Translating a Typing-Based Adaptive Learning Model to Speech-Based L2 Vocabulary Learning

Research output: Contribution to conferencePaperAcademic

2 Citations (Scopus)
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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. These computer-based systems measure learning performance in real time to create optimal study strategies for individual learners. While such adaptive learning systems have been successfully applied to written word learning, they have thus far seen little application in spoken word learning. Here we present a system for adaptive, speech-based word learning. We show that it is possible to improve the efficiency of speech-based learning systems by applying a modified adaptive model that was originally developed for typing-based word learning. This finding contributes to a better understanding of the memory processes involved in speech-based word learning. Furthermore, 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. Speech-based learning applications are educationally relevant because they focus on what may be the most important aspect of language learning: to practice speech.
Original languageEnglish
Pages245-250
Number of pages6
DOIs
Publication statusPublished - Jun-2021
Event29th ACM Conference on User Modeling, Adaptation and Personalization -
Duration: 21-Jun-202125-Dec-2021
Conference number: 29
https://www.um.org/umap2021/

Conference

Conference29th ACM Conference on User Modeling, Adaptation and Personalization
Abbreviated titleUMAP
Period21/06/202125/12/2021
Internet address

Keywords

  • Memory
  • Adaptive Learning
  • Vocabulary Learning
  • Pronunciation
  • Speech
  • ACT-R

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