Demo Data from EDBT 2026 / reviewers in the wild / expert
Sankalp Boorugu
dblp:324/0954
· 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% | |
| Theoretical computer science
1 paper |
Graph algorithms and graph theory · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computing education › programming education
block-based programming |
0.6 | 1 | 2022 | Towards an AI-Infused Interdisciplinary Curriculum for Middle-Grade Classrooms · AAAI 2022 |
Computing education › AI education
K-12 AI education |
0.6 | 1 | 2022 | Towards an AI-Infused Interdisciplinary Curriculum for Middle-Grade Classrooms · AAAI 2022 |
Graph algorithms and graph theory › graph traversal
breadth-first search |
0.2 | 1 | 2022 | Towards an AI-Infused Interdisciplinary Curriculum for Middle-Grade Classrooms · AAAI 2022 |
Methods — techniques the papers use, named apart from their topics
block-based programming · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Towards an AI-Infused Interdisciplinary Curriculum for Middle-Grade ClassroomsabstractAs AI becomes more widely used across a variety of disciplines, it is increasingly important to teach AI concepts to K-12 students in order to prepare them for an AI-driven future workforce. Hence, educators and researchers have been working to develop curricula that make these concepts accessible to K-12 students. We are designing and developing a comprehensive AI curriculum delivered through a series of carefully crafted activities in an adapted \emph{Snap!} environment for middle-grade students. In this work, we lay out the proposed content of our curriculum and present the design, development, and implementation results of the first unit of our curriculum that focuses on teaching the breadth-first search algorithm. The activities in this unit have been revised after being piloted with a single high-school student. These activities were further refined after a group of K-12 teachers examined and critiqued them during a two-week professional development workshop. Our teachers created a lesson plan around the activities and implemented that lesson in a summer workshop with 14 middle school students. Our results demonstrated that our activities were successful in helping many of the students in understanding and implementing the algorithm through block-based programming while extra supplementary material was needed to assist some other students. In this paper, we explain our curriculum and technology, the results of implementing the first unit of our curriculum in a summer camp, and lessons learned for future developments. Bita Akram, Spencer Yoder, Cansu Tatar, Sankalp Boorugu, Ifeoluwa Aderemi, Shiyan Jiang |
AAAI | 4 |