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CoCoVis: Visualizing Multi-Sensorial Time Series Data (together with Prof. Torsten Möller) |
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| The CoConUT project (coconut.cosy.wien) features smartphone apps which collect sensor data (location, speed, noise, nearby Bluetooth devices, heart rate, etc.) for each participant during mobile field studies. Result is a time series which shows information about the context and possibly interesting events the field study participants encountered („Why did the participant slow down on the corner?“, „Why were so many people present nearby during this time period?“, etc.). These time series data should be visualized and enriched by meaningful analyses to enable exploration and potentially reasoning. The data set consists of sensor data which is collected each second during a field study on the participant’s smartphone. The app can be downloaded from the App Store and you can create your own data sets: https://play.google.com/store/apps/details?id=at.ac.univie.cosy.coconut |
| If you're interested, please contact Svenja Schröder (Svenja.schroeder@univie.ac.at). |
Bringing CoConUT into the Cloud |
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| The CoConUT toolkit ("Context Collection for non-stationary Use Testing" - http://coconut.cosy.wien) is a framework for supporting short-term mobile field studies, e.g. usability tests on smartphones in realistic environments. It features several apps and wearables which collect quantitative and qualitative data about surrounding context and human behavior directly in the field. In this thesis / praktikum you will enhance the toolkit by a cloud-based web server on which the data gathered in field studies will be sent to, stored and preliminarily analyzed. Moreover, a frontend web application should be implemented which will be used by operator for field studies configuration and studies' results analysis presented using e.g. logs or charts. With this data the field study operator can review the course of the current study and get a first overview over the gathered data. |
| If you're interested, please contact |
Bursts of Interaction across App Types and Times of Day |
Oulasvirta et al. (2005) https://dl.acm.org/citation.cfm?id=1055101 showed that interaction during mobile web browsing occured in "interaction bursts" of 4 seconds. Although this research happened more than 10 years ago, today mobile interaction in the wild still happens in "bursts" due to contextual factors. In this work a study app will be developed which enables long time measurements of interaction bursts across app types (web browsing, chatting, etc.) and across times of day in the background of the participants' smartphones. This data will be gathered and analyzed in order to show patterns in interaction, e.g. different types of interaction "bursts". |
| Contact: Svenja Schröder (Svenja.schroeder@univie.ac.at) |
Bringing CoConUT into the Cloud |
| The CoConUT toolkit ("Context Collection for non-stationary Use Testing" - http://coconut.cosy.wien) is a framework for supporting short-term mobile field studies, e.g. usability tests on smartphones in realistic environments. It features several apps and wearables which collect quantitative and qualitative data about surrounding context and human behavior directly in the field. In this thesis / praktikum you will enhance the toolkit by a cloud-based web server on which the data gathered in field studies will be sent to, stored and preliminarily analyzed. Moreover, a frontend web application should be implemented which will be used by operator for field studies configuration and studies' results analysis presented using e.g. logs or charts. With this data the field study operator can review the course of the current study and get a first overview over the gathered data. |
| and Nemanja Ignjatov (nemanja.ignjatov@univie.ac.at). |
Bursts of Interaction across App Types and Times of Day |
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Oulasvirta et al. (2005) https://dl.acm.org/citation.cfm?id=1055101 showed that interaction during mobile web browsing occured in "interaction bursts" of 4 seconds. Although this research happened more than 10 years ago, today mobile interaction in the wild still happens in "bursts" due to contextual factors. In this work a study app will be developed which enables long time measurements of interaction bursts across app types (web browsing, chatting, etc.) and across times of day in the background of the participants' smartphones. This data will be gathered and analyzed in order to show patterns in interaction, e.g. different types of interaction "bursts". |
| Contact: Svenja Schröder (Svenja.schroeder@univie.ac.at)If you're interested, please contact Svenja Schröder (Svenja.schroeder@univie.ac.at) and Nemanja Ignjatov (nemanja.ignjatov@univie.ac.at). |
Language Learning on the Go |
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| Nowadays learning a new language is as easy as never before. Dozens of apps enable learners to engage in language learning while being on the move, for example while commuting to university / work. This so called „micro learning“ therefore happens in a multiplicity of different contexts and with different levels of attention the learner can spare for the learning task. In this thesis / praktikum you will build on an existing micro language learning app and try to find out how, when and where users prefer to improve their language skills. |
| If you're interested, please contact Svenja Schröder (Svenja.schroeder@univie.ac.at). |
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