EDBT 2026 Demo / reviewers in the wild / expert
Renu Balyan
dblp:115/5422
· DBLP profile ↗
17ranked-venue papers
6as first author
9since 2021 · last 2025
0000-0003-1393-2416ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 13 · 4 first-author · 8 since 2021Human-computer interaction and ubiquitous computing · 8 · 2 first-author · 4 since 2021Artificial intelligence and machine learning · 4 · 2 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | LLMs for Question-Answer and Synthetic Data Generation and Evaluation
Renu Balyan, Kayla Thompson, Francisco Iacobelli |
ITS (2) | 1 |
| 2023 | Automated strategy feedback can improve the readability of physicians' electronic communications to simulated patients
Rod D. Roscoe, Renu Balyan, Danielle S. McNamara, Michelle P. Banawan, Dean Schillinger |
Int. J. Hum. Comput. Stud. | 2 |
| 2022 | Modeling One-on-one Online Tutoring Discourse using an Accountable Talk Framework
Renu Balyan, Tracy Arner, Karen Taylor, Jinnie Shin, Michelle P. Banawan, Walter L. Leite, Danielle S. McNamara |
EDM | 1 |
| 2022 | Integrating Speech Technology into the iSTART-Early Intelligent Tutoring System
Renu Balyan, Tracy Arner, Ellen Orcutt, Reese Butterfuss, Panayiota Kendeou, Danielle S. McNamara |
ITS | 1 |
| 2022 | iSTART-Early: Interactive Strategy Training for Early Readers
Panayiota Kendeou, Ellen Orcutt, Tracy Arner, Renu Balyan, Reese Butterfuss, Micah Watanabe, Danielle S. McNamara |
ITS | 5 |
| 2022 | Math Discourse Linguistic Components (Cohesive Cues within a Math Discussion Board Discourse)abstractThis study presents the results of a computational discourse analysis of discussion threads within an online Math tutoring platform. This work is theoretically motivated by prior work that established the importance of linguistic and semantic features in the discourse in mathematics education. The end goal of this study is to understand the characteristics of language that is produced and used within a discussion board for math. The discussion board corpus comprises of posts from 4,720 students, teachers, and study experts who interacted within an online teaching and learning tutoring platform for math. Linguistic profiles of the discussion board discourse were estimated using Principal Component Analysis (PCA) based on Coh-Metrix linguistic features related to cohesion, language sophistication, and lexical characteristics. The PCA analysis yielded seven Math Discourse Linguistic Components, which collectively explained 49% of the variance in the dataset. Theoretical and conceptual validation of components revealed that the linguistic features align with the communication goal and the nature of mathematics. The linguistic profiles that characterized the discussion board discourse included referential cohesion, information density, instructional language, lexical variation, compare and contrast devices, explicit relations devices, and syntactic complexity. The dominance of cohesive cues within the linguistic profiles demonstrate the communication goals within the Math discourse such as elaboration, providing instruction, compare and contrast, establishing explicit relations, and presenting information. As such, these components characterize the Math Discussion Board discourse in terms of variations in cohesive and task-oriented cues within communication among students. Michelle P. Banawan, Jinnie Shin, Renu Balyan, Walter L. Leite, Danielle S. McNamara |
L@S | 3 |
| 2021 | Automated Claim Identification Using NLP Features in Student Argumentative Essays
Qian Wan 0005, Scott A. Crossley, Michelle P. Banawan, Renu Balyan, Danielle S. McNamara, Laura K. Allen |
EDM | 4 |
| 2021 | Linguistic Features of Discourse within an Algebra Online Discussion Board
Michelle P. Banawan, Renu Balyan, Jinnie Shin, Walter L. Leite, Danielle S. McNamara |
EDM | 2 |
| 2021 | Challenges and solutions to employing natural language processing and machine learning to measure patients' health literacy and physician writing complexity: The ECLIPPSE study
William Brown III 0001, Renu Balyan, Andrew J. Karter, Scott A. Crossley, Wagahta Semere, Nicholas D. Duran, Courtney R. Lyles, Jennifer Y. Liu, Howard H. Moffet, Ryane Daniels, Danielle S. McNamara, Dean Schillinger |
J. Biomed. Informatics | 2 |
| 2019 | Automated Scoring of Self-explanations Using Recurrent Neural Networks
Marilena Panaite, Stefan Ruseti, Mihai Dascalu, Renu Balyan, Danielle S. McNamara, Stefan Trausan-Matu |
EC-TEL | 4 |
| 2018 | Bring It on! Challenges Encountered While Building a Comprehensive Tutoring System Using ReaderBench
Marilena Panaite, Mihai Dascalu, Amy M. Johnson, Renu Balyan, Jianmin Dai, Danielle S. McNamara, Stefan Trausan-Matu |
AIED (1) | 4 |
| 2018 | Predicting Question Quality Using Recurrent Neural Networks
Stefan Ruseti, Mihai Dascalu, Amy M. Johnson, Renu Balyan, Kristopher J. Kopp, Danielle S. McNamara, Scott A. Crossley, Stefan Trausan-Matu |
AIED (1) | 4 |
| 2018 | Scoring Summaries Using Recurrent Neural Networks
Stefan Ruseti, Mihai Dascalu, Amy M. Johnson, Danielle S. McNamara, Renu Balyan, Kathryn S. McCarthy, Stefan Trausan-Matu |
ITS | 5 |
| 2017 | Combining Machine Learning and Natural Language Processing Approach to Assess Literary Text Comprehension
Renu Balyan, Kathryn S. McCarthy, Danielle S. McNamara |
EDM | 1 |
| 2015 | Translating noun compounds using semantic relations
Renu Balyan, Niladri Chatterjee |
Comput. Speech Lang. | 1 |
| 2013 | A Diagnostic Evaluation Approach for English to Hindi MT Using Linguistic Checkpoints and Error Rates
Renu Balyan, Sudip Kumar Naskar, Antonio Toral, Niladri Chatterjee |
CICLing (2) | 1 |
| 2011 | Context Resolution of Verb Particle Constructions for English to Hindi Translation
Niladri Chatterjee, Renu Balyan |
PACLIC | 2 |