SLA closer together’ (Gilquin, 2015: 24).
This article aims to present the CEDEL2 design, compilation and free web interface
in the context of SLA and L2 Spanish acquisition. CEDEL2 takes an SLA-motivated
approach to learner corpus design with a view to offering answers to SLA questions. This
article does neither intend to discuss the role of learner corpora in SLA theory (for dis-
cussions, see Lozano, 2021b; Lozano and Mendikoetxea, 2013; Mendikoetxea, 2014;
Myles, 2015, 2021) nor to present an overview of L2 Spanish corpora (see overviews in
Alonso-Ramos, 2016; Mendikoetxea, 2014; Rojo, 2021, and the L2 Spanish corpus
index at http://repositorios.fdi.ucm.es/corpus_aprendices_español).
The article is structured as follows: Section II contextualizes CEDEL2. Section III
presents its design principles, current holdings, data collection, web-based search inter-
face, and some of its limitations. Section IV concludes with thoughts on the way forward
in L2 (Spanish) corpus research.
II Some representative L2 Spanish acquisition corpora
While L2 English corpora predominate in the LCR field, the increasing interest in L2
Spanish has triggered the creation of corpora for L2 Spanish acquisition research. To
Lozano 967
contextualize CEDEL2, we will focus on a few representative corpora used in L2 Spanish
acquisition research: Spanish Learner Language Oral Corpora (SPLLOC: Mitchell et
al., 2008); Language and Social Networks Abroad Project (LANGSNAP: Tracy-Ventura
et al., 2016); and Corpus de Aprendices de Español ‘Spanish Learner Corpus’ (CAES:
Rojo and Palacios Martínez, 2016). We will see how CEDEL2 v.2 ultimately comple-
ments these corpora in terms of written-spoken data (next paragraphs) and adds function-
alities in terms of SLA-informed design features (Section III).
Regarding medium (Figure 1), the four corpora range from the entirely spoken
SPLLOC (100% of spoken files) to the entirely written CAES (100% written), with the
mixed LANGSNAP (65% spoken, 35% written) and the mostly written CEDEL2 (97.5%
written, 2.5% spoken) in between. Crucially, spoken data collection (and their corre-
sponding transcriptions) are more labour intensive and costly than written data collection
(Callies, 2015; Tracy-Ventura and Paquot, 2021). This results in spoken corpora like
LANGSNAP (331,554 words) and SPLLOC (unreported number of words) being smaller
in word size than written corpora like CAES (573,718) and CEDEL2 (1,106,013). For
SLA, however, representativeness is more relevant than corpus size since the corpus is
designed to represent the language type it intends to sample (see 3.1 for details on
representativeness).
Concerning the spoken vs. written dichotomy, LCR researchers have often assumed
that spoken data reflect learners’ competence better than written data (e.g. Myles, 2015).
However, many written learner corpora have been used to investigate competence and to
test SLA hypotheses (Granger et al., 2015) and most of the learner corpus studies in the
seminal book by Le Bruyn and Paquot (2021) use written corpora to ‘provide powerful
evidence that written data can make a significant contribution to some major SLA issues’
(Granger, 2021: 248). A written corpus is thus a valid and valuable source of evidence to
tap into learners’ competence and to test SLA hypotheses (Lozano, 2021b; Mendikoetxea,
2014), despite ‘SLA’s insistence on the supremacy of spoken data’ (Granger, 2021: 248).
LCR researchers could move beyond this dichotomy by exploring new avenues like the
triangulation of spoken and written data from different corpora. For example, Vázquez
Veiga (2016) triangulated spoken SPLLOC data and written CEDEL2 data to investigate
discourse markers at the lexicon-pragmatics interface. This approach ultimately yields a
more fully-rounded picture of the linguistic phenomenon under investigation.
Triangulation is even more promising when the spoken and written data come from the
same learners, same task and same corpus (Granger, 2021), as is the case in CEDEL2
(see Section III.1).
Regarding their speakers, SPLLOC (n = 150 speakers) samples British secondary-
school and university learners of L2 Spanish. LANGSNAP (n = 37) samples a cohort of
British university learners of L2 Spanish longitudinally over two academic courses at
SPOKEN ●●●●●●●●●
WRITTEN
SPLLOC LANGSNAP CEDEL2 CAES
Figure 1. Four representative L2 Spanish corpora plotted along the spoken–written
continuum.
968 Second Language Research 38(4)
three points in time (before, during and after their year abroad in Spain/Mexico). CAES
(n = 1,423) samples instructed learners of Spanish at the Instituto Cervantes from six
different L1 backgrounds. CEDEL2 (n = 4,334) samples more heterogeneous learners,
with eleven L1s and diverse backgrounds in terms of countries, proficiency levels,
chronological ages, ages of exposure to L2 Spanish, lengths of instruction in Spanish and
learning environments. L2 Spanish researchers have thus at their disposal cross-sectional
data with a variety of instructed and naturalistic backgrounds, plus longitudinal data
(LANGSNAP).
As for the number of variables sampled, SPLLOC and LANGSNAP register 5 varia-
bles each (4 learner variables and 1 task variable), CAES 10 (9 learner, 1 task) and
CEDEL2 25 (20 learner, 5 task). In terms of the number of subcorpora, SPLLOC
and LANGSNAP contain 2 subcorpora each (1 learner, 1 native), CAES 6 (learner only)
and CEDEL2 16 (11 learner, 6 native). Further details can be found in Appendix 1 in
supplemental material.
III The CEDEL2 (version 2) corpus
We describe CEDEL2 next: its general corpus design principles and SLA-motivated fea-
tures (3.1), its holdings (3.2), the data collection (3.3), its online web search interface
(3.4), the process and product (3.5) and its limitations (3.6).
1 CEDEL2 corpus design
While many learner corpora are not built according to specific design principles (Gilquin,
2015; Tono, 2016), Gilquin (2015) proposes CEDEL2 as a good-practice case in learner
corpus design since it rests on 10 corpus-design principles (Sinclair, 2005) that were
adapted for SLA purposes (Lozano and Mendikoetxea, 2013). Sinclair’s (2005) most
relevant principles are:
1. content selection: select corpus contents based on external (communicative func-
tion of the texts) and not internal (the language of the texts) criteria;
2. representativeness: select contents to be as representative as possible of the lan-
guage it samples;
3. topic: select the subject matter of the corpus based on external criteria;
4. contrast: compare only those subcorpora that have been equally designed;
5. documentation: fully document the contents of the corpus (i.e. its linguistically-
motivated variables or metadata).
Based on Sinclair’s design principles and the seven recommendations by Tracy-Ventura
and Paquot’s (2021) seen above, CEDEL2 showcases nine features that make it a suita-
ble and valuable tool for L2 Spanish acquisition research.
Feature 1: Same design across subcorpora to ensure maximum comparability. The principle
of contrast requires all subcorpora to be equally designed and balanced. In line with
Lozano 969
Tracy-Ventura and Paquot’s (2021) second recommendation and Granger’s (2015) Con-
trastive Interlanguage Analysis (CIA), all CEDEL2 subcorpora follow the same design
principles to ensure maximum comparability. Both between-subcorpora and within-sub-
corpus contrasts allow to test different effects (e.g. L2 development, L1 transfer, univer-
sal mechanisms, ultimate attainment, exposure, bimodality, bidirectionality, etc.), as will
be discussed in the following subsections.
Feature 2: SLA-motivated variables (learner profile). Though learner variables ‘have not
been controlled consistently across corpora and are seldom incorporated in metadata’
(Díaz-Negrillo and Thompson, 2013: 13), CEDEL2 registers, in line with Tracy-Ventura
and Paquot’s (2021) fourth recommendation, 20 learner variables (metadata) that can be
exploited in the investigation of many SLA phenomena (Table 1).
Proficiency level is a key learner variable that is often lacking in learner corpora
(Gilquin, 2015: 24). Some corpora use ad hoc criteria like course year as a proxy for
proficiency level (Callies, 2021), but ‘[b]eing in the same year group at school is not
always a sufficiently rigorous indication, and it is advisable to carry out independent
measures of proficiency’ (Myles, 2015: 316). CEDEL2 uses four measures: (1) objective
independent measure: learners do a 43-point standardized Spanish placement test
(University of Wisconsin, 1998); (2) subjective measure: learners self-rate each of their
skills (speaking, writing, listening, and reading) according to a 6-point scale (lower/
upper beginner, lower/upper intermediate, lower/upper advanced). Gilquin (2015) argues
that the ‘double proficiency measure in CEDEL2 is therefore a major asset (also because
. . . it makes it possible to compare self-rated and real proficiency)’ (p. 24). Additionally,
CEDEL2 records (3) language certificate (learners state any certificates they hold, if
any); and (4) length of instruction (LoI) in Spanish (learners report how long they have
been learning Spanish for). All these metadata provide a good estimation of the learner’s
proficiency level.
Feature 3: SLA-relevant variables (task profile). Five task metadata were recorded:
1. Task title (14 tasks to choose from; see Appendix 1 in supplemental material);
2. Task text (written text/spoken text transcription with audio file);
3. Approximate time to produce the task (in minutes);
4. Where the task was done (in class/outside class/both);
5. Resources used to produce the task (help from Spanish native/bilingual diction-
ary/monolingual dictionary/spellchecker/grammar book/background readings/
none).
Representativeness relates to both the sampled language (see this subsection) and the
sampled speakers (see Feature 9 below). Lozano and Mendikoetxea (2013) argue that L2
corpus design should adhere to external criteria to ensure that their language is repre-
sentative and authentic. This is achieved via ‘tasks that allow learners to choose their
own wording rather than being requested to produce a particular word or structure’
(Granger, 2008: 261), which ultimately leads to ‘a high degree of inclusiveness and a low
degree of language bias’ (Mendikoetxea, 2014: 14). Following these and Tracy-Ventura
970 Second Language Research 38(4)
Table 1. Learner variables (numbered list) and likely second language acquisition (SLA)
phenomena to investigate (bulleted list) in CEDEL2.
① Learner’s L1
② L1 of the learner’s father
③ L1 of the learners’ mother
④ Languages spoken at home
• L1 transfer effects by contrasting the 11 different L1s of the learner subcorpora
• L1 transfer effects vs. the effects of general/universal cognitive mechanisms by
comparing L1s that are typologically similar/different
• Indirect L2 input effects by analysing the native Spanish control subcorpus
• Likely language-dominance patterns via the parents’ L1 and language spoken at home
⑤ Standardized placement test score (1–43 points)
⑥ Proficiency level based on the placement score (lower/upper beginner, lower/upper
intermediate, lower/upper advanced)
⑦ Proficiency-level self-evaluation on each skill in Spanish (speaking, listening, writing, reading)
⑧ Proficiency-level self-evaluation on each skill in an additional foreign language (speaking,
listening, writing, reading)
⑨ Spanish language certificates held, if any
• L2 developmental effects by comparing beginner vs. intermediate vs. advanced learners
• I nterlanguage knowledge at a given proficiency level within a learner subcorpus or
across learner subcorpora
• Likely influence from the learners’ additional foreign language
• Correlation between the learners’ objective placement test score and their self-
evaluation score
⑩ Sex
• Sex-related linguistic differences (males/females)
⑪ Age (chronological)
• Maturational effects due to age (e.g. linguistic abilities in young/adult/senior learners)
• Linguistic phenomena across the lifespan
⑫ Age of Exposure (AoE) to L2 Spanish
• Likely AoE effects (critical periods in L2)
⑬ Years studying Spanish (length of instruction, LoI)
• LoI effects (or lack thereof)
⑭ Stay(s) in Spanish-speaking countries for longer than 1 month? (yes/no)
⑮ Stay(s): Where?
⑯ Stay(s): When? (periods of residence)
⑰ Stay(s): How long? (length of residence)
• Effects of Length of Residence (LoR) and the recency of the stays in Spanish-
speaking country/countries
• Effects of exposure to naturalistic input in a naturalistic setting
• Ultimate attainment effects in near natives (e.g. learners with high proficiency levels
and long stays in a Spanish speaking country) vs. Spanish natives
⑱ School/University/Educational institution
⑲ Major degree (if any)
⑳ Year at university (if any)
• Likely effects of general educational background (school/university)
• Likely effects of educational background in Spanish (for those majoring in Spanish/
Hispanic Studies vs. those who are not)
Lozano 971
and Paquot’s (2021) fourth and fifth recommendations, CEDEL2 tasks meet five criteria
so as to potentially elicit authentic and varied linguistic phenomena within the text types
it samples (see additional details in Appendix 1 in supplemental material):
1. Range of text types: descriptive (task 1 and 2 on the description of your region
and a famous person), narrative (3–7 on the narration of a film, your last holi-
days, your future plans, a recent trip and a life experience; 13–14 picture- and
video-based narratives), and argumentative (8–12 on terrorism, anti-tobacco law,
gay marriages, marijuana legalization, and immigration). Some tasks may trigger
a blending of descriptive-narrative styles (e.g. tasks 3–7).
2. Range of control: Most tasks are relatively open-ended (tasks 1–12), but tasks 13
and 14 impose a certain degree of control since participants narrate the same
visual prompts.
3. Range of difficulty: Some descriptive tasks are linguistically undemanding and
suitable for beginners (e.g. task 1), while narrative tasks require tense–aspect
contrasts and argumentative tasks (e.g. 8–12) are linguistically demanding.
4. Pedagogical and replication criteria: Tasks 1–12 were selected from essay topics
found in mainstream Spanish language textbooks. Tasks 13 (picture-based narra-
tive Frog, where are you?) and 14 (short video from Charles Chaplin’s The kid)
have been extensively used in SLA research, which allows for replication.
5. Tense–aspect contrasts are triggered by different tasks, e.g. 3 Describe a film you
have recently seen and 6 Describe a trip you have recently made (present perfect)
vs. 4 What did you do last year during your holidays? (preterite) vs. 5 Which are
your plants for the future? (future).
Purpose-designed, theoretically-motivated tasks are also necessary in learner corpora
(Callies and Paquot, 2015a; Myles, 2015; Tracy-Ventura and Myles, 2015; Tracy-Ventura
and Paquot, 2021). Domínguez et al. (2013) and Tracy-Ventura and Myles (2015) showed
that such tasks in SPLLOC revealed tense/aspect contrasts that would have gone unde-
tected if only one generic past-tense narrative task had been used. These contrasts are
also allowed in CEDEL2; see point (5) above.
Theoretically-motivated CEDEL2 tasks (7 Retell a recent film you have seen recently;
13 Retell the frog story and 14 Retell the Chaplin video) trigger different information-status
contexts (topic continuity vs. topic shift with varying number of potential antecedents) that
constrain anaphora resolution in native and L2 Spanish. Such tasks have shed light on SLA
theoretical issues like: (1) the Pronominal Feature Geometry hypothesis (Lozano, 2009b)
since learners’ well-known deficits with anaphora resolution selectively affect only 3rd
person human anaphoric pronouns; (2) the Pragmatic Principles Violation Hypothesis
(Lozano, 2016; Martín-Villena and Lozano, 2020) since learners are more redundant (in
topic-continuity contexts) than ambiguous (in topic-shift contexts); and (3) the Position of
Antecedent Hypothesis and the Accessibility Hierarchy (Georgopoulos, 2017).
Feature 4: Multiple L1 backgrounds. A classic debate in SLA is L1 influence vs. language
universals. Following Tracy-Ventura and Paquot’s (2021) first and second recommenda-
tions, CEDEL2 samples L2 Spanish learners from eleven L1s (English, German, Dutch,
972 Second Language Research 38(4)
Portuguese, Italian, French, Russian, Greek, Japanese, Chinese, and Arabic). Addition-
ally, based on the principle of contrast, CIA and Tracy-Ventura and Paquot’s (2021) third
recommendation, all CEDEL2 subcorpora were equally designed to allow contrasts
between typologically-(un)related L1s (e.g. Germanic: English vs. German vs. Dutch;
Romance: Portuguese vs. Italian vs. French; Germanic vs. Romance vs. Slavic vs.
Semitic vs. Sino-Tibetan vs. Japonic).
Feature 5: Cross-sectional, developmental corpus. Following Tracy-Ventura and Paquot’s
(2021) second recommendation, CEDEL2 samples learners at all proficiency levels
(based on a standardized placement test, see Feature 2 above), so L2 development can be
traced. Unlike CAES, CEDEL2 does not restrict composition topics to different profi-
ciency levels. CEDEL2 is a cross-sectional corpus, like SPLLOC and CAES, but unlike
the longitudinal LANGSNAP.
Feature 6: Bidirectionality. CEDEL2 has an equally-designed, mirror-image L2 English
corpus, COREFL (Corpus of English as a Foreign Language: http://corefl.learnercor-
pora.com) (Lozano et al., 2021), so it adheres to the principle of contrast and to Tracy-
Ventura and Paquot’s (2021) third recommendation. The same phenomenon can be
explored bidirectionally (L1↔L2), e.g. L1 English–L2 Spanish (CEDEL2) vs. L1
Spanish–L2 English (COREFL), which allows to uncover L1-specific vs. universal
effects that are independent of the L1–L2 combinations. Bidirectionality is an under-
researched area in SLA/LCR.
Feature 7: Bimodal contrasts. As discussed above, spoken data are costly to collect and
process, hence written corpora are the norm in LCR (Tracy-Ventura and Paquot, 2021).
While CEDEL2 is predominantly a written corpus, it incorporates some data from par-
ticipants who did the same task twice (written then spoken) with a two-week gap to avoid
task-habituation effects (n = 104 participants: 26 L1 English–L2 Spanish learners, 59 L1
Spanish native controls, and 19 L1 English native controls; see Appendix 1 in supple-
mental material). The classic argument that spoken data better reflect learners’ compe-
tence than written data (see discussion in Section II) can be reliably tested in CEDEL2
since medium varies (spoken/written) but task and speaker are constant, as recommended
by Granger (2021: 248): ‘bimodal corpora allow interesting comparisons, especially
when the data are collected from the same learners’.
Feature 8: ‘Dual’ native control subcorpora. Some learner corpora include a native control
subcorpus as a benchmark of the (variety of) language learners are exposed to. Control
corpora are justified in LCR –see the ‘comparative fallacy’ vs. ‘comparative hypocrisy’
debate (Granger, 2009; Tracy-Ventura and Paquot, 2021). Following the recommenda-
tion of using several native norms in learner corpus research (Gilquin, 2021b) and Tracy-
Ventura and Paquot’s (2021) sixth recommendation, CEDEL2 samples data from 1,112
natives across Spanish-speaking countries (Peninsular and Latin American varieties),
which turns it into a Spanish native corpus in its own right. Importantly, to determine
whether learners’ L2 knowledge is due to their L1, L2 input, or universal cognitive
mechanisms, two native control subcorpora are required: (1) the learners’ target (L2)
Lozano 973
language to check for potential effects of input on L2 acquisition; (2) the learners’ L1 to
check for possible L1 transfer effects. Using two native control subcorpora provides
more information about the likely sources of knowledge than using only one control
corpus, as demonstrated in L2 Spanish fluency (Huensch and Tracy-Ventura, 2017) and
L2 English reference (Kang, 2004). CEDEL2 incorporates this ‘dual’ native-corpus per-
spective (Table 2).
Feature 9: Heterogeneous sample. LCR researchers complain that many current learner
corpora oversample argumentative essays produced in university settings by young
adults who are advanced learners (Paquot and Plonsky, 2017), so, following Tracy-
Ventura and Paquot’s (2021) second recommendation, CEDEL2 samples heterogeneous
learners: eleven typologically (un)related L1s, as well as different proficiency levels,
chronological ages, AoE to Spanish, LoI and LoR, learning environments (instructed/
uninstructed) and educational backgrounds (universities/secondary schools). Such
CEDEL2 variety is argued to be an asset (Gilquin, 2015). The CEDEL2 web-based
search interface (Section III.4) allows researchers to filter results according to 12 meta-
data (e.g. L1, age, proficiency level, AoE, LoR, LoI), so researchers can select repre-
sentative samples of the population they intend to investigate.
2 CEDEL2 holdings: Subcorpora and statistics
CEDEL2 was designed and compiled by Cristóbal Lozano, who directs the project since
2004 (Lozano, 2009a; Lozano and Mendikoetxea, 2013). Online written data collection
started in 2006. CEDEL2 v.2 currently holds 4,399 written and spoken files coming from
4,334 speakers, amounting to over one million word-tokens (n = 1,105,936) (see statis-
tics in Appendix 1 in supplemental material and on the CEDEL2 website) thanks to the
voluntary data-collection collaboration of both local (Universidad de Granada, UGR)
Table 2. Current native control subcorpora in CEDEL2 v.2.
Native control subcorpus 1
(learners’ mother tongue)
Learner subcorpus Native control subcorpus 2
(learners’ target language)
L1 English L1 English–L2 Spanish L1 Spanish
L1 Portuguese L1 Portuguese–L2 Spanish
L1 Greek L1 Greek–L2 Spanish
L1 Arabic L1 Arabic–L2 Spanish
L1 Japanese L1 Japanese–L2 Spanish
L1 German* L1 German–L2 Spanish
L1 Dutch* L1 Dutch–L2 Spanish
L1 Italian* L1 Italian–L2 Spanish
L1 French* L1 French–L2 Spanish
L1 Russian* L1 Russian–L2 Spanish
L1 Chinese* L1 Chinese–L2 Spanish
Note. * under development.
974 Second Language Research 38(4)
and international collaborators (for details, see Appendix 2 in supplemental material). Its
17 subcorpora (Figure 2) are still growing for the future CEDEL2 version 3.
3 CEDEL2 data collection
CEDEL2 written data were collected via dedicated online forms (v.1) from 2006 to 2016
and via Google Forms (v.2) from late 2016 (http://learnercorpora.com). L2 corpus online
data collection has been argued to be ‘exciting for conceptualizing new avenues for data
collection that could reach L2 users, rather than learners, that have largely been ignored’
(Bell and Payant, 2021: 64).
Forms are written in the participant’s native language to ensure full understanding
(Figure 3). Participants completed three sections (instructions and informed consent,
learner profile, and task profile), and learners additionally completed a Spanish place-
ment test. CEDEL2 participation is voluntary. Ethics approval was granted by the Human
Research Ethics Committee at the Universidad de Granada. Calls for participation were
advertised in distribution lists (Linguist List, Infoling, Corpora List, social media, etc.).
In return for their written participation, learners received their placement-test score and,
upon request, a written statement of participation.
An international team (Appendix 2 in supplemental material) collaborated in the data
collection and in the translation of the forms into the participants’ mother tongue.
Approximately 27% of the data (1,185 files out of 4,440 files) were collected by collabo-
rators and 73% (3,215 out of 4,399 files) were collected by C. Lozano. Data collection
was coordinated from the University of Granada (89% or 3,936 out of 4,399 files) and
the remaining 11% from other international universities (464 out of 4,400 files). These
figures represent the coordinating institutions and not the actual data collection
institution.
1931
243 216164 101 83 82 74 60 59 22
1112
172 47 16 12 6
0
200
400
600
800
1000
1200
1400
1600
1800
2000
L1Eng-L2Spa
L1Jap-L2Spa
L1Greek-L2Spa
L1Port-L2Spa
L1Russ-L2Spa
L1Ital-L2Spa
L1Ger-L2Spa
L1Arab-L2Spa
L1Dut-L2Spa
L1Fre-L2Spa
L1Chi-L2Spa
Spa
Eng
Jap
Portu
Greek
Arab
Learners Naves
seliffoN
Figure 2. Number of files per subcorpus (CEDEL2 v.2).
Lozano 975
While written data were collected online from participants all over the world, spoken
data were collected in situ at the Universidad de Granada in a quiet room with recording
equipment (Audio Technica AT2020: Cardioid condenser microphone, 74 dB, 1 kHz at
1 Pa) to ensure optimal audio quality. Audio files were orthographically transcribed into
text files. Transcripts followed basic transcription conventions (see the User guide in the
CEDEL2 website for details) e.g. xxx for unintelligible words, / for silent pauses, eh for
filled pauses, and = for false starts).
4 CEDEL2 search and download engine
Following the Open Science philosophy and Tracy-Ventura and Paquot’s (2021) seventh
recommendation, the newly developed CEDEL2 v.2 web-based interface (Figure 4) was
freely released in September 2020 under a Creative Commons license (CC BY-NC-ND
3.0 ES) at http://cedel2.learnercorpora.com.
The CEDEL2 interface offers multiple and sophisticated search and download options
(for details, see Appendix 1 in supplemental material, the user Guide and the circled
question mark ( ) on the CEDEL2 website). There are several (sub)types of results (i.e.
outputs), which can be refined by the 12 filters Figure 4.
a Output type
1. Texts: The output shows a tabulated list of corpus files with 10 columns repre-
senting variables (Figure 5). Each text can be visualized by clicking on the
Figure 3. CEDEL2 v.2 forms.
976 Second Language Research 38(4)
tabulated list. The list of texts can be additionally filtered (see Section III.4.c
below), sorted according to certain criteria (Figure 6, left image), and down-
loaded in several formats: TXT (actual written text or spoken text transcription),
TXT with metadata (text together with the 20 linguistic-profile variables and the
4 task variables), CSV (Comma Separated Values) for Excel, CSV for other soft-
ware. The MP3 audio files can also be downloaded.
2. Concordances: The searched element is displayed in the centre accompanied by
its surrounding context, i.e. keywords in context (KWIC, Figure 7). Concordances
can be filtered (see Section III.4.c below), sorted (Figure 6, right image) and
downloaded.
3. Simple frequency: The output shows the frequency of the searched element(s)
(e.g. word, lemma, grammatical word) out of the total number of words and doc-
uments in the corpus, e.g. lemma SER ‘to be’ (21,016/851,675 results [24.68
million] in 2,713/3,034 documents) vs. lemma ESTAR ‘to be’ (5,038/851,675
results [5.92/million] in 1,752/3,034 documents).
4. Full frequency: The output shows the frequency of the searched element(s)
according to eleven variables (L1, medium, L1 and medium, proficiency level,
text title, years studying Spanish (LoI), stay abroad (LoR), age of exposure (AoE)
to Spanish, age, sex, and placement test score).
Figure 4. CEDEL2 v.2 web-based interface (http://cedel2.learnercorpora.com).
Lozano 977
b Output subtype. For concordances and frequencies, the searchable element(s) can be:
1. Words: Strings (characters, word, word combinations) that can incorporate wild-
cards: * (any number of characters), ? (for any 1 character), | (for either one
string or the other).
2. Grammatical elements: In CEDEL2 v.2, all words in the Spanish and English texts
have been automatically tagged (i.e. part-of-speech annotated) with Freeling
(http://nlp.lsi.upc.edu/freeling), which is an automatic annotator (see details on
the CEDEL2 website’s User guide). The searchable grammatical elements can be:
a. Part-of-speech (POS) tags, available from a drop-down menu containing word
categories (e.g. Noun, Adjective, Verb, Adverb, etc.) and subcategories (e.g.
noun.masculine.plural; verb.indicative.imperfect; pronoun.personal.3rd.singular.
Figure 5. Text output.
Figure 6. Sorting options.
978 Second Language Research 38(4)
masculine; etc.). Researchers can search for, e.g. imperfect/perfect past tense
contrasts to test the well-known Aspect Hypothesis; 3rd person singular personal
pronouns to test hypotheses on anaphora resolution (see above); etc.
b. Lemmas (e.g. SER and ESTAR would search for all verbal forms of ser and estar
‘to be’) to test hypotheses that can account for this well-known verbal contrast in
L2 Spanish.
3. Words proxim.: It searches for a first word separated N words from a second
word. The user can define the separation (N), e.g. yo + 2 + estoy would retrieve
cases like yo no|siempre|nunca|ahora estoy.
4. Grammatical elements proxim.: It searches for a first grammatical word sepa-
rated N words from a second grammatical word, e.g. determiner-article-mascu-
line-singular + 1 word + noun-feminine-singular, which would find cases of
masculine articles followed by feminine nouns (el acción, el alcantarilla . . .).
This can help researchers test hypotheses on gender (mis)agreement. Other
sophisticated lemma/tags combinations are possible, e.g. lemma LLEGAR + ⩽2
+ noun, would retrieve instances of postverbal subjects: llegar ‘arrive’ followed
by either a noun or by another word (e.g. article) before the noun, as in Llega un
policía, Llega otro personaje, etc.) to test the well-known unaccusative
hypothesis.
c Filtering options. The output (results) can be filtered according to 12 filters (Figure 4):
learners’ L1, task medium (written/spoken/written and spoken by the same person), sex,
proficiency level on a 6-point scale (lower/upper beginner, intermediate, and advanced),
placement test score in Spanish (range: 0%–100%), self-evaluated proficiency level in
Spanish on a 6-point scale, task title (14 tasks to choose from), filename, age, AoE to
Spanish, LoI in Spanish (in years), and LoR in a Spanish-speaking country (in months).
Filters allow to target those elements (learners, concordances, texts, grammatical words,
etc.) that meet the researcher’s criteria.
5 CEDEL2 process and product
Learners/natives participate in the written component via online forms (http://learnercor-
pora.com). Data and metadata are received in spreadsheet format. Based on a protocol,
the corpus managers manually clean, standardize and join the spreadsheet data of the
different subcorpora, which are then exported to CSV files for automatic validation. The
Figure 7. Concordance output.
Lozano 979
task texts are POS-annotated with Freeling and, together with their metadata (headers),
are joined. The resulting tagged documents are loaded onto a database, which can finally
be searched/filtered/downloaded by users via the online interface (see Figure 8).
At the time of printing of this paper, CEDEL2 data are the source of over 50 publica-
tions and dissertations covering SLA phenomena like anaphora, collocations, lexicon,
morphology, orthography, reflexives, unaccusativity, (in)transitivity, determiners, lexi-
con-pragmatics interface, lexicon-discourse interface, error analysis, interference and
transfer, automatic proficiency-level classification, natural language processing, and com-
puter-assisted language learning (see http://cedel2.learnercorpora.com/about/studies).
6 CEDEL2: Current limitations and future improvements
Corpus balance requires having equally-proportioned samples of each language variety
(Mendikoetxea, 2014: 14). Data size in each cell (i.e. cells resulting from crossing the
corpus-design factors) must be balanced to ensure representativeness across subcorpora
(Lozano, 2021a: 143). Future versions of CEDEL2 will therefore need to strike a balance
between: (1) learner subcorpora, since the L1-English–L2-Spanish subcorpus is the larg-
est in word size, speakers and number of tasks when compared to the other L2 subcor-
pora, which are smaller in size and contain either one task (Chaplin) or two tasks (Chaplin
and Frog); (2) control subcorpora, since not all learner subcorpora have their equivalent
native control subcorpus (Table 2); (3) spoken/written subcorpora, since only a small
spoken sample has been included in CEDEL2 v.2. As for tagging, it has been done auto-
matically with the Freeling tagger, which sometimes misclassifies words (particularly
learners’ novel words) into an incorrect word category. This feature is intended to be a
useful search tool for SLA researchers, though it needs to be improved in future versions.
Figure 8. CEDEL2 flowchart.
Source. Copyright of this flowchart by NLPGo (www.nlpgo.com).
980 Second Language Research 38(4)
Finally, a promising feature for future versions is the use of automated text metrics (e.g.
lexical sophistication/diversity, text cohesion, grammatical complexity, text readability,
etc.) as an additional measure of proficiency level.
IV Learner corpora: The way forward
LCR has a lot to offer to SLA (Granger, 2021; Tracy-Ventura and Paquot, 2021), particularly
to SLA theoretical models (Lozano, 2021b for an overview). This can be achieved via SLA-
motivated corpus design and fine-grained, theoretically-informed tagsets to test particular
hypotheses, as done with CEDEL2 (e.g. Georgopoulos, 2017; Lozano, 2009b, 2016; Martín-
Villena and Lozano, 2020) and SPLLOC (Domínguez et al., 2013; Tracy-Ventura and
Myles, 2015).
Most L2 Spanish corpora are cross-sectional, so longitudinal corpora like LANGSNAP
are needed (Tracy-Ventura and Paquot, 2021). More spoken and written data coming
from the same learner and task would be ideal to test the effect of medium (Granger,
2021). Multi-task (as opposed to single-task) corpora are also welcome since task vari-
ability is a key factor to understand learners’ interlanguage (Domínguez et al., 2013;
Tracy-Ventura and Myles, 2015).
SLA/LCR researchers argue for ‘complementing the corpus data with experimental
data’ (Tracy-Ventura and Myles, 2015: 89). Such triangulation is gaining momentum
(Gilquin, 2021a) since complementing large quantities of corpus data with controlled
experimental data can offer SLA theories a more solid empirical base (Callies and
Paquot, 2015b). Researchers have already triangulated experimental and spoken
SPLLOC corpus data to advance our understanding of tense–aspect in L2 Spanish
(Domínguez et al., 2013). Triangulation is most fruitful when done in a cyclic fashion to
investigate the same phenomenon: the corpus results can provide insights that can be
later implemented in an experiment, the results of which can additionally shed light on
new aspects that can be later interrogated in the corpus, and so on, as done in the inves-
tigation of word order in L2 English under the unaccusative hypothesis with corpus data
(Lozano and Mendikoetxea, 2010) and with experimental data (Mendikoetxea and
Lozano, 2018). Cyclic triangulation is therefore a welcome step in the contribution of
LCR to SLA.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/
or publication of this article: CEDEL2 has been publicly funded over the past 15 years by several
research project grants from the Spanish Government, which we gratefully acknowledge: FFI2016-
75106-P (Ministerio de Economía y Competitividad); FFI2012-30755 (Ministerio de Economía y
Competitividad); FFI2008-01584/FILO (Ministerio de Ciencia e Innovación); HUM2005-01728/
FILO (Ministerio de Educación y Ciencia).
Lozano 981
ORCID iD
Cristóbal Lozano https://orcid.org/0000-0003-0068-154X
Supplemental material
Supplemental material for this article is available online.
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Appendix 1: A comparative overview of four large publicly available L2 Spanish corpora
Corpus
SPLLOC
LANGSNAP
CAES
CEDEL2
Version
v. 1 and v. 2 (March 2020)
v. 3 (March 2020)
v. 1.2 (Aug 2018)
v. 1 (Sept 2017), v. 2 (Sept 2020)
Website
http://www.splloc.soton.ac.uk
http://langsnap.soton.ac.uk
https://scholarcommons.usf.edu/langsnap/
http://galvan.usc.es/caes
http://cedel2.learnercorpora.com
Reference
Mitchell et al. (2008)
Tracy-Ventura et al. (2016)
Rojo & Palacios (2016))
Lozano (2009) for CEDEL2 version 1
Lozano & Mendikoetxea (2013) for CEDEL2 version 1
Lozano (this paper) for CEDEL2 version 2
Web-based search interface
Interface features:
①Search options: string only.
②Search sensitivity: not
available.
③Search output format: audio
files, transcripts.
④File filtering options: corpus,
task, level of participant, sex, years
learning Spanish, other languages
studied, speakers’ ID.
⑤Sorting options: not available.
⑥File formats: audio files, text
transcriptions (CHAT format).
⑦File download options: not
available.
(Note: SPLLOC is also
downloadable via TALKBANK)
Interface features:
①Search options: not available.
②Search sensitivity: not available.
③Search output format: not available.
④ File filtering options: task, data collection
time (before, during, after), speaker’s ID number.
⑤Sorting options: no available.
⑥File formats: audio files, text transcriptions
(CHAT format).
⑦File download options: not available.
(Note: LANGSNAP is also downloadable via
TALKBANK)
Interface features:
①Search options: string, POS, lemma.
②Search sensitivity: orthographic
accents (on|off), case-sensitive (on|off).
③Search output format: concordances,
simple statistics, complex statistics.
④File filtering options: proficiency level,
L1, country, sex, age range.
⑤Sorting options: yes.
⑥File formats: not available.
⑦File download options: not available.
Interface features:
CEDEL2 v.1 [interface no longer available]:
①Search options: string.
②Search sensibility: not available.
③Search output format: texts.
④File filtering options: age of learner, placement test score,
proficiency level (self-evaluation), age of exposure to Spanish,
years studying Spanish, length of stay abroad in a Spanish-
speaking country (in months), other foreign languages, essay
title.
⑤Sorting options: no.
⑥File formats: text (txt format).
⑦File download options: text with metadata (txt format).
CEDEL2 v.2 [interface available since September 2020]:
①Search options (type of result output): concordances
(KWIC), texts, simple frequency, full frequency.
②Search options (subtype of results output): strings/words,
grammatical elements (part-of-speech tags, lemmas).
③Search sensibility: orthographic accents (on|off), case-
sensitive (on|off).
④ Filters: medium (written, spoken, written & spoken by same
person), L1, sex, age, composition title, years studying Spanish,
placement test score, proficiency level, self-evaluated
proficiency level, age of exposure to Spanish, length of stay
abroad in a Spanish-speaking country (in months).
⑤Sorting options: age, concordance to match, element 1,
filename, L1, lemma 1, medium, next element, placement test
score %, previous element, second next element, second
previous element, sex, tag 1, task title, unit 1.
Lozano (2021). CEDEL2: Design, compilation and web interface of an online corpus for L2 Spanish
acquisition research. Second Language Research. First Published online October 16, 2021
https://doi.org/10.1177/02676583211050522
Corpus
SPLLOC
LANGSNAP
CAES
CEDEL2
⑥File formats (downloadable): text (txt format), text with
metadata (txt format), CSV (Excel), CSV (other), MP3 (audio
files only).
⑦File download options: text without metadata (txt format),
text with metadata (txt format), CSV (Excel), CSV (other).
Mode
Spoken
Spoken and written
Written
Written (v.1), written and some spoken (v.2)
Subcorpora statistics:
speakers, files, words
: No. of speakers
🔊: No. of spoken audio files
: No. of written text files
: No. of words
Shaded cells: native control
subcorpus
GRAND TOTAL:
150
🔊
585
0
Not reported
BREAKDOWN:
SPPLOC v.1
🔊
L1Eng-L2Spa
60
286
L1Spa (natives)
15
TOTAL
75
286
SPPLOC v.2
🔊
L1Eng-L2Spa
60
299
L1Spa (natives)
15
TOTAL
75
299
Note: Audio files contain their
corresponding transcription.
GRAND TOTAL:
37
🔊
364
192
331,554
BREAKDOWN:
LANGSNAP
🔊
L1Eng-
L2Spa
27
364
192
303920
L1Spa
(natives)
10
27634
TOTAL
37
364
192
331554
Note: Audio files contain their corresponding
transcription.
GRAND TOTAL:
1,423
🔊
0
3,878
573,718
BREAKDOWN:
CAES
L1 Arab-
L2 Spa
497
1342
16,8231
L1 Chin-
L2 Spa
128
373
53,163
L1 Fren-
L2 Spa
143
371
59,412
L1 Eng-
L2 Spa
227
615
106,968
L1 Portu-
L2 Spa
361
1001
165,231
L1 Russ-
L2 Spa
67
176
20,713
TOTAL
1,423
3,878
573,718
GRAND TOTAL:
4,333
🔊
111
4,399
1,106,936
BREAKDOWN:
CEDEL2 v.1
L1Eng-L2Spa
1609
1609
512873
L1Greek-L2Spa
173
173
58,575
L1Spa (natives)
796
796
230,571
TOTAL
2,578
2,578
802,019
CEDEL2 v.2
🔊
L1 English-L2 Spa
296
322
27
45,858
L1 Greek-L2 Spa
43
43
0
5,530
L1 Italian-L2 Spa
83
83
0
14,426
L1 Portug-L2 Spa
164
164
0
21,662
L1 French-L2 Spa
58
58
0
8,135
L1 German-L2 Spa
82
82
0
16,164
L1 Dutch-L2 Spa
60
60
0
9,069
L1 Russian-L2 Spa
101
101
0
16,117
L1 Japanese-L2 Spa
243
243
0
23,049
L1 Chinese-L2 Spa
22
22
2
4,373
L1 Arabic-L2 Spa
74
74
0
9,118
L1 Spa (natives)
257
316
61
73,640
L1 English (natives)
153
172
23
40,805
L1 Greek (natives)
12
12
0
2,031
L1 Portug (natives)
16
16
0
3,348
L1 Japanese (natives)
47
47
0
9,126
L1 Arabic (natives)
6
6
0
1,465
TOTAL
1717
1821
113
303,916
Note: CEDEL2 v.2 Audio files contain their corresponding
transcription.
Corpus
SPLLOC
LANGSNAP
CAES
CEDEL2
Files/Speakers ratio
585 files / 150 speakers = 3.9 files
per speaker
556 files / 37 speakers = 15.02 files per speaker
3,873 files / 1,423 speakers = 2.72 files
per speaker
4,400 files / 4,334 speakers = 1.02 files per speaker
L1 Spanish native control
subcorpus
Yes
Variety: Peninsular Spanish
Yes
Variety: Peninsular Spanish, Mexican Spanish
No
Yes
Variety: all varieties of Spanish (Peninsular and Latin American)
Other control corpora
(learners’ L1)
No
No
No
Yes:
L1 English, L1 Portuguese, L1 Greek, L1 Japanese, L1 Arabic.
Age range
13 – 22 years old
20 – 27 years old
15 – 61+ years old
13 – 88 years old
Learners: types
Instructed only (secondary
education, university in the UK).
Instructed only (university students from the UK)
Instructed (students at the Instituto
Cervantes in different countries)
Instructed (secondary education, university) and naturalistic in
different countries and institutions.
Learners: Independent
proficiency measure
No: Placement according to hours
of instruction
No
Yes (CEFR-based proficiency from
Instituto Cervantes)
Yes: Standardised placement test (University of Wisconsin,
1998)
Learners: proficiency levels
All (beginner, intermediate,
advanced)
Unknown/Not reported
All (beginner, intermediate and advanced:
A1 to C1 according to CEFR)
All (beginner, intermediate, advanced)
Learners: profile and
linguistic variables
①Educational level (year 9, year
10, year 13, undergraduate)
②Sex
③Years learning Spanish (3, 4, 6
years)
④Other foreign languages studied
①Age
②Other foreign languages studied
③Educational level (university)
④Work type while abroad (exchange student,
teaching assistant)
①L1
②Proficiency level (A1-C1)
③Age
④Sex
⑤Age of exposure to Spanish
⑥Months studying Spanish
⑦Length of stay in a Spanish-speaking
country (in months)
⑧Educational level (primary, secondary,
university, other).
⑨Contacts in Spanish-speaking countries
(no, friends, family, friends & family,
other).
①L1
②L1 of the father
③L1 of the mother
④Language(s) spoken at home
⑤Placement test score (1-43 points)
⑥Proficiency level (lower beginner up to upper advanced)
⑦Proficiency level self-evaluation on each skill in Spanish
(speaking, listening, writing, reading).
⑧Proficiency level self-evaluation on each skill in additional
foreign language (speaking, listening, writing, reading).
⑨Spanish language certificates held, if any
⑩Sex
⑪Age
⑫Age of exposure to L2 Spanish (AoE)
⑬Years studying Spanish (Length of instructed exposure, in
years)
⑭Stays in Spanish-speaking countries? (yes/no): ⑮Stay(s):
Where?
⑯Stay(s): When? (period(s) of residence)
⑰Stay(s): How long? (length of residence in months)
⑱School/University/Educational institution
⑲Major degree (if any)
⑳Year at university (if any)
Learners: Task variables
①Type of task (see cell below)
①Type of task (see cell below)
①Type of task (see cell below)
①Task title [see titles in cell below]
②Task text (written text/spoken text/audio file)
Corpus
SPLLOC
LANGSNAP
CAES
CEDEL2
③Approximate time to produce the task (in minutes).
④Where was the task done? (in class, outside class, both)
⑤Resources used to produce the task (help from Spanish
native/bilingual dictionary/monolingual
dictionary/spellchecker/grammar book/background
readings/none)
Task types
9 tasks in total:
SPLLOC v. 1:
Clitic production task
Loch Ness narrative
Chaplin Modern Times narrative
Pair discussion task
One-to-one picture description &
interview task
SPLLOC v. 2:
Past-tense guided interview task
2 past-tense picture-based
narratives (varying degree of
control: Nati & Pancho; Hermanas)
Present-tense simultaneous
actions task
7 tasks in total:
•Oral task: Personal semi-structured interview.
•Oral task: Past-tense picture-based narrative
(brothers’ story), as in SPLLOC v.2.
•Oral task: Present-tense picture-based narrative
(cat story), as in SPLLOC v.2.
•Oral task: Past-tense picture-based narrative
(sisters’ story), as in SPLLOC v.2.
•Written argumentative task: Do you think
marihuana should be legalised? (as task no. 11 in
CEDEL2 v.1).
•Written argumentative task: Do you think gay
couples have the right to get married and adopt
children? (as task no. 10 in CEDEL2 v.1).
•Written argumentative task: Do you think junk
food should be taxed?
13 tasks in total:
Tasks are graded according to proficiency
level with an increasing number of words.
There is combination of genres and
communicative functions (descriptive,
narrative, argumentative, requests and
complaints).
A1 level (lower beginner):
1. Write an email to your work/class
mates introducing yourself (75-100
words).
2. Write a note to your flatmates telling
them you will be late (30-40 words).
3. Write an email to a friend talking about
your family (75-100 words)
A2 level (upper beginner), 100-150
words:
4. Write a postcard to your friends about
your last holidays.
5. Write a biography about a person you
admire.
6. Make a booking for a hotel room.
B1 level (lower intermediate), 175-200
words:
7. Write a letter to a friend requesting
several favours.
8. Write to a flight company complaining
about your lost luggage.
9. Write a (real or imaginary) funny story.
B2 level (upper intermediate), 275-300
words:
10. Write an admission letter to a
university programme.
14 tasks in total:
CEDEL2 v. 1: 12 tasks to choose from (independently of the
learners’ proficiency level), ranging in degree of difficulty and
without word limit, and containing a combination of genres
(descriptive, narrative and argumentative):
1. Describe the region where you live.
2. Talk about a famous person.
3. Describe a film you have recently seen.
4. What did you do last year during your holidays?
5. Which are your plans for the future?
6. Describe a trip you have recently made.
7. Narrate an experience you have lived.
8. Talk about the problem of terrorism in the world.
9. What do you think about the anti-tobacco law?
10. Do you think gay couples have the right to get married and
adopt children?
11. Do you think marihuana should be legalised?
12. Analyse the main aspects of immigration.
CEDEL2 v. 2: (2 new tasks, no. 13 and 14).
2. Talk about a famous person. (as in CEDEL2 v.1)
3. Describe a film you have recently seen. (as in CEDEL2 v.1)
13. Describe the frog story (picture-bask task).
14. Describe the Charles Chaplin silent video clip.
Corpus
SPLLOC
LANGSNAP
CAES
CEDEL2
11. Write an essay on the importance of
new technologies.
C1 level (lower intermediate), 400-500
words:
12. Write a critical commentary about a
recent film you have seen.
13. Write an email to your electricity
company complaining about power cuts
and high prices.
Appendix 2: Main data collaborators (ranked in order of N of files collected as of 15th July 2020)
CORPUS
SUBCORPUS
MAIN DATA COLLECTOR(S)
# documents
collected
# audios
collected
CEDEL2 v.1
L1 English - L2 Spanish
Cristóbal Lozano (Uni Granada)
1609
0
CEDEL2 v.1
Control: L1 Spanish
Cristóbal Lozano (Uni Granada)
796
0
CEDEL2 v.2
L1 English – L2 Spanish
Cristóbal Lozano (Uni Granada)
322
27
CEDEL2 v.2
Control: L1 Spanish
Cristóbal Lozano (Uni Granada)
316
61
CEDEL2 v.2
L1 Japanese – L2 Spanish
Nobuo Ignacio López-Sako (Uni Granada)
243
0
CEDEL2 v.1
L1 Greek - L2 Spanish
Athanasios Georgopoulos (Uni Granada)
173
0
CEDEL2 v.2
Control: L1 English*
Cristóbal Lozano & Ana Díaz-Negrillo (Uni Granada)
172
23
CEDEL2 v.2
L1 Portuguese – L2 Spanish
Joana Teixeira & Ana Madeira (Uni Nova Lisboa UNL)
164
0
CEDEL2 v.2
L1 Russian - L2 Spanish
Tatiana Portnova & Benamí Barros (Uni Granada)
101
0
CEDEL2 v.2
L1 Italian – L2 Spanish
Pau Montserrat (Uni Florence)
83
0
CEDEL2 v.2
L1 German – L2 Spanish
Jacopo Torregrossa (Uni Frankfurt)
82
0
CEDEL2 v.2
L1 Arabic – L2 Spanish
Amal Haddad (Uni Granada)
74
0
CEDEL2 v.2
L1 Dutch – L2 Spanish
Kim Collewaert (Vrije Uni Brussel- Uni Granada), An Vande Casteele (Vrije Uni Brusseel), Mª Carmen Parafita (Uni Leiden)
60
0
CEDEL2 v.2
L1 French – L2 Spanish
Hugues Lacroix (Uni Montréal), Ismael Ramos Ruíz (Uni Paris Diderot)
59
0
CEDEL2 v.2
Control: L1 Japanese
Nobuo Ignacio López-Sako (Uni Granada)
47
0
CEDEL2 v.2
L1 Greek – L2 Spanish
Athanasios Georgopoulos (Uni Athens), Aphrodite Amanatidou (Uni Ioanina - Uni Granada)
43
0
CEDEL2 v.2
L1 Chinese – L2 Spanish
Juan José Ciruela (Uni Granada), Cristóbal Lozano (Uni Granada) Aida García (Universidad Autónoma de Madrid UAM)
22
2
CEDEL2 v.2
Control: L1 Portuguese
Joana Teixeira & Ana Madeira (Uni Nova Lisboa UNL)
16
0
CEDEL2 v.2
Control: L1 Greek
Aphrodite Amanatidou (Uni Ioanina - Uni Granada)
12
0
CEDEL2 v.2
Control: L1 Arabic
Amal Haddad (Uni Granada)
6
0
*The L1 English native data are part of COREFL (Corpus of English as a Foreign Language) (Lozano et al., 2021), available at http://corefl.learnercorpora.com
