DSpace Collection:
http://hdl.handle.net/10722/44478
2024-03-28T17:06:54ZEmotional Intelligence in Science
http://hdl.handle.net/10722/342078
Title: Emotional Intelligence in Science
Authors: Yang, Yunhan; Zhang, Chenwei; Ding, Ying
Abstract: <p>This is the first on-going project presenting a four-dimension metric to identify scholars' emotional intelligence (EI), which has yet to receive much attention. Current study proposes a data-driven metric rather than a subjective survey to reflect EI's first dimension, self-awareness. By employing paired-T-tests on the DBLP dataset, we found that highly self-aware scholars are more likely to strive to improve with higher stability, leading to higher productivity and impact. Meanwhile, they have a more significant number of higher diverse collaborators. This research highlights the importance of one's self-awareness to his/her scientific performance.</p>2023-10-22T00:00:00ZUnderstanding scientific collaboration from the perspective of collaborators and their network structures
http://hdl.handle.net/10722/342079
Title: Understanding scientific collaboration from the perspective of collaborators and their network structures
Authors: Zhang, Chenwei; Bu, Yi; Ding, Ying
Abstract: <p>Scientific collaboration is one of the key factors to trigger innovations. Coauthorship networks have been taken as representations of scholars’ collaboration for a long time. This study investigates how the authors’ attributes and the coauthorship network structures simultaneously influence the scientific collaboration among them. Exponential random graph models (ERGMs) are adopted in this research. We find that an author has a propensity to coauthor with the other scholar if they have different levels of productivity. We also find that the effect of network’s transitivity strongly influence authors’ collaboration. We demonstrate that taking the effects from both authors’ attributes and the network structures into consideration helps gain a comprehensive understanding of scientific collaboration.</p>2016-03-15T00:00:00ZUsing multimodal learning analytics to examine learners’ responses to different types of background music during reading comprehension
http://hdl.handle.net/10722/342076
Title: Using multimodal learning analytics to examine learners’ responses to different types of background music during reading comprehension
Authors: Hu, Xiao; QUE, Ying; NG, Tzi Dong; Mak, MKF; Yip, PTY2024-03-18T00:00:00ZHeterogenous Network Analytics of Small Group Teamwork: Using Multimodal Data to Uncover Individual Behavioral Engagement Strategies.
http://hdl.handle.net/10722/342077
Title: Heterogenous Network Analytics of Small Group Teamwork: Using Multimodal Data to Uncover Individual Behavioral Engagement Strategies.
Authors: Feng, S; Yan, L; Zhao, L; Martinez-Maldonado, R, Gašević, D
Abstract: <p>Individual behavioral engagement is an important indicator of active learning in collaborative settings, encompassing multidimensional behaviors mediated through various interaction modes. Little existing work has explored the use of multimodal process data to understand individual behavioral engagement in face-to-face collaborative learning settings. In this study we bridge this gap, for the first time, introducing a heterogeneous tripartite network approach to analyze the interconnections among multimodal process data in collaborative learning. Students’ behavioral engagement strategies are analyzed based on their interaction patterns with various spatial locations and verbal communication types using a heterogeneous tripartite network. The multimodal collaborative learning process data were collected from 15 teams of four students. We conducted stochastic blockmodeling on a projection of the heterogeneous tripartite network to cluster students into groups that shared similar spatial and oral engagement patterns. We found two distinct clusters of students, whose characteristic behavioural engagement strategies were identified by extracting interaction patterns that were statistically significant relative to a multinomial null model. The two identified clusters also exhibited a statistically significant difference regarding students’ perceived collaboration satisfaction and teacher-assessed team performance level. This study advances collaboration analytics methodology and provides new insights into personalized support in collaborative learning.</p>2024-03-18T00:00:00Z