Demo Data from EDBT 2026 / reviewers in the wild / expert
Hamze Muse
dblp:336/3936
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Question answering and dialogue systems · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computing education · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Question answering and dialogue systems › question generation
educational question generation |
0.7 | 1 | 2023 | Pre-training with Scientific Text Improves Educational Question Generation (Student Abstract) · AAAI 2023 |
Methods — techniques the papers use, named apart from their topics
pre-training · 1.3large language model · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Pre-training with Scientific Text Improves Educational Question Generation (Student Abstract)abstractWith the boom of digital educational materials and scalable e-learning systems, the potential for realising AI-assisted personalised learning has skyrocketed. In this landscape, the automatic generation of educational questions will play a key role, enabling scalable self-assessment when a global population is manoeuvring their personalised learning journeys. We develop EduQG, a novel educational question generation model built by adapting a large language model. Our initial experiments demonstrate that EduQG can produce superior educational questions by pre-training on scientific text. Hamze Muse, Sahan Bulathwela, Emine Yilmaz |
AAAI | 1 |
| 2023 | Scalable Educational Question Generation with Pre-trained Language Models
Sahan Bulathwela, Hamze Muse, Emine Yilmaz |
AIED | 2 |