In 2022, the National Education Work Conference proposed the "Education Digitalization Strategic Action", pointing out that it is necessary to strengthen demand traction, deepen integration, innovation empowerment, and application drive, promote the construction of new educational infrastructure, strengthen data mining and analysis, and improve education informationization standards and specifications system, injecting new impetus into promoting the high-quality development of education.
Today, the information system construction of major universities is basically complete, and they are beginning to try to build a data application system, tap the value of data, and promote the development of smart campuses. How to carry out work in an orderly manner to solve the characteristics of data applications?
Current Situation of Digitalization in Chinese Universities
At present, my country's education is moving from informatization to digitalization, so as to improve the quality of education and school operation, and realize the high-quality development of higher education. Digitization has become a key innovation path for the development of my country's education modernization and an important strategy for building a high-quality education system.
According to data from the Ministry of Education, in 2021 China's education financial investment in education informatization will reach 464 billion yuan, a year-on-year increase of 9.4%.
2012-2021 Education Informatization Funding Situation
Data source: Ministry of Education
The development of university informatization has gone through 30 years. From the perspective of informatization construction stage, it can be divided into four stages.
In the first stage, equipment and network are the core of construction. The main responsibilities are campus network planning, construction, management, maintenance and school Internet access. The construction results are various centers, such as "audio-education center" and "computer center";
The second stage focuses on the construction of digital campus, and its main responsibilities are the planning, construction, management and maintenance of digital campus, such as "smart classroom" and "online teaching platform";
The third stage takes data as the core, and its main responsibilities are to focus on the planning, construction, management, and maintenance of smart campuses, and take on the important tasks of decision support, organization, and business process reengineering. For example, some schools have renamed "network information center" to "big data center". This is also the stage that most colleges and universities are currently in.
The fourth stage is the development stage of establishing a smart brain and an artificial intelligence problem-solving platform, such as automatically answering a large number of questions, intelligently handling school business, and establishing an artificial intelligence problem-solving platform.
It can be seen that the digital transformation of colleges and universities is not just to make offline classrooms online, but to digitize all school-related work, realize full-scenario and full-process data applications, and deeply integrate data into the school's daily education management process. , Realize the refinement, intelligence and personalization of various tasks.
Pain points in data application in colleges and universities
Business Pain Points: Disconnected Management, Ineffective Decision-Making
(1) Teaching
Curriculum is out of touch with practical application, theory and hands-on practice are separated, teachers' expectations are far from what students need, teaching quality is lacking, teacher assessment fairness and academic warning are all pain points in the development of college teaching.
(2) Management services
At present, many colleges and universities in my country are still unable to break away from the "administrative" management method. Although departments at all levels continue to optimize business processes, sort out service catalogs, and improve working methods, due to the closed loop of business flows and the proliferation of data islands, the lack of effective sharing and interaction of cross-departmental business data within universities still cannot solve the cumbersome administrative affairs management process. Difficult and inefficient handling of cross-departmental business. It is difficult to prevent students' physical and mental health, especially student safety issues in advance.
(3) Comprehensive decision-making
The objects involved in comprehensive decision-making are the leaders and managers of universities. Comprehensive decision-making requires an in-depth grasp of basic data, preparations to grasp development deficiencies, and a sensitive grasp of policy dynamics. It requires decision-making at the school level, such as: enrollment plan decisions, financial status and policy decisions, faculty development status and talent policy decisions, and scientific research development status A series of decision-making items related to the life and death of colleges and universities, such as discipline construction decision-making, teaching evaluation status and talent training mode decision-making. However, most colleges and universities have not yet established an effective data application system to support management decision-making with data, and the phenomenon of "slapping the head" is still common.
Data pain points: poor data foundation, difficult application
(1) Lack of basic data: It is impossible to provide basic information data services for school teaching, scientific research and management. At the same time, there are big differences in the infrastructure construction of various schools. Some schools only use one card, the data type is single, and many important basic data are missing. As a result, most colleges and universities just "do what they have" and have many personalities. needs. For example, the educational affairs data of some colleges and universities are relatively complete, but lack of data such as postgraduate system and asset system.
(2) Lack of data standardization: The data of the whole school lacks a unified standard, and no unified data standard has been formed, resulting in poor data quality. Problems such as complex data sources, uneven data quality, scattered basic data, inconsistent data, and inconsistent statistical caliber have occurred, resulting in unguaranteed data quality during application, data that cannot be matched, unidentifiable, inconsistent, and redundant Problems such as duplication, lack of timeliness, and insufficient accuracy occur frequently, and the data results do not match the actual situation. They cannot be used as a reference for business improvement and business decision-making, and it is difficult to support upper-level applications and fully release the true value of data.
In the process of data input and management, there are problems such as inconsistent data input specifications. Different departments have different understandings of the same data index, which leads to differences in the description and understanding of data elements during input, resulting in data conflicts or contradictions. This type of error is difficult to correct during the cleaning process, and the cost of supplementing and improving in the later stage is high.
The Way to Break the Situation of Data Application in Colleges and Universities
Digital transformation is a systematic project with a huge system, a long process, and a wide range of businesses involved. In the process of transformation, colleges and universities face the difficulties of limited resources such as time, funds, and human resources. Especially under external environmental conditions such as the epidemic, many colleges and universities have reduced the funds for digital transformation, and most of them focus on the introduction of teaching equipment and platforms. Investment in information technology construction.
Therefore, when building a data application system, it is necessary to clarify the overall thinking and purpose, establish clear and definite strategic goals, deeply explore business needs, and combine business and technology to achieve agility and efficiency and reduce waste of resources.
For example, colleges and universities can set up a strategic goal of "improving the management ability of colleges and universities and improving the quality and reputation of colleges and universities", and establish sub-goals for the business end and data end respectively, so that the two can be closely integrated to jointly support the achievement of strategic goals:
(1) Business: It is most important to cultivate data thinking and leaders to pay attention to it.
- Spread the value of data: Concise and intuitive is the most important, everyone can understand;
- Consistency: unified data caliber, unified business indicators;
- Stimulate vitality: comprehensive comparative analysis, establish internal competition awareness;
- Closed business loop: discover problems through data, and drive improvement through closed loop management;
- Multi-terminal access: mobile phones, computers, and large screens, to keep track of the situation of colleges and universities at any time.
(2) Technology: Open up all data and improve data analysis capabilities.
- Data access: unified standards, unified quality, breaking down barriers;
- Integration of external data: education industry data, public opinion;
- In-depth business improvement indicator system: result indicators, process indicators, behavior indicators;
- Agile platform: improve data analysis efficiency and quickly respond to business;
- Self-service analysis: business empowerment, root tracing, and continuous optimization.
The key to the effective implementation of the data application system is to closely integrate the data with the business of the university, so that the data is born and used in the business. Under the background of digitalization, the platform construction of colleges and universities should actively rely on teaching big data and develop around various activities of teachers and students. Data dynamic correction, data early warning, guided by data static labels, dynamic labels, model labels, etc., inject human vitality into data , to build a big data roadmap for the cultivation of talents in colleges and universities.
(1) Build a panoramic portrait system based on big data to support school evaluation and decision-making analysis
Conduct visual data analysis on professional conditions, teachers' conditions, students' conditions, teaching conditions, personnel training, educational research, innovation and entrepreneurship, ideological and political education, etc., to improve the modernization efficiency of education and teaching governance in colleges and universities.
(2) Teaching evaluation driven by big data to more precisely describe the characteristics of teachers and students' teaching and learning
Driven by big data, innovate teaching evaluation tools, actively explore the whole process and all-factor evaluation of students' learning situation based on the OBE education concept, and implement targeted teaching content and services, promote the close integration of the evaluation process and the learning process, and form a large-scale education The educational evaluation system that integrates with personalized training empowers the realization of personalized teaching, thereby promoting the transformation and development of educational quality.
