# Gamifying Math Education using Object Detection

arXiv: https://arxiv.org/abs/2304.06270 · HTML: https://mlsanity.com/paper/2304.06270/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [A General and Adaptive Robust Loss Function](https://mlsanity.com/paper/1701.03077/) (2017)
- [Designing Deep Convolutional Neural Networks for Continuous Object Orientation Estimation](https://mlsanity.com/paper/1702.01499/) (2017)

## Built on by (most cited first, 2 of 2)

- [Multimodality of AI for Education: Towards Artificial General Intelligence](https://mlsanity.com/paper/2312.06037/) (2023)
- [Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home](https://mlsanity.com/paper/2306.00482/) (2023)

## Abstract (arXiv)

Manipulatives used in the right way help improve mathematical concepts leading to better learning outcomes. In this paper, we present a phygital (physical + digital) curriculum inspired teaching system for kids aged 5-8 to learn geometry using shape tile manipulatives. Combining smaller shapes to form larger ones is an important skill kids learn early on which requires shape tiles to be placed close to each other in the play area. This introduces a challenge of oriented object detection for densely packed objects with arbitrary orientations. Leveraging simulated data for neural network training and light-weight mobile architectures, we enable our system to understand user interactions and provide real-time audiovisual feedback. Experimental results show that our network runs real-time with high precision/recall on consumer devices, thereby providing a consistent and enjoyable learning experience.