摘要：
Online collaborative learning (OCL) has become a common instructional strategy in higher education for developing students' skills in collaboration, problem-solving, and critical thinking. Cognitive engagement in OCL evolves dynamically, but we do not yet fully understand which patterns of cognitive engagement are conducive to OCL and when to promote them. This study used entropy analysis, sequential pattern mining, and temporal network analysis to examine the online discourse of 44 college students who participated in three OCL tasks. Results showed that, compared with the low-performance groups, the high-performance groups exhibited patterns of continuous perspective elaboration and low-level regulation, as well as frequent shifts from perspective expression to perspective elaboration. In addition, there were differences in the longitudinal evolution patterns of cognitive engagement between the high- and low- performance groups. These findings have important implications for learning tool design and improving collaborative learning design.
摘要：
Temporal knowledge graph (TKG) reasoning aims to infer the missing links from the massive historical facts. One of the big issues is that how to model the entity evolution from both the local and especially global perspectives. The primary temporal dependency models often fail to disentangle both perspectives due to the lack explicit annotations to distinguish the boundary of these two representations. To address these limitations, we propose a contrastive learning framework to Disentangle Local and Global perspectives for TKG Reasoning with selfsupervision framework (DLGR). Our proposed DLGR can jointly utilize the local and global perspectives on two separate graphs and disentangle them in a self -supervised manner. Firstly, we construct a temporal subgraph and a temporal unified graph to effectively learn the local and global perspective representations, respectively. Second, we extract proxies regarding the different neighbors as pseudo labels to supervise the local and global disentanglement in a contrastive manner. Finally, we adaptively fuse the learned two perspective representations for TKG reasoning. The empirical results show that our DLGR significantly outperforms other baselines (e.g., compared to the strong baseline HGLS, our DLGR achieves 4.3%, 3.4%, 1.6% and 1.1% improvements on ICEWS14, ICEWS18, YAGO and WIKI using MRR).
作者机构：
[Zhou, Jin] Cent China Normal Univ CCNU, Sch Educ Informat Technol, Wuhan, Peoples R China.;[Ye, Jun-min] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.
通讯机构：
[Zhou, Jin] C;Cent China Normal Univ CCNU, Sch Educ Informat Technol, Wuhan, Peoples R China.
期刊：
Information Processing & Management,2023年60(1):103114 ISSN：0306-4573
通讯作者：
Weizhong Zhao
作者机构：
[Zhao, Weizhong; Xia, Jun; He, Tingting; Jiang, Xingpeng] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Hubei, Peoples R China.;[Zhao, Weizhong] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Zhao, Weizhong] Cent China Normal Univ, Natl Language Resources Monitoring & Res Ctr Netwo, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Weizhong Zhao] H;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, Hubei, China<&wdkj&>School of Computer, Central China Normal University, Wuhan 430079, Hubei, China<&wdkj&>National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan 430079, Hubei, China
关键词：
Deep knowledge tracing;Forgetting and learning mechanisms;Intelligent education
期刊：
Information Processing & Management,2023年60(2):103207 ISSN：0306-4573
通讯作者：
Chen Qiu
作者机构：
[Gu, Jinguang; Qiu, Chen; Xu, Zhaoyang; Liu, Maofu; Fu, Haidong] Wuhan Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430081, Peoples R China.;[Gu, Jinguang; Qiu, Chen; Xu, Zhaoyang; Liu, Maofu; Fu, Haidong] Wuhan Univ Sci & Technol, Inst Big Data Sci & Engn, Wuhan 430081, Peoples R China.;[Zhou, Guangyou] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Chen Qiu] S;School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430081, China<&wdkj&>Institute of Big Data Science and Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China
期刊：
Journal of Computing in Higher Education,2023年:1-24 ISSN：1042-1726
通讯作者：
Yu, S;Wang, JH
作者机构：
[Yu, Shuang; Liu, Qingtang; Yu, S; Zheng, Xinxin; Wu, Linjing] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.;[Ye, Junmin] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Wang, Jianhu] Xinjiang Normal Univ, Coll Educ Sci, Urumqi, Peoples R China.
通讯机构：
[Yu, S ] C;[Wang, JH ] X;Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.;Xinjiang Normal Univ, Coll Educ Sci, Urumqi, Peoples R China.
摘要：
Interdisciplinary collaboration is widely used in research, industry, and education. Understanding the differences in cognitive processes between cross-discipline and same-discipline groups can improve instruction in collaborative learning. In this study, students volunteered to participate in cross-discipline or same-discipline collaborative learning groups to collaboratively complete three assignments. Epistemic network analysis was employed to identify the differences in cognitive engagement, the roles of group leaders, and the trajectory differences between these two kinds of groups. The results showed that compared to same-discipline groups, cross-discipline groups have lower-order cognitive engagement. Group leaders in cross-discipline groups invested most of their energy into inquiring or starting the discussion. In addition, cognitive engagement of cross-discipline groups dropped sharply over time in collaborative learning. These differences imply that to achieve better collaboration in cross-discipline groups, teachers should provide more interventions, such as evaluation and analytical support, to help students reach high-level cognitive engagement.
期刊：
INTERNET AND HIGHER EDUCATION,2022年55:100875 ISSN：1096-7516
通讯作者：
Jin Zhou
作者机构：
[Ye, Jun -min] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Zhou, Jin] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.
通讯机构：
[Jin Zhou] F;Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China
摘要：
Evidence suggests that learning sentiments are inextricably related to cognitive processing, and the exploration of the relationship remains to be an important research topic. This study collected discourse data from 40 college students in online collaborative learning activities. Epistemic network analysis (ENA) was employed to explore the connection between learning sentiments and cognitive processing and compare the ENA network characteristics of the higher- and lower-engagement groups. The results indicated that there was a joint connection between understand-analyze-neutral, and insightful sentiments had more association with neutral sentiments and understanding. Besides, distinctions existed between higher- and lower-engagement groups with respect to the association between learning sentiments and cognitive processing. The higher-engagement group had stronger associations around positive and confused sentiments, while the lower-engagement group had stronger associations around off-topic discussion. The findings of this research may serve as a reference for designing and implementing collaborative learning activities to increase cognitive levels.
作者：
Zhang, Qixuan;Weng, Xinyi;Zhou, Guangyou;Zhang, Yi;Huang, Jimmy Xiangji
期刊：
Information Processing & Management,2022年59(3):102933 ISSN：0306-4573
通讯作者：
Guangyou Zhou
作者机构：
[Zhou, Guangyou; Zhang, Yi; Weng, Xinyi; Zhang, Qixuan] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Huang, Jimmy Xiangji] York Univ, Sch Informat Technol, Toronto, ON, Canada.
通讯机构：
[Guangyou Zhou] S;School of Computer, Central China Normal University, Wuhan, China
摘要：
Knowledge graph representation learning (KGRL) aims to infer the missing links between target entities based on existing triples. Graph neural networks (GNNs) have been introduced recently as one of the latest trendy architectures serves KGRL task using aggregations of neighborhood information. However, current GNN-based methods have fundamental limitations in both modelling the multi-hop distant neighbors and selecting relation-specific neighborhood information from vast neighbors. In this study, we propose a new relation-specific graph transformation network (RGTN) for the KGRL task. Specifically, the proposed RGTN is the first pioneer model that transforms a relation-based graph into a new path-based graph by generating useful paths that connect heterogeneous relations and multi-hop neighbors. Unlike the existing GNN-based methods, our approach is able to adaptively select the most useful paths for each specific relation and to effectively build path-based connections between unconnected distant entities. The transformed new graph structure opens a new way to model the arbitrary lengths of multi-hop neighbors which leads to more effective embedding learning. In order to verify the effectiveness of our proposed model, we conduct extensive experiments on three standard benchmark datasets, e.g., WN18RR, FB15k-237 and YAGO-10-DR. Experimental results show that the proposed RGTN achieves the promising results and even outperforms other state-of-the-art models on the KGRL task (e.g., compared to other state-of-the-art GNN-based methods, our model achieves 2.5% improvement using H@10 on WN18RR, 1.2% improvement using H@10 on FB15k-237 and 6% improvement using H@10 on YAGO3-10-DR).
作者机构：
[Chi, Maomao; Li, Weiqing] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Dan, Qianyi] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;[Wang, Weijun] Cent China Normal Univ, Key Lab Adolescent Cyberpsychol & Behav, Minist Educ, Wuhan 430079, Peoples R China.
通讯机构：
[Chi, Maomao; Wang, Weijun] C;Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Key Lab Adolescent Cyberpsychol & Behav, Minist Educ, Wuhan 430079, Peoples R China.
关键词：
consumer information search behavior;price level;perceived price dispersion;durables and consumables;moderating effect
摘要：
The methods consumers use to reduce their perceived risk and make reasonable purchase decisions can be synthesized under the umbrella term “consumer information search behavior” (CISB). As one key factor that conveys a product’s value and quality, price has a significant impact on CISB. There are few studies that comprehensively consider the impact of price level (PL) and perceived price dispersion (PPD) on CISB, and there is a certain disagreement about the impact of PPD specific to the online shopping environment. To address this research gap, we construct a model using the data from 5515 consumers’ purchasing and browsing behavior on a B2C e-commerce website, selecting six products as our research objects. We use a hierarchical regression analysis method to study the influence of product PL and PPD on CISB, and to explore the moderating effect of product categories (durables and consumables) on the relationship between PL, PPD and CISB. The results show that PL significantly affects CISB, and that product categories have a significant moderating effect on the relationship between PL and CISB. For durable goods, when the PL is high, consumers tend to increase their search behavior, both in depth and in breadth, and for consumables with low PL but higher purchase frequency, consumers likewise tend to increase their search behavior. In the B2C online shopping environment, PPD has a significant positive effect on CISB, and product category has a moderating effect on the relationship between PPD and CISB. When consumers purchase consumables, the higher the PPD, the higher the depth of CISB. The findings have several implications for marketing practitioners and enterprises advertising, also can help customers save time and energy in their search behaviors.
作者机构：
[Yuan, Shuai] Cent China Normal Univ, Natl Engn Res Ctr E Leaming, Wuhan 430079, Peoples R China.;[He, Tingting] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;[He, Tingting] Cent China Normal Univ, Informat Retrieval & Knowledge Management Res Lab, Wuhan 430079, Peoples R China.;[Huang, Huan] South Cent Univ Nationalities, Sch Educ, Wuhan 430074, Peoples R China.;[Hou, Rui] South Cent Univ Nationalities, Coll Comp Sci, Wuhan 430074, Peoples R China.
通讯机构：
[He, Tingting] C;Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Informat Retrieval & Knowledge Management Res Lab, Wuhan 430079, Peoples R China.
作者：
Pan, Min;Huang, Jimmy Xiangji*;He, Tingting（何婷婷）;Mao, Zhiming;Ying, Zhiwei;...
期刊：
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY,2020年71(3):264-281 ISSN：2330-1635
通讯作者：
Huang, Jimmy Xiangji
作者机构：
[Pan, Min] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Informat Retrieval & Knowledge Management Res Lab, Wuhan, Peoples R China.;[Pan, Min; Mao, Zhiming] Hubei Normal Univ, Sch Comp & Informat Engn, Huangshi, Hubei, Peoples R China.;[Huang, Jimmy Xiangji; Pan, Min; Ying, Zhiwei] York Univ, Sch Informat Technol, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON, Canada.;[He, Tingting; Tu, Xinhui; Mao, Zhiming] Cent China Normal Univ, Sch Comp, Informat Retrieval & Knowledge Management Res Lab, Wuhan, Peoples R China.;[Ying, Zhiwei] Cent China Normal Univ, Sch Informat Management, Informat Retrieval & Knowledge Management Res Lab, Wuhan, Peoples R China.
通讯机构：
[Huang, Jimmy Xiangji] Y;York Univ, Sch Informat Technol, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON, Canada.
摘要：
Pseudo-relevance feedback is a well-studied query expansion technique in which it is assumed that the top-ranked documents in an initial set of retrieval results are relevant and expansion terms are then extracted from those documents. When selecting expansion terms, most traditional models do not simultaneously consider term frequency and the co-occurrence relationships between candidate terms and query terms. Intuitively, however, a term that has a higher co-occurrence with a query term is more likely to be related to the query topic. In this article, we propose a kernel co-occurrence-based framework to enhance retrieval performance by integrating term co-occurrence information into the Rocchio model and a relevance language model (RM3). Specifically, a kernel co-occurrence-based Rocchio method (KRoc) and a kernel co-occurrence-based RM3 method (KRM3) are proposed. In our framework, co-occurrence information is incorporated into both the factor of the term discrimination power and the factor of the within-document term weight to boost retrieval performance. The results of a series of experiments show that our proposed methods significantly outperform the corresponding strong baselines over all data sets in terms of the mean average precision and over most data sets in terms of P@10. A direct comparison of standard Text Retrieval Conference data sets indicates that our proposed methods are at least comparable to state-of-the-art approaches.
期刊：
Information Processing & Management,2020年57(6):102342 ISSN：0306-4573
通讯作者：
He, Tingting
作者机构：
[Wang, Junmei] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.;[Pan, Min] Hubei Normal Univ, Sch Comp & Informat Engn, Huangshi 435002, Hubei, Peoples R China.;[He, Tingting; Wang, Xueyan; Tu, Xinhui; Huang, Xiang] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;[He, Tingting; Wang, Junmei; Wang, Xueyan; Tu, Xinhui; Pan, Min; Huang, Xiang] Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Peoples R China.;[He, Tingting; Wang, Junmei; Wang, Xueyan; Tu, Xinhui; Pan, Min; Huang, Xiang] Natl Language Resources Monitor & Res Ctr Network, Wuhan 430079, Peoples R China.
通讯机构：
[He, Tingting] C;[He, Tingting] H;[He, Tingting] N;Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Peoples R China.
关键词：
Information retrieval;Pseudo-relevance feedback;Semantic matching;Text similarity
摘要：
Pseudo-relevance feedback (PRF) is a well-known method for addressing the mismatch between query intention and query representation. Most current PRF methods consider relevance matching only from the perspective of terms used to sort feedback documents, thus possibly leading to a semantic gap between query representation and document representation. In this work, a PRF framework that combines relevance matching and semantic matching is proposed to improve the quality of feedback documents. Specifically, in the first round of retrieval, we propose a reranking mechanism in which the information of the exact terms and the semantic similarity between the query and document representations are calculated by bidirectional encoder representations from transformers (BERT); this mechanism reduces the text semantic gap by using the semantic information and improves the quality of feedback documents. Then, our proposed PRF framework is constructed to process the results of the first round of retrieval by using probability-based PRF methods and language-model-based PRF methods. Finally, we conduct extensive experiments on four Text Retrieval Conference (TREC) datasets. The results show that the proposed models outperform the robust baseline models in terms of the mean average precision (MAP) and precision P at position 10 (P@10), and the results also highlight that using the combined relevance matching and semantic matching method is more effective than using relevance matching or semantic matching alone in terms of improving the quality of feedback documents.
作者机构：
[Ying, Zhiwei] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.;[Huang, Jimmy Xiangji; Ying, Zhiwei] York Univ, Sch Informat Technol, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON M3J 1P3, Canada.;[Zhou, Jie] East China Normal Univ, Dept Comp Sci & Technol, Shanghai 200062, Peoples R China.;[Jian, Fanghong] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[He, Tingting] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Huang, Jimmy Xiangji] Y;York Univ, Sch Informat Technol, Informat Retrieval & Knowledge Management Res Lab, Toronto, ON M3J 1P3, Canada.
关键词：
digital signal processing;Information retrieval;probabilistic and statistical models
摘要：
Recently, researchers mainly focus on three categories of models in the field of Information Retrieval (IR), namely vector-space models, probabilistic models, and statistical language models. The existing studies have always developed IR models through refining or combining these traditional models. However, some new frameworks (e.g., digital signal processing (DSP)-based IR framework) have not been well-developed. In our research, we propose a new DSP-based IR Framework (DSPF) introducing the theories from the field of the DSP and present two corresponding DSP-based IR models, denoted as DSPF-BM25 and DSPF-DLM, which incorporate the term weighting schemes from two well-performed probabilistic IR models, the BM25, and the Dirichlet Language Model (DLM). In particular, first, we consider each query term as a spectrum with Gaussian form. Second, instead of transforming the signals from the time domain to frequency domain, we directly represent the query terms in the frequency domain. It is much more controllable and precise to adjust the values of the parameters for getting better performance of our proposed models. To testify the effectiveness of our proposed models, we conduct extensive experiments on seven standard datasets. The results show that in most cases our proposed models outperform the strong baselines in terms of MAP.
摘要：
Due to the emergence of new big data technology, mobility data such as flows between origin and destination areas have increasingly become more available, cheaper, and faster. These improvements to data infrastructure have boosted spatial and temporal modeling of OD (origin-destination) flows, which require the consideration of spatial dependence and heterogeneity. Both ordinary least square (OLS) and negative binomial (NB) regression methods have been used extensively to calibrate OD flow models by processing flow data as different types of dependent variables. This paper aims to compare both global and local spatial interaction modeling of OD flows between traditional and geographically weighted OLS (GWOLSR) and NB (GWNBR) modeling methods. From this study with empirical data it is concluded that GWNBR outperforms GWOLSR in reducing spatial autocorrelation and in detecting spatial non-stationarity. Although, it is noted that both local modeling methods show improvement when compared against the equivalent global models.
