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Vocabulary Learning Based on Learner-Generated Pictorial Annotations: Using Big Data as Learning Resources

Author

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  • Di Zou

    (Department of English Language Education, The Education University of Hong Kong, Hong Kong, China)

  • Haoran Xie

    (Department of Computing and Decision Sciences, Lingnan University, Hong Kong, China)

Abstract

This research discusses the potential of using big data for vocabulary learning from the perspective of learner-generated pictorial annotations. Pictorial annotations lead to effective vocabulary learning, the creation of which is however challenging and time-consuming. As user-generated annotations promote active learning, and in the big data era, data sources in social media platforms are not only huge but also user-generated, the proposal of using social media data to establish a natural and semantic connection between pictorial annotations and words seems feasible. This research investigated learners’ perceptions of creating pictorial annotations using Google images and social media images, learners’ evaluation of the learner-generated pictorial annotations, and the effectiveness of Google pictorial annotations and social media pictorial annotations in promoting vocabulary learning. A total of 153 undergraduates participated in the research, some of whom created pictorial annotations using Google and social media data, some evaluated the annotations, and some learned the target words with the annotations. The results indicated positive attitudes towards using Google and social media data sets as resources for language enhancement, as well as significant effectiveness of learner-generated Google pictorial annotations and social media pictorial annotations in promoting both initial learning and retention of target words. Specifically, we found that (i) Google images were more appropriate and reliable for pictorial annotations creation, and therefore they achieved better outcomes when learning with the annotations created with Google images than images from social media, and (ii) the participants who created word lists that integrate pictorial annotations were likely to engage in active learning when they selected and organized the verbal and visual information of target words by themselves and actively integrated such information with their prior knowledge.

Suggested Citation

  • Di Zou & Haoran Xie, 2021. "Vocabulary Learning Based on Learner-Generated Pictorial Annotations: Using Big Data as Learning Resources," Sustainability, MDPI, vol. 13(11), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:5767-:d:558975
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    References listed on IDEAS

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    1. Clifford Lynch, 2008. "How do your data grow?," Nature, Nature, vol. 455(7209), pages 28-29, September.
    2. Xinyi Huang & Di Zou & Gary Cheng & Haoran Xie, 2021. "A Systematic Review of AR and VR Enhanced Language Learning," Sustainability, MDPI, vol. 13(9), pages 1-28, April.
    3. Soo Young Rieh, 2002. "Judgment of information quality and cognitive authority in the Web," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 53(2), pages 145-161.
    4. Opresnik, David & Taisch, Marco, 2015. "The value of Big Data in servitization," International Journal of Production Economics, Elsevier, vol. 165(C), pages 174-184.
    5. Lee, In, 2017. "Big data: Dimensions, evolution, impacts, and challenges," Business Horizons, Elsevier, vol. 60(3), pages 293-303.
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    1. Yi-Chieh Chen & Kuo-Kuang Fan & Kwo-Ting Fang, 2021. "Effect of Flipped Teaching on Cognitive Load Level with Mobile Devices: The Case of a Graphic Design Course," Sustainability, MDPI, vol. 13(13), pages 1-26, June.

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