Exploring the Effectiveness of Vocabulary Proficiency Diagnosis Using Linguistic Concept and Skill Modeling
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| Title: | Exploring the Effectiveness of Vocabulary Proficiency Diagnosis Using Linguistic Concept and Skill Modeling |
|---|---|
| Language: | English |
| Authors: | Ma, Boxuan, Hettiarachchi, Gayan Prasad, Fukui, Sora, Ando, Yuji |
| Source: | International Educational Data Mining Society. 2023. |
| Availability: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
| Peer Reviewed: | Y |
| Page Count: | 11 |
| Publication Date: | 2023 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Descriptors: | Vocabulary Development, Second Language Instruction, Second Language Learning, Language Proficiency, Item Response Theory, Comparative Analysis, Learning Processes, Artificial Intelligence, Task Analysis, Evaluation Methods |
| Abstract: | Vocabulary proficiency diagnosis plays an important role in the field of language learning, which aims to identify the level of vocabulary knowledge of a learner through his or her learning process periodically, and can be used to provide personalized materials and feedback in language-learning applications. Traditional approaches are widely applied for modeling knowledge in science or mathematics, where skills or knowledge concepts are well-defined and easy to associate with each item. However, only a handful of works focus on defining knowledge concepts and skills using linguistic characteristics for language knowledge proficiency diagnosis. In addressing this, we propose a framework for vocabulary proficiency diagnosis based on neural networks. Specifically, we propose a series of methods based on our framework that uses different linguistic features to define skills and knowledge concepts in the context of the language learning task. Experimental results on a real-world second-language learning dataset demonstrate the effectiveness and interpretability of our framework. We also provide empirical evidence with ablation testing to prove that our knowledge concept and skill definitions are reasonable and critical to the performance of our model. [For the complete proceedings, see ED630829.] |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| Accession Number: | ED630845 |
| Database: | ERIC |
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