VLDB 2026 Research / reviewers in the wild / expert
Robert G. Moulder
dblp:332/2573
· DBLP profile ↗
4ranked-venue papers
2as first author
4since 2021 · last 2025
0000-0001-7504-9560ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 4 · 2 first-author · 4 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 2 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Improving Tutor Discourse Practices via AI-Enhanced Coaching: A Piecewise Latent Growth Curve Modeling Approach
Sandra Sawaya, Jennifer Jacobs 0002, Robert G. Moulder, Chelsea Chandler, Brent Milne, Tom Fischaber, Sidney K. D'Mello |
AIED (4) | 3 |
| 2023 | Recurrence Quantification Analysis of Eye Gaze Dynamics During Team CollaborationabstractShared visual attention between team members facilitates collaborative problem solving (CPS), but little is known about how team-level eye gaze dynamics influence the quality and successfulness of CPS. To better understand the role of shared visual attention during CPS, we collected eye gaze data from 279 individuals solving computer-based physics puzzles while in teams of three. We converted eye gaze into discrete screen locations and quantified team-level gaze dynamics using recurrence quantification analysis (RQA). Specifically, we used a centroid-based auto-RQA approach, a pairwise team member cross-RQAs approach, and a multi-dimensional RQA approach to quantify team-level eye gaze dynamics from the eye gaze data of team members. We find that teams differing in composition based on prior task knowledge, gender, and race show few differences in team-level eye gaze dynamics. We also find that RQA metrics of team-level eye gaze dynamics were predictive of task success (all ps < .001). However, the same metrics showed different patterns of feature importance depending on predictive model and RQA type, suggesting some redundancy in task-relevant information. These findings signify that team-level eye gaze dynamics play an important role in CPS and that different forms of RQA pick up on unique aspects of shared attention between team-members. Robert G. Moulder, Brandon M. Booth, Angelina Abitino, Sidney K. D'Mello |
LAK | 1 |
| 2023 | 'Location, Location, Location': An Exploration of Different Workplace Contexts in Remote Teamwork during the COVID-19 PandemicabstractMuch emphasis has been placed on how the affordances and layouts of an office setting can influence co-worker interactions and perceived team outcomes. Little is known, however, whether perceptions of teamwork and team conflict are affected when the location of work changes from the office to the home. To address this gap, we present findings from a ten-week,in situ study of 91 information workers from 27 US-based teams. We compare three distinct work locations---private and shared workspaces at home as well at the office---and explore how each location may impact individual perceptions of teamwork. While there was no significant association with participants' perceptions of teamwork, results revealed associations of work location with team conflict: participants who worked in a private room at home reported significantly lower team conflict compared to those working in the office. No difference was found for the office and the shared workspace. We further found that the influence of work location on team conflict interacted with job decision latitude and the level of task interdependence among co-workers. We discuss practical implications for full-time work from home (WFH) on teams. Our study adds an important environmental dimension to the literature on remote teaming, which in turn may help organizations as they consider, prepare, or implement more permanent WFH and/or hybrid work policies in the future. Thomas Breideband, Robert G. Moulder, Gonzalo J. Martínez, Megan Caruso, Gloria Mark, Aaron Striegel, Sidney K. D'Mello |
Proc. ACM Hum. Comput. Interact. | 2 |
| 2022 | Assessing Multimodal Dynamics in Multi-Party Collaborative Interactions with Multi-Level Vector AutoregressionabstractMulti-level vector autoregression (mlVAR) is a recently developed dynamic network model for assessing multimodal temporal data streams derived from multiple users over time. Importantly, mlVAR facilitates investigations into highly complex collaborative interactions within a unified framework. In order to demonstrate the utility of mlVAR for understanding the temporal dynamics of multimodal multi-party (MMP) interactions, we apply it to 9 signals measured from 201 users (67 triads) who engaged in a 15-minute collaborative problem solving task. Measured signals reflect participants’ affective states (positive valence and negative valence), physiological states (skin conductance and heart rate), attention (gaze fixation duration and gaze dispersion), nonverbal communication (head acceleration and facial expressiveness), and verbal communication (speech rate). Using node-level metrics of in-strength, out-strength, and synchrony, we show that mlVAR is capable of teasing apart complex role-based dynamics (controller, primary contributor, or secondary contributor) between participants. Our findings also provide evidence for a complex feedback system between individuals where internal states (i.e., skin conductance) are influenced by external signals of shared attention and communication (i.e., gaze and speech). Robert G. Moulder, Nicholas D. Duran, Sidney K. D'Mello |
ICMI | 1 |