Learning personal conscientiousness from footprints in e-learning systems

Lo Pang Yun Ting, Shan Yun Teng, Kun Ta Chuang, Ee Peng Lim

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

Personality inference has received widespread attention for its potential to infer psychological well being, job satisfaction, romantic relationship success, and professional performance. In this research, we focus on Conscientiousness, one of the well studied Big Five personality traits, which determines if a person is self-disciplined, organized, and hard-working. Research has shown that Conscientiousness is related to a person's academic and workplace success. For an expert to evaluate a person's Conscientiousness, long-term observation of the person's behavior at work place or at home is usually required. To reduce this evaluation effort as well as to cope with the increasing trend of human behavior turning digital, there is a need to conduct the evaluation using digital traces of human behavior. In this paper, we propose a novel framework, called HAPE, to automatically infer an individual's Conscientiousness scores using his/her behavioral data in an E-learning system. We first determine how users learn in the E-learning system, and design a novel Pattern Relational Graph Embedding method to learn the representations of users, their learning actions, and learning situations. The interaction between users, learning actions and situations characterizes the learning style of a user. Through experimental studies on real data, we demonstrate that HAPE framework outperforms the baseline methods in the Conscientiousness inference task.

原文English
主出版物標題Proceedings - 20th IEEE International Conference on Data Mining, ICDM 2020
編輯Claudia Plant, Haixun Wang, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1292-1297
頁數6
ISBN(電子)9781728183169
DOIs
出版狀態Published - 2020 11月
事件20th IEEE International Conference on Data Mining, ICDM 2020 - Virtual, Sorrento, Italy
持續時間: 2020 11月 172020 11月 20

出版系列

名字Proceedings - IEEE International Conference on Data Mining, ICDM
2020-November
ISSN(列印)1550-4786

Conference

Conference20th IEEE International Conference on Data Mining, ICDM 2020
國家/地區Italy
城市Virtual, Sorrento
期間20-11-1720-11-20

All Science Journal Classification (ASJC) codes

  • 一般工程

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