Finding Similar Exercises in Online Education Systems

author: Qui Liu, University of Science and Technology of China
published: Nov. 23, 2018,   recorded: August 2018,   views: 822

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In online education systems, finding similar exercises is a fundamental task of many applications, such as exercise retrieval and student modeling. Several approaches have been proposed for this task by simply using the specific textual content (e.g. the same knowledge concepts or the similar words) in exercises. However, the problem of how to systematically exploit the rich semantic information embedded in multiple heterogenous data (e.g. texts and images) to precisely retrieve similar exercises remains pretty much open. To this end, in this paper, we develop a novel Multimodal Attention-based Neural Network (MANN) framework for finding similar exercises in large-scale online education systems by learning a unified semantic representation from the heterogenous data. In MANN, given exercises with texts, images and knowledge concepts, we first apply a convolutional neural network to extract image representations and use an embedding layer for representing concepts. Then, we design an attention-based long short-term memory network to learn a unified semantic representation of each exercise in a multimodal way. Here, two attention strategies are proposed to capture the associations of texts and images, texts and knowledge concepts, respectively. Moreover, with a Similarity Attention, the similar parts in each exercise pair are also measured. Finally, we develop a pairwise training strategy for returning similar exercises. Extensive experimental results on real-world data clearly validate the effectiveness and the interpretation power of MANN.

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Reviews and comments:

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Comment2 kaden smith, February 22, 2019 at 7:17 a.m.:

When it comes to online education systems, discovery comparable exercises are a basic task of many apps, like exercise retrieval and student modeling. There are many methods that have been planned for this task by utilizing the definite textual content in exercises. They found very useful. The issue how to exploit the rich semantic info rooted in many other varied data to precisely recover related exercises residues pretty much open.

Comment3 khanasuea, February 23, 2019 at 1:01 p.m.:

When it comes to finding similar exercises for online education systems then it is not a very simple task to do. Specifically in MANN, there are specified exercises with texts, pictures and data concepts, and some people initially apply a convolutional neural network to excerpt duplicate symbols and utilize an implanting layer for signifying concepts. I found very informative. The next step is to design a signifying long short-term memory network to get to know more about a combined semantic depiction of each exercise in a multimodal pattern. Mostly there are dual attention plans that are being planned to detent the links of transcripts and images, texts and data, singly. With the help of a Similarity Attention, the comparable parts in each exercise pair are also calculated. You can also make a pairwise drill plan for recurring comparable exercises. Wide-ranging investigational results on real-world data evidently legalize the efficiency and the clarification control of MANN.

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