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Register a new userMistake Captioning: A Machine Learning Approach for Detecting Mistakes and Generating Instructive Feedback
خطأ في التسمية: نهج التعلم الآلي للكشف عن الأخطاء وتوليد ردود الفعل المفيدة
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إن تقديم ملاحظات للطلاب ليس فقط في وضع علامة على إجاباتهم على النحو الصحيح أو غير صحيح، ولكن أيضا العثور على أخطاء في عملية التفكير التي دفعتهم إلى الإجابة غير الصحيحة.في هذه الورقة، نقدم تقنية لتعلم الآلات بسبب التسمية التوضيحية، وهي مهمة تحاول تحديد الأخطاء وتوفير التعليقات مخصصة لمساعدة المتعلمين على تصحيح هذه الأخطاء.نقوم بذلك عن طريق تدريب شبكة تسلسل إلى تسلسل لتوليد هذه التعليقات بناء على خبراء المجال.لتقييم هذا النظام، نستكشف كيف يمكن استخدامه في مهمة اللغويات التي تدرس قانون جريم.نظهر أن نهجنا يولد ردود الفعل التي تتفوق على خط أساس على مجموعة من مقاييس NLP الآلية.بالإضافة إلى ذلك، نقوم بإجراء سلسلة من دراسات الحالة التي ندرس فيها مخرجات النظام الناجحة وغير الناجحة.
Giving feedback to students is not just about marking their answers as correct or incorrect, but also finding mistakes in their thought process that led them to that incorrect answer. In this paper, we introduce a machine learning technique for mistake captioning, a task that attempts to identify mistakes and provide feedback meant to help learners correct these mistakes. We do this by training a sequence-to-sequence network to generate this feedback based on domain experts. To evaluate this system, we explore how it can be used on a Linguistics assignment studying Grimm's Law. We show that our approach generates feedback that outperforms a baseline on a set of automated NLP metrics. In addition, we perform a series of case studies in which we examine successful and unsuccessful system outputs.
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