Uncertainty-aware Personalized Readability Assessment Framework for Second Language Learners

• Item type: Journal(1)
• 公開日: 2022-05-15
• タイトル: Uncertainty-aware Personalized Readability Assessment Framework for Second Language Learners
• タイトル / 言語: en
• タイトル: Uncertainty-aware Personalized Readability Assessment Framework for Second Language Learners
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集:情報システム論文] uncertainty, vocabulary tests, readability assessments, natural language processing
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Tokyo Gakugei University
• 著者所属(英): en
• 著者所属(英): Tokyo Gakugei University
• 著者名: Yo, Ehara
• 著者名(英): Yo, Ehara
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Assessing whether an ungraded second language learner can read a given text quickly is important for supporting learners of diverse backgrounds. Second language acquisition (SLA) studies have tackled such assessment tasks wherein only a single short vocabulary test result is available to assess a learner. Such studies have shown that the text-coverage or namely the percentage of words the learner knows in the text, is the key assessment measure. Currently, count-based percentages are used, in which each word in the given text is classified as being known/unknown to the learner, and the words classified as known are then simply counted. When each word is classified, we can also obtain an uncertainty value as to how likely each word is known to the learner. However, how to leverage these informative values to guarantee their use as an assessment measure that is comparable to that of the previous values remains unclear. We propose a novel framework that allows assessment methods to be uncertainty-aware while guaranteeing comparability to the text-coverage threshold. Such methods involve a computationally complex problem for which we also propose a practical algorithm. In our evaluation using newly created crowdsourcing-based dataset, our best method under our framework outperformed conventional methods.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.352; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Assessing whether an ungraded second language learner can read a given text quickly is important for supporting learners of diverse backgrounds. Second language acquisition (SLA) studies have tackled such assessment tasks wherein only a single short vocabulary test result is available to assess a learner. Such studies have shown that the text-coverage or namely the percentage of words the learner knows in the text, is the key assessment measure. Currently, count-based percentages are used, in which each word in the given text is classified as being known/unknown to the learner, and the words classified as known are then simply counted. When each word is classified, we can also obtain an uncertainty value as to how likely each word is known to the learner. However, how to leverage these informative values to guarantee their use as an assessment measure that is comparable to that of the previous values remains unclear. We propose a novel framework that allows assessment methods to be uncertainty-aware while guaranteeing comparability to the text-coverage threshold. Such methods involve a computationally complex problem for which we also propose a practical algorithm. In our evaluation using newly created crowdsourcing-based dataset, our best method under our framework outperformed conventional methods.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.352; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 63, 号 5, 発行日 2022-05-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764