his SBIR Phase I project will develop an innovative application, geared towards the growing emergent bilingual population which enters the educational system. Despite their growing size (approximately 20 percent of the total US population), emergent bilinguals still do not have adequate support to succeed in an academic setting, and are often victims of a subtractive bilingualism process, which eventually favors the acquisition of English at the expense of the home language. When this happens, the individual child loses a valuable economic and cultural resource, as well as the cognitive benefits associated with bilingualism. In turn, society loses its multilingual resources and a wealth of cultural knowledge, which is necessary for trade and diplomacy with foreign nations, among other things. Therefore, the goal of the project is to track the progress of bilingual students throughout their education. It aims to support the students' bilingual development by providing a research-based, standard-aligned, and child-friendly bilingual literacy and cognitive skills assessment tool accompanied by an educational platform where teachers, parents, and students can collectively collaborate to promote bilingualism.
The project consists of a mobile-device and/or web-based automated bilingual assessment of reading, writing and oral skills, that provides real-time results to teachers (and parents) of emergent bilinguals. The assessment is intuitive, adapting to each child, and is complemented by recommended activities that are automatically tailored to each student's needs. The goal is to help teachers gain an in-depth understanding of the bilingual student's strengths and weaknesses, and to rule out cognitive deficits that are usually wrongly ascribed to bilinguals. It is also to help teachers and parents gauge, develop, and nurture the specific skill set that emergent bilinguals bring with them and that strongly benefit many aspects of society. The R&D plan aims at developing an innovative product that stands out from other existing solutions. The key differentiating features are: its focus on bilingual children and its adaptability in accepting bilingual answers for the assessments, its capacity of assessing oral skills in children thanks to the integration of a speech recognizer that will be refined by integrating machine learning algorithms trained on children speech data, and the option for each student to receive a personalized bundle of analytics and activities that fit his/her profile.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
