作者机构：
[Liu, Sannyuya; Yuan, Xin; Yue, Jieyu; Li, Zhen; Li, Qing; Liu, SNYY; Hu, Tianhui; Chen, Sijing; Sun, Jianwen] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.;[Liu, Sannyuya; Liu, SNYY] Cent China Normal Univ, Natl Engn Res Ctr E Elearning, Wuhan 430079, Peoples R China.
通讯机构：
[Liu, SNYY ; Chen, SJ] C;Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Natl Engn Res Ctr E Elearning, Wuhan 430079, Peoples R China.
摘要：
The purpose of this study was to investigate the frontier, science, and public engagement of educational science research. This paper conducted a systematic literature review of 101 educational science research articles published in Nature and Science in 1982-2021 based on the Web of Science database and analyzed the current status of research in terms of basic publication characteristics, research themes, and research processes. Five research topics were recognized, namely, education policy evaluation and reform, learning mechanisms and learning interventions, science education, educational technology, and education equity. Content of each topic had a distinctive emphasis. Findings revealed that most studies were dominated by empirical research, involving causal relationships between various educational phenomena, diverse range of research subjects, rigorous scientific randomized experiments, and quantitative analysis. We encourage more research on educational science in the future from four feasible directions, namely, developing active learning approaches to promoting effective learning, extending the research subjects and objectives of science education, conducting long-term, large-scale and practice-oriented research, and introducing new research methods into educational research.
摘要：
Intelligent tutoring systems (ITS) have received much attention recently as online learning has taken off and is replacing offline instruction in many cases. It analyses user behavior and customizes personalized learning strategies for users through artificial intelligence technology. ITS encompasses a variety of entities and multiple relations, making it suitable to be represented as a graph. This perfectly aligns with the utilization of graph embedding (GE) for downstream ITS tasks. Existing GE methods cannot effectively model ITS data because the user evolution in ITS is discrete in time. The patterns of variation in user states are similar to each other but not correlated at the temporal level. Because of the hierarchical structure caused by the discrete evolution, encoding ITS data in a hyperbolic space is more sensible. We define a discrete evolution graph (DEG) to characterize ITS and propose a method called DEGE to embed it. The static nodes in a DEG are projected randomly and then transformed into hyperbolic space. Next, employ hyperbolic evolution networks to generate the embedding of dynamic nodes. The aggregated features of each node are then delivered by hyperbolic aggregation networks and are concatenated to generate the final higher-order features. To validate the superiority, design a multi-objective loss function with preserving pairwise proximity and preserving link types to train the model on several real datasets. The experimental results demonstrate that our method outperforms other baselines on both question annotation and performance prediction in ITS.
摘要：
Computer-supported collaborative concept mapping (CSCCM) integrates technology and concept mapping to support students’ knowledge understanding, and much research on the behavioral patterns involved in CSCCM activities has been conducted. However, there is limited understanding of the differences in knowledge understanding and behavioral patterns between students with different levels of collaboration perception. This study examined the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns in the CSCCM activity. A total of 36 individuals from the same university participated in this study. The findings suggested that compared with students with a low level of collaborative perception, students with a high level of collaborative perception could obtain better conceptual knowledge understanding. However, there was no significant difference in factual knowledge understanding between students with different levels of collaboration perception. For behavioral patterns, students with a high level of collaboration perception demonstrated more diverse behavioral transition sequences, students with a middle level of collaboration perception demonstrated more repetitive behavioral sequences, and students with a low level of collaboration perception demonstrated less behavioral transition sequences. The findings of this research can provide a reference for teachers to design CSCCM activities in the classroom.
期刊：
User Modeling and User-Adapted Interaction,2023年:1-33 ISSN：0924-1868
通讯作者：
Liang, RX
作者机构：
[Shen, Xiaoxuan; Yang, Zongkai; Liu, Sannyuya; Li, Qing; Liang, Ruxia; Du, Shangheng; Sun, Jianwen] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.;[Shen, Xiaoxuan; Yang, Zongkai; Liu, Sannyuya; Li, Qing; Liang, Ruxia; Du, Shangheng; Sun, Jianwen] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Yang, Zongkai; Liu, Sannyuya] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.
通讯机构：
[Liang, RX ] C;Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
关键词：
Recommender systems;Group recommender systems;Adversarial learning;Knowledge transfer
摘要：
Many online services allow users to participate in various group activities such as online meeting or group buying and thus need to provide user groups with services that they are interested. The group recommender systems emerge as required and provide personalized services for various online user groups. Data sparsity is an important issue in group recommender systems, since even fewer group-item interactions are observed. Transfer learning has been one efficient tool to alleviate the data sparsity issue in recommender systems for individual users, but have not been utilized for group recommendation. Moreover, the group and the group members have complex and mutual relationships with each other, which exacerbates the difficulty in modelling the preferences of both a group and its members for recommendation. Therefore, group recommender systems face three main challenges that may significantly impact its quality and accuracy: (1) taking consideration of group member relationship and their interactions in modelling user and group preferences; (2) ensuring latent feature spaces between the users and groups are maximally matched; and (3) constructing a deep group recommendation method that both the individual user and group domains can benefit from a knowledge exchange. Hence, in this paper, we propose a deep adversarial group recommendation method, called DA-GR. User feature are separated into two subspaces to ensure only consistent group members’ feature knowledge can be extracted and shared with group preference modelling. Adversarial learning is used to effectively transfer consistent knowledge from individual user interactions to the group interaction domain through the bridge of group-user relationships. Extensive experiments, which demonstrate the effectiveness and superiority of our proposal, providing accurate recommendation for both individual users and groups, are conducted on public datasets. The source code of DA-GR is in
https://github.com/ccnu-mathits/DA-GR
.
作者：
Su, Zhu;Li, Yue;Liu, Zhi;Sun, Jianwen;Yang, Zongkai;...
期刊：
ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT,2023年71(5):1941-1963 ISSN：1042-1629
通讯作者：
Liu, S
作者机构：
[Yang, Zongkai; Liu, Sannyuya; Liu, S; Liu, Zhi; Su, Zhu; Li, Yue; Sun, Jianwen] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;[Yang, Zongkai; Liu, Sannyuya; Liu, S] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;[Yang, Zongkai; Liu, Sannyuya; Liu, S; Liu, Zhi; Su, Zhu; Li, Yue; Sun, Jianwen] Cent China Normal Univ, Fac Artificial Intelligence Educ, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Liu, S ] C;Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Fac Artificial Intelligence Educ, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
期刊：
Expert Systems with Applications,2022年207:117680 ISSN：0957-4174
通讯作者：
Liu, Sannyuya(lsy5918@mail.ccnu.edu.cn)
作者机构：
[Liu, Sannyuya; Zou, Rui; Li, Qing; Liang, Ruxia; Sun, Jianwen] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Liu, Sannyuya; Gao, Lu] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.;[Liu, Sannyuya; Zou, Rui; Gao, Lu; Li, Qing; Liang, Ruxia; Sun, Jianwen] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Zhang, Kai] Yangtze Univ, Sch Comp Sci, Jingzhou 434025, Peoples R China.;[Jiang, Lulu] Nanhai Expt Sch, Foshan 528299, Peoples R China.
通讯机构：
[Sannyuya Liu; Qing Li] N;National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, 430079, China<&wdkj&>Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China<&wdkj&>National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, 430079, China<&wdkj&>National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, 430079, China<&wdkj&>Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China
关键词：
Deep neural network;Knowledge Tracing;Learning interactions;User modeling
期刊：
Information Sciences,2022年596:567-587 ISSN：0020-0255
通讯作者：
Shen, Xiaoxuan;Sun, JW
作者机构：
[Shen, Xiaoxuan; Liu, Sannyuya; Li, Qing; Shen, XX; Sun, Jianwen; Liang, Ruxia; Zhang, Yunhan] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Liu, Sannyuya; Yu, Jianwei] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.;[Shen, Xiaoxuan; Liu, Sannyuya; Li, Qing; Shen, XX; Sun, Jianwen; Liang, Ruxia; Zhang, Yunhan; Yu, Jianwei] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
通讯机构：
[Shen, XX; Sun, JW ] C;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
摘要：
Knowledge tracing (KT) has become an increasingly relevant problem in intelligent education services, which estimates and traces the degree of learner's mastery of concepts based on students' responses to learning resources. The existing mainstream KT models, only attribute learners' feedback to the degree of knowledge mastery and leave the influence of mental ability factors out of consideration. Although ability is an essential component of the problem-solving process, these knowledge-centered models cause a contradiction between data fitting and rationalization of the model decision-making process, making it difficult to achieve high precision and readability simultaneously.In this paper, an innovative KT model, ability boosted knowledge tracing (ABKT)(1) is pro-posed, which introduces the ability factor into learning feedback attribution to enable the model to analyze the learning process from two perspectives, knowledge and ability, simul-taneously. Based on constructive learning theory, continuous matrix factorization (CMF) model is proposed to simulate the knowledge internalization process, following the initiative growth and stationarity principles. In addition, the linear graph latent ability (LGLA) model is proposed to construct learner and item latent ability features, from graph-structured learner interaction data. Then, the knowledge and ability dual-tracing framework is constructed to integrate the knowledge and ability modules. Experimental results on four public databases indicate that the proposed methods perform better than state-of-the-art knowledge tracing algorithms in terms of prediction accuracy in quantitative assessments, displaying some advantages in model interpretability and intelligibility.(c) 2022 Elsevier Inc. All rights reserved.
作者机构：
[Li, Qing; Sun, Jianwen] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.;[Lu, Zijian; Zhou, Jianpeng; Zhang, Kai] Cent China Normal Univ, Natl Engn Res Ctr Learning, Wuhan, Peoples R China.
会议名称：
14th International Conference on Knowledge Science, Engineering, and Management (KSEM)
会议时间：
AUG 14-16, 2021
会议地点：
Tokyo, JAPAN
会议主办单位：
[Sun, Jianwen;Li, Qing] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.^[Zhou, Jianpeng;Zhang, Kai;Lu, Zijian] Cent China Normal Univ, Natl Engn Res Ctr Learning, Wuhan, Peoples R China.
摘要：
Knowledge tracing predicts students' future performance based on their past performance. Most of the existing models take skills as input, which neglects question information and further limits the model performance. Inspired by item-item collaborative filtering in recommender systems, we propose a question-question Collaborative embedding method for Knowledge Tracing (CoKT) to introduce question information. To be specific, we incorporate student-question interactions and question-skill relations to capture question similarity. Based on the similarity, we further learn question embeddings, which are then integrated into a neural network to make predictions. Experiments demonstrate that CoKT significantly outperforms baselines on three benchmark datasets. Moreover, visualization illustrates that CoKT can learn interpretable question embeddings and achieve more obvious improvement on AUC when the interaction data is more sparse.
作者机构：
[Duan, Chao] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Li, Kaiqi; Li, Qing; Sun, Jianwen] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
通讯机构：
[Qing Li] N;National Engineering Laboratory for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
Accelerated development of mobile networks and applications leads to the exponential expansion of resources, which causes problems such as trek and overload of information. One of the practical approaches to ease these problems is recommendation systems (RSs) that can provide individualized service. Video recommendation is one of the most critical recommendation services. However, achieving satisfactory recommendation service on the sparse data is difficult for video recommendation service. Moreover, the cold start problem further exacerbates the research challenge. Recent state-of-the-art works attempted to solve this problem by utilizing the user and item information from some other perspective. However, the significance of user and item information changes under different applications. This paper proposes an autoencoder model to improve recommendation efficiency by utilizing attribute information and implementing the proposed algorithm for video recommendation. In the proposed model, we first extract the user features and the video features by combining the user attribute and the video category information simultaneously. Then, we integrate the attention mechanism into the extracted features to generate the vital features. Finally, we incorporate the user and item potential factor to generate the probability matrix and defines the user-item rating matrix using the factorized probability matrix. Experimental results on two shared datasets demonstrates that the proposed model can effectively ameliorate video recommendation quality compared with the state-of-the-art methods.
