摘要：
Many knowledge graphs, especially those that are collaboratively or automatically generated, are prone to noise and cross-domain entries, which can impede domain-specific applications. Existing methods for pruning inaccurate or out-of-domain information from knowledge graphs often rely on topological graph-pruning strategies. However, these approaches have two major drawbacks: they may discard logical structure and semantic information, and they allow multiple inheritance. To address these limitations, this study introduces KGPruning, which is a novel approach that can effectively clean and prune noisy knowledge graphs by guiding tasks with a given set of concepts and automatically generating a domain-specific taxonomy. Specifically, KGPruning employs a graph hierarchy inference method that is based on the Agony model to precisely identify and eliminate noisy entries while striving to preserve the underlying hierarchy of semantic relations as much as possible. Furthermore, to establish a tree-structured taxonomy, KGPruning integrates semantic relations and structural characteristics to effectively eliminate out-of-domain informa-tion and multiple inheritance. Through extensive experimental evaluations conducted on open benchmark datasets as well as large-scale real-world problems, the superior performance of KGPruning over state-of-the-art methods is demonstrated on the task of pruning noisy knowledge graphs.
期刊：
Expert Systems with Applications,2023年214:118943 ISSN：0957-4174
通讯作者：
Zengzhao Chen
作者机构：
[Wang, Hu; Chen, Zengzhao; Li, Jiawen; Liu, Hai; Zheng, Qiuyu] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Li, Jiawen; Zheng, Qiuyu] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.;[Wang, Hu; Chen, Zengzhao; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Wang, Xuyang] Aviat Ind Corp, Luoyang Inst Electroopt Equipment, Luoyang 471023, Henan, Peoples R China.
通讯机构：
[Zengzhao Chen] F;Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China<&wdkj&>National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China
关键词：
Speech emotion recognition;Features fusion;Connection attention mechanism;Frame-level features;Utterance-level features
摘要：
Speech emotion recognition (SER) has become a crucial topic in the field of human-computer interactions. Feature representation plays an important role in SER, but there are still many challenges in feature representation such as the inability to predict which features are most effective for SER and the cultural differences in emotion expression. Most previous studies use a single type of feature for the recognition task or conduct early fusion of features. However, a single type of feature cannot well reflect the emotions of speech signals. Also, different features contain different information, direct fusion cannot integrate the advantages of different features. To overcome these challenges, this paper proposes a parallel network for multi-scale SER based on a connection attention mechanism (AMSNet). AMSNet fuses fine-grained frame-level manual features with coarse-grained utterance-level deep features. Meanwhile, it adopts different speech emotion feature extraction modules according to the temporal and spatial features of speech signals, which enriches features and improves feature characterization. The network consists of a frame-level representation learning module (FRLM) based on the time structure and an utterance-level representation learning module (URLM) based on the global structure. Besides, improved attention-based long short-term memory (LSTM) is introduced into FRLM to focus on the frames that contribute more to the final emotion recognition result. In URLM, a convolutional neural network with the squeeze-and-excitation block (SCNN) is introduced to extract deep features. In addition, the connection attention mechanism is proposed for feature fusion, which applies different weights to different features. Extensive experiments are conducted on the IEMOCAP and EmoDB datasets, and the results demonstrate the effectiveness and performance superiority of AMSNet. Our code will be publicly available at https://codeocean.com/capsule/8636967/tree/v1.
期刊：
Journal of Science Education and Technology,2023年:1-14 ISSN：1059-0145
通讯作者：
Zhao, L
作者机构：
[Sun, Chengzhang; Dai, Zhicheng] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.;[Zhao, Liang; Zhu, Xiaoliang] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Wuhan 430079, Peoples R China.;[Sun, Chengzhang; Dai, Zhicheng; Zhao, Liang; Zhu, Xiaoliang] Cent China Normal Univ, Fac Artificial Intelligence Educ, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Zhao, L ] 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, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
摘要：
Classroom interaction affects the classroom atmosphere as well as students' behavior and participation, thus affecting the quality of classroom teaching. In traditional classrooms, inherent problems (e.g., inflexible tables and chairs, rigid multimedia consoles, and traditional software) have seriously restricted the overall quality of classroom interpersonal interaction. In recent years, the problem of enhancing classroom interaction has gradually attracted the attention of scholars. The application of project-based learning (PBL) in higher education is effective, but few studies have analyzed the differences in interaction between smart classrooms and traditional classrooms in PBL courses. In this study, through the proposed teacher-student classroom interaction behavior analysis framework, 20 sessions in smart classrooms and 20 sessions in traditional classrooms were encoded to illustrate the differences between interaction in these two types of classrooms. Furthermore, 765 student questionnaires on satisfaction with and participation in smart classrooms were collected to determine whether smart classrooms affect students' satisfaction and participation in PBL courses. The questionnaires were analyzed using SPSS 27.0. The results showed that there were significant differences in four dimensions of teachers' behavior, students' behavior, technology, and other interactions between the smart classroom and the traditional classroom. After taking PBL courses in a smart classroom, students were generally satisfied and thought that the smart learning environment could help them improve their thinking and learning. Suggestions on the further construction and application of smart classrooms are proposed.
作者：
Hai Liu;Cheng Zhang;Yongjian Deng;Tingting Liu;Zhaoli Zhang;...
期刊：
IEEE Transactions on Image Processing,2023年PP:1-1 ISSN：1057-7149
作者机构：
[Hai Liu; Cheng Zhang; Zhaoli Zhang] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China;[Yongjian Deng] College of Computer Science, Beijing University of Technology, Beijing, China;[You-Fu Li] Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong, China;School of Education, Hubei University, No. 368 Youyi Road, Wuhan, Hubei, China;[Tingting Liu] School of Education, Hubei University, No. 368 Youyi Road, Wuhan, Hubei, China<&wdkj&>Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong, China
关键词：
Head pose estimation;attention mechanism;relationship perception;deep learning;Transformer
摘要：
Head pose estimation (HPE) is an indispensable upstream task in the fields of human-machine interaction, self-driving, and attention detection. However, practical head pose applications suffer from several challenges, such as severe occlusion, low illumination, and extreme orientations. To address these challenges, we identify three cues from head images, namely, critical minority relationships, neighborhood orientation relationships, and significant facial changes. On the basis of the three cues, two key insights on head poses are revealed: 1) intra-orientation relationship and 2) cross-orientation relationship. To leverage two key insights above, a novel relationship-driven method is proposed based on the Transformer architecture, in which facial and orientation relationships can be learned. Specifically, we design several orientation tokens to explicitly encode basic orientation regions. Besides, a novel token guide multi-loss function is accordingly designed to guide the orientation tokens as they learn the desired regional similarities and relationships. Experimental results on three challenging benchmark HPE datasets show that our proposed TokenHPE achieves state-of-the-art performance. Moreover, qualitative visualizations are provided to verify the effectiveness of the token-learning methodology.
作者：
Wang, Wenlong;Qi, Feifei;Wipf, David Paul;Cai, Chang;Yu, Tianyou;...
期刊：
IEEE Transactions on Pattern Analysis and Machine Intelligence,2023年45(12):15632-15649 ISSN：0162-8828
作者机构：
[Cai, Chang] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, Hubei, China;[Yu, Zhuliang] Department of Bioengineering, Lehigh University, Bethlehem, PA, USA;School of Internet Finance and Information Engineering, Guangdong University of Finance, Guangzhou, Guangdong, China;Pazhou Lab, Guangzhou, Guangdong, China;School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China
摘要：
Decoding brain activity from non-invasive electroencephalography (EEG) is crucial for brain-computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG decoding has gained widespread popularity in recent years owing to the remarkable advances in deep learning research. However, many EEG studies suffer from limited sample sizes, making it difficult for existing deep learning models to effectively generalize to highly noisy EEG data. To address this fundamental limitation, this paper proposes a novel end-to-end EEG decoding algorithm that utilizes a low-rank weight matrix to encode both spatio-temporal filters and the classifier, all optimized under a principled sparse Bayesian learning (SBL) framework. Importantly, this SBL framework also enables us to learn hyperparameters that optimally penalize the model in a Bayesian fashion. The proposed decoding algorithm is systematically benchmarked on five motor imagery BCI EEG datasets ( N=192) and an emotion recognition EEG dataset ( N=45), in comparison with several contemporary algorithms, including end-to-end deep-learning-based EEG decoding algorithms. The classification results demonstrate that our algorithm significantly outperforms the competing algorithms while yielding neurophysiologically meaningful spatio-temporal patterns. Our algorithm therefore advances the state-of-the-art by providing a novel EEG-tailored machine learning tool for decoding brain activity.
作者机构：
[Zhang, Lishan] National Engineering Research Center for E-learning, Central China Normal University, Wuhan, People’s Republic of China;[Zhang, Jing] School of Educational Technology, Faculty of education, Beijing Normal University, Beijing, People’s Republic of China;Jingshi Liyun School of Shunde, Foshan, Guangdong, People’s Republic of China;Advanced Innovation Center for Future Education, Faculty of education, Beijing Normal University, Beijing, People’s Republic of China;[Pan, Mengqi] School of Educational Technology, Faculty of education, Beijing Normal University, Beijing, People’s Republic of China<&wdkj&>Jingshi Liyun School of Shunde, Foshan, Guangdong, People’s Republic of China
通讯机构：
[Ling Chen] S;School of Educational Technology, Faculty of education, Beijing Normal University, Beijing, People’s Republic of China<&wdkj&>Advanced Innovation Center for Future Education, Faculty of education, Beijing Normal University, Beijing, People’s Republic of China
摘要：
This paper introduces a system that supports student-centered online one-to-one tutoring and evaluates the practical value of the system by running an experiment with 64 experienced mathematics teachers and 810 students in Grade 7. The experiment lasted for 50 days. A comprehensive evaluation was performed using students’ academic performance before and after usage of the system and the system log files. By classifying the students into active and inactive usage groups, it was determined that active students significantly outperformed inactive students on posttests, but with a small effect size. The results also suggested that high prior knowledge students tended to benefit more from using the system than low prior knowledge students. An explanation for this result was that students with a high level of prior knowledge were more likely to have good-quality interactions with their teachers. Therefore, although some advantages of this type of student-centered online one-to-one tutoring are observed, in this system, both the students and the teachers need to be further facilitated to produce more effective tutoring interactions.
作者机构：
[Yang, Zongkai; Liu, Sannyuya; Liu, Zhi; Kong, Weizheng; Peng, Xian; Liu, Shiqi; Wen, Chaodong] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Educ Big Data, Wuhan, Peoples R China.;[Yang, Zongkai; Liu, Sannyuya] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Learning, Wuhan, Peoples R China.
通讯机构：
[Xian Peng; Zongkai Yang] N;National Engineering Research Center for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, PR China<&wdkj&>National Engineering Research Center for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, PR China<&wdkj&>National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, PR China
关键词：
Cognitive engagement classification;Semi-supervised learning;Dual feature embedding;Linguistic Inquiry and Word Count (LIWC);Course discussion
摘要：
Online course discussions contain abundant cognitive information from learners. Previous models required a large amount of labeled data to classify cognitive engagement from the perspective of semantic features alone. However, these models only contain semantic features but cannot fully represent textual information and have poor performance in cases of scarce labeled data. Moreover, cognitive psychological features imply important information that cannot be captured by semantic features. Therefore, this paper proposes a dual feature embedding-based semi-supervised cognitive classification method that exploits the additional inductive biases caused by implicit cognitive features to supplement generic semantic features. Additional inductive biases facilitate the propagation of labeled and unlabeled data and improve the consistency between unlabeled and augmented data. Unsupervised data augmentation (UDA) is used to obtain augmented data by inserting advanced noise into unlabeled data in semi-supervised learning. Furthermore, bidirectional encoder representations from transformers (BERT) are used to extract generic semantics, and linguistic inquiry and word count (LIWC) are adopted to fetch implicit cognitive features from discussion texts. Therefore, we refer to the proposed method as B-LIWC-UDA, sequentially fusing the dual features in the explicit and hidden levels to obtain dual feature embeddings. The cognitive engagement classification model was trained using supervised and consistent training methods. We conducted experiments using datasets obtained from two real-world online course discussions. The experimental results demonstrate that, in terms of major evaluation metrics, the proposed B-LIWC-UDA method performs better than state-of-the-art text classification methods used for identifying cognitive engagement. (c) 2022 Elsevier B.V. All rights reserved.
作者机构：
[Dai, Zhicheng; Xiong, Junxia] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China;[Dai, Zhicheng; Xiong, Junxia; Zhao, Liang; Zhu, Xiaoliang] Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China;[Zhao, Liang; Zhu, Xiaoliang] National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan 430079, China
关键词：
Ecological;Higher education;Learning environment preferences;Smart classroom;Teachers and students
摘要：
By evaluating learners' perceived preferences for the learning environment, we can understand the important characteristics and better improve the learning environment, ultimately to provide great potential for the optimization of teaching practice. Seeing that the current research pays less attention to teachers' and students' preferences for the space environment simultaneously, based on the survey of 1937 undergraduates and 107 teachers from a university in central China, this study aims to explore their preferences for smart learning environment. Based on the ecological theory and research results of the existing learning environment, this paper constructed an ecological model and a conceptual model of learning space preferences. An empirical study was conducted to explore the impact of sociodemographic variables on personal spatial preference. The results showed that teachers and students had a positive attitude towards the smart learning environment, and gender, age, grade, subject category and other variables had limited impact on spatial preference.
期刊：
Journal of Science Education and Technology,2023年32(3):379-389 ISSN：1059-0145
通讯作者：
Xiao Yang
作者机构：
[Lu, Chun] Cent China Normal Univ, Minist Educ, Educ Informatizat Strategy Res Base, Wuhan, Hubei, Peoples R China.;[Yang, Wei; Yang, Xiao] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan, Hubei, Peoples R China.;[Wu, Longkai] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Hubei, Peoples R China.;[Lu, Chun; Wu, Longkai; Yang, Wei; Yang, Xiao] Cent China Normal Univ, Sci Hall, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Xiao Yang] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China<&wdkj&>Science Hall, Central China Normal University, Wuhan, China
摘要：
Understanding factors that influence k-12 students’ Science, Technology, Engineering, and Mathematics (STEM) performance is essential to improving their problem-solving ability. Most studies have focused on the relationship between students’ psychological factors and STEM performance and have paid little attention to the relationship between behavioral factors and STEM performance. This study explored the impact of behavioral factors (i.e., information and communications technology (ICT) readiness and online interaction (OI)) and psychological factors (i.e., internet self-efficacy (ISE)) on k-12 students’ STEM performance. The sample included 851 fifth graders and 535 eighth graders from cities in central China. The results of structural equation modeling analysis showed that ISE and ICT readiness (IR) significantly impacted the STEM performance of eighth graders. More importantly, ISE, a psychological factor, had the greatest effect on STEM performance and played a mediating role in the relationship between IR, OI, and STEM performance. These findings have important implications for STEM teachers. To improve students’ STEM performance, teachers should intervene to improve ISE according to students’ grades and cognitive ability, guide students to use ICT correctly, and encourage them to actively engage in OI.
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：
[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：
Laboratory experience is critical to foster college students' collaborative problem-solving (CPS) abilities, but whether students stay cognitively engaged in CPS tasks during online laboratory sessions remains unknown. This study applied multimodal data analysis to examine college students' (N = 36) cognitive engagement in CPS during their online experimentation experience. Groups of three collaborated on CPS tasks via shared worksheets and computer-based simulations on videoconferences. Portable electroencephalogram instruments were used to determine students' levels of cognitive engagement in CPS activities. The multimodal data analysis (e.g., electroencephalogram, surveys, and artifacts) results showed a significant difference in students' cognitive engagement between different phases of CPS. The students' cognitive engagement significantly differed between groups who did and did not complete the task. Additionally, intrinsic motivation predicted students' cognitive engagement in the completion groups while self-efficacy was the primary predictor of cognitive engagement for the groups who did not complete the task.
期刊：
IEEE Transactions on Learning Technologies,2023年16(4):528-542 ISSN：1939-1382
通讯作者：
Kong, X
作者机构：
[Yang, Zongkai; Liu, Sannyuya; Kong, Xi; Liu, Zhi; Kong, X; Chen, Hao] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
通讯机构：
[Kong, X ] C;Cent China Normal Univ, Natl Engn Res Ctr Elearning, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
关键词：
Cognitive presence identification;community of inquiry model;MOOC-BERT;online discussions;pretrained language model;text analysis
摘要：
In a massive open online courses (MOOCs) learning environment, it is essential to understand students' social knowledge constructs and critical thinking for instructors to design intervention strategies. The development of social knowledge constructs and critical thinking can be represented by cognitive presence, which is a primary component of the community of inquiry model. However, identifying learners’ cognitive presence is a challenging problem, and most researchers have performed this task using traditional machine learning methods that require both manual feature construction and adequate labeled data. In this article, we present a novel variant of the bidirectional encoder representations from transformers (BERT) model for cognitive presence identification, namely MOOC-BERT, which is pretrained on large-scale unlabeled discussion data collected from various MOOCs involving different disciplines. MOOC-BERT learned deep representations of unlabeled data and adopted Chinese characters as inputs without any feature engineering. The experimental results showed that MOOC-BERT outperformed the representative machine learning algorithms and deep learning models in the performance of identification and cross-course generalization. Then, MOOC-BERT was adopted to identify the unlabeled posts of the two courses. The empirical analysis results revealed the evolution and differences in MOOC learners’ cognitive presence levels. These findings provide valuable insights into the effectiveness of pretraining on large-scale and multidiscipline discussion data in facilitating accurate cognitive presence identification, demonstrating the practical value of MOOC-BERT in learning analytics.
