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Register a new userText Retrieval for Language Learners: Graded Vocabulary vs. Open Learner Model
استرجاع النص لمتعلمي اللغة: مفردات متدرجة مقابل نموذج المتعلم المفتوح
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يعيد نظام استرجاع النص للتعلم اللغوي مواد القراءة في مستوى الصعوبة المناسب للمستخدم.يحافظ النظام عادة على نموذج متعلم على معرفة المفردات للمستخدم، وتحدد النصوص التي تناسب النموذج.مع زيادة الكفاءة في اللغة للمستخدم، تكون التحديثات النموذجية ضرورية لاسترداد النصوص مع التعقيد المعجمي المقابل.نحن نتحقق في نموذج متعلم مفتوح يتيح تعديل المستخدم لمحتواه، وتقييم فعاليته فيما يتعلق بمبلغ جهد تحديث المستخدم.قارنا هذا النموذج مع النهج المتدرج، حيث يقوم النظام بإرجاع النصوص في الصف الأمثل.عندما يقوم المستخدم بإجراء ما لا يقل عن نصف التحديثات المتوقعة لنموذج المتعلم المفتوح، تظهر نتائج المحاكاة أنه يتفوق على النهج المتدرج في استرجاع النصوص التي تناسب تفضيلات المستخدم كثافة كلمة جديدة.
A text retrieval system for language learning returns reading materials at the appropriate difficulty level for the user. The system typically maintains a learner model on the user's vocabulary knowledge, and identifies texts that best fit the model. As the user's language proficiency increases, model updates are necessary to retrieve texts with the corresponding lexical complexity. We investigate an open learner model that allows user modification of its content, and evaluate its effectiveness with respect to the amount of user update effort. We compare this model with the graded approach, in which the system returns texts at the optimal grade. When the user makes at least half of the expected updates to the open learner model, simulation results show that it outperforms the graded approach in retrieving texts that fit user preference for new-word density.
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