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Does automation in typing really make you faster?
Analyze a large amount of mobile typing data to answer the question if predictive methods such as autocorrection and word prediction really make you faster in typing.
Keywords: Text input, smartphone, word prediction, autocorrection
Typing on smartphones is regarded as slow and cumbersome. Nevertheless we use it every day even to type longer texts. Most keyboards try to make typing faster and easier by implementing predictive methods such as autocorrection and word prediction. However, it is unclear in how far these methods are useful for typing on smartphones and some studies even showed that word prediction impairs typing performance [1].
The of this thesis is to answer the question whether autocorrection and word prediction are useful for mobile text input or not and potentially develop methods to improve their usefulness. Therefore, we offer a large dataset of mobile text input which should be analyzed with regard to this question.
[1] Quinn, P., & Zhai, S. (2016, May). A cost-benefit study of text entry suggestion interaction. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 83-88). ACM.
Typing on smartphones is regarded as slow and cumbersome. Nevertheless we use it every day even to type longer texts. Most keyboards try to make typing faster and easier by implementing predictive methods such as autocorrection and word prediction. However, it is unclear in how far these methods are useful for typing on smartphones and some studies even showed that word prediction impairs typing performance [1]. The of this thesis is to answer the question whether autocorrection and word prediction are useful for mobile text input or not and potentially develop methods to improve their usefulness. Therefore, we offer a large dataset of mobile text input which should be analyzed with regard to this question.
[1] Quinn, P., & Zhai, S. (2016, May). A cost-benefit study of text entry suggestion interaction. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 83-88). ACM.
Analyze a large dataset of mobile typing data to answer the question whether autocorrection and word prediction are useful for mobile text input or not and develop methods to improve it.
Analyze a large dataset of mobile typing data to answer the question whether autocorrection and word prediction are useful for mobile text input or not and develop methods to improve it.
Each year the IDEA League offers the students of its partner universities over 180 monthly grants for a short-term research exchange. In general, these grants are awarded based on academic merit. For more information visit http://idealeague.org/student-grant/
Semester Project
Bachelor Thesis
Master Thesis
CLS Student Project [managed by Max Planck ETH Center for Learning Systems]