Abstract
Artificial intelligence was first proposed in the 1950s, when it was only a forward-looking concept. If machines can have the same learning ability as human beings and the computing power of computers themselves, this concept has been placed high hopes. Until about 2010, with the explosion of data volume and the improvement of computer performance, machine learning has become a leader in breaking through the bottleneck of artificial intelligence. Research on machine learning in education and teaching has attracted much attention. From the above research status, we can see that in the current period of the vigorous development of machine learning, many applications are still not perfect and ordinary education and teaching evaluation is difficult to meet people’s requirements, so how to gradually improve its effectiveness is a significant goal with research significance and practical interests. However, in the environment of colleges and universities, prediction information and evaluation methods have important application value and development space in education and teaching. In this context, according to the theory of machine science, the effectiveness of several conventional prediction and evaluation methods is analyzed. In this paper, machine learning theory is used to study college students’ performance prediction and credit evaluation, as well as teaching quality evaluation and comprehensive ability evaluation in colleges and universities. Questionnaire survey is used to investigate and analyze the results. The effectiveness of machine theory in teaching is analyzed. It is found that machine learning has great advantages in education and teaching evaluation. It builds models in complex computing environment and is not affected by human factors; the effectiveness of prediction and evaluation is significant.
Highlights
Nowadays, with the development and popularization of mobile products, people’s demand for content information products is increasingly urgent
The report of the 19th National Congress of the Communist Party of China in 2018 stressed that education should be built as the basis for the great rejuvenation of the Chinese nation [3]
By analyzing the comprehensive ability of colleges and universities and its influencing factors and combining the comprehensive ability evaluation system of colleges and universities with the principles and methods of construction, we take Guangxi colleges and universities as an example and use the theory of machine science to evaluate the comprehensive ability of colleges and universities, and the content is shown in Table 2: According to the ranking results of Guangxi and 8 normal universities planned for key construction, the national key universities rank only Guangxi University, while other universities rank below the average level in the country, ranking in 300 ordinary universities, which shows that Guangxi university education is not high
Summary
With the development and popularization of mobile products, people’s demand for content information products is increasingly urgent. Because the difficulty of the test paper is too small, the test results of students are generally on the high side, which will make students have self-expansion and pride These factors will lead to the evaluation result of students’ learning effect being not objective. A large number of student evaluation data have been accumulated, which can provide more information for teachers’ teaching ability Based on this idea, this paper puts forward how to make full use of the evaluation data of students and make more effective use of machine theory to evaluate and diagnose teachers’ classroom teaching ability. The comprehensive evaluation of students’ academic examination results is taken as the research object, the comprehensive evaluation and prediction based on machine learning theory are discussed, and the effectiveness of the evaluation and prediction is simulated and analyzed
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