作者机构：
[Chen, Zengzhao; Miao, Bingchen; Liu, Hai] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Chen, Zengzhao; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.;[Chen, Zengzhao] Cent China Normal Univ, Natl Intelligent Soc Governance Expt Base Educ, Wuhan 430079, Peoples R China.;[Zhang, Aijun] China Telecom Corp Henan Branch, Zhengzhou 450016, Peoples R China.
通讯机构：
[Zhang, AJ ] C;China Telecom Corp Henan Branch, Zhengzhou 450016, Peoples R China.
作者：
Li, Miaoyun;Lu, Chun;Yang, Harrison H.;Wu, Di;Yang, Xiao
期刊：
ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT,2023年71(5):2137-2154 ISSN：1042-1629
通讯作者：
Yang, HH
作者机构：
[Li, Miaoyun; Yang, Harrison H.] Cent China Normal Univ, Fac Artificial Intelligence Educ, Nanhu Campus Complex Bldg,382 Xiongchu Ave, Wuhan, Hubei, Peoples R China.;[Lu, Chun] Strateg Res Base Minist Educ, Res Ctr Sci & Technol Promoting Educ Innovat & Dev, Sci Hall,152 Luoyu Rd, Wuhan, Hubei, Peoples R China.;[Yang, Harrison H.] SUNY Coll Oswego, Sch Educ, 7060 State Route 104, Oswego, NY 13126 USA.;[Wu, Di] Cent China Normal Univ, Educ Informatizat Strategy Res Base Minist Educ, Sci Hall,152 Luoyu Rd, Wuhan, Hubei, Peoples R China.;[Yang, Xiao] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Sci Hall,152 Luoyu Rd, Wuhan, Hubei, Peoples R China.
通讯机构：
[Yang, HH ] C;Cent China Normal Univ, Fac Artificial Intelligence Educ, Nanhu Campus Complex Bldg,382 Xiongchu Ave, Wuhan, Hubei, Peoples R China.;SUNY Coll Oswego, Sch Educ, 7060 State Route 104, Oswego, NY 13126 USA.
期刊：
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING,2023年31:826-834 ISSN：2329-9290
作者机构：
[Dong, Ming; Tu, Xinhui; Wang, Yufan; Mei, Jie; He, Tingting] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China.;[Dong, Ming; Tu, Xinhui; Wang, Yufan; Mei, Jie; He, Tingting] Cent China Normal Univ, Natl Language Resources Monitor & Res Ctr Network, Wuhan 430079, Peoples R China.;[Dong, Ming; Tu, Xinhui; Mei, Jie; He, Tingting] Cent China Normal Univ, Sch Comp Sci & Technol, Wuhan 430079, Peoples R China.;[Wang, Yufan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
关键词：
Training;Correlation;Bit error rate;Semantics;Natural languages;Logic gates;Filling;Natural language processing;spoken language understanding;intent detection;slot filling
摘要：
Spoken language understanding (SLU) is an essential part of a task-oriented dialogue system, which mainly includes intent detection and slot filling. Some existing approaches obtain enhanced semantic representation by establishing the correlation between two tasks. However, those methods show little improvement when applied to BERT, since BERT has learned rich semantic features. In this paper, we propose a BERT-based model with the probability-aware gate mechanism, called PAGM (<underline>P</underline>robability <underline>A</underline>ware <underline>G</underline>ated <underline>M</underline>odel). PAGM aims to learn the correlation between intent and slot from the perspective of probability distribution, which explicitly utilizes intent information to guide slot filling. Besides, in order to efficiently incorporate BERT with the probability-aware gate, we design the stacked fine-tuning strategy. This approach introduces a mid-stage before target model training, which enables BERT to get better initialization for final training. Experiments show that PAGM achieves significant improvement on two benchmark datasets, and outperforms the previous state-of-the-art results.
作者机构：
[Zhu, Sha; Yang, Harrison Hao] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Guo, Qing] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Yang, Harrison Hao] SUNY Coll Oswego, Sch Educ, Oswego, NY 13126 USA.
通讯机构：
[Harrison Hao Yang] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China<&wdkj&>School of Education, State University of New York at Oswego, Oswego, NY 13126, USA<&wdkj&>Author to whom correspondence should be addressed.
关键词：
assessment methodologies;digital games;21st century skills;media in education
摘要：
Traditional methods of student assessment (SA) include self-reported surveys, standardized tests, etc. These methods are widely regarded by researchers as inducing test anxiety. They also ignore students' thinking processes and are not applicable to the assessment of higher-order skills. Digital game-based assessment (DGBA) is thought to address the shortcomings of traditional assessment methods. Given the advantages of DGBA, an increasing number of empirical studies are working to apply digital games for SA. However, there is a lack of any systematic review of DGBA studies. In particular, very little is known about the characteristics of the games, the content of the assessment, the methods of implementation, and the distribution of the results. This study examined the characteristics of DGBA studies, and the adopted games on SA in the past decade from different perspectives. A rigorous systematic review process was adopted in this study. First, the Web of Science (WOS) database was used to search the literature on DGBA published over the last decade. Then, 50 studies on SA were selected for subsequent analysis according to the inclusion and exclusion criteria. The results of this study found that DGBA has attracted the attention of researchers around the world. The participants of the DGBA studies were distributed across different educational levels, but the number of participants was small. Among all game genres, educational games were the most frequently used. Disciplinary knowledge is the most popular SA research content. Formative assessment modeling with process data and summative assessment using final scores were the most popular assessment methods. Correlation analysis was the most popular analysis method to verify the effectiveness of games on SA. However, many DGBA studies have reported unsatisfactory data analysis results. For the above findings, this study further discussed the reasons, as well as the meanings. In conclusion, this review showed the current status and gaps of DGBA in the SA application; directional references for future research of researchers and game designers are also provided.
作者机构：
[Tang, Hengtao] Univ South Carolina, Dept Educ Studies, Columbia, SC USA.;[Dai, Miao; Du, Xu; Li, Hao] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan, Peoples R China.;[Hung, Jui-Long] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.;[Hung, Jui-Long] Boise State Univ, Dept Educ Technol, Boise, ID USA.
通讯机构：
[Du, X ] C;Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan, Peoples R China.
摘要：
Real-time emotion recognition in conversations (ERC), which relies on only the historical utterances to achieve ERC, has recently gained increasing attention due to its significance in providing real-time empathetic services. Although utilizing multimodal information can mitigate the issues of unimodal approaches, few real-time ERC studies consider the differences in representation ability of different modalities and explore comprehensive conversational context from different perspectives based on different structures. Furthermore, the heavy annotation cost makes it difficult to collect sufficient labeled data, which also limits the performance of current supervised ERC approaches. To address these issues, we propose a novel framework SMFNM for real-time ERC, which integrates semi-supervised learning with multimodal fusion under the guidance of main-modal. Specifically, SMFNM utilizes additional unlabeled data to extract high-quality intra-modal representations, and implements cross-modal interaction to capture complementary information to enhance the audio representations. Then SMFNM employs the directed acyclic graph and the Gated Recurrent Units for exploring more accurate conversational context from both the multimodal and main-modal perspectives, respectively. Finally, these two types of contextual features are fused for emotion identification. Extensive experiments on benchmark datasets (i.e., IEMOCAP (4-way), IEMOCAP (6-way) and MELD) demonstrate the effectiveness, superiority and rationality of our SMFNM.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
期刊：
Behaviour & Information Technology,2023年 ISSN：0144-929X
通讯作者：
Chen, JY
作者机构：
[Sun, Jianchi; Wang, Guangshuai; Liu, Xiaodi; Chen, Jingying; Zhang, Kun; Ma, Pianpian] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Wang, Guangshuai] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.;[Zhang, Rujing] Liaocheng Univ, Sch Media & Technol, Liaocheng, Peoples R China.
通讯机构：
[Chen, JY ] C;Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
关键词：
Evaluation;autism;gross motor skills;PEP-3
摘要：
To effectively evaluate the gross motor ability of autistic children, we proposed a method of computerised evaluation of gross motor skills (CEGM). The CEGM integrates Dynamic Time Warping (DTW) method and OpenPose technology to automatically detect key joints and return a score. Ten items were selected for evaluation based on the gross motor subtest of the Psychoeducational Profile - Third Edition (PEP-3) scale, including upper limb movement, lower limb movement, and body coordination performance. 30 autistic participants (males: 23, female: 7) with an average age of 5.00 years were recruited in this study. Then we compared the results of evaluation using CEGM and the original PEP-3 gross motor subtest in autistic children. The results showed that in the evaluations using CEGM and PEP-3, Cronbach's alpha coefficients and Spearman-rank correlation coefficients were all greater than 0.80, intraclass correlation coefficient (ICC) were all greater than 0.90, indicating good agreement in evaluating the gross motor ability of autistic children. Moreover, compared to the PEP-3, the evaluation using CEGM provided precise quantitative indicators (trajectory, velocity, and angle of joint). Therefore, our findings demonstrate that CEGM can be used in the initial evaluation of the gross motor ability of autistic children.
作者：
Zhou, Chi;Wu, Di;Li, Yating;Yang, Harrison Hao;Man, Shuo;...
期刊：
Education and Information Technologies,2023年28(2):2207-2227 ISSN：1360-2357
通讯作者：
Min Chen
作者机构：
[Li, Yating; Zhou, Chi; Man, Shuo; Chen, Min] Cent China Normal Univ, Natl Engn Res Ctr Learning, Wuhan 430079, Hubei, Peoples R China.;[Zhou, Chi; Wu, Di] Cent China Normal Univ, Res Ctr Sci & Technol Promoting Educ Innovat & De, Strateg Res Base, Minist Educ, Wuhan 430079, Hubei, Peoples R China.;[Zhou, Chi] Cent China Normal Univ, Educ Informatizat Strategy Res Base Minist Educ, Wuhan 430079, Hubei, Peoples R China.;[Wu, Di] Cent China Normal Univ, Hubei Res Ctr Educ Informatizat Dev, Wuhan 430079, Hubei, Peoples R China.;[Yang, Harrison Hao] SUNY Coll Oswego, Sch Educ, Oswego, NY 60543 USA.
通讯机构：
[Min Chen] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
关键词：
Teacher learning;Technological pedagogical content knowledge;Student engagement;Stimulus-organism-response framework;Integrative model of behavior prediction
摘要：
Traditional Generative Adversarial Network (GAN) based Generalized Zero Shot Learning (GZSL) methods usually suffer from a problem that these methods ignore the differences between classes when using the standard normal distribution to fit the true distribution of each category, and the incompleteness of a single adversarial training makes the model unable to capture all the characteristics of the samples. To address this problem, a data-driven recurrent adversarial generative network is proposed in this paper. We first synthe-size visual prototypes for unseen classes using the transformation from semantic attributes to visual prototypes learned on seen classes. Then, some noise is generated from these pro-totypes to synthesize the unseen samples according to the corresponding semantic attri-butes. During the sample generation process, a recurrent generative adversarial network is designed to facilitate the generated visual features to be more representative. Extensive experiments on five popular datasets as well as detailed ablation studies demon-strate the effectiveness and superiority of the proposed method.(c) 2023 Elsevier Inc. All rights reserved.
作者：
Miao, Tian-Chang;Gu, Chuan-Hua;Liu, Shengyingjie;Zhou, Z. K.*
期刊：
Behaviour & Information Technology,2023年42(11) ISSN：0144-929X
通讯作者：
Zhou, Z. K.
作者机构：
[Miao, Tian-Chang; Gu, Chuan-Hua; Zhou, Z. K.] Cent China Normal Univ, Sch Psychol, Wuhan 430079, Hubei, Peoples R China.;[Miao, Tian-Chang; Gu, Chuan-Hua; Zhou, Z. K.] Cent China Normal Univ, Minist Educ, Key Lab Adolescent Cyberpsychol & Behav, Wuhan, Peoples R China.;[Liu, Shengyingjie] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Zhou, Z. K.] Minist Educ, Key Lab Adolescent Cyberpsychol & Behav CCNU, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Zhou, Z. K.] C;[Zhou, Z. K.] M;Cent China Normal Univ, Sch Psychol, Wuhan 430079, Hubei, Peoples R China.;Minist Educ, Key Lab Adolescent Cyberpsychol & Behav CCNU, Wuhan 430079, Hubei, Peoples R China.
摘要：
Tian-Chang Miao, Chuan-Hua Gu, Shengyingjie Liu & Z. K. Zhou (2020) Internet literacy and academic achievement among Chinese adolescent: a moderated mediation model, Behaviour & Information Technology, DOI: 10.1080/0144929X.2020.1831074
作者：
Li, Huan;Zhu, Sha*;Wu, Di;Yang, Harrison Hao;Guo, Qing
期刊：
Education and Information Technologies,2023年28(10):13485-13504 ISSN：1360-2357
通讯作者：
Zhu, Sha;Yang, HH;Zhu, S
作者机构：
[Guo, Qing; Yang, HH; Zhu, Sha; Yang, Harrison Hao; Li, Huan; Zhu, S; Wu, Di] Cent China Normal Univ, Natl Engn Res Ctr Elearning, 152 Luoyu Rd, Wuhan, Hubei, Peoples R China.;[Zhu, Sha; Zhu, S; Wu, Di] Cent China Normal Univ, Res Ctr Sci & Technol Promoting Educ Innovat & Dev, Strateg Res Base, Minist Educ, Wuhan, Peoples R China.;[Yang, HH; Yang, Harrison Hao] SUNY Coll Oswego, Sch Educ, 232 Wilber Hall, Oswego, NY 12306 USA.
通讯机构：
[Yang, HH ; Zhu, S ; Zhu, S] C;Cent China Normal Univ, Natl Engn Res Ctr Elearning, 152 Luoyu Rd, Wuhan, Hubei, Peoples R China.;Cent China Normal Univ, Res Ctr Sci & Technol Promoting Educ Innovat & Dev, Strateg Res Base, Minist Educ, Wuhan, Peoples R China.;SUNY Coll Oswego, Sch Educ, 232 Wilber Hall, Oswego, NY 12306 USA.
摘要：
The adoption of online learning for adolescent students accelerated with the outbreak of the COVID-19 pandemic. However, few studies have investigated the mechanisms influencing adolescent students’ online learning engagement systematically and comprehensively. This study applied the Presage-Process-Product (3P) model of learning to investigate the direct effects of presage factors (i.e., information literacy and self-directed learning skills) and process factors (i.e., academic emotions) on high school students’ online learning engagement; and the mediating role of process factors. Data from 1993 high school students in China (49.3% males and 50.7% females) were analyzed using structural equation modeling. The result showed that students’ information literacy, self-directed learning skills, and positive academic emotions positively predicted their online learning engagement. Moreover, the positive impact of self-directed learning skills on students’ online learning engagement was significantly and largely enhanced through the mediation effects of positive academic emotions (
$${\upbeta }$$
= 0.606, 95% CI = [0.544, 0.674]). Based on these results, to enhance adolescent students’ online learning engagement, it is important for school administrators, teachers, and parents to improve students’ information literacy, self-directed learning skills, and positive academic emotions.
