作者机构：
[Han, Shuyun; Xue, Zengcan; Zhang, Zhaoli; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.;[Kong, Weiliang] Henan Normal Univ, Fac Educ, Xinxiang, Peoples R China.;[Cao, Taihe] Beijing Normal Univ, Fac Educ, Beijing, Peoples R China.;[Shu, Jiangbo] Cent China Normal Univ, Natl Engn Lab Educ BIG DATA, Wuhan, Peoples R China.
通讯机构：
[Liu, H ] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
作者机构：
[Chen, Jingying; Xu, Ruyi; Zhang, Kun] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, People's Republic of China;School of Media and Technology, Liaocheng University, Liaocheng, People’s Republic of China;[Wang, Guangshuai] School of Computer Science, Wuhan University, Wuhan, People’s Republic of China;[Wang, Jidong] School of Teacher Education, Huzhou University, Huzhou, People’s Republic of China;[Zheng, Wenming] Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, People’s Republic of China
通讯机构：
[Jingying Chen] N;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, People's Republic of China
关键词：
autism spectrum disorder;computer games;computerized evaluation tool;quantitative indicator;validity;Visual motor integration
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.
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.
期刊：
Expert Systems with Applications,2023年214:118943 ISSN：0957-4174
通讯作者：
Chen, Zengzhao(zzchen@ccnu.edu.cn)
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：
Connection attention mechanism;Features fusion;Frame-level features;Speech emotion recognition;Utterance-level features
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 E-Learning, 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
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.
期刊：
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2023年PP:1-12 ISSN：2168-2194
作者机构：
[Xueli Pan; Frank van Harmelen] Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China;National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China;National Language Resources Monitor Research Center for Network Media, Central China Normal University, Wuhan, China;School of Computer Science, Central China Normal University, Wuhan, China
摘要：
It is commonly known that food nutrition is closely related to human health. The complex interactions between food nutrients and diseases, influenced by gut microbial metabolism, present challenges in systematizing and practically applying knowledge. To address this, we propose a method for extracting triples from a vast amount of literature, which is used to construct a comprehensive knowledge graph on nutrition and human health. Concurrently, we develop a query-based question answering system over our knowledge graph, proficiently addressing three types of questions. The results show that our proposed model outperforms other state-of-art methods, achieving a precision of 0.92, a recall of 0.81, and an F1 score of 0.86in the nutrition and disease relation extraction task. Meanwhile, our question answering system achieves an accuracy of 0.68 and an F1 score of 0.61 on our benchmark dataset, showcasing competitiveness in practical scenarios. Furthermore, we design five independent experiments to assess the quality of the data structure in the knowledge graph, ensuring results characterized by high accuracy and interpretability. In conclusion, the construction of our knowledge graph shows significant promise in facilitating diet recommendations, enhancing patient care applications, and informing decision-making in clinical research.
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：
Journal of Science Education and Technology,2023年32(6):858-871 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：
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.
