IIS: Collaborative Research: Harnessing Big Data for Improving Career Mobility
Investigator(s)
| Hui Xiong (hxiong@rutgers.edu) | Principle Investigator |
Award Information
| Award Number | 2006387 |
| Duration | October 1, 2020 - September 30, 2023 (Estimated) |
| Title | IIS: Collaborative Research: Harnessing Big Data for Improving Career Mobility |
| NSF Program(s) | IIS |
Project Abstract
U.S. college students are facing critical challenges for their career development and job mobility, which is vital for their long-term career success, especially during global pandemic times. Indeed, the questions that often puzzle students include what career choices to choose next, how to update skills for future new jobs, and which learning opportunities to take. These challenges have been increasingly observed among different groups of students in different majors and socioeconomic statuses at many universities. This project collects and analyzes academic curriculum and student career data, discovers useful patterns about college curriculum and students’ career development, studies students’ career choices, and develops sophisticated solutions to improve their career mobility. This study makes significant contributions to the fields of data mining, machine learning, and education and career data analytics. The results of this project can bring new ways for understanding and improving college graduates’ career success, provide useful insights and tools for students to make their decisions on career development, and augment the service capability of college career and academic advising offices. This project integrates the research with education through new course module development, involving graduate and undergraduate students in research, and research showcases for local K-12 students.
This project focuses on the following three specific aims (SA): mining and informing useful semantics and patterns about college curriculum and graduates’ career development; studying the career choices of college graduates; and developing sophisticated solutions to improve career mobility of college graduates. To achieve the first SA, this project develops a novel context-aware deep learning method for mining semantics from heterogenous textual data and discovers insightful horizontal and vertical patterns. To solve the second SA, this project mines multiple-scale career path patterns and develops a new hierarchical neural network method to model and predict graduates’ career choices. To achieve the third SA, this project develops novel reinforcement learning methods to recommend learning items for both graduated students and enrolled ones. The results of this project will be disseminated in the form of peer-reviewed publications, publicly available data set, tutorials, seminars, and workshops.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Publications
- Shengming Zhang, Hao Zhong, Zixuan Yuan, Hui Xiong. Scalable Heterogeneous Graph Neural Networks for Predicting High-potential Early-stage Startups. The 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021).