Analysis of influencing factors and teaching reform of nuclear professional English based on logistic regression

Abstract

Nuclear professional English course can help students of nuclear energy engineering major to understand the corresponding course content accurately and effectively while learning and researching frontier topics. This course has an important guiding role for their future development. This experiment obtained a series of data from students who had completed the course by questionnaire survey, and logistic regression algorithm was used to analyse the influencing factors of professional English test, then obtained the P value. The significant relationship between the independent variable and the dependent variable can be seen from the P value, thus reflecting the problems existing in the current course. The odds ratio (OR value) value of 0.030 is the biggest influence on the passing rate of the examination factor. In the questionnaire survey on the distribution of students’ intention, 96.25% of the students hope to have a new teaching mode, while 38.10% of the students do not take the initiative to read English books.

Development and Construction of Internet of Things Training Practice Platform for Employment Skills Assessment

“…The participation of teachers or the guidance of students in the competition can help teachers understand and master the latest developments in the development of the Internet of Things industry. The teachers' ideas are conducive to the timely update of professional course content and experimental content, so that students' knowledge structure can keep up with the development of the industry and improve the employment competitiveness of Internet of Things student [16].…”

An Analysis of Students’ failing in University Based on Least Square Method and a New arctan − exp Logistic Regression Function

“…In the quantitative model, the main methods used are machine-learning algorithms, such as deep neural network [10], logistic regression model [11][12][13][14][15][16], and some other combined models. Yousafzai et al [10] have used the attention-based bidirectional long short-term memory (BiLSTM) network combined with an attention mechanism model, which is based on advanced feature classification and prediction.…”

“…Nurhasanah et al [12] received a model of interest from the Banda Aceh city students through a binary logistic regression approach, and its results show that the factors that influence students' interest in continuing their studies at the Universitas Syiah Kuala are the ability to be affiliated with others, goals, and expectations. Wang et al [13] have conducted a student survey and analyzed the results of professional English, which provided strong support for teaching reform. Yan and Liu, Sugilar et al and Okewu et al [14][15][16] have also made progress in quantitative analysis of student data and research on student behavior and achievement.…”