ICNALE: The International Corpus Network of Asian Learners of English
A collection of controlled essays and speeches produced by learners of English in 10 countries and areas in Asia
Project Leader: Dr. Shin'ichiro Ishikawa, Kobe University, Japan (iskwshin@gmail.com)

Last updated 2023/6/30

Why should I use the download version?

Although the ICNALE Online is an easy and powerful query tool, we would like to advise you to use the ICNALE download version for your research. Using the download version has many merits.

1) You can access learner attribute data, which is available only in the download version.
2) You can make varied sub-copora for your research. For example, if you are interested in the gender difference in learner essays, you can compare the essays written by male students and those by female students. (See how to do it)
3) You can access the updated data. Please note that we currently do not update the data on the ICNALE Online.
4) You can scrutinize the data with your favorite concordancers and/or corpus analytical tools.
5) Using the data stored in your PC guarantees replicability of your research.

How can I download the data?

The ICNALE Development Team offers a full access to the data collected in the project as well as several tools developed by the project team. Please download the file(s) you need from the links below. Registered users are given a password for unzipping the file(s).

First of all, obtain the "ICNALE Learner Background Survey Sheet (202306)," which includes detailed background data about the participants.

Core Modules
Modules	Updated	N. of samples	N. of tokens	Contents
Spoken Monologues 2.1 > Data (Incl. audio link)	2023/6/30	4,400	c 500,000	60-second monologues about two common topics.
Spoken Dialogues 1.4 > Data (incl. video link)	2023/6/30	4,250	c 1,600,000	30-40-minutes oral interviews including picture descriptions and role plays, and 3-5-minutes L1 follow-up interviews
Written Essays 2.5 > Data	2023/6/30	5,600	c 1,300.000	200-300-words essays about two common topics
Edited Essays 3.1 > Data	2023/6/30	656	c 150,000	Fully edited versions of learner essays about two common topics. Rubric-based essay evaluation data is also included.
Additional Modules
Written Essays UAE 0.1 > Data	2018/04	200	c 47,000	200-300-words essays about two common topics written by college students in the United Arab Emirates.
Global Rating Archives 2.0 > To be released soon	To appear soon	NA	---	Ratings of 140 learner speeches and the same number of learner essays by international raters with varied L1 and occupational backgrounds
Software
Speech Morphing System 1.0 > Program (10MB)	2014/10	---	---	Computer software developed for morphing the audio data, which was used in the ICNALE Spoken Monologue project.
Annotation Data (Developed by the third party)
ICNALE Written Essays with Phrase Structure Annotation 1.0 > Data (2MB)	2016/06	134	c 33,000	ICNALE essays manually annotated with phrase structures. This is a product by Dr. Ryo Nagata, Konan University.

How can I obtain the password?

Register from the The ICNALE User Registration Form. After filling out the form, you can obtain the passwords soon.

Fig. 1 The ICNALE User Registration Form

For researchers in mainland China

You may not be able to access the registration site from China. Please send your information (name, institute, position, and the purpose of the use of the data) directly to Dr. Shin Ishikawa (iskwshin@gmail.com)

Frequently Asked Questions

1. I already registered, but have not received passwords. What should I do?
---> Please contact the ICNALE team.

2. I am using a Mac PC and cannot open the file. Any helps?
---> Please read this.

3. I am in China and cannot access the registration form on the Google Server. Any helps?
---> Send your name, your institute, your position (eg. Prof./ Grad Student/ Undergrad/ Independent researcher etc.), and the purpose of using the ICNALE directly to the ICNALE team.

4. How should I cite the ICNALE in my research papers?
---> Please read this.

5. How can I analyze the download version?
---> You need a corpus analytical software. Please try Antconc and/or Wordsmith (Free Version).

6. Encoding seems to be incompatible with my software. Any helps?
---> All the ICNALE texts are encoded in the UTF-8 containing the BOM character (Info). When using a concordance software, you may need to set the character code before conducting analyses.

Fig. How to set the character code on the Antconc v3.5.2 (Global Setting --> Character Encoding)

7. I am using the Wordsmith. It always tries to convert ICNALE UTF8 files to some other format. How can I stop this?
---> Please remove the check of "Convert from UTF8."

Fig. How to stop the text format conversion on the Wordsmith (Settings --> Advanced)

Folder Structure

Each module includes unmerged and merged text sets. The latter includes POS-tagged texts.

Unmerged --- Individual (Only for the ICNALE Edited Essays) / Classified
Merged --- Plain Text / Tagged

File Naming Rules

Fully updated in 2023 June

General Rules
1. Spoken Monologues (SM)
Module _Region _Task    _Student ID   _CEFR
SM          _CHN      _PTJ1    _001           _B1_2

2. Spoken Dialogues (SD)
Module   _Region _Task     _Student ID   _CEFR    _Editing
EE            _CHN      _PTJ0   _001        _B1_1     _ORIG

3. Written Essays (WE)
Module   _Region _Task    _Student ID _CEFR
WE          _CHN       _PTJ0   _001              _B1_1

4. Edited Essays (EE)
Module   _Region    _Task (Code, Topic, Task, main/QA) _Student ID   _CEFR
SD           _CHN                      _04   _PTJ   _PIC _QA            _001       _B1_2

5. Global Rating Archives (GRA)
N/A

Module
    SM: Spoken Monologues
    SD: Spoken Dialogues
    WE: Written Essays
    EE: Edited Essays

Region
    CHN, ENS, HKG, IDN, JPN, KOR, PAK, PHL, SIN, THA, TWN (SD only) MYS

Task
       PTJ0: part-time job essay
       PTJ1/2: 1st / 2nd Part-time job speech
       SMK0: non-smoking essay
       SMK1/2: 1st / 2nd no-smoking speech

SD Task
   01_XXX_INT_xx (Introduction)
   03_PTJ_PIC_xx (Picture Description)
   04_PTJ_PIC_QA
   06_PTJ_ROL_xx (Roleplay)
   07_PTJ_ROL_QA
   09_SMK_PIC_xx
   10_SMK_PIC_QA
   12_SMK_ROL_xx
   13_SMK_ROK_QA
   14_XXX_REF_xx (Final Refelction)

Student ID
    001-999: Participant identification codes     (For NNS)

CEFR
   (For Learners)
   A2_0: CEFR A2
       B1_1: B1 lower
   B1_2: B1 upper
       B2_0: B2+

   (For NS)
      XX_0 Unclassified
      XX_1: Students
      XX_2: Teachers
      XX_3: Others

How to Read a Participant Information Sheet

The number of words
PTJ1 (wds)
   Number of words in part-time job essays OR monologue (1st trial)
PTJ2 (wds)
   Number of words in part-time monologue (2nd trial)
SMK1 (wds)
   Number of words in non-smoking essays OR monologue (1st trial)
SMK2 (wds)
   Number of words in non-smoking monologue (2nd trial)

Personal Attributes
Country
   Countries and regions
Sex
   Male or Female
Age
   y/o
Grade/ Degree
   1-4, or MA for learners/ BA, MA, Ph.D. for ENS
Yrs of Stay (< Yrs)
   Years of stay in English-speaking countries
Yrs of Working (NS)
   Years of working (only for ENS)
ENS Type
   1 (College students), 2 (Teachers), 3 (Other adults)
Major/ Occupation
   Faculty or Department for Learners/ Jobs (for ENS only)
Acad. Genre
   Humanities, Social Sciences, Science & Technology, Life Science

Proficiency
Test
   Type of the English proficiency test that a participant has taken (e.g.: TOEIC, TOEFL, IELTS)
Score
   Scores in the test mentioned above
TOEIC-Converted
   Test scores converted to the TOEIC. Scores in the TOEFL PBT test were converted into the TOEIC score, using the regression formula: TOEIC = TOEFL*4.167 - 1497
TOEIC-S
   Scores in the TOEIC Speaking Test
TOEIC-W
   Scores in the TOEIC Writing Test
VST
   Scores in the 5k-words voc size test (/50) (Nation & Beglar, 2007)
CEFR
   CEFR level (A2, B11, B12, B2+) estimated from the scores in the proficiency test or Voc Size Test
Self Ev
   Self evaluation scores (only for Spoken Monologue)

Attitudes
INTM
   Integrative motivation score (See http://language.sakura.ne.jp/icnale/about.html)
INSM
   Instrumental motivation score
INTM+INSM
   Strength of motivation in general
INTM-INSM
   The difference between two kinds of motivations
Like to talk in L2
   Willingness to communicate in L2
Like to talk in L1
   Willingness to communicate in L1

L2 Learning Background
Primary/ Secondary/ College
   How much have learners used English at each of the school levels? (See http://language.sakura.ne.jp/icnale/about.html )
Inschool/ Outschool
   How much have learners used English in classes or out of the classes? (See http://language.sakura.ne.jp/icnale/about.html)
Listening/ Reading/ Speaking/ Writing
   How much have learners studied each of the four basic English skills? (See http://language.sakura.ne.jp/icnale/about.html)
NS
   How often have learners been taught by English native speakers?
Pronunciation
   How often have learners been taught English pronunciation?
Presentation
   How often have learners been taught speeches and presentations?
Essay W
   How often have learners been taught essay writing?

Additional info in the Written Essays Module
Editor
   HAC, JES, LC, MH, NH (Editors’ codes)
A (Added) /   D (Deleted)
   Number of words added/ deleted in editing
A+D
   Total number of words changed (namely added or deleted) in editing (a.k.a “edit distance”)
Content (/12), Organization (/12), Vocabulary (/12), Language Use (/12), Mechanics (/12)
    Scores given by the editors. Rating rubrics are based on the ESL Composition Profile (Jacobs et al., 1981). Also see Ishikawa (2018).
Total 1 (%)
    Simple Mean: T1= (Con+Org+Voc+LanU+Mec)/60*100
Total 2 (Weighted %)
    Weighted Mean (Based on the weights proposed in Jacobs, et. al., 1981): T2= (Con/12*30 + Org/12*20 + Voc/12*20 + LanU/12*25 + Mec/12*5) (Corrected in April, 2018)

Additional info in the Spoken Dialogue Module
Time (L2/ L1 Intv) (mm:ss/ ss)
   Duration of an L2 interview/ Duration of a follow-up L1 interview
Time (All) (mm:ss)
   Duration of the total interview
L2 Tokens
   The number of tokens in an L2 interview (student's turn only)
L2 Types
   The number of types in an L2 interview (student's turn only)
L2 STTR (/100)
   Standardized type/token ratio
L2 MWL
   Mean word length (in the number of letters)
L2 MSL
   Mean sentence length (in the number of words)
L2 Fluency (Tokens/ M)
L2 Speed Fluency Index
L1 Tokens (Letters)
   The number of tokens in a follow-up L1 interview (student's turn only)
L1 Fluency (Letters/ M)
L1 Speed Fluency Index
File Size (MP4 All Intv: MB)/ File Size (MP3 L2 Intv: MB)
   Sizes of the accompanying video and audio files