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William Hanna

dblp:341/8121 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
0000-0003-1855-9542ORCID · reported

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 2021Human-computer interaction and ubiquitous 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.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 100%
Artificial intelligence
1 paper
Deep learning architectures and training · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computing education
AI education
0.912025
Learning to Think like a Neuron in Middle School · AAAI 2025
Machine learning › Deep learning architectures and training › feedforward neural network
linear threshold unit
0.312025
Learning to Think like a Neuron in Middle School · AAAI 2025

Methods — techniques the papers use, named apart from their topics

visualization · 1.7scaffolding · 1.7
YearPublicationVenuePosition
2025 Learning to Think like a Neuron in Middle School
abstract
Neuron Sandbox is a browser-based tool that helps middle school students grasp basic principles of neural computation. It simulates a linear threshold unit applied to binary decision problems, which students solve by adjusting the unit's threshold and/or weights. Although Neuron Sandbox provides extensive visualization aids, solving these problems is challenging for students who have not yet been exposed to algebra. We collected survey, video, and worksheet data from 21 seventh grade students in two sections of an AI elective, taught by the same teacher, that used Neuron Sandbox. We present a scaffolding strategy that proved effective at guiding these students to achieve mastery of these problems. While the amount of scaffolding required was more than we originally anticipated, by the end of the exercise students understood the computation that linear threshold units perform and were able to generalize their understanding of the worksheet’s "solve for threshold" strategy to also solve for weights.
David S. Touretzky, Christina Gardner-McCune, William Hanna, Angela Chen, Neel Pawar
AAAI3
2023 Co-Designing an AI Curriculum with University Researchers and Middle School Teachers
abstract
Over the past year, our AI4GA team of university faculty and middle school teachers have co-designed a middle school AI curriculum. In this poster we share how we used co-design both as a tool for collaboratively developing engaging AI activities and as a mechanism for mutual professional development. We explain our co-design process, give examples of curriculum materials provided to teachers, and showcase several teacher-created activities. We believe this approach to curriculum development centers the lived experiences of teachers and leverages the knowledge and expertise of university researchers to create high quality and engaging AI learning experiences for K-12 students.
Christina Gardner-McCune, David S. Touretzky, Bryan Cox, Judith Uchidiuno, Yerika Jimenez, Betia Bentley, William Hanna, Amber Jones
SIGCSE (2)7