The requirement of performing assessments continually on a larger scale necessitates the implementation of automated systems for evaluation of the learners' responses to free-text questions. We target children of age group 8-14 years and use an ASR integrated assessment app to crowdsource learners' responses to free text questions in Hindi. The app helped collect 39641 user answers to 35 different questions of Science topics. Since the users are young children from rural India and may not be well-equipped with technology, it brings in various noise types in the answers. We describe these noise types and propose a preprocessing pipeline to denoise user's answers. We showcase the performance of different similarity metrics on the noisy and denoised versions of user and model answers. Our findings have large-scale applications for automated answer assessment for school children in India in low resource settings.

No abstract

No abstract

1One out of four children in India are leaving grade eight without basic reading skills. Measuring the reading levels in a vast country like India poses significant hurdles. Recent advances in machine learning opens up the possibility of automating this task. However, the datasets of children's speech are not only rare but are primarily in English. To solve this assessment problem and advance deep learning research in regional Indian languages, we present the ASER dataset of children in the age group of 6-14. The dataset consists of 5,301 subjects generating 81,330 labeled audio clips in Hindi, Marathi and English. These labels represent expert opinions on the child's ability to read at a specified level. Using this dataset, we built a simple ASR-based classifier. Early results indicate that we can achieve a prediction accuracy of 86% for the English language. Considering the ASER survey spans half a million subjects, this dataset can grow to those scales.