Abstract
Career choice has a pivotal role in college students’ life planning. In the past, professional career appraisers used questionnaires or diagnoses to quantify the factors potentially influencing career choices. However, due to the complexity of each person’s goals and ideas, it is difficult to properly forecast their career choices. Recent evidence suggests that we could use students’ behavioral data to predict their career choices. Based on the simple premise that the most remarkable characteristics of classes are reflected by the main samples of a category, we propose a model called the Approach Cluster Centers Based On XGBOOST (ACCBOX) model to predict students’ career choices. The experimental results of predicting students’ career choices clearly demonstrate the superiority of our method compared to the existing state-of-the-art techniques by evaluating on 13 M behavioral data of over four thousand students.
Highlights
According to Erikson’s theory [1], identity development primarily relates to career identity, which is mainly developed during adolescence
We propose a new regularization method to compensate for the gap between the examples of students and prototypical cluster centers
We have studied college students’ career choices based on their professional skills, behavior regularity, and other related behaviors
Summary
According to Erikson’s theory [1], identity development primarily relates to career identity, which is mainly developed during adolescence. This means that all behavioral data of students on campus can be recorded in real time through the campus information system. Using behavior data to predict students’ career choices is a challenging task. As there is aggregation in student groups [10], cluster centers is used to help the model capture information in behavioral data. We propose a new regularization method to compensate for the gap between the examples of students and prototypical cluster centers. In order to predict students’ career choices based on behavior data, data mining approaches such as feature engineering [12] is introduced. The cluster center can be a new instance with the average label Such prototype approach brings multiple important advantages for multiclass learning. Framework of the proposed Approach Cluster Centers Based On XGBOOST (ACCBOX) model
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