In the era of big data, educational decision-making, educational policy, educational concept, educational scientific research paradigm, and cultivation of school core competence are all impacted, and the importance of educational big data is becoming important. The big data is applied in the engineering education of colleges and universities. It is of great significance to construct modern engineering education through big data.

This guest post first studies big data and its characteristics then analyzes the characteristics of educational big data. It will then analyze the current situation and trend of big data applications in engineering education in colleges and universities. With the development of the Internet, big data is applied in engineering education in colleges and universities. Big data is applied to the construction of an adaptive learning system. Big data will become a powerful assistant to teachers teaching and help teachers more.

The Definition and Characteristics of Big Data

Big Data engineering courses provide detailed education on how to handle a large set of data collected from many sources in multiple forms. It can be with real-time performance. In the case of enterprise sales, these data may be available from social networks, e-commerce sites, customer visit records, and many other sources. These data are not normal data sets for the company's undefined customer relationship management database. Big data has a wide range of data sources, including traditional relational databases and semi-structured data such as XML, as well as unstructured data in video, audio, text, and other forms. The main problem to be solved in data extraction and integration is to collect all kinds of fragmented data, clean the data, ensure the data quality, update the data pattern according to the evolution of time, and determine the data entity and its relationship. Finally, the data can be stored in a uniform format to be provided to the upper layer for data analysis.

The Definition and Characteristics of Big Data Engineering Courses in Education

In terms of the composition of educational big data, online learning data bear the brunt. It can be said that the widespread concern of educational big data is closely related to the prevalence of online teaching and learning. In Steinberger Peer with Big Data: the Future of Learning and Education, the first case of a big data education application comes from online learning. With the increasing popularity of online teaching, the massive data recorded by the learning management system and various mobile devices has become an important source of teaching analysis in the process of teaching and learning. These data include the behavior data of the learning process, the evaluation data of the learning results, and the data of the social network relationship formed by the learning.

Through the expansion of these data, educational big data also contains all kinds of student personal information data, teaching management data, and so on. Therefore, educational big data comes from the main body and process of education and teaching. According to different levels of subjects and contents of educational activities, educational big data can be divided into four levels and six types. Four levels include individual, school, region, and country; six types include basic data, teaching data, scientific research data, management data, service data, and public opinion data. Among them, the basic data include the basic information data of learners represented by demography, and the teaching data include the process, content, and result data involved in the teaching process. Scientific research data include data obtained from various educational experiments and scientific research projects, management data include data recorded in various educational management systems, such as student status data, archival data, and various statistical data, etc.

The Application of Big Data Engineering Courses in Higher Engineering Education:

Current Situation and Trend It is well known that large data are characterized by huge volume, a wide variety, and mostly unconventional, unstructured (unstructured) and high-speed data information. It can be quickly analyzed and provide analysis results to decision-makers in the "almost" real-time situation. The purpose of using large data is to provide advice or to solve practical questions. Rather than creating new theories or exploring the reasons for interpreting data, it can be used to identify and predict patterns to improve the economic efficiency of enterprises. More than 10 years ago, big data was first developed in large enterprises and large companies seeking efficiency. The reasons for the rise of big data can be attributed to two factors:

  • Advances in data collection technologies, such as the use of various sensors, data digitization, and collection of sound, images, coordinates, speed, and data from the Internet and social networks. The technical ability of various data inside;
  • Development of computer hardware. These improvements have improved the functions of data processing, analysis, and visualization.