Education is a super-complex system, involving teaching, management, teaching and research, service and many other businesses. Different from the clear, standardized and consistent business process of the financial system, the educational business in different regions and schools has certain characteristics, but the differences are also very prominent. The differences in business directly lead to more diverse sources of educational data and more complicated data collection.
Education big data comes from various educational practice activities, including teaching activities, management activities, scientific research activities and campus life in the campus environment, as well as learning activities in informal environments such as families, communities, museums and libraries. It includes both online education and teaching activities and offline education and teaching activities.
The core data sources of educational big data are "people" and "things"-"people" include students, teachers, managers and parents, and "things" include educational equipment such as information systems, campus websites, servers and multimedia devices.
According to different sources and scope, education big data can be divided into six types: individual education big data, curriculum education big data, class education big data, school education big data, regional education big data and national education big data.
Second, the classification of education big data
There are many classifications of educational data.
From the business sources of data generation, it includes teaching data, management data, scientific research data and service data.
From the technical scene of data generation, it includes perceptual data, business data and Internet data.
In terms of data structure, it includes structured data, semi-structured data and unstructured data. Structured data is suitable for two-dimensional table storage.
From the link of data generation, it includes process data and result data. Procedural data is difficult to quantify collected in the course of activities (such as classroom interaction, online homework, network search, etc.). ); Results data often show some quantifiable results (such as grades, grades, quantities, etc.). ).
The data collected by the state are mainly managerial, structured and result-oriented, focusing on the overall situation of macro-education development. In the era of big data, comprehensive collection and deep mining analysis of educational data are becoming more and more important. The focus of educational data collection will shift to unstructured and procedural data.
Thirdly, the structural model of educational data.
On the whole, educational big data can be divided into four layers from the inside out, namely, the basic layer, the state layer, the resource layer and the behavior layer.
Basic layer: that is, the most basic data in China is highly confidential data; Including all the data mentioned in the seven series standards of educational management information issued by the Ministry of Education on 20 12, such as school management information, administrative management information, educational statistical information, etc.
Status layer, data of various equipment, environment and business operation status; Inevitable energy consumption, failure, running time, campus air quality, classroom lighting, teaching progress;
The resource layer, the top layer is about the user behavior data in the field of education. For example, PPT courseware, micro-lessons, teaching videos, pictures, games, teaching software, posts, questions and test papers;
Behavior layer: store the behavior data of users (teachers, students, teaching and research personnel, education administrators, etc.). ) related to expanding education, such as students' learning behavior data, teachers' teaching behavior data, teaching researchers' teaching guidance behavior data and administrators' system maintenance behavior data.
Different levels of data should have different collection methods and educational data application scenarios.
With regard to the iceberg model of education big data, at present, we collect more explicit and structured data, while more unstructured data exists under the iceberg, which really produces the greatest value for education.
References:
Source and collection technology of educational big data? Xing beibei