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17. Teaching How to Teach Promotes Learning by Teaching

Author

Matsuda, Noboru, Lv, Dan, and Zheng, Guoguo

Abstract

When students learn skills to solve problems by teaching others, they often need to receive scaffolding to benefit from learning by teaching. To facilitate learning by teaching (aka tutor learning), two types of scaffolding have been commonly studied--the scaffolding on how to teach (to induce appropriate teaching activities) and the one on how to solve problems (to ensure correctness of solutions taught). The comparison between these two types of scaffolding and the adaptive control among them, however, has been left unresearched. The primary goal of the current study is to understand how to best control the scaffolding for learning by teaching. A technique for exhaustive differential pattern mining was used to correlate a behavioral pattern of learning by teaching with tutor learning. The results showed that only the scaffolding on how to teach facilitated tutor learning. Students who received the scaffolding on how to teach typically interleaved teaching and assessing their peers' competency more often than those who received the scaffolding on how to solve problems. The results imply an importance of implementing adaptive scaffolding on how to teach to better facilitate learning by teaching.

Published
2023
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42. Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs

Author

Su, Jingchao, Chen, Xu, Zhang, Ya, Chen, Siheng, Lv, Dan, and Li, Chenyang

Subjects
Computer Science - Information Retrieval
Abstract

Relational learning aims to make relation inference by exploiting the correlations among different types of entities. Exploring relational learning on multiple bipartite graphs has been receiving attention because of its popular applications such as recommendations. How to make efficient relation inference with few observed links is the main problem on multiple bipartite graphs. Most existing approaches attempt to solve the sparsity problem via learning shared representations to integrate knowledge from multi-source data for shared entities. However, they merely model the correlations from one aspect (e.g. distribution, representation), and cannot impose sufficient constraints on different relations of the shared entities. One effective way of modeling the multi-domain data is to learn the joint distribution of the shared entities across domains.In this paper, we propose Collaborative Adversarial Learning (CAL) that explicitly models the joint distribution of the shared entities across multiple bipartite graphs. The objective of CAL is formulated from a variational lower bound that maximizes the joint log-likelihoods of the observations. In particular, CAL consists of distribution-level and feature-level alignments for knowledge from multiple bipartite graphs. The two-level alignment acts as two different constraints on different relations of the shared entities and facilitates better knowledge transfer for relational learning on multiple bipartite graphs. Extensive experiments on two real-world datasets have shown that the proposed model outperforms the existing methods., Comment: 8 pages. It has been accepted by IEEE International Conference on Knowledge Graphs (ICKG) 2020

Published
2020

43. Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph

Author

Li, Chenyang, Chen, Xu, Zhang, Ya, Chen, Siheng, Lv, Dan, and Wang, Yanfeng

Subjects
Computer Science - Information Retrieval
Abstract

Knowledge graphs (KGs) composed of users, objects, and tags are widely used in web applications ranging from E-commerce, social media sites to news portals. This paper concentrates on an attractive application which aims to predict the object-tag links in the KG for better tag recommendation and object explanation. When predicting the object-tag links, both the first-order and high-order proximities between entities in the KG propagate essential similarity information for better prediction. Most existing methods focus on preserving the first-order proximity between entities in the KG. However, they cannot capture the high-order proximities in an explicit way, and the adopted margin-based criterion cannot measure the first-order proximity on the global structure accurately. In this paper, we propose a novel approach named Dual Graph Embedding (DGE) that models both the first-order and high-order proximities in the KG via an auto-encoding architecture to facilitate better object-tag relation inference. Here the dual graphs contain an object graph and a tag graph that explicitly depict the high-order object-object and tag-tag proximities in the KG. The dual graph encoder in DGE then encodes these high-order proximities in the dual graphs into entity embeddings. The decoder formulates a skip-gram objective that maximizes the first-order proximity between observed object-tag pairs over the global proximity structure. With the supervision of the decoder, the embeddings derived by the encoder will be refined to capture both the first-order and high-order proximities in the KG for better link prediction. Extensive experiments on three real-world datasets demonstrate that DGE outperforms the state-of-the-art methods., Comment: 8 pages. It has been accepted by IEEE International Conference on Knowledge Graphs (ICKG) 2020

Published
2020

48. Research on International Chinese Education Informatization based on artificial intelligence Technology

Author

Lv, Dan, Lv, Jin’e, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Kassim, Hafizoah, editor, Malik, Nadeem, editor, Rajagopal, Premkumar, editor, and Singh, Manjet Kaur Mehar, editor

Published
2023
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