Wang Jingwei

The biology major has developed rapidly in recent years. Biology is a science that penetrates every aspect of human life and is one of the core majors in most agricultural colleges and universities. However, many teachers lack practical experience in the subject. To overcome this problem, in recent years, we have been trying to introduce new reforms into our teaching. This article provides some insight into the way that biology majors have been reformed, which will help educators in agricultural colleges and universities. At present, teachers implement the “Industrial Innovation and Entrepreneurship Talent Cultivation” model, but it is not clear whether this helps biology majors to master the course and improve their practical skills. In this study, the IIETC model is outlined, and the academic achievement and satisfaction of students taught under the IIETC model are assessed. A T-test is used to examine potential differences between IIETC and traditional teaching models. In-depth interviews and questionnaires were given to two groups of students who followed different teaching models as part of an exploratory study. The aim was to explore how effective IIETC is at helping biology majors master the course and improve students’ wellbeing. Our results show that compared with traditional teaching methods, the IIETC model has a significant positive impact on the academic performance and happiness of biology students. Students trained under the IIETC model were more active and scored more highly in their final exams. They were more likely to feel that they had achieved success and happiness through the course. The outcomes of this research reveal a novel teaching reform that improved students’ enthusiasm for innovation and entrepreneurship during the ongoing COVID-19 pandemic. The effects are very encouraging and deserve further exploration and expansion in future work.

With the emergence of numerous link prediction methods, how to accurately evaluate them and select the appropriate one has become a key problem that cannot be ignored. Since AUC was first used for link prediction evaluation in 2008, it is arguably the most preferred metric because it well balances the role of wins and the role of draws. However, in many cases, AUC does not show enough discrimination when evaluating link prediction methods, especially those based on local similarity. Hence, we propose a new metric, called W-index, which considers only the effect of wins rather than draws. Our extensive experiments on various networks show that the W-index makes the accuracy scores of link prediction methods more distinguishable, and it can not only widen the local gap of these methods but also enlarge their global distance. We further show the reliability of the W-index by ranking change analysis and correlation analysis. In particular, some community-based approaches, which have been deemed effective, do not show any advantages after our reevaluation. Our results suggest that the W-index is a promising metric for link prediction evaluation, capable of offering convincing discrimination.