Career Choice Prediction Based on Campus Big Data—Mining the Potential Behavior of College Students
Source abstract
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.
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Research context
OpenAlex topics: Online Learning and Analytics · Higher Education Research Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Big Data Research Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Faculty of Business and Economics, University of Hong Kong, Hong Kong 999077, China
Center for Statistical Research, Southwestern University of Finance and Economics, Chengdu 611130, China
Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Funding
the National Key Research and Development Program of China · the National Natural Science Foundation of China
References (25)
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