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 2022/4/12



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).


Cannot download the file(s)? Then, try "right click + save as."  


Core Modules
 Modules  Updated  N. of samples  N. of tokens  Contents
Spoken Monologues 2.0
   > Transcripts (6MB)
   > Audio (1GB)
   > ICNALE_Infosheet_201912		  2017/08  4,400  c 500,000 60-second monologues about two common topics.
Spoken Dialogues 1.3
   > Speaker Info & Transcripts (17MB).
The zipped file above includes the link to the  video data (300GB).		  2021/11  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 2.4
   > Data (12MB)
  > ICNALE_Infosheet_201912		  2020/12  5,600  c 1,300.000 200-300-words essays about two common topics
Edited Essays 3.0
   > Data (12MB)
   > ICNALE_Infosheet_201912		  2022/04  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 0.1
    > Data		  2021/08  11,200  --- 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

(Eg.) W_  CHN_   PTJ1_   026_   A2_0.   txt
         [1]      [2]          [3]          [4]         [5]      [6]

1st: Production mode
    SM: Spoken Monologue
    SD: Spoken Dialogue
    W: Written Essays
    W_EDIT: Edited Essays (NB: The essay type code, “ORIG” (original essays) or “EDIT” (edited essays) is added at the last of the file names.)


2nd: Country   
    CHN, ENS, HKG, IDN, JPN, KOR, PAK, PHL, SIN, THA, TWN (SD only) MYS

3rd: Topic/ Task   
   (Essays)
       PTJ0: part-time job essay
       SMK0: non-smoking essay
   (Monologue)
       PTJ1/2: 1st / 2nd Part-time job speech
       SMK1/2:　1st / 2nd no-smoking speech
   (Dialogue)
       EN: English Interview

4th: Code   
    001-999: Participant identification codes     (For NNS)

5th: 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

6th: File type
   txt: text
   vert: tagged text (vertical) [Merged File Only]

     



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