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When reputation evaluation indicators become targets, existing indicators will lose the role of indicating the true quality; At present, the evaluation of discipline reputation mostly focuses on subjective evaluation based on objective data, and there is a dispute about reliability and validity; Due to different indicators and weight settings, it is difficult to make horizontal comparison among disciplines; The evaluation also has a certain time lag. In order to solve the above four problems, this study explores a new method of discipline reputation evaluation. Taking the business administration discipline as an example, it collects data of 5848 doctoral graduates who first entered teaching posts, establishes a directed adjacency matrix from the employment unit to the doctoral degree awarding unit, and uses the theory and method of social network analysis to conduct quantitative analysis on the doctoral mutual employment network. The results show that: (1) PhD exchange network can explain discipline reputation and is a new indicator to measure discipline reputation; (2) From the perspective of employment behavior among colleges and universities, there is horizontal flow and downward flow between the head colleges and universities, and downward flow is mainly among the middle and lower colleges. There is a time lag between college talent recruitment and academic achievement output. Therefore, the mining of the structural characteristics and network evolution trend of the PhD exchange network based on the "foot voting" of doctoral graduates is faster than the discipline ranking based on the follow-up achievement indicators to reflect the changes in the discipline quality, which can be used to warn the changes in the discipline quality., Comment: 18 pages, 2 figures

In the institutional research mode, in order to explore which characteristics are the best indicators for predicting academic risk from the student behavior data sets that have high-dimensional, unbalanced classified small sample, it transforms the academic risk prediction of college students into a binary classification task. It predicts academic risk based on the LightGBM model and the interpretable machine learning method of Shapley value. The simulation results show that from the global perspective of the prediction model, characteristics such as the quality of academic partners, the seating position in classroom, the dormitory study atmosphere, the English scores of the college entrance examination, the quantity of academic partners, the addiction level of video games, the mobility of academic partners, and the degree of truancy are the best 8 predictors for academic risk. It is contrary to intuition that characteristics such as living in campus or not, work-study, lipstick addiction, student leader or not, lover amount, and smoking have little correlation with university academic risk in this experiment. From the local perspective of the sample, the factors affecting academic risk vary from person to person. It can perform personalized interpretable analysis through Shapley values, which cannot be done by traditional mathematical statistical prediction models. The academic contributions of this research are mainly in two aspects: First, the learning interaction networks is proposed for the first time, so that social behavior can be used to compensate for the one-sided individual behavior and improve the performance of academic risk prediction. Second, the introduction of Shapley value calculation makes machine learning that lacks a clear reasoning process visualized, and provides intuitive decision support for education managers., Comment: 15 pages,7 figures

Academic lobification refers to a collection of academic performance control strategies, methods, and means that a student deliberately hides academic behaviors, or deliberately lowers academic performance, or deliberately delays academic returns for a certain long-term purpose, but does not produce academic risks. Understanding academic lobification is essential to our ability to compensate for inherent deviations in the evaluation of students' academic performance, discover gifted student, reap benefits and minimize harms. It outlines a set of questions that are fundamental to this emerging interdisciplinary research field, including research object, research question, research scope, research method, and explores the technical, legal and other constraints on the study of academic lobification., Comment: 8 pages, 2 figures

University evaluation and ranking is an extremely complex activity. Major universities are struggling because of increasingly complex indicator systems of world university rankings. So can we find the meta-indicators of the index system by simplifying the complexity? This research discovered three meta-indicators based on interpretable machine learning. The first one is time, to be friends with time, and believe in the power of time, and accumulate historical deposits; the second one is space, to be friends with city, and grow together by co-develop; the third one is relationships, to be friends with alumni, and strive for more alumni donations without ceiling., Comment: 4 pages, 1 figure