Demo Data from EDBT 2026 / reviewers in the wild / expert
Anyao Ma
dblp:324/1338
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computing education · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
User interface design and tools · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computing education › AI education
K-12 AI education |
0.6 | 1 | 2022 | Paving the Way for Novices: How to Teach AI for K-12 Education in China · AAAI 2022 |
User interface design and tools › visual programming
visual programming environment |
0.2 | 1 | 2022 | Paving the Way for Novices: How to Teach AI for K-12 Education in China · AAAI 2022 |
Methods — techniques the papers use, named apart from their topics
course design · 1.1block-based programming · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Paving the Way for Novices: How to Teach AI for K-12 Education in ChinaabstractIn response to the trend that artificial intelligence (AI) is becoming the main driver for social and economic development, enhancing the readiness of learners in AI is significant and important. The state council and the ministry of education of China put AI education for K-12 schools on a high priority in order to foster local AI talents and reduce educational disparities. However, the AI knowledge and technical skills are still limited for not only students but also the school teachers. Furthermore, many local schools in China, especially in the rural areas, are lack of the necessary software and hardware for teaching AI. Hence, we designed and implemented a structured series of AI courses, built on an online block-based visual programming platform. The AI courses are free and easily accessible for all. We have conducted the experimental classes in a local school and collected the results. The results show that the learners in general gained significant learning progress on AI knowledge comprehension, aroused strong interests in AI, and increased the degree of satisfaction towards the course. Especially, our practices significantly increased computational thinking of the students who were initially staying at a lower level. Jiachen Song, Linan Zhang, Jinglei Yu, Anyao Ma, Yu Lu 0003 |
AAAI | 5 |