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On the interplay between vocal production effect and learning content types in e-learning settings

10 pagesPublished: September 20, 2022

Abstract

The concept of “production effect” from experimental psychology suggests that produc- ing a word aloud during study improves explicit memory as compared to reading the word silently. In this study, we investigate the effect of the different vocal production behav- iors on recollection rates concerning varying content types delivered through an e-learning platform and inquire whether there is any possibility of improving the e-learning system by integrating vocal production instructions. As for different sorts of vocal production behaviors, we considered as the usual depiction (uttering) as well as lack (free view) and suppression (mouthing) of vocal production. As for content types, one numerical content and two verbal contents with varying levels of pronunciation difficulty are considered. Our results indicate that there is no statistically significant difference on recollection rates be- tween various vocal production behaviors. However, it is observed that by uttering, the content which is relatively harder to pronounce, can be recalled better than the others in a statistically significant way. This unexpected result indicates that there is a potential to increase the performance of learners, who study unfamiliar verbal content (e.g. foreign vocabulary) by integrating vocal production into e-learning systems.

Keyphrases: information registration, memory retention, uttering

In: Tokuro Matsuo (editor). Proceedings of 11th International Congress on Advanced Applied Informatics, vol 81, pages 303-312.

BibTeX entry
@inproceedings{IIAIAAI2021-Winter:interplay_between_vocal_production,
  author    = {Kazuma Ohta and Zeynep Yucel and Parisa Supitayakul and Akito Monden and Pattara Leelaprute},
  title     = {On the interplay between vocal production effect and learning content types in e-learning settings},
  booktitle = {Proceedings of 11th International Congress on Advanced Applied Informatics},
  editor    = {Tokuro Matsuo},
  series    = {EPiC Series in Computing},
  volume    = {81},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/xXVz},
  doi       = {10.29007/mph5},
  pages     = {303-312},
  year      = {2022}}
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