Construction Of Smart Campus Research Articles

Design and Implementation of Teaching Quality Evaluation System Based on SpringBoot

In order to enhance the integration, standardization and network of teaching quality evaluation, SWPU (Southwest Petroleum University) proposes teaching quality evaluation system based on SpringBoot as a part of smart campus construction. This system adopts the B/S structure to design the system function module and system database.In addition, it utilizes the IDEA tool management project, which enables a normative and consistent way to integrate the information resources, such as personnel system and educational administration system. As such, the system realizes sharing data and ensures practicability and scalability.

Research Progress of Construction of Smart Campus

Smart campus is a new stage in the development of educational informatization. Focusing on present construction situation of smart campus in universities in China and abroad, the paper firstly reviews of typical construction modes, types of design architecture and major functional modules of smart campus in universities. Secondly, from the perspective of application, service and management for smart education, some key technologies of construction of smart campus in universities are analyzed and summarized. Finally, further work of promoting the construction standards and evaluation of smart campus, training and improving the big data literacy for teachers and students in campus, and exploring effectively on sharing and constructing mechanism on smart campus for local application-oriented universities is expected in this paper.

A Personalized Hybrid Recommendation Algorithm for Location-Based Service on Smart Campus

In order to provide location-based service for teachers and students, this paper proposes a personalized hybrid recommendation algorithm (GTRA) based on user’s geographical locations and tags. First, by analyzing the user's historical stay points and the length of stay time in different feature areas, the user's geographical adjacent user set is identified, which well solves scoring matrix sparse problem; then, we propose a tag growth strategy in which club administrators participate to give an effective solution to the problem of cold start; Finally, personalized push service was offered according to the user’s identity and the current location. Through the construction of personalized recommendation system and execution of the experiment, the algorithm proposed in this paper is superior to the traditional collaborative filtering algorithm, and it is suitable for campus application scenarios, which has some reference to the construction of smart campus in which personalized services for teachers and students are emphasized.

Algorithm Analysis for Big Data in Education Based on Depth Learning

The construction of campus network has provided an advanced comprehensive information environment for teaching, scientific research and management of colleges. In the process of digitization and intelligentization, the data produced by all kinds of application systems in college are growing, and the large data environment of campus has been formed. Big data of college contain abundant information, so we need to use new data storage and analysis tools to store and analyze huge amounts of college data and get useful information from them. In this paper, a depth learning analysis algorithm based on Map Reduce is proposed to deal with college data. Using Map Reduce parallel computing framework to achieve campus data computing, we studied the analysis and application systems of campus big data in different themes and levels and dug out valuable information hidden behind college data. The experimental results show that the high school data mining algorithm based on Map Reduce is effective. It provides new research ideas for large data mining in colleges and provides technical reference for the construction of smart campus.

Teaching Performance Evaluation in Smart Campus

With the rapid development of modern teaching technology, the construction of smart campus has become the focus of modern college education reform. The application of technologies, such as the Internet of Things and big data, plays an important role in improving the teaching environment of colleges and universities, improving the utilization of teaching resources, and the flexibility of education. As an important part of campus activities, teaching performance evaluation scientifically and effectively utilizes teaching information and teacher and student interaction information to evaluate teachers’ teaching performance, which helps to motivate teachers’ work enthusiasm, improve teaching quality, and enhance school core competitiveness. This paper analyzes the salient features of smart campus from the perspectives of technology, business, and construction mode, and proposes a smart campus architecture model. According to the research content of teaching performance evaluation, the framework model of smart campus education data collection and storage platform is established, which provides a reference model for the construction of smart campus in colleges and universities. The evaluation of teaching performance in smart campus first analyzes the shortcomings of traditional evaluation methods and proposes the necessity of combining teaching performance evaluation with modern technology. Second, six principal components were determined using the PCA algorithm. Then, use the AHP to calculate the weights of each layer of the indicator set, avoiding the decision errors caused by subjective factors. Finally, the gray correlation degree is used to improve the TOPSIS algorithm for multi-objective decision analysis. The evaluation results of the AHP-TOPSIS teaching performance model are consistent with the actual situation. The application of the smart campus education data platform combined with the AHP and the gray correlation improvement TOPSIS algorithm is more targeted to the teacher’s teaching performance evaluation and provides a new evaluation method for scientific performance evaluation, and avoid the problem of strong subjectivity of traditional teaching performance evaluation.

An Efficient Image Matching Algorithm Based on Adaptive Threshold and RANSAC

The education plays a more and more important role in disseminating knowledge because of the explosive growth of knowledge. As one kind of carrier delivering knowledge, image also presents an explosive growth trend and plays an increasingly important role in education, medical, advertising, entertainment, and so on. Aiming at the long time of massive image feature extraction in the construction of smart campus, the traditional Harris corner has problems, such as low detection efficiency and many non-maximal pseudocorner points. This paper proposes a Harris image matching method that combines adaptive threshold and random sample consensus (RANSAC). First, the Harris feature points are selected based on the adaptive threshold and the Forstner algorithm in this method. On the one hand, candidate points are filtered based on the adaptive threshold. On the other hand, the Forstner algorithm is used to further select the corner points. Second, the normalized cross correlation matching and the RANSAC are applied to precisely match the detected Harris corners. The experimental results show that compared with the existing algorithms, the proposed method not only obtains a matching accuracy higher than 20% of Cui’s algorithm but also saves more than 30% detection time of corner detection and image matching. Furthermore, the proposed method obtains a matching accuracy higher than 50% of the Cui’s algorithm and saves more than 50% detection time of corner detection and image matching.

Investigating Learners’ Technology Engagement - A Perspective From Ubiquitous Game-Based Learning in Smart Campus

With the increasing development of information technology and emerging of modern mobile devices, researchers have been shedding light on the integration of ubiquitous learning and online games in the construction of smart campus, aiming to enhance the online users’ learning performance and perceptions. However, challenges of such blend learning model, for example, how to avoid the learners’ distraction from online games, triggered many concerns from software engineers and educators. This paper investigated the factors and driving mechanism to construct a learner’s technology engagement (TE) model towards ubiquitous game-based learning (UGL) in the smart campus context. Based on the technology acceptance model and the media system dependency theory, this paper identified the relationships among individual expectation (IE), facility environment, social environment (SE), and TE. A total of 168 participants, who were randomly assigned in the UGL settings, took part in the investigation. SPSS 19.0 was employed to analyze factorial validity, internal consistency reliability, correlation, and multiple regression analyses. The result shows that learners’ TE toward UGL was influenced directly by IE (including fragmented learning and infotainment learning) and SE (including peer coaching and parental supports). In addition, the ease of use significantly moderated the fragmented learning and TE in the context of smart campus, while it was an insignificant moderator of infotainment. Besides, the ease of use significantly moderated the coaching of peers and TE, while it failed to moderate the parental supports and TEs. The theoretical and practical implications are finally discussed in terms of the development tendency of the construction of smart campus with UGL model.

On Study of Building Smart Campus under Conditions of Cloud Computing and Internet of Things

two new concepts in the information era are cloud computing and internet of things, although they are defined differently, they share close relationship. It is a new measure to realize leap-forward development of campus by virtue of cloud computing, internet of things and other internet technologies to build smart campus. This paper, centering on the construction of smart campus, analyzes and compares differences between network in traditional campus and that in smart campus, and makes proposals on how to build smart campus finally from the perspectives of cloud computing and internet of things.

On Study of Application of Big Data and Cloud Computing Technology in Smart Campus

We live in an era of network and information, which means we produce and face a lot of data every day, however it is not easy for database in the traditional meaning to better store, process and analyze the mass data, therefore the big data was born at the right moment. Meanwhile, the development and operation of big data rest with cloud computing which provides sufficient space and resources available to process and analyze data of big data technology. Nowadays, the proposal of smart campus construction aims at improving the process of building information in colleges and universities, therefore it is necessary to consider combining big data technology and cloud computing technology into construction of smart campus to make campus database system and campus management system mutually combined rather than isolated, and to serve smart campus construction through integrating, storing, processing and analyzing mass data.

The Application of Big Data in the Construction of Smart Campus Information

With the rapid development of information technology, university information construction has entered the era of big data, big data as a new database processing technology in the construction of smart campus has played a crucial role. This paper expounds the importance of the construction of the smart campus in the background of big data, and gives the frame of the smart campus structure under the perspective of big data, hoping to promote the development of the smart campus in big data perspective, through the analysis of the key technologies of smart campus in universities and the present situation and challenges of university information construction. The innovation of informatization construction of university 's smart campus in the view of data.

Research on the Application of Smart Campus Construction under the Background of Big Data

With the rapid development of big data, Internet of Things and cloud computing, the construction of smart campus has become the main trend of campus construction. Based on the theory of big data and smart campus, this paper introduces the current situation and defects of smart campus data analysis, the research on the status and construction of smart campus, analyzes the methods and methods of smart campus construction overall structure of higher education analysis and construction. It has a positive significance for the higher level of teaching management and service.

Research on Smart Campus Based on the Internet of Things and Virtual Reality

Smart campus is an inevitable trend in the development of digital campus construction. With the development of the Internet of Things (IoT) and Virtual Reality (VR) technology, these technologies become the key to the construction of smart campus. The major objective of this study put forward a smart campus system prototype based on Internet technology. This paper firstly introduces the definition and characteristics of the IoT and VR technology. Secondly, presents the architecture and implementation methodology of the system prototype, and analyzes the core idea of smart campus. Finally, discuss the problems should be noticed in the smart campus construction.

Research on the Construction of Smart Campus Based on the Internet of Things and Cloud Computing

Smart campus is an inevitable trend in the development of digital campus construction. The internet of things and cloud computing are the key technologies in the construction of smart campus. The authors expound the concepts of smart campus and put forward overall architecture of smart campus based on the internet of things and cloud computing. They elaborate on the network foundation platform, service platform and the construction of intelligent application platform in detail. Finally, discuss the problems should be noticed in the smart campus construction.