III: Small: Collaborative Research: Harnessing Big Data for Improving Career Mobility

III：小：协作研究：利用大数据提高职业流动性

• 批准号: 2006387
• 负责人: Xiaodong Lin
• 金额: $ 25万
• 依托单位: Rutgers University Newark
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006387&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Harnessing

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.

美国大学生的职业发展和工作流动性面临严峻挑战，这对他们的长期职业成功至关重要，特别是在全球大流行时期。事实上，经常困扰学生的问题包括下一步选择什么职业，如何为未来的新工作更新技能，以及应该抓住哪些学习机会。这些挑战在许多大学不同专业和社会经济地位的不同学生群体中越来越多地被观察到。该项目收集和分析学术课程和学生职业生涯数据，发现大学课程和学生职业发展的有用模式，研究学生的职业选择，并制定复杂的解决方案，以提高他们的职业流动性。这项研究为数据挖掘、机器学习、教育和职业数据分析领域做出了重大贡献。本研究的成果可以为理解和提高大学生的职业成功提供新的思路，为大学生的职业发展决策提供有用的见解和工具，并增强高校就业和学术咨询机构的服务能力。该项目通过开发新的课程模块，将研究与教育相结合，让研究生和本科生参与研究，并为当地K-12学生提供研究展示。本研究的主要目的有三：一是挖掘和揭示大学课程与毕业生职业发展之间的语义和模式;二是研究大学毕业生的职业选择;三是研究提高大学毕业生职业流动性的解决方案。为了实现第一个SA，该项目开发了一种新颖的上下文感知深度学习方法，用于从异构文本数据中挖掘语义，并发现有见地的水平和垂直模式。为了解决第二个SA问题，本项目挖掘了多尺度职业路径模式，并开发了一种新的分层神经网络方法来建模和预测毕业生的职业选择。为了实现第三个SA，本项目开发了新的强化学习方法，为毕业生和注册学生推荐学习项目。 该项目的成果将以同行评审的出版物、公开数据集、教程、研讨会和讲习班的形式传播。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Multi-Faceted Knowledge-Driven Pre-Training for Product Representation Learning

• DOI: 10.1109/tkde.2022.3200921
• 发表时间: 2023-07
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Denghui Zhang;Yanchi Liu;Zixuan Yuan;Yanjie Fu;Haifeng Chen;Hui Xiong

MANE: Organizational Network Embedding With Multiplex Attentive Neural Networks

• DOI: 10.1109/tkde.2022.3140866
• 发表时间: 2023-04
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Yuyang Ye;Zheng Dong;Hengshu Zhu;Tong Xu;Xin Song;Runlong Yu;Hui Xiong

Scalable Heterogeneous Graph Neural Networks for Predicting High-potential Early-stage Startups

• DOI: 10.1145/3447548.3467383
• 发表时间: 2021-08
• 期刊: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Shengming Zhang;Hao Zhong;Zixuan Yuan;Hui Xiong