VLDB 2026 Research / reviewers in the wild / expert
Yuanru Tan
dblp:218/0275
· DBLP profile ↗
3ranked-venue papers
0as first author
3since 2021 · last 2025
0000-0001-5154-9667ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 3 · 3 since 2021Applied, interdisciplinary, general and emerging computing · 3 · 3 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Exploring students' epistemic orientation, learning trajectories, and outcomesabstractThe influence of students’ epistemic orientations on their learning behavior and outcomes is well-documented. However, limited research explores students’ epistemic orientations in terms of conceptual engagement and learning outcomes. This study, set within the context of higher education, examined the patterns of conceptual engagement among two performance groups and identifies differences in their epistemic orientations. Both epistemic network analysis (ENA) and ordered network analysis (ONA) methods were used. The results from the ENA revealed distinct trajectories and patterns of conceptual engagement between high-performing and low-performing students during different periods in their learning journey. High-performing students were able to establish a more interconnected and distributed epistemic network earlier than their low-performing counterparts. ONA results revealed that (1) high-performing students were more inclined to employ abstract theoretical concepts to address empirical concerns, doing so more frequently and earlier; and (2) low-performing students benefitted from forum interactions with high-performing students to expand their knowledge resources and engagement with theoretical constructs over time. These discoveries contribute to our comprehension of epistemic orientations in different learners. The implications of this study could help generate learning analytics that monitor students’ conceptual engagement in forum discussion and provide feedback to guide the design of learning. Pakon Ko, Cong Liu 0026, Nancy Law, Yuanru Tan, David Williamson Shaffer |
LAK | 4 |
| 2025 | A Dual-Method Examination of Nursing Students' Teamwork in Simulation-Based Learning: Combining CORDTRA and Ordered Network Analysis to Reveal Patterns and DynamicsabstractThis study examines nursing students’ teamwork during a simulated pediatric scenario by combining Chronologically Ordered Representations of Discourse and Tool-Related Activity (CORDTRA) with Ordered Network Analysis (ONA). CORDTRA revealed each dyad's progression and critical moments during the scenario, while ONA illustrated how roles were divided. Our findings show that patient and parent interactions, education, and assessments were typically shared between students, whereas technical tasks such as dosage calculations were led by one student with support from the other. These findings highlight the nuanced ways in which manikin-based simulations foster essential teamwork skills, such as communication, task delegation, and problem-solving. This study highlights the methodological benefit of integrating CORDTRA and ONA to capture both temporal and relational dynamics, along with the practical implication that targeted feedback and debriefing informed by these approaches can enhance nursing students’ individual and team performance, and by extension their practice readiness. Mamta Shah, Yuanru Tan, Brendan R. Eagan, Brittny Chabalowski, Yahan Chen |
LAK | 2 |
| 2023 | Analysing Verbal Communication in Embodied Team Learning Using Multimodal Data and Ordered Network Analysis
Linxuan Zhao, Yuanru Tan, Dragan Gasevic, David Williamson Shaffer, Lixiang Yan, Riordan Alfredo, Xinyu Li 0004, Roberto Martínez-Maldonado |
AIED | 2 |