Bibliographic Details
| Title: |
An intelligent vocabulary size measurement method for second language learner |
| Authors: |
Tian Xia, Xuemin Chen, Hamid R. Parsaei, Feng Qiu |
| Source: |
Language Testing in Asia, Vol 13, Iss 1, Pp 1-24 (2023) |
| Publisher Information: |
SpringerOpen, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Language and Literature |
| Subject Terms: |
Vocabulary size test, Computerized adaptive testing, Intelligent vocabulary size measurement, Artificial neural network, Long short-term memory, Robot testers, Language and Literature |
| More Details: |
Abstract This paper presents a new method for accurately measuring the vocabulary size of second language (L2) learners. Traditional vocabulary size tests (VSTs) are limited in capturing a tester’s vocabulary and are often population-specific. To overcome these issues, we propose an intelligent vocabulary size measurement method that utilizes massive robot testers. They are equipped with randomized and word-frequency-based vocabularies to simulate L2 learners’ variant vocabularies. An intelligent vocabulary size test (IVST) is developed to precisely measure vocabulary size for any population. The robot testers “take” the IVST, which dynamically generates quizzes with varying levels of difficulty adapted to the estimated tester’s vocabulary size in real-time using an artificial neural network (ANN) through iterative learning. The effectiveness of the IVST is factually verified by their visible vocabularies. Additionally, we apply a long short-term memory (LSTM) model to further enhance the method’s performance. The proposed method has demonstrated high reliability and effectiveness, achieving accuracies of 98.47% for the IVST and 99.87% for the IVST with LSTM. This novel approach provides a more precise and reliable method for measuring vocabulary size in L2 learners compared to traditional VSTs, offering potential benefits to language learners and educators. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2229-0443 |
| Relation: |
https://doaj.org/toc/2229-0443 |
| DOI: |
10.1186/s40468-023-00258-w |
| Access URL: |
https://doaj.org/article/c2437384a31c42e3948c45b3193d3455 |
| Accession Number: |
edsdoj.2437384a31c42e3948c45b3193d3455 |
| Database: |
Directory of Open Access Journals |
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