In the rapidly evolving digital age, universities face unprecedented challenges in their administration, operations, and management. Data provides a critical basis for evidence-based decision-making in higher education institutions, directly affecting the efficient use of resources, operational management, and academic development. The School of Science (SCI) at Xi’an Jiaotong-Liverpool University (XJTLU) has fully utilized the university’s innovative no-code platform to create the “SCI Data Center,” which is committed to significantly improving academic management and research efficiency through digital means. In March 2025, the SCI Data Center was awarded the “Best Platform Application Award” at the XJTLU Digital Platform Empowerment Competition. This achievement recognizes and highlights the School of Science’s innovative efforts in digital transformation and provides valuable insights and learnt experiences for other Schools and universities.

 

 

 

Prior to entering its digital transformation phase, the School of Science faced a series of significant challenges in its data and process management:

 

1. Diverse Data Collection Tools

 

• Previously, the traditional data collection tools used in the School of Science were quite varied, and included not only standardized data platforms such as PURE, Intranet forms, Learning Mall, and eBridge, but also multiple means and channels such as e-mails, BOX cloud storage, Wenjuanxing, and offline forms. These tools were of limited functionality since they lacked a unified integration mechanism, leading to inefficiency in data collection and processing. For example, academic staff had to enter data repeatedly on multiple platforms, increasing their workload, increasing frustration, and also causing data inconsistencies.

 

2. Difficult Data Integration

 

• With data sources scattered across platforms and different tools having various data formats and storage methods, data integration became a significant This not only affected the accuracy and integrity of the data but also caused delays in data analysis and reporting, failing to meet the School’s need for accurate and real-time data.

 

3. Inefficient Workflows and Manual Data Processing

 

• Most workflows in the School relied on manual operations, such as the annual Personal Development Review (PDR), conference funding and publication fee applications, as well as information collection and collation for academic seminars. These processes were not only time-consuming and labor-intensive but also prone to errors, seriously affecting the School’s operational efficiency and decision-making quality.

 

4. Lack of Real-time Data Support

 

• In key decision-making processes such as course planning and faculty allocation, among others, the lack of real-time data led to an inability to mount a rapid response mechanism. This seriously hindered the decision-making processes within the School.

 

5. Room for Improvement in Conference and Event Organization Efficiency

 

• When organizing academic conferences and seminars, the traditional scoring and feedback processes by the judges were typically cumbersome and inefficient. For example, in the School’s graduate student seminars, scoring and feedback had to be recorded and collated manually, which was not only time-consuming but also prone to omissions and errors.

 

In the School of Science’s first Annual Research Report (for the 2023/24 academic year), these challenges were highlighted and reported. Therefore, it became imperative for the School to find effective solutions to address these critical problems.

 

 

 

Starting in May 2024, XJTLU organized a workshop on the innovative use of no-code platforms. The School of Science, recognizing the potential value of the no-code platforms, was eager to participate and began to explore how its use may help solve the actual problems faced in the School. In October 2024, the School of Science received professional training on the no-code platform, mastering how to quickly develop efficient data management tools. After the training, the School officially began developing its SCI Data Center. After several months of significant effort, by integrating multiple data sources, the use of the platform achieved automated data collection, processing, and analysis. The design of the no-code platform (Figure 1) fully considered the user’s needs and optimized processes by providing real-time data visualization and helpful automated workflow management functions.

 

 

 

 

Figure 1. Structure of the SCI Data Center

 

 

 

1. Real-time Data Visualization Dashboard

 

Recently, the School of Science launched a new AI+Science, PGCert programme. To ensure its quality of teaching, we designed an application form and a real-time visualization dashboard (Figure 2). Students submit requested materials by filling out the application form, and the dashboard can display the application data in real time, including the number of students, year of study, and academic background, providing strong decision-making support for course planning and faculty allocation. Through this innovation, the School can adjust the course settings according to real-time data to ensure that the course content remains consistent with the students' needs and aligned with the academic frontiers of excellence.

 

 

Figure 2. Application for AI-Science PGCert Programme

 

2. Closed-loop Workflow Management

 

• The School of Science provides a certain amount of conference funding for its staff on an annual basis. Previously, applicants had to fill out a PDF application form and send it to the School’s Research Committee (SRC) for approval via email, which was followed by approval from the School Senior Management Team (SSMT). Managing the application forms was quite tedious and inconvenient. Later, the School decided to transfer this approval process to an online application form on the Intranet, which solved some of the problems to a certain extent, but there were still significant inconveniences. The primary reason was that the process was not a closed loop:

 

• The application form was filled out before the applicant attended the conference and ended with the approval by the School leadership. Although the estimated costs were filled in, the actual costs were unknown.

 

• The application form required applicants to commit to publishing the conference presentation as an academic peer-reviewed paper in a well-respected journal, but whether this was actually done was also unknown.

 

• To achieve a closed loop, we designed a multi-step workflow form using the innovative no-code platform. This process not only includes the application and approval before the conference, but also provides the ability to track and ensure follow-up feedback after the conference. Through this innovation, the School can ensure enhanced research outputs, increase accountability, and improve transparency.

 

Figure 3. A closed-loop workflow of the application for the conference fund

 

3. Instant Marking

 

• In March 2025, the School of Science hosted its Postgraduate Symposium, and there was a need to develop an instant marking system mechanism. This system integrates multiple functions of the no-code platform, including backend support tables and a frontend scoring form, as well as the use of a real-time ranking dashboard. Judges can mark the student’s presentations in a paperless manner by using a computer or mobile phone, selecting the marking task directly, entering their score and comments, and then submitting it. The ranking dashboard updates were happening in real time, displaying the latest ranking information of the students in six sub-panels according to the three Departments in the School and taking into account the report formats (oral or poster presentation). Thanks to this system, we successfully held the Postgraduate symposium and award ceremony on the same day, bringing the event to a successful conclusion.

 

• At the recently held inaugural XJTLU-UoL-XJTU Joint Postgraduate Conference, March 2025, this marking system mechanism was upgraded and performed even better. We further improved the system by adding new functions. The tasks that judges had completed and submitted automatically disappear from their marking to-do list, avoiding duplicate submissions and adding accuracy and efficiency to the marking process. In addition, we also added a reminder function for marks that were missing by certain judges, and therefore, our improved system automatically listed the names of the judges who had yet to submit their marking forms. This innovation greatly improved the efficiency of scoring, better supported the difficult work of the judges, and enhanced the organization and convenience of the conference.

 

 

 

The successful establishment of the SCI Data Center and its innovative use of the no-code platform have brought about a new standard of efficiency and improved outcomes:

 

1. Improved Data Management Efficiency

 

• By integrating multiple data sources, the SCI Data Center has achieved automated data collection and processing mechanisms, significantly improving the efficiency and accuracy of data management. The real-time data visualization function enables the School to quickly obtain and analyze key data, providing a scientific basis for evidence-based decision-making.

 

2. Optimized Research Management

 

• In terms of the School’s various funding applications, the closed-loop workflow management function of the SCI Data Center has improved transparency and accountability, motivating and better supporting our researchers’ outputs. Real-time data support enables the School to better plan research projects and allocate resources, improving the scientific nature and effectiveness of our research management.

 

3. Efficient Organization of Academic Activities

 

• The instant marking function to better support the work of judges during School academic activities has greatly improved the convenience of conference organization. The platform automatically completes the ranking and provides statistics, reducing the cumbersome and error-prone nature of manual operations.

 

4. Value of Data Storage and Use

 

• The SCI Data Center supports data storage and use, providing an efficient means for the School to effectively and quickly prepare its annual research reports and publicity work. By collating, integrating, and analyzing the research data from previous year(s), the School can better prepare, summarize, and present its accomplishments and plan its future development directions.

 

5. Enhanced Team Collaboration and Innovation

 

• The establishment of the SCI Data Center has helped solve numerous practical problems and also enhanced the School team’s innovation capabilities and collaboration spirit. Team members, through the use and participation in the no-code platform training and development practices, have mastered new technologies and innovative tools, laying a solid foundation for the School’s continued digital transformation.

 

The SCI Data Center team has formulated short-term, medium-term, and long-term development plans. In the short term, the team will continue to develop new applications to meet the growing needs of the School. In the medium term, the goal is to integrate artificial intelligence technology into the SCI Data Center to further enhance the platform’s intelligence level and data analytics capabilities. In the long term, the team plans to transform data users into data providers and achieve the full transformation of data from cost to value.