Yoichiro Ashida


English Vocabulary Learning Software Developed by Students at a National Institute of Technology (KOSEN)
Yoichiro Ashida, Marshall Kiyoshi Higa

This presentation summarizes the development of software that enables EFL learners to perform analyses of texts in order to build personalized word lists, which are stored in a cloud system for review. It will begin by explaining the pedagogical rationale for the system by surveying previous literature and reviewing related concepts in second-language vocabulary acquisition. A demonstration of the software in use will follow with an explanation of key features. The final portion of the presentation will consider various technical aspects of the platform and how these can aid in improving vocabulary acquisition. This project is a collaboration between instructors and students, who have lent their programming expertise and provided important feedback as learners hoping to optimize the software for their own study purposes.
The primary programming language utilized in this project is Python, along with open-source Python libraries such as Pandas and Natural Language Toolkit (NLTK), which allow users to perform text analyses and obtain information such as the frequency of a word, difficulty, and genre. In order to provide learners with more specific information about words they encounter in texts, the text-analysis tool uses data from commonly used word lists as well as custom lists developed within the researchers’ institutions to provide additional data. To aid efficient review, students can save the selected vocabulary in a spaced repetition system (SRS). SRS is a tool that optimizes learning by presenting information at increasing intervals of time based on the user's performance, thereby facilitating long-term retention. It is based on the Leitner System, which is a method of organizing flashcards into boxes that are reviewed at increasingly wider intervals of time in order to maximize the retention process. SRS utilizes algorithms to determine the optimal interval of time to review a vocabulary word for efficient retention.
While similar web-based tools exist, the combination of text analysis functions and SRS is unique and offers learners a flexible and convenient way to personalize their vocabulary lists. This will be particularly useful for learners studying a specific specialism. By drawing on word lists and vocabulary specific to their specialism, students can better prepare themselves to use English in their future professions. For students at institutes of technology, this software provides a way to tailor their own personal word lists to the subjects that they are studying and gain more autonomy and control over the learning process.

Education Research and Practice
Terrsa Hall B