Abstract
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 word learning where spoken instead of typed input is used. Here we present a framework for 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. We extend earlier findings demonstrating that a response-time based adaptive learning approach outperforms an accuracy-based, Leitner flashcard approach in learning efficiency (demonstrated by higher average accuracy and lower response times after a learning session). 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. We discuss the implications for our approach for the development of educationally relevant, adaptive speech-based learning applications.
Highlights
Recent advances in cognitive psychology have led to the development of adaptive learning systems that aim to improve the process of word learning by determining optimal learning strategies for individual learners
We aimed to assess whether the distributions of speech-based reaction times and typing-based reaction times differed when using an adaptive learning method
We hypothesised that—given the assumed functional similarity between typing- and speechbased learning—both types of learning are likely to result in similar reaction time distributions, and that both voice onset times and keypress response times can be used in adaptive learning systems to estimate internal memory parameters
Summary
Storing word representations in the mental lexicon is one of the most important aspects of learning a language. Recent advances in cognitive psychology have led to the development of adaptive learning systems that aim to improve the process of word learning by determining optimal learning strategies for individual learners These digital systems typically focus on teaching orthography (i.e., the letters that spell a word) and require the learner to respond by typing or selecting the correct answer in response to a cue (e.g., Wozniak and Gorzelanczyk, 1994; Van Rijn et al, 2009; Lindsey et al, 2014; Papousek et al, 2014). Several variables, such as accuracy and reaction times, are Adaptive Speech-Based Learning measured during the learning process and are used in real time to determine optimal repetition schedules for individual learners. Using such adaptive learning systems results in higher learning efficiency than learning with traditional, non-adaptive methods, which often translates into better retention at the end of the study sessions (Wozniak and Gorzelanczyk, 1994; Van Rijn et al, 2009; Lindsey et al, 2014; Papousek et al, 2014; van der Velde et al, 2021a)
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