Design and evaluation of korean text entry methods for mobile phones

Ilinkin, Ivaylo; Kim, Sunghee

doi:10.1145/1358628.1358773

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Consequently, the existing prediction technique is not culturespecific, as it can even be used with non-Latin-based alphabets. Proof of concepts can be found in works related to predictive evaluation of Korean text entry [17] and Chinese text entry [18] on a multitap-based mobile phone keypad with 12 buttons.…”

Section: Modeling Upper-bound Text Entry Speedsmentioning

confidence: 99%

Predicting upper-bound text entry speeds for discrete-tilt-based input on smartphones

2014

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Motion sensors integrated into contemporary smartphones allow the introduction of new mobile interaction paradigms, here including tilt-based input control in the mobile context. Namely, as opposed to existing implementations that typically apply continuous feedback on tilting, we define Pitch and Roll movement sequences that change the orientation of the mobile device as discrete-tilt input primitives. The respective commands are then used to manage text entry within three discrete-tilt-based methods thus introduced: keyboard bisection, single cursor, and quad cursor. Each method is based on the use of a particular QWERTY-based keyboard layout with related strategy for character input. We model upper-bound text entry speeds for the input methods, taking into account both movement aspects and language context. The movement model corresponds to both the tilt-based shortest path between two consequent characters, which is theoretically defined, and the time of discrete-tilt execution, which is obtained from user testing experiment we conducted. The linguistic model, comprising digraph statistics, is constructed basing on available English corpora. This modeling approach provides discrete-tilt-based text entry speed predictions representing efficiency rates for expert behavior, i.e. for optimal performance. The results obtained enable the evaluation of the proposed designs without need to test with real users, and can furthermore serve as a baseline for efficiency of text entry implementations that rely on discrete tilt.

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Section: Modeling Upper-bound Text Entry Speedsmentioning

confidence: 99%

Predicting upper-bound text entry speeds for discrete-tilt-based input on smartphones

2014

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“…The predictive modeling of text entry efficiency based on Fitts's law and the digram probability distribution has been successfully applied in several domains, including the development of various keyboards and input devices [3,5,6] and in target languages other than English [7,8]. This approach proved particularly important for modeling text entry methods that rely on a single pointing device.…”

Section: Introductionmentioning

confidence: 99%

Generating Representative Phrase Sets for Text Entry Experiments by GA-Based Text Corpora Sampling

Ljubić

Salkanović

2023

Mathematics

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In the field of human–computer interaction (HCI), text entry methods can be evaluated through controlled user experiments or predictive modeling techniques. While the modeling approach requires a language model, the empirical approach necessitates representative text phrases for the experimental stimuli. In this context, finding a phrase set with the best language representativeness belongs to the class of optimization problems in which a solution is sought in a large search space. We propose a genetic algorithm (GA)-based method for extracting a target phrase set from the available text corpus, optimizing its language representativeness. Kullback–Leibler divergence is utilized to evaluate candidates, considering the digram probability distributions of both the source corpus and the target sample. The proposed method is highly customizable, outperforms typical random sampling, and exhibits language independence. The representative phrase sets generated by the proposed solution facilitate a more valid comparison of the results from different text entry studies. The open source implementation enables the easy customization of the GA-based sampling method, promotes its immediate utilization, and facilitates the reproducibility of this study. In addition, we provide heuristic guidelines for preparing the text entry experiments, which consider the experiment’s intended design and the phrase set to be generated with the proposed solution.

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ACT-R Predictive Model of Korean Text Entry on Touchscreen

Lim¹,

Jo²,

Myung³

et al. 2012

Journal of the Ergonomics Society of Korea

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Objective: The aim of this study is to predict Korean text entry on touchscreens using ACT-R cognitive architecture. Background: Touchscreen application in devices such as satellite navigation devices, PDAs, mobile phones, etc. has been increasing, and the market size is expanding. Accordingly, there is an increasing interest to develop and evaluate the interface to enhance the user experience and increase satisfaction in the touchscreen environment. Method: In this study, Korean text entry performance in the touchscreen environment was analyzed using ACT-R. The ACT-R model considering the characteristics of the Korean language which is composed of vowels and consonants was established. Further, this study analyzed if the prediction of Korean text entry is possible through the ACT-R cognitive model. Results: In the analysis results, no significant difference on performance time between model prediction and empirical data was found. Conclusion:The proposed model can predict the accurate physical movement time as well as cognitive processing time. Application: This study is useful in conducting model-based evaluation on the text entry interface of the touchscreen and enabled quantitative and effective evaluation on the diverse types of Korean text input interfaces through the cognitive models.

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Design and evaluation of korean text entry methods for mobile phones

Cited by 3 publications

References 7 publications

Predicting upper-bound text entry speeds for discrete-tilt-based input on smartphones

Predicting upper-bound text entry speeds for discrete-tilt-based input on smartphones

Generating Representative Phrase Sets for Text Entry Experiments by GA-Based Text Corpora Sampling

ACT-R Predictive Model of Korean Text Entry on Touchscreen

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