Literacy Screening
Rank:
Scoring audio clips from literacy screeners, helping educators provide effective early literacy intervention
About the competition
Literacy is a fundamental skill that shapes personal growth, academic success, career opportunities, and societal participation. Yet, 250 million children worldwide struggle to meet basic reading standards. Addressing literacy gaps early, starting in preschool, has proven to be a promising approach.
Teachers need reliable tools to identify students who need support, but current manual literacy assessments are slow and inconsistent. Machine learning can improve this process, providing faster, more accurate results.
This competition aims to develop an AI model to score audio recordings from literacy screener exercises for children through 3rd grade, helping educators pinpoint students who would benefit from early literacy intervention.
Reach Every Reader
The competition is organized by “Reach Every Reader,” a collaboration between Harvard, MIT, and Florida State University. This initiative unites educators, researchers, and technologists to address literacy challenges at scale.
Focused on early identification and personalized support, it combines research, evidence-based practices, and technology to empower teachers, engage families, and inspire students, aiming for a world where every child learns to read.
Relevance
Literacy is a critical skill for personal and academic growth, yet many children in the U.S. face significant challenges in developing strong language abilities.
By automating this process with machine learning, we can provide teachers with fast, reliable insights to identify students who need support. This has the potential to improve early literacy intervention, reduce disparities in education, and set children on a path to greater success in school and beyond.
Technical Details
The objective of this competition is to develop an automated scoring model that evaluates audio recordings from literacy assessments given to students in kindergarten through 3rd grade.
A key challenge in this competition is developing models capable of performing Automatic Speech Recognition (ASR) on children's speech. Due to factors like smaller vocal tracts and unpredictable pronunciations, children’s speech introduces greater acoustic variability, making it difficult for ASR systems designed for adult voices to perform effectively. Additionally, the lack of diverse datasets for children's speech further complicates the development of effective ASR systems.
The team is exploring various machine learning techniques, such as encoder-decoder, Transformer models and Contrastive models for audio processing. To improve efficiency, data augmentation strategies are being used to enhance the model’s robustness and generalization capabilities.
UN Sustainable Development Goals
This competition supports UN Sustainable Development Goal #4: Quality Education. By improving early literacy screening, the AI model helps more children succeed academically, promoting educational equity and opportunity.
