Affordable Mobile Data Collection Platform and IT Training in Benin: Analysis of Performance and Usability

2020 ◽  
pp. 635-639
Author(s):  
Andreas Vassilakos ◽  
Damon Walker ◽  
Maurice Dawson
Keyword(s):  
Data Collection ◽  
Mobile Data ◽  
Mobile Data Collection ◽  
It Training ◽  
Analysis Of Performance

Mobile Data Collection with Smartphones

2021 ◽  
pp. 3262-3264
Author(s):  
Alexander Seifert ◽  
Gabriella M. Harari
Keyword(s):  
Data Collection ◽  
Mobile Data ◽  
Mobile Data Collection

Open data kit (ODK) in crop farming: mobile data collection for seed yam tracking in Ibadan, Nigeria

2019 ◽  
Vol 33 (5) ◽  
pp. 605-619 ◽  
Author(s):  
Turry Ouma ◽  
Agnes Kavoo ◽  
Cornelius Wainaina ◽  
Busayo Ogunya ◽  
Margaret Karanja ◽  
...  
Keyword(s):  
Data Collection ◽  
Open Data ◽  
Mobile Data ◽  
Mobile Data Collection ◽  
Crop Farming ◽  
Open Data Kit ◽  
Ibadan Nigeria

scholarly journals Measuring Mental Effort for Creating Mobile Data Collection Applications

2020 ◽  
Vol 17 (5) ◽  
pp. 1649
Author(s):  
Johannes Schobel ◽  
Thomas Probst ◽  
Manfred Reichert ◽  
Winfried Schlee ◽  
Marc Schickler ◽  
...  
Keyword(s):  
Data Collection ◽  
Prior Knowledge ◽  
Performance Measures ◽  
Large Scale ◽  
Performance Metrics ◽  
Positive Impact ◽  
Mental Effort ◽  
The Self ◽  
Mobile Data ◽  
Mobile Data Collection

To deal with drawbacks of paper-based data collection procedures, the QuestionSys approach empowers researchers with none or little programming knowledge to flexibly configure mobile data collection applications on demand. The mobile application approach of QuestionSys mainly pursues the goal to mitigate existing drawbacks of paper-based collection procedures in mHealth scenarios. Importantly, researchers shall be enabled to gather data in an efficient way. To evaluate the applicability of QuestionSys, several studies have been carried out to measure the efforts when using the framework in practice. In this work, the results of a study that investigated psychological insights on the required mental effort to configure the mobile applications are presented. Specifically, the mental effort for creating data collection instruments is validated in a study with N = 80 participants across two sessions. Thereby, participants were categorized into novices and experts based on prior knowledge on process modeling, which is a fundamental pillar of the developed approach. Each participant modeled 10 instruments during the course of the study, while concurrently several performance measures are assessed (e.g., time needed or errors). The results of these measures are then compared to the self-reported mental effort with respect to the tasks that had to be modeled. On one hand, the obtained results reveal a strong correlation between mental effort and performance measures. On the other, the self-reported mental effort decreased significantly over the course of the study, and therefore had a positive impact on measured performance metrics. Altogether, this study indicates that novices with no prior knowledge gain enough experience over the short amount of time to successfully model data collection instruments on their own. Therefore, QuestionSys is a helpful instrument to properly deal with large-scale data collection scenarios like clinical trials.

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