%0 Journal Article %1 Wu2019 %A Wu, Wen %A Chen, Li %A Yang, Qingchang %A Li, You %D 2019 %J International Journal of Artificial Intelligence in Education %K individual-differences log-mining %N 2 %P 189--216 %R 10.1007/s40593-018-00173-9 %T Inferring Students' Personality from Their Communication Behavior in Web-based Learning Systems %U https://doi.org/10.1007/s40593-018-00173-9 %V 29 %X Communication tools have been popular in web-based learning systems because of their ability to promote the interaction and potentially alleviate the high dropout issue. In recent years, with the increased awareness among researchers about the individual difference of the students, more and more personalized learning supports have been developed. Although personality has been considered as a valuable personal factor being incorporated into the provision of personalized learning, existing studies mainly acquire students' personality via questionnaires, which unavoidably demands user efforts. In this paper, we are motivated to derive students' Big-Five personality from their communication behavior in web-based learning systems. Concretely, we first identify a set of features that are significantly influenced by students' personality, which not only include their communication activities carried out in both synchronous and asynchronous web-based learning environment, but also their linguistic content in conversational texts. We then develop inference model to unify these features for determining students' five personality traits, and find that students' usage of different communication tools can be effective in predicting their Big-Five personality.

@article{Wu2019, abstract = {Communication tools have been popular in web-based learning systems because of their ability to promote the interaction and potentially alleviate the high dropout issue. In recent years, with the increased awareness among researchers about the individual difference of the students, more and more personalized learning supports have been developed. Although personality has been considered as a valuable personal factor being incorporated into the provision of personalized learning, existing studies mainly acquire students' personality via questionnaires, which unavoidably demands user efforts. In this paper, we are motivated to derive students' Big-Five personality from their communication behavior in web-based learning systems. Concretely, we first identify a set of features that are significantly influenced by students' personality, which not only include their communication activities carried out in both synchronous and asynchronous web-based learning environment, but also their linguistic content in conversational texts. We then develop inference model to unify these features for determining students' five personality traits, and find that students' usage of different communication tools can be effective in predicting their Big-Five personality.}, added-at = {2019-06-27T22:58:06.000+0200}, author = {Wu, Wen and Chen, Li and Yang, Qingchang and Li, You}, biburl = {https://www.bibsonomy.org/bibtex/26569e4ec98349850bb5919178a5fb83e/brusilovsky}, day = 01, description = {Inferring Students’ Personality from Their Communication Behavior in Web-based Learning Systems | SpringerLink}, doi = {10.1007/s40593-018-00173-9}, interhash = {7b4bcdf37bd16cf5233b9a8a19fef4b9}, intrahash = {6569e4ec98349850bb5919178a5fb83e}, issn = {1560-4306}, journal = {International Journal of Artificial Intelligence in Education}, keywords = {individual-differences log-mining}, month = may, number = 2, pages = {189--216}, timestamp = {2019-06-27T22:58:06.000+0200}, title = {Inferring Students' Personality from Their Communication Behavior in Web-based Learning Systems}, url = {https://doi.org/10.1007/s40593-018-00173-9}, volume = 29, year = 2019 }