VLDB 2026 Research / reviewers in the wild / expert
Mamta Shah
dblp:141/1718
· DBLP profile ↗
7ranked-venue papers
4as first author
5since 2021 · last 2025
0000-0002-4932-2831ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 7 · 4 first-author · 5 since 2021Human-computer interaction and ubiquitous computing · 6 · 3 first-author · 4 since 2021Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 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 | 1 |
| 2024 | ChatGPT for Education Research: Exploring the Potential of Large Language Models for Qualitative Codebook Development
Amanda Barany, Nidhi Nasiar, Chelsea Porter, Andres Felipe Zambrano, Juliana Ma. Alexandra L. Andres, Dara Bright, Mamta Shah, Xiner Liu, Sabrina Gao, Jiayi Zhang 0004, Shruti Mehta, Jaeyoon Choi, Camille Giordano, Ryan Baker 0001 |
AIED (2) | 7 |
| 2024 | Examining Student Engagement in Online Learning Platforms for Promoting Exam Readiness and Success in Undergraduate Nursing EducationabstractPromoting students' readiness and first-time success on the National Council Licensure Examination for Registered Nurses (NCLEX-RN) is an important driver of investigations and interventions in undergraduate nursing education. However, few studies have linked nursing students' engagement in online learning platforms to their exam performance. We address this gap in the field by applying feature engineering and prediction modeling to engagement and outcome data available for approximately 600 students enrolled in the Bachelor of Science in Nursing program for registered nurses (BSN-RN) from 2015-2022 at a mid-size private university in the Southern U.S. Mamta Shah, Ryan Baker 0001, Peter Granville, Kimberly Sharp |
L@S | 1 |
| 2022 | Undergraduate Nursing Students' Experiences and Perceptions of Self-Efficacy in Virtual Reality SimulationsabstractImmersive virtual reality (VR) simulations are gaining prominence in undergraduate nursing education because they (a) can mimic the dynamism of clinical settings for students, (b) enable educators to scaffold theory-practice integration and multiple skill development, and (c) provide programs with a costeffective curricular solution for supporting clinical practice-readiness. In this paper, we investigate first- and second-year nursing students’ experiences with Simulation Learning System with Virtual Reality (SLS with VR) during a 7-week study in Fall 2020 and assess change in their self-efficacy from pre-post. An inductive thematic analysis of students’ responses revealed that they experienced a heightened sense of engagement in VR, and SLS with VR afforded roleplaying in authentic clinical situations. A statistically significant improvement was found in students’ ratings of self-efficacy variables such as their understanding of content, ability to make clinical judgments, caring for patients, working in teams, and ensuring safety and quality in clinical situations. These findings contribute to a growing body of literature that is focused on supporting nursing education stakeholders to understand and embrace the effectiveness of VR in preparing future nurses for patient well-being and the complexities of health care settings. Mamta Shah, Christine Gouveia, Benjamin Babcock |
iLRN | 1 |
| 2021 | Modeling Educator Use of Virtual Reality Simulations in Nursing Education Using Epistemic Network AnalysisabstractSimulations are widely adopted in undergraduate nursing education because they offer low-risk, experiential ways to expose pre-licensure students to clinical environments, and to situate the development of requisite knowledge and skills for patient care. Virtual reality (VR) simulations present novel opportunities for clinical education. Research in this area is burgeoning around questions related to perception about VR modality, adoption of the technology, and educational outcomes VR simulations can help facilitate. In this paper, we demonstrate the application of epistemic network analysis (ENA), a quantitative ethnography (QE) technique, to model how one nursing educator facilitated clinical judgment, and nurtured quality and safety education for nurses' competencies through the use of the Simulation Leaning System with Virtual Reality (SLS with VR). We modeled the discourse obtained from three simulation sessions in October and November 2020, all involving a fundamentals scenario requiring second-year nursing students to practice basic assessment and care management. Our work aims to advance research in healthcare education, particularly nursing education, using immersive learning environments by way of applying theory-backed learning analytic techniques. Mamta Shah, Amanda Siebert-Evenstone, Brendan R. Eagan, Roxanne Holthaus |
iLRN | 1 |
| 2020 | Design-Based Research Iterations of a Virtual Learning Environment for Identity ExplorationabstractThis paper reports the iterative design-based research and implementation of Virtual Ci1ity Planning, a course that leveraged a virtual learning environment (VLE) and supportive classroom curricula to encourage students' exploration of environmental science and urban planning identities. Iterative course design and assessment was informed by Projective Reflection - a theoretical and methodological framework that conceptualizes learning as a role-specific process of identity exploration over time. This work describes the cyclical process of contextual analysis, design and implementation, and efficacy evaluation across three sessions of Virtual City Planning, which were implemented in a science museum with 57 high school students. The design case demonstrates how each session was modified to adapt to contextual needs and encourage deeper and more integrated processes of identity exploration as defined by Projective Reflection. The work concludes with lessons learned for future research on identity exploration in VLEs. Amanda Barany, Aroutis Foster, Mamta Shah |
iLRN | 3 |
| 2018 | Virtual Learning Environments for Promoting Self Transformation: Iterative Design and Implementation of Philadelphia Land Science
Aroutis Foster, Mamta Shah, Amanda Barany, Mark Eugene Petrovich Jr., Jessica Cellitti, Migela Duka, Zach Swiecki, Amanda Siebert-Evenstone, Hannah Kinley, Peter Quigley, David Williamson Shaffer |
iLRN | 2 |