M. Parvez Rashid

dblp:245/1787 · DBLP profile ↗
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9ranked-venue papers
3as first author
8since 2021 · last 2024
0009-0007-1796-9079ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 8 · 3 first-author · 8 since 2021Databases, data management, data science and information retrieval · 1Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021
YearPublicationVenuePosition
2024 LLM-generated Feedback in Real Classes and Beyond: Perspectives from Students and Instructors
Qinjin Jia, Jialin Cui, Haoze Du, M. Parvez Rashid, Ruijie Xi, Ruochi Li, Edward F. Gehringer
EDM4
2024 On Assessing the Faithfulness of LLM-generated Feedback on Student Assignments
Qinjin Jia, Jialin Cui, Ruijie Xi, M. Parvez Rashid, Ruochi Li, Edward F. Gehringer
EDM5
2024 Generative AI for Peer Assessment Helpfulness Evaluation
Jialin Cui, Ruixuan Shang, Qinjin Jia, M. Parvez Rashid, Edward F. Gehringer
EDM5
2024 Navigating (Dis)agreement: AI Assistance to Uncover Peer Feedback Discrepancies
abstract
Engaging students in the peer review process has been recognized as a valuable educational tool. It not only nurtures a collaborative learning environment where reviewees receive timely and rich feedback but also enhances the reviewer’s critical thinking skills and encourages reflective self-evaluation. However, a common concern arises when students encounter misaligned or conflicting feedback. Not only can such feedback confuse students; but it can also make it difficult for the instructor to rely on the reviews when assigning a score to the work. Addressing this pressing issue, our paper introduces an innovative, AI-assisted approach that is designed to detect and highlight disagreements within formative feedback. We’ve harnessed extensive data from 170 students, analyzing 15,500 instances of peer feedback from a software development course. By utilizing clustering techniques coupled with sophisticated natural language processing (NLP) models, we transform feedback into distinct feature vectors to pinpoint disagreements. The findings from our study underscore the effectiveness of our approach in enhancing text representations to significantly boost the capability of clustering algorithms in discerning disagreements in feedback. These insights bear implications for educators and software development courses, offering a promising route to streamline and refine the peer review process for the betterment of student learning outcomes.
M. Parvez Rashid, Edward F. Gehringer, Hassan Khosravi
LAK1
2023 "Can we reach agreement?": A context- and semantic-based clustering approach with semi-supervised text-feature extraction for finding disagreement in peer-assessment formative feedback
M. Parvez Rashid, Divyang Doshi, Sai Venkata Vinay, Qinjin Jia, Edward F. Gehringer
EDM1
2022 Insta-Reviewer: A Data-Driven Approach for Generating Instant Feedback on Students' Project Reports
Qinjin Jia, Mitchell Young, Yunkai Xiao, Jialin Cui, M. Parvez Rashid, Edward F. Gehringer
EDM6
2022 Going beyond "Good Job": Analyzing Helpful Feedback from the Student's Perspective
M. Parvez Rashid, Yunkai Xiao, Edward F. Gehringer
EDM1
2021 ALL-IN-ONE: Multi-Task Learning BERT models for Evaluating Peer Assessments
Qinjin Jia, Jialin Cui, Yunkai Xiao, M. Parvez Rashid, Edward F. Gehringer
EDM5
2019 The design and implementation of AIDA: ancient inscription database and analytics system
abstract
This paper describes the development of AIDA, the Ancient Inscription Database and Analytics system. The AIDA system currently stores three types of ancient Minoan inscriptions: Linear A, Cretan Hieroglyph and Phaistos Disk inscriptions. In addition, AIDA provides candidate syllabic values and translations of Minoan words and inscriptions into English. The AIDA system allows the users to change these candidate phonetic assignments to the Linear A, Cretan Hieroglyph and Phaistos symbols. Hence the AIDA system provides for various scholars not only a convenient online resource to browse Minoan inscriptions but also provides an analysis tool to explore various options of phonetic assignments and their implications. Such explorations can aid in the decipherment of Minoan inscriptions.
Peter Z. Revesz, M. Parvez Rashid, Yves Tuyishime
IDEAS2