Machine Learning for Automated Child Reading Assessment and Intervention

Supervisors:

Primary supervisor Dr Saeed Afshar

Description:

Traditional methods of assessing children's reading abilities often require substantial human effort and operate best in controlled, quiet environments. Moreover, existing machine learning models for speech recognition and assessment tend to perform poorly when faced with the dynamic, varied speech patterns of children, particularly those who speak with regional accents or in non-English languages. This project seeks to overcome these hurdles through the design and implementation of a tailored machine learning system for automated reading assessment and intervention.

Our machine learning-based system will be designed to capture and understand a child's reading in real-time, assess their abilities, identify errors, and provide valuable feedback for improvement. The system will also be developed with a strong focus on accent and language inclusivity, striving to accurately analyze and assess reading across a broad range of accents, dialects, and languages.

Outcomes:

The project aims to leverage machine learning for the development of an automated system to assess children's reading skills and provide tailored intervention strategies. The goal is to build a system that can accurately assess reading abilities, offer personalized feedback, and enhance reading skills of children across different accents, dialects, and non-English languages, even in the face of unique challenges presented by the speech patterns of young learners. This will include the following tasks:

• Literature Review: Conduct a comprehensive review of current research in child reading assessment, intervention strategies, and machine learning models for speech recognition, with a particular focus on models capable of handling varied accents, dialects, and languages.
• Data Collection and Pre-processing: Collect and preprocess diverse reading samples from children across different accents, dialects, and languages. Ensure the collected data has enough variation and complexity to train a robust machine learning model.
• Model Design and Training: Design and train machine learning models for real-time speech recognition, reading assessment, and feedback generation.
• Validation and Optimization: Test the models with different types of reading errors and various accents, dialects, and languages. Depending on the results, optimize the models for improved accuracy and efficiency.
• Evaluation: Compare the performance of the developed system against traditional methods of reading assessment and intervention.
• Communication and Publication: Write up the results in a format suitable for publication in a scientific journal. Present the results at relevant conferences and workshops.

Eligibility criteria:

Experience in Python or other languages commonly used in machine learning is necessary. Familiarity with various machine learning frameworks, data preprocessing techniques, and experience with speech recognition models is beneficial. Knowledge of reading assessment methods, intervention strategies, and an understanding of linguistics or education will be advantageous.