ABSTRACT
With the rapid development of Internet technology and the reform of the education model, online education has been widely recognized and applied. In the process of online learning, various types of browsing behavior characteristic data such as learning engagement and attitude will be generated. These learning behaviors are closely related to academic performance. In-depth exploration of the laws contained in the data can provide teaching assistance for education administrators. In this paper, the random forest algorithm is used to determine the importance of factors for the relationship between 11 learning behavior data and students' psychological quality test data, a total of 12-dimensional feature data and grades, and extracts six factors that have a greater impact on grades. Through the research of this paper, the method of random forest is innovatively used, and it is found that the psychological factor is one of the six important factors. This paper innovatively uses BP neural network as the prediction model, takes six important factors as input, and establishes a complete method of online learning performance prediction. The research in this paper can help teachers monitor students' learning status, detect abnormal learning behaviors and problems in time, and make timely and effective teaching interventions and adjustments in advance according to the abnormal status of students found.
ABSTRACT
Objective To investigate the current learning status of medical students and related influencing factors, and to improve the quality of medical education. Methods The method of formative assessment and summative assessment was used. Questionnaire survey and data collection were performed among 11760 students from 11 medical colleges and universities in China to analyze their learning motivations, learning attitudes, academic record, and expectations. An analysis of variance and the t-test were used for comparison of general demographic factors. Results The overall score of learning motivations was (1.95±0.57) points (with a full score of 3 points), while there were significant differences between the colleges and universities in different regions , the colleges/universities at different levels , and different specialties (F=49.694, 12.354, and 8.094, P<0.01). The overall score of self-appraised learning attitudes was (2.68±0.78) points (with a full score of 4 points), and there were significant differences between the medical students from different regions, different types of colleges/universities, and colleges at different levels, between the medical students with different sexes, specialties, and grades, and between the military and non-military medical students (F/t=49.476, 19.241, 57.275, 11.513, 4.660, 30.177, and 42.788, P<0.01). The score of expectations for academic record was (2.14±0.95) points (with a full score of 4 points), and there were significant differences between different groups of medical students (F/t=458.503, 149.679, 553.090, 409.184, 29.584, 199.268, and 821.113, P<0.01). The score of overall academic record was (2.43±0.79) points (with a full score of 4 points), and there were significant differences between different groups of medical students (F/t=44.938, 20.305, 47.850, 45.174, 3.769, 20.819, and 81.909, P<0.01). There were significant differences in academic record between the medical students with different learning motivations , expectations , and self-appraised learning attitudes ( F/t=78 . 845 , 1146.000, and 715.012, P<0.01). Conclusion Medical students in China generally have good learning status, which is greatly influenced by general demographic factors. The academic score of medical students in China is positively correlated with learning expectations and attitudes , and learning motivations have a special effect on academic record.
