Abstract
The coronavirus disease 2019 (COVID-19) pandemic has brought much attention to contactless services. Therefore, strengthening the construction of artificial intelligence in universities and improving the level of campus intelligent construction could reduce the infection rate of COVID-19 to a certain extent. The sudden outbreak of the COVID-19 pandemic in 2020 has posed severe challenges to university governance and campus safety; however, it has also created a good opportunity to promote the construction of smart campuses. Based on the questionnaire survey method, this paper uses the structural equation model as the methodology to verify whether smart campuses of colleges and universities could achieve a low infection rate in the post-pandemic era. The study findings were: (1) A great correlation was observed between the construction of smart campuses and the realization of the low infection possibility of the campus. The more effective the university's smart campus is, the more likely it is to achieve its low infection rate target; (2) The core intermediary transmission mechanism of the management using smart campuses is informatization. Symmetry improves information processing efficiency to reduce security risks; (3) Although smart campuses in colleges and universities in the post-pandemic era could achieve a low infection rate to a certain extent, still deficiencies exist encouraging further development. Thus, the construction of smart campuses should be promoted by raising awareness, setting up a special management department, and improving information utilization.
Keywords: Post-pandemic era, intelligent campus of universities, low infection rate, information technology, coronavirus disease 2019
The connotations and carriers of a university intelligent campus in the post-pandemic era
Smart campus concept for universities in the post-pandemic era
With the vigorous development of emerging information technologies, such as the Internet of Things, big data, cloud computing, blockchain, and new-generation artificial intelligence, universities have started to employ these technologies to enable a digital campus management mode. The “smart campus” is a set of technical solutions for smart education based on China's information technology development and construction, 1 which is a concept from the “Chinese perspective.” The smart campus could be considered as a campus management model based on information technology to integrate and optimize campus resources for providing intelligent services. 2
Based on the definition of the concept of smart campus by different researchers, its connotation could be interpreted based on the following aspects.
First, based on resource integration of service management, the smart campus is a mode of intelligent linkage and integration of campus resources through the development of the Internet of Things and provides a unified system platform for users to integrate school business management information through the interchange and interaction of various data.
A smart campus usually requires some type of application system as the carrier. In a smart campus, whether a classroom, library, or study room is considered the venue, or teaching courseware or book files as the knowledge resources, they can all be connected in the form of digital codes, and constitute both an online and offline aggregate. A common example in routine life is the campus card that marks the identity of students or staff, which also contains an electronic QR code that can be scanned to complete identity authentication. The online service hall provides a series of convenient “one-stop services” for those who need them; moreover, business processors can also work online to simplify the process. The smart campus allows students, faculty, and staff to enjoy campus resources more freely and conveniently, thereby enriching campus life. In the post-pandemic era, Smart Campus, which is a previously built unified system platform, further strengthened the interoperability of various data, using data platforms, such as “Campus Today” and “Online Office Hall” to provide basic information statistics of the university and key personnel. It also provides support and tools for the key tasks of pandemic prevention and control, such as health condition detection, daily health punching, abnormality reporting, and punching location of personnel, which further enriches the content and management mode of the Smart Campus and improves the efficiency of information processing.
The starting and ending points for the creation and development of a smart campus are the “people” themselves. A smart campus is an inevitable product of the development of modern science and information technology and the increasing demand for campus management. A smart campus can better meet the needs of teachers and students on campus, improve the level of education management, and provide more help to further cultivate outstanding talents. Focusing on effective service teaching methods, the smart campus is also quite rich in the resources that it provides for learning and teaching, integrating application systems with related businesses to achieve information interoperability in different time and space dimensions. 3 The combination of information technology and educational resources also allows the smart campus to have a good effect in protecting user privacy and data collection, which was fully reflected in the post-pandemic era. The instability and normalization of the pandemic influenced the development of online teaching platforms to achieve good results in a short period of time, which is fundamental for the normal teaching function of universities.
A smart campus is not the same as a digital campus. If a smart campus is merely used as a platform for information search and separated from the management mode of the university itself, then the smart campus would lose its original meaning. A mature smart campus system is comprehensive and can penetrate modern information technology, such as big data and the Internet of Things, into various management services on campus, which is especially important for pandemic prevention and control measures and information collection in the post-pandemic era. At a higher level, a smart campus can also realize personalized and customized services, i.e. different application modes can be generated according to different user needs, making the word “wisdom” profoundly applicable. In the post-pandemic era, the use of a smart campus is greatly reflected in online teaching, approval, and personal collection of student data, while its “management” characteristics are becoming increasingly prominent.
Therefore, in this paper, we tentatively defined the concept of university wisdom campus in the post-pandemic era as using emerging information technology, improving digitalization and informatization capabilities, creating an integrated platform for information management, integrating education management and normalized management of pandemic prevention and control, and using various application systems as the main carrier to construct a human-oriented campus wisdom management model.
Smart campus carrier for universities in the post-pandemic era
The carrier of the university's smart campus is usually an app, website, applet, or other application service platform, which are usually available on cell phones, computers, and tablets. These application service platforms can aid students, faculty, and staff in learning, working, and living.
Learning Pass, Wisdom Tree, Classroom Pie, etc. are applications for learning and working. Teachers can upload teaching resources on the platform and store them in the cloud for easy access to students for their own learning. Students can also ask questions to teachers online through the platform and retain the teacher's answer for repeated study. Colleges and universities usually also establish their own official websites, where they can save time by handling financial reimbursements, research declarations, classroom inquiries for class schedules, and other businesses all online with one click.
In the post-pandemic era, smart campuses are more likely to be reflected in applications with more integrated functions, such as Campus Today, and online approval systems to meet an individual's health monitoring needs and achieve potentially low infection rates. Application operations, such as face recognition temperature measurement, code check-in, code nucleic acid testing, vaccine registration, location check-in, remote location punch card, etc. record the data of the movement of people on campus, which can better control the overall risk of pandemic leakage or internal invasion. Certain individuals became aware of college smart campuses only after the emergence of the pandemic; however, smart campuses have been employed on campus long before the pandemic, only with a different focus on carrier functions.
Normalization of prevention and control in the post-pandemic era and smart campus construction in universities
Pandemic evolution and smart campus construction in universities
According to the WTO organization statistics, until December 2022 when China was fully liberalized, the level of pandemic control in China improved, and the infection rate in China was extremely low compared to the United States, the United Kingdom, India, and other countries that experienced several spikes of infections, owing to the health code and trip code for people's restraint, as well as the effective regulation of the pandemic normalization policy around the country. Schools, on the other hand, as places with a high risk of infection, are characterized by a high people density, high risk of transmission, and high requirement for efficient information transfer. Large-scale transmission following infection is very easy in a short period of time; smart campus construction is an important tool for pandemic prevention and control providing a strong grip for the realization of low infection rates in Chinese universities (Figure 1).
Figure 1.
January 2020 to February 2023 monthly additions of coronavirus disease 2019 (COVID-19) in major countries.
Normalization of prevention and control in the post-pandemic era provides new opportunities for the promotion of intelligent campus construction in universities
Society and the economy have been greatly affected since the outbreak of the pandemic. Since the pandemic has experienced several spikes throughout the country and the world, normalizing the prevention and control of the pandemic is warranted. Colleges and universities gather a large and mobile population of students, faculty, and staff from different places. Since students in colleges and universities are still on the verge of psychological maturity, they could become drained and depressed concerning the emergence of pandemics; thus, more factors should be considered for their management.
The Ministry of Education has recommended to “stop teaching and learning without stopping classes.” Online teaching has become a normal teaching measure to facilitate normal and fair teaching of students and faculty in different regions and ensure the health and safety requirements of students and faculty. The MU platform and laboratory resource platform also provide services and support for different universities to further ensure the different needs of different grades for course teaching. The dual requirement of effective planning of pandemic prevention and control and teaching curriculum arrangement is a new opportunity for promoting the construction of a university smart campus.
The normalization of pandemic prevention and control requires colleges and universities to protect students’ health and safety and control the unnecessary movement of people; however, colleges and universities need to fulfill the responsibility of education and ensure that the student's normal course study would not be greatly affected. The original traditional management mode of colleges and universities is thereby impacted. If such conventional management methods continue to be adopted during a pandemic, not only an unreasonable allocation of resources but also negligence and mistakes could result in inefficient decision-making, thereby increasing the security risks of pandemic infection or even spreading and failing to cope with the real challenges. Strengthening the use of smart campuses is an inevitable choice in the general environment of a pandemic. A smart campus can integrate and link various resources together, use information technology to the maximum extent to achieve information sharing, and realize centralized management and classification of business processes. The problems revealed by the use of smart campus management mode in the pandemic and increased demands of teachers and students can also promote further improvement of the construction of university smart campuses and provide experience for the future construction of universities under the development of information technology.
Special role of a smart campus in pandemic prevention and control in universities
Pandemic prevention and control is a systematic project, which is complex and challenging. For universities, complete and timely communication and coordination with relevant outside departments, and also building a unified emergency system internally to prepare emergency plans for handling unexpected events are necessary. In pandemic prevention and control, the smart campus is mainly used for information collection of personnel, screening and monitoring, and coordination and arrangement of materials. The special role of using smart campuses can be witnessed in different scenarios through smart colleges and universities.
For example, the school's pandemic prevention and control team can use the online meeting platform to communicate, follow up on the pandemic situation outside the school, implement measures inside the school, coordinate and arrange internal and external resources, receive and study the documents and notices issued by the government and other relevant departments to formulate the next plan of action, prepare a good early warning record, report in time, and announce the development of the school's pandemic to the society, thereby improving the transparency and timeliness of information communication. Online meetings are safe and efficient, which can effectively alleviate the problem of security risks of personnel contact and ensure the efficiency of decision-making. During the closed campus control period, the identity and health status of students, faculty, and staff entering and leaving the school are verified and authenticated with the aid of smart campus to reduce the flow of outsiders, while the movement trajectory of individuals entering and leaving the campus is tracked for refined monitoring and management. Face recognition and infrared thermal camera body temperature detection devices can be placed in dormitories and cafeterias to detect abnormal body temperatures on campus and protect the campus from pandemic outbreaks. This approach could provide further targeted decision support in planning and approving the entry of graded and graded returners to campus.
Smart campuses also ensure normal teaching schedules in the midst of a pandemic. The main function of colleges and universities is to provide an educational platform for students, which is an important matter issue that cannot be abandoned owing to the disruption attributed to the pandemic. Thus, students can rely on the online teaching platform of the smart campus for their own learning and achieve the same results as offline teachings, such as Q&A, homework, and quizzes. The online teaching platform has its inherent advantages, for example, it can penetrate the limitation of time and space dimensions, and provide greater possibilities for teachers and students to choose. Online teaching platforms can provide both live classrooms with online real-time interaction and a complete free-form catechism learning platform, allowing students to access different teaching resources according to their needs. Considering teaching quality control, access to different online classes should be open and transparent, and the Academic Affairs Office can monitor the classes by obtaining data from them, and school college inspectors can randomly join the classes to participate in auditions, thereby further guaranteeing the quality of teaching during the pandemic.
The essence of a smart campus is to meet user needs, integrate resources, and optimize the campus management model. Therefore, smart campus management in the post-pandemic era should focus on linking the information system of the entire campus, realizing the interoperability of information inside and outside the campus, publishing information on pandemic monitoring and investigation, the safety of campus entry and exit prevention and control, online teaching and meeting situation, and life and procurement of pandemic prevention materials to ensure the timeliness and transparency of information circulation and relieve the psychological worries of teachers and students in school. Universities should pay attention to the physiological and psychological health of teachers, students, and staff, reduce manual pressure through smart campus, improve the satisfaction of teachers, students, and staff, further release the campus vitality, create a good campus life atmosphere, and truly play the role of intelligent campus management under the pandemic.
Analysis of the current situation and utility of intelligent campus construction in universities in the post-pandemic era
Questionnaire content
To investigate the perceptions, evaluations, and outlooks of students and faculty members of universities on the construction of an intelligent campus with low infection potential in the context of the pandemic, we propose the corresponding suggestions for the problems to promote benign and effective development of an intelligent campus with low infection potential in the post-pandemic era. The main theme of the study was the effect of the post-pandemic era on the construction of the intelligent campus of universities with low infection potential. The questionnaire begins with the identity of the respondents, their perceptions of the awareness and effectiveness of the university's smart campus, and their views on future construction and development. The raw data obtained from the research are organized, screened, and analyzed, while conclusions are drawn and improvement suggestions are put forward. For this research survey, we randomly distributed 280 questionnaires to teachers, students, and staff through the questionnaire star app, of which 277 were valid, with a recovery rate of 98.93%.
Questionnaire data analysis and processing
According to the survey results, the number of individuals who were very much aware of college smart campuses before the beginning of the pandemic was only 59, while the number of those unaware was 76. The number of individuals who had only occasionally heard of college smart campuses was 142, accounting for 51.26% of the total number of individuals surveyed. The specific proportion of the data is shown in Figure 2. During the pandemic period, the number of individuals who perceived the development of a university smart campus was 217, accounting for 78.34%. Thus, before the outbreak of the pandemic, teachers and students on campus did not possess a high degree of knowledge about the smart campus of colleges and universities, and they only knew that the platform of smart campus existed in colleges and universities; however, they were unaware of the specific examples, and the outbreak of the pandemic promoted the individual's perception of the smart campus at some level.
Figure 2.
Statistics on the proportion of individuals aware of smart campuses in colleges and universities.
During the pandemic, students, faculty, and staff on campus possessed different views on whether a smart campus could be an important tool to solve various management problems on campus. Thus, we enquired whether the use of a smart campus was effective in achieving a low likelihood of infection on campus; the data demonstrated that smart campus software was effective in collecting pandemic data and preventing and controlling the pandemic. It was also effective in online teaching and administrative offices; however, certain individuals thought that a smart campus does not play a major role in solving various management problems on campus.
Overall, more than half of the students, faculty, and staff on campus were positive about the usefulness of the used university smart campus software in solving campus management problems, that is, they considered that the complete use of smart campus could solve some problems more efficiently and improve the campus management mode of universities to a certain extent in the context of the pandemic.
The specific statistics are presented in Table 1.
Table 1.
Perception statistics of the use of smart campuses in colleges and universities.
| Management challenges/use outcomes | Significant results | Average utility | Little effect | No correlation | Negative effect |
|---|---|---|---|---|---|
| Comprehensive management of school information | 186 (67.15%) | 60 (21.66%) | 28 (10.11%) | 2 (0.72%) | 1 (0.36%) |
| Information investigation of key personnel in the school | 186 (67.15%) | 62 (22.38%) | 26 (9.39%) | 2 (0.72%) | 1 (0.36%) |
| Student safety management | 186 (67.15%) | 68 (24.55%) | 21 (7.58%) | 1 (0.36%) | 1 (0.36%) |
| Student teaching management | 171 (61.73%) | 76 (27.44%) | 27 (9.75%) | 2 (0.72%) | 1 (0.36%) |
| Full-time teacher education and teaching efficiency | 171 (61.73%) | 74 (26.71%) | 28 (10.11%) | 2 (0.72%) | 2 (0.72%) |
| Administrative staff office efficiency | 171 (61.73%) | 75 (27.08%) | 28 (10.11%) | 2 (0.72%) | 1 (0.36%) |
Moreover, we also conducted a survey on the views of teachers and students on the specific application of smart campuses in colleges and universities during the pandemic. We mainly collected information on the challenges that may be exposed in the specific application of the smart campus platform and assessed the contradictions that need to be solved urgently in the actual application of the smart campus based on the different degrees of recognition of the existence of these challenges by teachers and students.
Therefore, in our question, “During the pandemic, what do you think are the problems in the current construction of smart campus platforms in colleges and universities?” most people generally agreed that still many areas exist for improvement in the construction and development of smart campus platforms in colleges and universities. The proportion of the number of individuals who had “No correlation” or “Negative effect” on the college smart campus platforms with certain challenges was relatively low, all below 70, and the number of “Negative effect” was basically in single digits, which is extremely low. Among them, 67 people did not recognize the “lack of mobile terminals” of the smart campus platform of colleges and universities, accounting for a relatively high proportion, but only 24.19%. This demonstrates that teachers and students believe that the mobile application of smart campuses in colleges and universities is relatively sufficient.
Overall, these data further illustrate that the construction of smart campus platforms in colleges and universities still lacks details and needs to be further improved and supplemented. The specific statistics are presented in Table 2.
Table 2.
Statistics on the recognition of challenges in the construction of smart campus platforms in colleges and universities.
| There is a problem/degree of recognition | Greatly recognized | Recognition | Generally | Not recognized | Greatly disapproved |
|---|---|---|---|---|---|
| Does not highlight high collaboration and intelligence | 75(27.08%) | 79 (28.52%) | 77 (27.8%) | 40 (14.44%) | 6 (2.17%) |
| Lack of mobile terminals | 67 (24.19%) | 61 (22.02%) | 74 (26.71%) | 67 (24.19%) | 8 (2.89%) |
| The user interface is not appealing enough | 68 (24.55%) | 59 (21.3%) | 95 (34.3%) | 46 (16.61%) | 9 (3.25%) |
| The function does not meet the needs well | 73 (26.35%) | 61 (22.02%) | 93 (33.57%) | 43 (15.52%) | 7 (2.53%) |
| The operation interface is not friendly enough and the operation is not convenient enough | 74 (26.71%) | 53 (19.13%) | 93 (33.57%) | 51 (18.41%) | 6 (2.17%) |
| Information delivery is slow to update, making it difficult to respond in a timely manner | 74 (26.71%) | 71 (25.63%) | 76 (27.44%) | 50 (18.05%) | 6 (2.17%) |
| Dispersed and complex functions | 76 (27.44%) | 64 (23.1%) | 80 (28.88%) | 52 (18.77%) | 5 (1.81%) |
However, in response to the question, “In the post-pandemic era, how much of a correlation do you think is there between building a smart campus and achieving a low likelihood of infection on campus?” This question was answered by students, faculty, and staff on campus, who generally agreed on a greater correlation between the two. The data demonstrated that the number of people who agreed that the correlation between the construction of the smart campus and the realization of low infection possibility on campus was large; their percentages were 102 (36.82%) and 106 (38.27%) respectively, that is, more than 75% of the individuals agree that the correlation was large. Individuals believe that the construction of a smart campus platform in universities can effectively reduce the possibility of infection on campus (Figure 3).
Figure 3.
Statistics on the degree of correlation between the construction of smart campuses and the possibility of achieving a low campus infection rate.
Structural equation model
Preprocessing
We used Q1–Q4 to denote the frequency of participation in various smart campus carriers for learning and life use, Q5–Q8 to denote the frequency of participation in various smart campus carriers for pandemic prevention and control use, and relve to denote the degree of influence that the construction of smart campus has on the safety of campus pandemic prevention and control, thereby forming three latent variables in the structural equation model test: the degree of association between learning and life use, pandemic prevention and control use, and the construction of the smart campus and low infection rate on campus (Table 3).
Table 3.
Classification of the scale questions.
| Factor | Question |
|---|---|
| School | Q1: Today's campus, online approval system, and other apps and webpages |
| Q2: Campus Network | |
| Q3: Face-swiping access control, electronic card swiping, etc. | |
| Q4: Online teaching (including lectures, training, etc.) | |
| Disease | Q5: Online pandemic data report and collection |
| Q6: Face recognition temperature measurement | |
| Q7: Scan code for registration, scan code nucleic acid detection, vaccine registration, etc. | |
| Q8: Location check-in, remote location check-in | |
| Relevant | relve: The degree of impact on the safety of campus pandemic prevention and control |
Confirmatory factor analysis
The distribution of the basic situation of the significant variables in each latent variable is shown in Table 4, and the unstandardized value is considered as the reference target for the first significant variable in each latent variable, and the unstandardized value/standard error is obtained as Z (combined reliability [CR] value), with Z > 1.96 and significant p-value. The standardized loading coefficient values show the correlation between the factors (latent variables) and the analyzed terms (significant variables/measurements), and if a term shows the significance and the standardized loadings coefficient value > 0.7, a strong correlation is indicated. For the measurement relationships, the absolute values of the standardized loadings were greater than 0.6 and showed significance, which indicates a good measurement relationship.
Table 4.
Loading factor table.
| Factor | Measurement item | Coef. | Standard error | Z (CR) | P-value | Standard load factor |
|---|---|---|---|---|---|---|
| School | Q1 | 0.454 | 0.049 | 9.351 | 0.000 | 0.803 |
| Q2 | 0.612 | 0.061 | 9.977 | 0.000 | 0.749 | |
| Q3 | 0.440 | 0.050 | 8.879 | 0.000 | 0.829 | |
| Q4 | 0.873 | 0.081 | 10.757 | 0.000 | 0.604 | |
| Disease | Q5 | 0.377 | 0.042 | 8.900 | 0.000 | 0.833 |
| Q6 | 0.868 | 0.084 | 10.355 | 0.000 | 0.700 | |
| Q7 | 0.470 | 0.048 | 9.879 | 0.000 | 0.763 | |
| Q8 | 0.377 | 0.041 | 9.200 | 0.000 | 0.817 |
CR: combined reliability.
The average variance extracted (AVE) values and the CR values were used for the convergent validity (convergent validity) analysis. Normally, an AVE > 0.5 and a CR < 0.7 indicate high convergent validity; if the AVE or CR is low, consider eliminating a factor and reanalyzing the convergent validity. Based on the results of the indicators in Table 5, we can conclude that the AVE values of all four factors are greater than 0.5 and the CR values are higher than 0.7, which indicates that the data of this analysis have good convergent validity.
Table 5.
AVE and CR index results of the model.
| Factor | Average variance extracted (AVE) value | Combined reliability (CR) value |
|---|---|---|
| School | 0.829 | 0.836 |
| Disease | 0.856 | 0.855 |
Structural equation model
The structural equation model was constructed with the help of R software, and the data fitting results are shown in Table 6. The cardinality/degree of freedom (χ²/df) value is 4.85; in terms of relative fit indicators, the comparative fit index (CFI) and goodness-of-fit index (GFI) are 0.937 and 0.893, respectively, and the absolute fit indicator standardized root mean square residual (SRMR) value is 0.05. Upon comparing the reference values in the fourth column of the table, the effect of the fit of the actual data with the established research model is relatively satisfactory; hence, no further corrections to the model were required.
Table 6.
Model fitting results.
| Fit metrics | Index value | Reference | Fit |
|---|---|---|---|
| χ2 | 121.430 | ||
| df | 25 | ||
| χ21df | 4.85 | < 3, ideal, < 5 acceptable | Acceptable |
| RMSEA | 0.122 | < 0.05, ideal; < 0.1 acceptable | Slightly worse |
| RMR | 0.066 | The smaller the better | Acceptable |
| SRMR | 0.050 | < 0.05, ideal; < 0.1 acceptable | Ideal |
| CFI | 0.927 | > 0.9, ideal | Ideal |
| GFI | 0.893 | > 0.9, ideal | Acceptable |
RMSEA: root mean square error of approximation; RMR: root mean square residual; SRMR: standardized root mean square residual; CFI: comparative fit index; GFI: goodness-of-fit index.
Finally, a structural equation model test was conducted, in which the latent variables were represented as ellipses and the observed variables were represented as boxes, and the values in the figure indicate the standardized path coefficients among the latent variables; the results are shown in Figure 4. According to the model path coefficients, the standardized path coefficients for the use of learning life and pandemic prevention and control are 0.34 and 0.33, respectively, with a p-value of 0.000, which indicate that these two types of factors have a significant impact on the degree of association between smart campus construction and low infection rate.
Figure 4.
Structural equation model results.
Research conclusions and recommendations
Research conclusions
Based on the data analysis and model construction of the survey questionnaire, we can conclude that smart campuses of colleges and universities in the post-pandemic era can achieve a low infection rate to a certain extent; however, deficiencies exist and there is still some room for further improvement.
The use of smart campuses in universities can reduce contact between individuals during the pandemic and improve their safety. Moreover, the use of data to “replace” manual work has greatly improved the efficiency of business processes and reduced the pressure on students, faculty, and staff during a pandemic. Furthermore, by monitoring the movement of individuals on campus through the university's smart campus, the risk of spreading the pandemic can be further reduced and low infection rates could be possibly achieved. Most students, faculty, and staff on campus have a positive attitude toward the use of college smart campuses during a pandemic, which proves that the construction of college smart campuses is beneficial to the management of pandemic prevention and control.
However, before the outbreak, many individuals did not know about college smart campuses. During the pandemic, the awareness of a smart campus was also based on the perception of a specific application. This exemplifies the challenge of promoting the use of smart campuses. During the survey, some students, faculty, and staff also raised certain practical and security privacy issues in the application of smart campuses. For example, the repeated utilization rate of certain application data is not high and practical, and the data exclusion statistics are reproduced, which results in further challenges. Whether data and information, such as punch cards, positioning, and personal situations are private, whether they can be disclosed, and whether the security of personal information could be guaranteed are still controversial. This is also the main reason why some students, faculty, and staff are not very satisfied with the smart campus application. Certain students, faculty, and staff also have doubts concerning whether the smart campus is just a formality and not very helpful for pandemic management. This is based on the fact that different universities sometimes have contradictory actions and purposes in the specific application of the smart campus management model. For example, the security guards scan the incoming and outgoing students, faculty, and staff through the equipment to verify their identities and monitor their health conditions. This is not a completely contact-free situation, and the scan results are not always accurate (especially in terms of body temperature); hence, the incoming and outgoing students, faculty, and staff have to wait in line for a completely error-free scan result before they can enter or leave the campus. This approach is not only time-consuming during a high turnover of people but also prone to security hazards, defeating the original purpose of using the equipment to quickly scan and confirm the safety of people. This hidden danger is related to whether the university sets up a personnel triage mechanism or not, and also to the limitation that current technology cannot achieve 100% fast and accurate scanning monitoring. Therefore, although college smart campuses could achieve low infection rates, further development, and improvement are still warranted.
Research recommendations
Based on the challenges observed in the survey and research, we put forward relevant suggestions in three aspects: promoting awareness, establishing a special department, and improving the information rate for promoting the development of smart campuses in universities in the post-pandemic era.
Grasp the opportunity for pandemic prevention and control, and raise awareness of smart campuses
With the continuous development of information technology, universities have also started to gradually use technologies such as big data, artificial intelligence, and the Internet of Things to establish new teaching interaction modes and improve education methods. Although the pandemic has hindered the normal teaching mode of colleges and universities, it has also made a variety of new education methods, such as online teaching, more popular. The pandemic has brought more attention to the use of information technology on campus and inspired the demand for upgrading the functions of smart campuses. However, to promote the future construction of smart campuses, more individuals should understand smart campuses and their connotations more deeply. Universities should fully grasp the opportunities presented by this pandemic prevention and control tool.
The first step is to raise awareness of smart campuses at a higher leadership level. The connotation and mode of smart campus should be understood and one should clarify the “what,” “why,” and “how” to build a smart campus in colleges and universities at the decision-making level. 4 After clarifying the necessity and importance of building a smart campus, a general plan should be drafted at the decision-making level for the use of a smart campus according to the actual situation of the campus, and subsequently, mobilize teachers and students to build a smart campus in the form of meetings and documents.
Second, it is necessary to raise awareness of smart campuses in different departments of the university. Different departments can employ different applications in the smart campus and instruct teachers and students to use the relevant functions appropriately. For example, if the Finance Office needs to use Smart Campus for online reimbursement and funding application, the Finance Office should issue a clear document to standardize the materials and formats required for online reimbursement and funding application and record the specific operation process as a manual to guide students, faculty, and staff in the specific operation. Different departments of the university are responsible for the implementation of the smart campus; thus, they should constantly improve their knowledge of the smart campus, keep up with the times, and make improvements and additions according to the different applications on campus.
Lastly, to improve the awareness of smart campus among students, faculty, and staff, the needs of the students, faculty, and staff should be met, and their satisfaction with learning, working, and living on campus should be enhanced since they comprise the most important participants, users, and beneficiaries of the smart campus construction. The smooth construction and development of smart campuses are also inseparable from the cooperation and opinions of students, faculty, and staff. Therefore, it is necessary to enhance the knowledge of students, faculty, and staff about the smart campus to ensure complete application to promote the further development of the smart campus.
At the leadership and decision-making level, specific implementation departments, teachers and students, and employees on the campus with a more in-depth knowledge of the smart campus through their personal experience should be included to explore the construction of the smart campus. Therefore, to promote the construction of smart campuses in the post-pandemic era, we should first grasp the opportunity to use this pandemic prevention and control tool. When colleges and universities raise awareness of smart campuses internally, they should also utilize the power of external professional organizations to carry out activities, such as question and answer, training and guidance to help solve some technical problems that colleges and universities cannot cope with independently, further improve people's awareness of smart campus and actually gain access from the construction of the smart campus. As people gradually become familiar with the existence of smart campuses as an emerging entity in their daily study, work, and life, a synergy gradually forming within the campus would support the further development of smart campuses thereby encouraging a positive attitude and practical actions.
Establish a bottom-up feedback channel and dedicated management department
Considering that the communication information channels between the decision-making leadership and the specific implementation departments, teachers, and students are not smooth enough, prompt resolution of the problems arising in the specific implementation process of smart campus is challenging. The decision-making leadership cannot devote all resources to the construction of a smart campus; thus, bottom-up feedback channels and special management departments are warranted to improve efficiency and save resources.
Colleges and universities should create an information feedback channel with openness, use the Internet to break the traditional manual collection mode, and promote efficient and optimal allocation of resources. For example, colleges and universities can set up official public numbers or websites, set keywords for feedback, filter invalid information in advance, get opinions in real time and suggestions from teachers, students, and staff of the entire university, and form opinion veins with big data, realize bottom-up information feedback, and promote good campus atmosphere in an open way.
Moreover, universities should tap high-quality talents and build specialized management departments. Colleges and universities can provide intensive training to existing talents for improving their management thinking and information technology application level and also consider absorbing certain professional talents to join the specialized management department of colleges and universities to solve the problems arising in the process of wisdom campus construction with a more scientific and professional perspective and create more favorable conditions for the future development of wisdom campus.
Colleges and universities use smart campuses more frequently during the pandemic period and teachers, students, and employees would be more willing to provide feedback; thus, a special management department should be established as early as possible to solve problems at the source. If fewer matters need to be handled, we can consider using the extra talents in other jobs in the university, such as the information management center and other departments, to realize the simple but precise function of the intelligent campus management department.
Improve information utilization and reduce homogeneous and repetitive statistics
As the pandemic information exclusion requires a large amount of data to provide support and requires real-time data update and aggregation, the task is complex and the work of information data collection personnel is more intensive. The survey respondents often have to fill out several questionnaires and regularly position themselves to punch the card. Owing to the high number of records, the ranked person would also become weary; thus, the accuracy and authenticity of the information when completing the information collection form may not be guaranteed. Data collection can be even more tedious when temporary personnel are present on campus. Therefore, repetitive and invalid data could exist in the process of these data statistics; moreover, there is also a security risk of information leakage. The essential purpose of information technology is to better serve people, and the start and end points of smart campus are also to provide convenience for teachers, students, and staff, and not to increase the burden.
Therefore, universities should prevent technology alienation and improve the processing efficiency and security governance of data. This requires universities to collaborate with professional organizations and use professional talents to optimize and upgrade the information management system, establish a sound comprehensive data security governance system, improve data processing ability, and achieve a balance between personal information protection and reasonable data use. 5 Universities should also calculate and estimate the utilization rate of information, reduce the number of questionnaires and questions and reject “formalistic” screening and redundancy (e.g. filling in specific body temperature multiple times at different times of the day), and establish a more concise method of periodic data feedback.
Moreover, universities should refine and integrate the data of students, faculty, and staff on campus and temporary entrants to reduce invalid data. At present, the authentication system of universities mainly included one card and online authentication, which can collect the identity information of students, faculty, and staff. However, the information demand attributed to the pandemic contains more data, such as physical health conditions, personnel mobility information, etc. This requires location punch cards and body temperature monitoring. For on-campus staff with a card, a campus temperature measuring device, card, website, or app could meet the data collection needs. Temporary personnel lacks a unified system for data collection and processing, and they do not have a unified identity and cannot use the online system to process their business in a timely manner. These personnel are also more likely to bring risk and hidden danger during the pandemic. Therefore, universities can expand sub-systems or platforms on the original system to classify and manage their information for improving statistical efficiency and security. 6
Limitations and further research in the future
This study began in the first half of 2022, and the collected filled questionnaires were dated July 2022; thus, the perspective of this article is mainly based on the study of the relationship between a smart campus and low infection rates in the post-pandemic era. After December 2022, China's pandemic prevention and control policy underwent a major shift, with the policy shifting from prevention and control to liberalization. To avoid mass infections and medical crowding situations, a vast majority of colleges and universities across the country went on early vacation; thus, whether smart campus construction can continue to be helpful in achieving low infection rates warrants further future examination and research.
Conclusion
The construction of a smart campus is an inevitable choice to realize the sharing of campus information resources and promote the reform and development of a new-age university teaching system. 7 In the post-pandemic era, the construction and development of a smart campus are also conducive to potentially achieving low infection rates on campus. Through the structural equation model established by the questionnaire data of university teachers and students, we can observe that the core of the management of university smart campuses lies in informatization, and further development warrants a timely grasp of the opportunity for pandemic prevention and control, improving people's awareness of smart campus, forming a collaborative effort to promote the construction of the smart campus, and setting up bottom-up feedback channels and special management departments to solve the problems of improving information utilization and reducing homogeneous and repetitive statistics. Colleges and universities can keep pace with the development tide of the times only through continuous improvement of digitalization and informatization and complete institutional reform to provide a better campus environment for teachers, students, and staff.
Acknowledgments
We thank the associate editor and the reviewers for their useful feedback that improved this paper. Thanks to Yufang Chen for supporting us with the data.
Abbreviations
- AVE
average variance extracted
- COVID-19
coronavirus disease 2019
- CR
combined reliability
- CFI
comparative fit index
- GFI
goodness-of-fit index
- SRMR
standardized root mean square residual.
Author biographies
Changpeng Sui (May 1983), Originally from Qingdao, Shandong, Associate Researcher, main research interests are smart campus.
Yeying Xie (March 1996), Originally from Quanzhou, Fujian, Master's degree, main research interests are smart campus.
Footnotes
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
ORCID iD: Yeying Xie https://orcid.org/0000-0001-5074-6319
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