## 2 Related Work  
In the domain of visual clues, there are four essential works, which are presented below [8; 9; 10; 11]. [PERSON] et al. [8] focus on the same problem with the same solution proposal as this paper, trying to improve the novice experience of alternative keyboard layouts. Similar to the approach of this paper, they wanted to decrease the scanning time in the novice situation via visual clues. Generally, their goal was to encourage users to use optimized keyboards, which is exactly the goal of our work. The main difference to the approach of this paper is that they just tested whether visual clues improve the input performance, while this approach tries to find out which clue results in the best performance. They indicate the next probable characters by making the font bold on the affected buttons. Three modes were used to check the performance. The first mode was without visual clues, the second one with perfect visual clues, and the third one with a 10% error rate. To keep the novice experience the same during the experiment, they decided to mix the letter assignment to the buttons after every input [8]. Twelve participants had to input 50 words per mode on a Pocket PC. Concerning the input speed they observed an improvement of 37.7% when all predictions were right. When 90% of all predictions were right, the performance increased by 27.5%. Therefore they state that visual clues can help in a novice experience setting but the performance is dependent on the prediction performance. This paper can be seen as the base knowledge of this paper. It describes that visual clues help in the novice experience situation. It is expected to see a difference in input performance depending on the visual clue, assuming that the bold font clue is not the most suitable clue. The bold font clue of this experiment is also used as one of the clues in the comparison of clues of this thesis. [PERSON] et al. [9] use a different kind of visual clue. They enlarge the keys according to their probability. To calculate the probability of the keys, they use single letters and digram frequency counts. They expected an improved performance based on two principles. First, they expect an improved character selection. They base this assumption on [PERSON]'s law. Second, they expect reduced visual scanning times, especially in a novice experience context. The experiment was performed on a Sony VAIO UMPC with a stylus and three keyboard modes were tested. First, no clue was shown; second, the size of the letter with the highest probability was increased; and, third, four keys were adapted. Adapting the size of four keys performed the best, with a 25.14% input speed improvement in comparison with no clue at all [9]. The performance increase is about 12% less than the one documented by [PERSON] et al. [8]. This can be attributed to the fact that the BigKey implementation of [PERSON] et al. [9] used the _Qwverty_ layout and therefore did not really operate in the domain of novice experience, because some participants might have had _Qwetry_ experience. This demonstrates a difference to the approach of this paper, as it focuses on the novice experience with high-performance keyboard layouts. In our approach, a similar visual clue is used as the size of buttons is also increased according to their probability. The difference is that in this approach more overlap is allowed and a higher scaling factor is used. A part of the proposal of [PERSON] et al. [11] also aimed to improve input speed and was also engaged with visual clues. Their problem domain was initially different from this paper. They tested the impact of their measures in the domain of elderly users. Elderly people can also be seen to be in the domain of novice experience, because of their limited experience with keyboard layouts. [PERSON] et al. [11] contemplated that similarly as they stated that they expected a better input performance with visual clues due to the unfamiliarity with the _Qwerty_ layout.