Learning New Words and Spelling with Autocorrections

被引：4

作者：

Arif, Ahmed Sabbir ^{[1
]}

Sylla, Cristina ^{[2
]}

Mazalek, Ali ^{[1
]}

机构：

[1] Ryerson Univ, Toronto, ON, Canada

[2] Univ Minho, Braga, Portugal

来源：

PROCEEDINGS OF THE 2016 ACM INTERNATIONAL CONFERENCE ON INTERACTIVE SURFACES AND SPACES, (ISS 2016) | 2016年

关键词：

Child; children; prediction; predictive keyboard; spelling; autocorrection; color-coded autocorrect;

D O I：

10.1145/2992154.2996790

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present a novel color-coded feedback method that highlights the types of corrections made by autocorrections. We then present results of a longitudinal user study with 7-8-year-old children that compared the new method with the conventional autocorrection feedback method, in terms of leaning new words and spelling. Results suggested that the new method better accommodates learning new words. Interestingly, learning was observed with the conventional feedback method as well, demanding further investigation into whether predictive methods are truly a barrier to learning new words and spelling.

引用

页码：409 / 414

页数：6

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