Abstract
Keystroke-Level Model (KLM) is an established HCI model for predicting users’ time on task. KLM was originally developed for desktop systems, but it has been extended for different interaction contexts. Fingerstroke-Level Model (FLM) is such an extension for touch-sensitive interactions with direct finger movements. This paper presents a novel tool, named FLM for Android Apps (FLM-2A), that supports automated FLM modeling of tasks performed in Android apps. The tool aims to support design and evaluation of Android apps in an effective and efficient manner. A study investigated the accuracy of FLM-2A predictions by comparing them to participants’ interaction times for three custom-built Android apps. Results showed that the error rate for the FLM-2A predictions with Fitts’ Law enabled were 0.3% and 2.8% for the first and second apps respectively. However, the error rate for the third custom-built Android app was 40.0%, indicating that additional steps are required to finetune the FLM-2A predictions.
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Theofilou, S., Vardas, N., Katsanos, C. (2021). FLM-2A: Towards Automated HCI Modeling of Android Applications Based on a Modified Version of the Keystroke Level Model. In: Kurosu, M. (eds) Human-Computer Interaction. Theory, Methods and Tools. HCII 2021. Lecture Notes in Computer Science(), vol 12762. Springer, Cham. https://doi.org/10.1007/978-3-030-78462-1_25
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