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Register a new userTowards the Application of Calibrated Transformers to the Unsupervised Estimation of Question Difficulty from Text
نحو تطبيق المحولات المعايرة إلى تقدير غير مدهش للصعوبة من النص
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أن تكون قادرا على أداء تقدير صعوبة الأسئلة بدقة (QDE) تحسين دقة تقييم الطلاب وتحسين تجربة التعلم. الأساليب التقليدية إلى QDE هي إما ذاتية أو إدخال تأخير طويل قبل أن يتم استخدام أسئلة جديدة لتقييم الطلاب. وبالتالي، اقترح العمل الأخير النهج القائم على التعلم في التعلم للتغلب على هذه القيود. يستخدمون أسئلة من صعوبة معروفة لتدريب النماذج القادرة على استنتاج صعوبة الأسئلة من نصها. بمجرد التدريب، يمكن استخدامها لأداء QDE من الأسئلة التي تم إنشاؤها حديثا. توفر الأساليب الحالية النماذج الخاضعة للإشراف والتي تعتمد على نطاق ومتطلبات مجموعة بيانات كبيرة من الأسئلة المعروفة للتدريب. لذلك، لا يمكن استخدامها إذا كانت هذه البيانات غير متوفرة (للدورات الجديدة على منصة التعلم الإلكتروني). في هذا العمل، نقوم بتجربة إمكانية أداء QDE من النص بطريقة غير منشأة. على وجه التحديد، نستخدم عدم اليقين من الأسئلة المعايرة في الرد النماذج كوكيل للصعوبة المتصورة للإنسان. تظهر تجاربنا نتائج واعدة، مما يشير إلى أن عدم اليقين النموذجي يمكن الاستفادة بنجاح لأداء QDE من النص، مما يقلل من كل من التكاليف والوقت المنقضي.
Being able to accurately perform Question Difficulty Estimation (QDE) can improve the accuracy of students' assessment and better their learning experience. Traditional approaches to QDE are either subjective or introduce a long delay before new questions can be used to assess students. Thus, recent work proposed machine learning-based approaches to overcome these limitations. They use questions of known difficulty to train models capable of inferring the difficulty of questions from their text. Once trained, they can be used to perform QDE of newly created questions. Existing approaches employ supervised models which are domain-dependent and require a large dataset of questions of known difficulty for training. Therefore, they cannot be used if such a dataset is not available ( for new courses on an e-learning platform). In this work, we experiment with the possibility of performing QDE from text in an unsupervised manner. Specifically, we use the uncertainty of calibrated question answering models as a proxy of human-perceived difficulty. Our experiments show promising results, suggesting that model uncertainty could be successfully leveraged to perform QDE from text, reducing both costs and elapsed time.
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