Conceptualization of an Assessment System to Measure Vocabulary in Science (Project MELVA-S)

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.