Code, Programs, How To

Code & Links for Tools / Programs that I Helped Write:

Code that finds all instances of "path" encoding in text files and categorizes as verb- or satellite-framed. Also separates between motion events, change of state events, and "other" events. Currently only available in English. To run with GUI, please open English_ECFinder.py. Requires tkinter, PIL, SpaCY, and the en_core_web_trf pipeline.

GitHub Link: https://github.com/springuistics/EventConflationFinder
How to Set Up Video: coming soon
Want your text analyzed without installing?
→  Email me your text files to me: spring.ryan.edward.c4 <at> tohoku.ac.jp

Be sure to cite:
Spring, R., & Ono, N. (2024). Creating an automated tool to assist with event-conflation studies: An explanation and argument for its importance. Research Methods in Applied Linguistics, 3(1), 100054. https://doi.org/10.1016/j.rmal.2023.100054   

Several different programs:

1. Code that combines Lu's (2010) L2 Syntactic Complexity Analyzer and Lu's (2012) Lexical Complexity Analyzer. "Main" version compares the measures run in NLTK versus SpaCY versus other parsers as per Spring & Johnson (2022). "SpaCy Full" provides all measures and a few others parsed with SpaCy as per Spring & Johnson (2022). This has also been used to fuel my human-AI integrated rater system

GitHub Link: https://github.com/mwjohnson/autograder

2. Code that provides a much wider range of complexity measure types (i.e., both large-grained and phrasal syntactic complexity measures, lexical complexity measures, discourse marker measures), but provides far FEWER actual measures - specifically those that have repeatedly and consistently shown high correlation (r>0.25) across numerous data sets of L2 production for L1 Japanese EFL learners (both in my own research and others'). Many of these measures are present in the human-AI integrated rater system

GitHub Link: https://github.com/springuistics/L1J_EFL_Measures

Cited Works:
Lu, X.  (2010). Automatic analysis of syntactic complexity in second language writing. International Journal of Corpus Linguistics, 15(4), 474-496.

Lu, X. (2012). The relationship of lexical richness to the quality of ESL learners' oral narratives. The Modern Language Journal, 96(2), 190-208. (Creator of the original code)

Spring, R., & Johnson, M.W. (2022). The possibility of improving automated calculation of measures of lexical richness for EFL writing: A comparison of the LCA, NLTK, and SpaCy tools. System,106, 770–786. https://doi.org/10.1016/j.system.2022.102770

Spring, R., & Johnson, M.W. (2022). Comparing Syntactic Complexity Measures Counted by Tregrex-based Tagging and UD-based Tagging for Evaluating L1 Japanese EFL Paragraph Writing. Proceedings of The 28th Annual Meeting of The Association for Natural Language Processing (pp.319-323). (Technology in "spacyfull")

Spring, R. (2023). A human-AI integrated rating scheme for improving second language writing: The case of Japanese learners of English for general academic purposes. Reports Vol. 15 of Japan Association for Language Education and Technology (LET), Kansai Chapter, Methodology Special Interest Group (SIG) (pp. 22–43). https://doi.org/10.21203/rs.3.rs-3350837/v2

Excel to Text File Exporter

Code that clean non-UTF characters (e.g. Japanese zenkakumoji) and then exports each cell in a column to an individual text file in a designated folder. Each file is named the value of the "A" column, and the text written into each file is the value of the "B" column.
TIP: In Excel 365, you can press "ALT + F11" to open the VBA code console needed for using this code.
→ Useful for teachers who need to create multiple files for student work.
→ Useful when using automatic complexity measurements tools (such as the one above in 2).
→ Only works with Windows based machines (not Linux or Mac OS systems)

This works very well preparing text files for the above programs.

Video Explanation : <https://youtu.be/8as1LmE-hLg>
Copy& Paste-able Code (VBA for Excel): Code Text File

Free Online Statistical Calculators

This is a free set of completely online statistics calculators. The website helps you to choose the proper test and runs checks to ensure that you are using an appropriate test for your data. It provides both checks for statistical significance and effect size, as well as post-hoc analysis when necessary. Furthermore, the website provides proper notation for researchers and explanation to help choose tests, interpret them, and understand their implications. Currently available in both English and Japanese.

 https://springsenglish.online/stats/

→ Good for educators who need to compare pre/post tests check for differences in survey questions, etc.
→ Check calculations on Github:  https://github.com/springuistics/BilingualStats
→ Read the details in the paper describing and explaining the code and calculations.

Spring, R. (2022) Free, Online, Multilingual Statistics for Linguistics and Language Education Researchers. Center for Culture and Language Education, Tohoku University 2021 Nenpo, 8, 32-38. https://doi.org/10.13140/RG.2.2.12037.63202  

Foreign Language Speaking Homework Code

Code and Explanation for Creating Submit-able Foreign Language Speaking Homework Assignments Utilizing Google Scripts and ASR Technology:

(Code)
Github link: https://github.com/springuistics/speaking-homework

(Simple Video Explanation)
Explanation of how to Use the Code (English / Japanese): <https://youtu.be/77uM8zhKb94>

Please cite:

Spring, R. (2022). Google-based Foreign Language Speaking Homework Script. [Computer Code]. Available at:  https://sites.google.com/view/ryanspring/code-misc?authuser=0

Teacher-Centric Text Checker w/ Vocabulary Levels

This is a free text-checker aimed at teachers. The website lemmatizes words and then checks them against the New General Service List (NGSL; Browne, 2014) and CEFR-J vocabulary lists (Tono, 2013). It provides a percentage of lexical coverage for each, but also provides a list of words and a button allowing users to claim that students do or do not likely know the word. The "analyze" button can then be clicked again to recalculate the percentages. This encourages teachers to think about what words THEIR particular students are likely to know and make a more educated guess based off of both previous research AND their own knowledge of their students. The tool also provides other simple measurements such as CTTR and number of different words for vocabulary variety, MLS for syntactic complexity, number of words for length, and Flesh-Kincaid scores (for L1 uses).

https://springsenglish.online/textCheck/textChecker.html

→ Good for educators who want to check the levels of their self-made materials and also include their intuitions / knowledge of students' vocabulary
→ Consider the tool from Mizumoto et al. (2021) for more research-oriented purposes: https://doi. org/10.7820/vli.v10.2.mizumoto

Resources:
Spring, R. (2024) A free human-AI integreated text-readability tool. LET関東支部第150回（2024年春季）予稿
Browne, C. (2014). A New General Service List: The better mousetrap we’ve been looking for? Vocabulary Learning and Instruction, 3(2), 1–10.
Mizumoto, A. Pinchbeck, G.G., and McLean, S. (2021). Comparisons of word lists on new word level checker. Vocabulary Learning and Instruction, 10(2), 30–41.
Tono, Y. (ed.) (2013). The CEFR-J Handbook. Taishukan.

Learn Phrasal Verbs App

This is a free application that assists with the learning of Phrasal Verbs. It provides a number of examples and 4-modalitiy practice (Spring & Takeda, 2024) for the 150 most common phrasal verbs, according to the PHaVE list (Garnier & Schmitt, 2015). It assists with memorization by allowing learners to learn phrasal verbs with common particles and cognitive linguistic similarities (Spring, 2018) or with common verbs (Nakata, 2024)

https://springsenglish.online/learnPhrasalVerbs

→ Good for educators who want to provide phrasal verb practice to  students or independent learnrers trying to master phrasal verbs

Resources (please cite if you use in research):
Spring, R., & Takeda, J. (2024). Teaching phrasal verbs and idiomatic expressions through multimodal flashcards: Confirming the importance of multimedia-based instruction. Journal of English Teaching through Movies and Media, 25(2), 40–53. https://doi.org/10.16875/stem.2024.25.2.40
Spring, R. (2018). Teaching phrasal verbs more efficiently: Using corpus studies and cognitive linguistics to create a particle list. Advances in Language and Literary Studies, 9(5), 121–135. https://doi.org/10.7575/aiac.alls.v.9n.5p.121
Garnier, M., Schmitt, N. (2015). The PHaVE List: a pedagogical list of phrasal verbs and their most frequent meaning senses. Language Teaching Research, 19(6), https://doi.org/10.1177/1362168814559798
Nakata, T. (2024).  Eigo Teikei Hyougen Kagaku (『英語定型表現の科学』) [The science of fixed expressions in English]. Kenkyusha. 

Code & Tools that I Explain, but did NOT write
(please cite appropriately):

1. Video Explanation of How to Use Praat to Automatically Analyze Oral Fluency
   
   Video: https://www.youtube.com/watch?v=utJMBCI5Zt8

Please cite:
De Jong, N.H. & Wempe, T. (2009). Praat script to detect syllable nuclei and measure speech rate automatically. Behavior research methods, 41 (2), 385 - 390.

Their site  contains the necessary code and written explanation :
https://sites.google.com/site/speechrate/

Visit my "Journal Articles" page for examples of the above being used in ELT research and kindly consider citing if appropriate:
  → E.g. Spring, Kato, & Mori 2019 (in Foreign Language Annals), Spring 2020 (in TESL-EJ; STEM), Spring 2021 (in TESL-EJ)

2. Video Explanation of How to Use NatTos for ASR-based Pronunciation Practice
   
   Video: https://youtu.be/MYHWF5K8myo

Please cite:
Spring, R., & Tabuchi, R. (2021). Assessing the practicality of using an automatic speech recognition tool to teach English pronunciation online. STEM Journal, 22(2), 93–104. https://doi.org/10.16875/stem.2021.22.2.93.

Spring, R., & Tabuchi, R. (2022). The role of ASR in EFL pronunciation improvement: An in-depth look at the impact of treatment length and guided practice on specific pronunciation points. Computer Assisted Language Learning Electronic Journal, 23(3), 163–185. http://callej.org/journal/23-3/Spring-Tabuchi2022.pdf.

The NatTos site can be found here:
https://www.mintap.com/nattos/