摘要：
Electronic portfolios are crucial means to evaluate the performance of students. However, the traditional student e-portfolio(SEP) system cannot meet the needs of guaranteeing students' information safety due to the system using centralized third-party storage. In addition, the SEP system involves multiple stakeholders and has an extreme volume of learning data, while the current access control model fails to satisfy the requirements of the SEP system. Blockchain technology provides a possible solution to the issues, while the massive data stored in the blockchain can cause congestion in the blockchain network. Therefore, we propose StuChain, an efficient blockchain-based SEP platform integrating a hybrid access control approach, to solve the above problems. Firstly, we design a hybrid access control model to manage and share student e-portfolio. Secondly, we propose to use blockchain technology to solve the security issue and design smart contracts to realize identity certification, recording, access control management, and sharing. Thirdly, we present an efficient storage approach, which achieves massive storage without threatening the system's efficiency. The student e-portfolio information is encrypted through the symmetric encryption scheme(AES-128), signed through the Edwards-curve digital signature algorithm (EdDSA), and recorded in the off-chain. We store the corresponding storage address and hash values in the blockchain. Finally, we conduct theoretical analysis and extensive experiments. Theoretical analysis demonstrates that our proposed approach outperforms other schemes. Experimental results show that our proposed StuChain can achieve secure and efficient storage, fine-grained access control, and our proposed system maintains high throughput.
摘要：
As a collection of various IoT devices, smart classroom can record various forms of teaching data and provide rich data for recognizing teachers' emotions. Recognizing and analyzing teachers' emotions can promote teachers' professional development. Nowadays, most of the automatic emotion recognition methods for teachers in smart classroom are based on facial expressions. However, since teachers usually keep smiling to mobilize the classroom atmosphere, the recognition results may not reflect the real mental state of teachers. By observing teaching videos, it is found that the prosody and text in the teachers' speech can reflect the implicit emotion of the teacher. Therefore, a multimodal teacher emotion dataset (MTED) was built based on teaching videos recorded by IoT cameras and microphones in smart classroom. A neural network combining multiple prosodic features and text content for teacher speech emotion recognition is proposed. The proposed method fills the gap in teacher speech emotion recognition, our proposed method has higher accuracy. Experimental results show that ProsodyBERT achieves 78.6 % UA4 and 66.2 % UA6 on IEMOCAP and MELD, respectively, surpassing the existing methods. The proposed method reached 82.1% UA6 on MTED self -built dataset, which is 9.6 %-21.4 % higher than that of unimodal method in teacher emotion recognition. An ablation experiment is designed and implemented on MTED dataset to explore the influence of each module in ProsodyBERT on teacher speech emotion recognition task. The experimental results in the smart classroom record show that ProsodyBERT has higher accuracy and stronger robustness than unimodal methods.
期刊：
TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES,2022年33(1) ISSN：2161-3915
通讯作者：
Di, Bingbing
作者机构：
[Di, Bingbing; Zhao, Gang; He, Hui] Cent China Normal Univ, Fac Artificial Intelligence Educ, Sch Educ Informat Technol, Wuhan, Peoples R China.
通讯机构：
[Di, Bingbing] C;Cent China Normal Univ, Fac Artificial Intelligence Educ, Sch Educ Informat Technol, Wuhan, Peoples R China.
摘要：
In the context of cloud computing, the interaction between clients in different application domains becomes more frequent, which makes cross-domain identity authentication safely and efficiently become an important research topic. Public key infrastructure (PKI) is a technology to solve cross-domain authentication. However, there are problems such as difficulty in mutual trust between multiple certificate authority nodes (CA), failure of single point, and low efficiency in the traditional PKI method. Blockchain is a promising technology for decentralized trust management by providing consistent data storage, which gives impetus to the further development of cross-domain identity authentication. Thus, this article apply blockchain to cross-domain identity authentication. To solve the defects of the traditional PKI method, the design requirements are analyzed firstly, based on the analysis result, we proposed a double-layer cross-domain identity authentication model by constructing a consortium blockchain which is comprised of authentication server nodes (AS) and some internal blockchain, the model can highly improve the scalability of the PKI system without changing the internal architecture. Then a novel authentication protocol was put forward. The protocol can improve the efficiency of online cross-domain identity authentication transactions by verifying the hash instead of the signature of their certificate. By putting the generation process of the blockchain certificate and the storage process of its hash in the registration operation and reducing the authentication process for AS and CA, the efficiency is further improved. Finally, the protocol was evaluated by security and performance analysis. The results display our protocol can guarantee security and has an excellent performance in cross-domain identity authentication transactions.
作者机构：
[Gang, Zhao; Qing, Xia; Biling, Hu; Jie, Chu; Wenjuan, Zhu; Hui, He] Cent China Normal Univ, Fac Artificial Intelligence Educ, Sch Educ Informat Technol, Wuhan, Peoples R China.
通讯机构：
[Zhu Wenjuan] S;School of Educational Information Technology, Faculty of Artificial Intelligence Education, Central China Normal University, Wuhan, China
关键词：
Teacher behavior;Behavior recognition;Motion region extraction;Key-points tracking;Teacher’s behavior rule
摘要：
The analysis of teacher behavior of massive teaching videos has become a surge of research interest recently. Traditional methods rely on accurate manual analysis, which is extremely complex and time-consuming for analyzing massive teaching videos. However, existing works on action recognition are difficultly transplanted to the teacher behavior recognition, because it is difficult to extract teacher’s behavior from complex teaching scenario, and teacher’s behaviors are given professional educational semantics. These methods are not adequate for the need of the teacher behavior recognition. Thus, a novel and simple recognition method of teacher behavior in the actual teaching scene for massive teaching videos is proposed, which can provide technical assistance for analyzing teacher behavior and fill the blank of automatic recognition of teacher behavior in actual teaching scene. Firstly, we discover the educational pattern which it be named “teacher set”, that is, the spatial region of the video of the whole class where teachers should exist. Based on this, the algorithm of teacher set identification and extraction (Teacher-set IE algorithm) is studied to identify the teacher in the teaching video, and reduce the interference factors of classroom background. Then, an improved behavior recognition network based on 3D bilinear pooling (3D BP-TBR) is presented to enhance fusion representation of three-dimensional features thus identifying the categories of teacher behavior, and experiments show that 3D BP-TBR can achieve better performance on public and self-built dataset (TAD-08). Hence, our whole approach can increase recognition accuracy of teacher behavior in the actual teaching scene to utilize the deep integration of educational characteristics and action recognition technology.
期刊：
International Journal of Human-Computer Interaction,2021年37(9):884-901 ISSN：1044-7318
通讯作者：
Liu, Shan;Zhu, Wen-Juan
作者机构：
[Qi, Yu-Heng; Liu, Shan; Zhu, Wen-Juan; Liu, S; Zhao, Gang] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Peoples R China.
通讯机构：
[Liu, S; Zhu, WJ] C;Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Peoples R China.
摘要：
Mobile augmented reality (AR) technology creates realistic learning situations and a strong sense of immersion, which is conducive to enhance learning experience and stimulate learning motivation. However, existing mobile outdoor augmented reality applications generally have a complicated operation process and a mismatch between learning resources and corresponding scenes, which leads to a poor learning experience. Therefore, we propose a lightweight mobile outdoor AR method that combines deep learning and knowledge modeling to perceive learning scenes with a goal to improve learning experience. This method improves the accuracy of scene perception and resources retrieval and provides a convenient mobile AR technology solution for outdoor learning. To evaluate the proposed method, we provide objective criteria to assess the effectiveness of the lightweight object detection model and the learning resources retrieval approach. Simultaneously, we investigate the evaluation of participants majoring in teacher education on the usability of the proposed method by the modified system usability scale questionnaire and net promoter score. Experimental results demonstrate that our method achieves high detection accuracy, good usability, and is of great significance to improve outdoor learning experience.
摘要：
In recent years, online education service platform has swept the world due to its high quality, openness and variety of education resource, and user registered in online platform has increased dramatically. However, the traditional centralized information storage method based on the third-party may cause problems such as the loss and disclosure of the transaction subject identity data, seriously threatening the legitimate rights and interests of users. Therefore, based on education service theory, this paper applies blockchain technology to identity authentication to design and implement a blockchain-based digital education transaction subject identity authentication system. The system proposed in this study can store identity information in ciphertext form and is jointly verified and maintained by the entire network node, thus it can guarantee the security and reliability of identity data in the digital identity authentication.
摘要：
With the rapid development of E-Learning, the demand for high-quality personalized education services and resources has increased rapidly. In order to meet this demand, a project called “Digital Education Crowdfunding and Crowdcreating Personalized Service Platform Development and Application Demonstration” was launched in China. The goal of the project is to provide a personalized education resource and service trading platform for producers, suppliers, and users by joint investment and collaborative creation. So how to ensure the authenticity and credibility of the objects (education resources and services) involved in this multi-party participated service platform becomes a challenging task. Blockchain is emerging as a promising technology that enables people to make secure and reliable transactions because of the decentralization and collective maintenance features. Thus in this letter we proposed a novel blockchain-based authentication service for digital education transaction object to resolve the problem of education transaction object authentication among different education institutions. We constructed information models for the two types object and stored the metadata information of the model on the blockchain to make it transparent and safe. And smart contracts are designed to authenticate education transaction object automatically among different education institutions. To improve the performance of blockchain-based authentication service, an improved consensus process for blockchain network is proposed and incorporated into our prototype system. Performance simulation is carried out and the experimental results demonstrate the practicality of our blockchain-based authentification service.
作者机构：
[Zhang, Lina; Zhao, Gang; Li, Yaxu] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan, Hubei, Peoples R China.;[Zhang, Lina] Da Li Univ Dali, Sch Math & Comp Sci, Dali, Peoples R China.
会议名称：
IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC)
会议时间：
JUL 12-14, 2019
会议地点：
Beijing, PEOPLES R CHINA
会议主办单位：
[Zhao, Gang;Li, Yaxu;Zhang, Lina] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan, Hubei, Peoples R China.^[Zhang, Lina] Da Li Univ Dali, Sch Math & Comp Sci, Dali, Peoples R China.
会议论文集名称：
IEEE International Conference on Electronics Information and Emergency Communication
摘要：
An energy-conserving protocol of routing is critical to a wireless sensor network (WSN) because of limited energy supply and communication capabilities of wireless sensor nodes. LEACH protocol has been widely used, which is based on a homogeneous network without considering most heterogeneous sensor networks in practical application. In addition, instantly sending data perceived by sensor nodes to the cluster head is not suitable for specific applications such as anti-submarine underwater, in this kind of application environment, most of the sensing data is redundant. Therefore, this paper proposes an improved clustering protocol with data transmission status switchable, which can be used in heterogeneous sensor networks. Cluster heads filter the perceived information and start data transmission link, and then send data to the sink when the information intensity perceived exceeds the preset threshold. Otherwise, cluster heads record the received data and continue receiving data sent by the next round of cluster nodes. We fortunately found in the simulation results that the network lifetime is prolonged several times than the protocol of LEACH.
关键词：
Community of practice;Teachers' professional development;Teaching thinking;Belief;Engagement
摘要：
This study investigated how teachers' beliefs (i.e., beliefs about teaching thinking, acceptance of the community of practice, and acceptance of the school culture) and engagement (i.e., engagement in learning and engagement in practice on teaching thinking) affected their perceived professional development in the Alliance of Thinking Schools (ATS), which is a multi-regional community of practice (CoP) on teaching thinking for K-12 teachers in China. A total of 478 teachers from 39 schools in 10 cities participated in this study. The regression analysis results indicated that teachers' beliefs about teaching thinking, followed by engagement in practice, engagement in learning, and acceptance of the CoP, were significant predictors to their perceived professional development. However, teachers' acceptance of the school culture was not a significant predictor. This study suggests that multi-regional CoPs could eliminate the barriers to teachers' professional development regarding the school culture. Schools should provide opportunities for teachers to engage in the practice, rather than one-shot training.
期刊：
International Journal of Wavelets, Multiresolution and Information Processing,2019年17(1):1950001 ISSN：0219-6913
通讯作者：
Wei, Yantao
作者机构：
[Yao, Huang; Shi, Yafei; Wei, Yantao; Tong, Mingwen; Liu, Qingtang; Chen, Tiantian; Deng, Wei; Zhao, Gang] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Hubei, Peoples R China.;[Pan, Donghui] Anhui Univ, Sch Math Sci, Hefei 230601, Anhui, Peoples R China.
通讯机构：
[Wei, Yantao] C;Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Hubei, Peoples R China.
关键词：
Student body gesture recognition;fisher broad learning system;learning analytics
摘要：
Observing student body gesture has been widely used to assess teaching effectiveness over the past few decades. However, manual observation is not suitable for the automatic data analysis in the field of learning analytics. Consequently, a student body gesture recognition method based on Fisher Broad Learning System (FBLS) and Local Log-Euclidean Multivariate Gaussian (L2EMG) is proposed in this paper. FBLS is designed by introducing the discriminative information into the hidden layer of Broad Learning System (BLS) and reducing the dimensionality of hidden-layer representations. FBLS has superiorities in accuracy and speed. In addition, L2EMG, which is a highly distinctive descriptor, characterizes the local image with a multivariate Gaussian distribution. So L2EMG features are fed into the FBLS for recognition in this paper. Extensive experimental results on self-built dataset show that the proposed student body gesture recognition method obtains better results than other benchmarking methods.
期刊：
Proceedings of 2017 6th International Conference on Computer Science and Network Technology, ICCSNT 2017,2018年2018-January:157-160
通讯作者：
Zan, Hui(zxydhh@163.com)
作者机构：
[Luo, Zhuoran; Yu, Peng; Zhao, Gang; Lu, Shuai] School of Educational Information Technology, Central China Normal University, Wuhan, 430079, China;[Zan, Hui] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, 430079, China;[Zhao, Dasheng] Wuhan Maritime Communication Research Institute, Wuhan, 430079, China
通讯机构：
National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
期刊：
Lecture Notes in Computer Science,2018年10749:314-325 ISSN：0302-9743
通讯作者：
He, Bin
作者机构：
[Yu, Xinguo; He, Bin; Jian, Pengpeng; Xia, Meng] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.;[Zhao, Gang] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan, Hubei, Peoples R China.
通讯机构：
[He, Bin] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
会议名称：
8th Pacific-Rim Symposium on Image and Video Technology (PSIVT)
会议时间：
NOV 20-24, 2017
会议地点：
Wuhan, PEOPLES R CHINA
会议主办单位：
[Yu, Xinguo;Jian, Pengpeng;He, Bin;Xia, Meng] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.^[Zhao, Gang] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan, Hubei, Peoples R China.
摘要：
This paper presents an algorithm for understanding problems from circuit schematics in exercise problems in physics at secondary school. This paper models the problem understanding as a problem of extracting a set of relations that can be used to solve problems with enough information. The challenges lie in not only analyzing the circuit schematics but also extracting the proper relations for a given exercise problem. To face these challenges a novel approach is proposed to detect circuit nodes with their current flows to extract the current equations for nodes. And the other novel approach is proposed to extract voltage equations of independent loops. The proposed approach was tested with a dataset collected from the text books and the exam papers for the students at secondary schools. Experimental results show that the effect of recognition and analysis we designed delivers promising result, and our approach can be adapted to more complex electrical circuit analysis.
期刊：
PROCEEDINGS OF 2018 IEEE 9TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS),2018年2018-November:1108-1111 ISSN：2327-0586
通讯作者：
Zhao, Gang
作者机构：
[Liu, Shan; Chu, Jie; Zhao, Gang; Zhang, Qing; Li, Yaxu] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan, Hubei, Peoples R China.;[Lin, Luyu] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
通讯机构：
[Zhao, Gang] C;Cent China Normal Univ, Sch Educ Informat Technol, Wuhan, Hubei, Peoples R China.
会议名称：
9th IEEE International Conference on Software Engineering and Service Science (ICSESS)
会议时间：
NOV 23-25, 2018
会议地点：
China Hall Sci & Technol, Beijing, PEOPLES R CHINA
会议主办单位：
China Hall Sci & Technol
会议论文集名称：
International Conference on Software Engineering and Service Science
摘要：
High school statistical graph classification is one of the key steps in intelligent mathematics problem solving system. In this paper, a hierarchial classification method is proposed for high school statistical graph classification. Firstly, the dense Scale-invariant Feature Transform (SIFT) features of the input images are extracted. Secondly, the sparse coding of the SIFT features are obtained. Thirdly, these sparse features are pooled in multiscale. Finally, these pooled features are concatenated and then fed into single-hidden layer feedforward neural network for classification. The effectiveness of the proposed method is demonstrated on the constructed dataset, which contains 400 statistical graphs. In contrast to several state-of-the-art methods, the proposed method achieves better performance in terms of classification accuracy, especially when the size of the training samples is small.
