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Register a new userHeterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data
رجال البيئة غير المتجانسة الشبكات العصبية لمفهوم الشرط الأساسي التعلم في البيانات التعليمية
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تعتبر العلاقات المتطلالة الأساسية بين المفاهيم أمرا حاسما للتطبيقات التعليمية، مثل تخطيط المناهج الدراسية والدروس الذكي.في هذه الورقة، نقترح نهجا للتعليم ذات العلاقات ذات العلاقات ذات الصلة بالمفهوم الجديد، والتي تجمع بين كل من تمثيل المفهوم المستفادة من الرسم البياني غير المتجانس والمفهوم المفهوم المميزات الزوجية.علاوة على ذلك، نقوم بتوسيع CPRL في ظل الإعدادات الخاضعة للإشراف ضعيفا لجعل طريقةنا أكثر عملية، بما في ذلك التعلم العلاقات المتطلبات الأساسية من تبعيات كائن التعلم وتوليد بيانات التدريب مع برامج البيانات.تظهر تجاربنا على أربع مجموعات البيانات أن النهج المقترح يحقق نتائج أحدث النتائج مقارنة بالأساليب الحالية.
Prerequisite relations among concepts are crucial for educational applications, such as curriculum planning and intelligent tutoring. In this paper, we propose a novel concept prerequisite relation learning approach, named CPRL, which combines both concept representation learned from a heterogeneous graph and concept pairwise features. Furthermore, we extend CPRL under weakly supervised settings to make our method more practical, including learning prerequisite relations from learning object dependencies and generating training data with data programming. Our experiments on four datasets show that the proposed approach achieves the state-of-the-art results comparing with existing methods.
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The encoder--decoder framework achieves state-of-the-art results in keyphrase generation (KG) tasks by predicting both present keyphrases that appear in the source document and absent keyphrases that do not. However, relying solely on the source docu
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The work aims to make benefit from existence
multi-CPU and multi-GPU, exploiting the calculation processes
which do multi-GPU, which aims to form mechanism to
scheduling a directed acyclic graph(DAG), it aims to reduce
communication between resources and inter linked task
scheduling in the best form.
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