In this presentation, we explore the development and deployment of a web-based flashcard application, designed for the Defense Language Institute's intensive 64-week language course for Modern Standard Arabic. The primary objective of this application is to facilitate efficient acquisition and retention of over 2,500 task-critical vocabulary terms using retrieval practice, a proven method for enhancing long-term memory retention.
The application balances the introduction of new vocabulary with refreshing previously learned words, ensuring continual progress without the loss of past learning. This dynamic balance is crucial given the extensive duration and scope of the course. The end goal of this project is to create a personalized, predictive model that adapts to each student's learning trajectory, offering tailored rehearsal items based on their current knowledge and skill level. The model will also communicate actionable insights back to students so they can make effective study choices.
However, at the inception of this project, a significant challenge was the lack of digitized, actionable data to build such a model. Thus, the flashcard application serves two functions: an effective educational tool and a means to gather essential data. This data collection is instrumental for the development of sophisticated predictive modeling capabilities in future iterations of the application.
Our talk will delve into the application's design, the methodologies employed for effective vocabulary retention, and the roadmap for future development. We will focus on preliminary findings and insights gained, setting the stage for future advancements in educational technology and personalized learning.