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 |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED630845 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
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| Items | – Name: Title Label: Title Group: Ti Data: Exploring the Effectiveness of Vocabulary Proficiency Diagnosis Using Linguistic Concept and Skill Modeling – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ma%2C+Boxuan%22">Ma, Boxuan</searchLink><br /><searchLink fieldCode="AR" term="%22Hettiarachchi%2C+Gayan+Prasad%22">Hettiarachchi, Gayan Prasad</searchLink><br /><searchLink fieldCode="AR" term="%22Fukui%2C+Sora%22">Fukui, Sora</searchLink><br /><searchLink fieldCode="AR" term="%22Ando%2C+Yuji%22">Ando, Yuji</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22International+Educational+Data+Mining+Society%22"><i>International Educational Data Mining Society</i></searchLink>. 2023. – Name: Avail Label: Availability Group: Avail Data: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 11 – Name: DatePubCY Label: Publication Date Group: Date Data: 2023 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Speeches/Meeting Papers<br />Reports - Research – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Vocabulary+Development%22">Vocabulary Development</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Instruction%22">Second Language Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Learning%22">Second Language Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Proficiency%22">Language Proficiency</searchLink><br /><searchLink fieldCode="DE" term="%22Item+Response+Theory%22">Item Response Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+Analysis%22">Comparative Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Processes%22">Learning Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Task+Analysis%22">Task Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Methods%22">Evaluation Methods</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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.] – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2023 – Name: AN Label: Accession Number Group: ID Data: ED630845 |
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| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 11 Subjects: – SubjectFull: Vocabulary Development Type: general – SubjectFull: Second Language Instruction Type: general – SubjectFull: Second Language Learning Type: general – SubjectFull: Language Proficiency Type: general – SubjectFull: Item Response Theory Type: general – SubjectFull: Comparative Analysis Type: general – SubjectFull: Learning Processes Type: general – SubjectFull: Artificial Intelligence Type: general – SubjectFull: Task Analysis Type: general – SubjectFull: Evaluation Methods Type: general Titles: – TitleFull: Exploring the Effectiveness of Vocabulary Proficiency Diagnosis Using Linguistic Concept and Skill Modeling Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ma, Boxuan – PersonEntity: Name: NameFull: Hettiarachchi, Gayan Prasad – PersonEntity: Name: NameFull: Fukui, Sora – PersonEntity: Name: NameFull: Ando, Yuji IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2023 Titles: – TitleFull: International Educational Data Mining Society Type: main |
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