SMU Journal of Undergraduate Research


Sisi KangFollow


This paper aims to report the conceptualization of a web-based Automated Speech Recognition Scoring System, project MELVA-S (Measuring the English Language Vocabulary Acquisition of Latinx Bilingual Students), to measure the science vocabulary of second- and third-grade Latinx students. ELVA (English Learner Vocabulary Acquisition First Iteration) and ELVA-2 (English Learner Vocabulary Acquisition Second Iteration) focused on student’s learning and comprehension on science vocabularies. Both of the iterations are the foundation to build MELVA-S, which intends to measure and evaluate student’s answers with greater accuracy with Machine Learning. As a web-based agent, this system increases satisfaction for both teachers’ and students’ User Experience (UX) from content, design, and engineering perspectives. The project utilized a design-thinking approach and prototyped both the algorithm and the automated system interfaces. Future iterations of ELVA-2 and MELVA-S could consider adopting a Human-Centered Machine Learning approach, implemented with incremental improvements that include evaluation and testing with users, to keep enhancing both usability and functionality of the system for better UX.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License