Wilschut Thomas, Sense Florian, van der Velde Maarten, Fountas Zafeirios, Maaß Sarah C, van Rijn Hedderik
Department of Experimental Psychology, University of Groningen, Groningen, Netherlands.
Department of Behavioral and Cognitive Neurosciences, University of Groningen, Groningen, Netherlands.
Front Artif Intell. 2021 Dec 7;4:780131. doi: 10.3389/frai.2021.780131. eCollection 2021.
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.
记忆词汇是正规外语学习的一个重要方面。认知心理学的进展推动了自适应学习系统的发展,这些系统能使词汇学习更高效。这些基于计算机的系统优化学习的一种方式是实时测量学习表现,为个体学习者创建最佳重复学习计划。虽然这种自适应学习系统已成功应用于基于键盘输入的单词学习,但到目前为止,在使用语音而非打字输入的单词学习中应用很少。在此,我们提出一个基于语音的单词学习框架,该框架使用一个为基于打字的单词学习而开发并经过测试的自适应模型。我们表明,基于打字和语音的学习会产生相似的行为模式,可用于可靠地估计个体记忆过程。我们扩展了早期的研究结果,证明基于反应时间的自适应学习方法在学习效率上优于基于准确性的 Leitner 抽认卡方法(学习会话后平均准确率更高且反应时间更短可证明)。简而言之,我们表明自适应学习的益处可以从基于打字的学习转移到基于语音的学习。我们的工作为开发使用实时发音评估软件来对学习者发音准确性进行评分的语言学习应用提供了基础。我们讨论了我们的方法对开发具有教育相关性的、基于语音的自适应学习应用的影响。