Memorising vocabulary is an important aspect of formal foreign-language learning. Advances in cognitive psychology have led to the development of adaptive learning systems that make vocabulary learning more efficient. One way these computer-based systems optimize learning is by measuring learning performance in real time to create optimal repetition schedules for individual learners. While such adaptive learning systems have been successfully applied to word learning using keyboard-based input, they have thus far seen little application in spoken word learning. Here we present a system for adaptive, speech-based word learning using an adaptive model that was developed for and tested with typing-based word learning. We show that typing- and speech-based learning result in similar behavioral patterns that can be used to reliably estimate individual memory processes, and we extend earlier findings demonstrating that a response-time based adaptive learning system outperforms an accuracy-based, Leitner flashcard learning algorithm. In short, we show that adaptive learning benefits transfer from typing-based learning, to speech based learning. Our work provides a basis for the development of language learning applications that use real-time pronunciation assessment software to score the accuracy of the learner's pronunciations. The development of adaptive, speech-based learning applications is important for two reasons. First, by focusing on speech, the model can be applied for individuals whose typing skills are insufficient---as is demonstrated by the successful application of the model in an elderly participant population. Second, speech-based learning models are educationally relevant because they focus on what may be the most important aspect of language learning: to practice speech.
Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-learning Content Personalization