WASS: Web-Based Annotation and Search System to Facilitate English Vocabulary Learning in Vocational High School

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Innovative Technologies and Learning (ICITL 2018)

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Abstract

In this study, we proposed a web-based annotation and search system named WASS, which not only provides annotation making but also peers’ annotation searching and reviewing for vocabulary learning. Four-week experiment was conducted among 75 vocational high school students, divided into experimental group and control group. The student learning behavior and their influence on learning achievement were explored, the results showed that experimental group outperformed control group in vocabulary post-test. Further investigation in experimental group showed low achievement students made more text annotation than the high achievement students and obtained more significant score gain, thereby indicating that our system is capable to motivate the low achievement students to make more text annotation, this is because through searching and learning peers’ annotation, low achievement students can make more annotation and acquire more vocabulary. Those findings may confirm that searching and reviewing peers’ annotation plays an important role in fostering vocabulary learning, especially for those low achievement students in vocational high school. Therefore, designing the supported powerful function to utilize annotations completely is vital (like searching tool for peers’ annotation in this study) and encouraging students to learn through their own annotation and peers’ by searching tool.

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Acknowledgements

Our thanks to National Science Council of the R.O.C. for funding us conducting this research. We also want to thank to those who participated the system design and experiment.

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Correspondence to Nguyen-Thi Huyen .

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Ji, YS., Huyen, NT., Hwang, WY., Ghinea, G. (2018). WASS: Web-Based Annotation and Search System to Facilitate English Vocabulary Learning in Vocational High School. In: Wu, TT., Huang, YM., Shadiev, R., Lin, L., Starčič, A. (eds) Innovative Technologies and Learning. ICITL 2018. Lecture Notes in Computer Science(), vol 11003. Springer, Cham. https://doi.org/10.1007/978-3-319-99737-7_41

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  • DOI: https://doi.org/10.1007/978-3-319-99737-7_41

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  • Online ISBN: 978-3-319-99737-7

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