TY - GEN
T1 - Learning personal conscientiousness from footprints in e-learning systems
AU - Ting, Lo Pang Yun
AU - Teng, Shan Yun
AU - Chuang, Kun Ta
AU - Lim, Ee Peng
N1 - Funding Information:
VI. CONCLUSIONS In this paper, we investigate how to infer a user’s scores of six facets for personality trait Conscientiousness based on his/her long-term learning data from an E-learning system. In the proposed HAPE framework, we take a user’s activity patterns and learning aptitudes into consideration, and also consider the action hierarchy to enhance the performance of inferring facets. In addition, to consider the importance of an activity pattern, we define the adaptive confidence for each triple (activity pattern) in the pattern relational graph to represent the possibility of a specific triple to be a fact. The experimental results prove that our method can outperform other baselines. To sum up, we conduct extensive experiments on real E-learning dataset to show the robustness of our HAPE framework. Acknowledgement: This work was supported in part by Ministry of Science and Technology, R.O.C., under Contract 107- 2221-E-006-165-MY2 and 109-2221-E-006-187-MY3. We also sincerely thank Junyi Academy for providing their valuable data.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
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U2 - 10.1109/ICDM50108.2020.00166
DO - 10.1109/ICDM50108.2020.00166
M3 - Conference contribution
AN - SCOPUS:85100905140
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1292
EP - 1297
BT - Proceedings - 20th IEEE International Conference on Data Mining, ICDM 2020
A2 - Plant, Claudia
A2 - Wang, Haixun
A2 - Cuzzocrea, Alfredo
A2 - Zaniolo, Carlo
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Data Mining, ICDM 2020
Y2 - 17 November 2020 through 20 November 2020
ER -