End-User Programming of Mobile Services: Empowering Domain Experts to Implement Mobile Data Collection Applications

Schobel, Johannes; Pryss, Rüdiger; Schickler, Marc; Ruf-Leuschner, Martina; Elbert, Thomas; Reichert, Manfred

doi:10.1109/mobserv.2016.11

Cited by 27 publications

(28 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A main concept in EUP is empowered computing -allowing users to personalize systems to their needs and preferences [37]. They are used widely in educational contexts [40,47,65,88], and are used in HRI, home automation, and healthcare contexts [16,17,19,26,29,39,41,49,64,67,73,83,84]. However, these methods are typically procedural, so users require a basic understanding of coding constructs.…”

Section: End-user Programmingmentioning

confidence: 99%

Jessie

Kubota

Peterson

Rajendren

et al. 2020

Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

View full text Add to dashboard Cite

JESSIE is a robotic system that enables novice programmers to program social robots by expressing high-level specifications. We employ control synthesis with a tangible front-end to allow users to define complex behavior for which we automatically generate control code. We demonstrate JESSIE in the context of enabling clinicians to create personalized treatments for people with mild cognitive impairment (MCI) on a Kuri robot, in little time and without error. We evaluated JESSIE with neuropsychologists who reported high usability and learnability. They gave suggestions for improvement, including increased support for personalization, multi-party programming, collaborative goal setting, and re-tasking robot role post-deployment, which each raise technical and sociotechnical issues in HRI. We exhibit JESSIE's reproducibility by replicating a clinician-created program on a TurtleBot 2. As an open-source means of accessing control synthesis, JESSIE supports reproducibility, scalability, and accessibility of personalized robots for HRI. CCS CONCEPTS • Computer systems organization → Robotic control; • Applied computing → Health informatics.

show abstract

Section: End-user Programmingmentioning

confidence: 99%

Jessie

Kubota

Peterson

Rajendren

et al. 2020

Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

View full text Add to dashboard Cite

show abstract

“…To allow untrained staff to use such a powerful modeling approach to graphically develop data collection instruments, so-called end-user programming techniques were evaluated [38]. The authors of [39] discuss the importance of empowering non-developers by providing sophisticated tools.…”

Section: Questionsys Framework Background Informationmentioning

confidence: 99%

Measuring Mental Effort for Creating Mobile Data Collection Applications

Schobel

Probst

Reichert

et al. 2020

IJERPH

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Particularly, this configurator uses its own (graphical) modeling notation based on BPMN 2.0 in order to allow end users without any programming skills or knowledge in process modeling to graphically specify data collection instruments themselves. Therefore, there should be no need for involving IT experts anymore when developing such mobile data collection instruments [28]. To be more precise, QuestionSys particularly focuses on scenarios in which the instruments need to be frequently adapted.…”

Section: Introductionmentioning

confidence: 99%

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study

Schobel¹,

Pryss²,

Probst³

et al. 2018

JMIR Mhealth Uhealth

Self Cite

View full text Add to dashboard Cite

BackgroundMany research domains still heavily rely on paper-based data collection procedures, despite numerous associated drawbacks. The QuestionSys framework is intended to empower researchers as well as clinicians without programming skills to develop their own smart mobile apps in order to collect data for their specific scenarios.ObjectiveIn order to validate the feasibility of this model-driven, end-user programming approach, we conducted a study with 80 participants.MethodsAcross 2 sessions (7 days between Session 1 and Session 2), participants had to model 10 data collection instruments (5 at each session) with the developed configurator component of the framework. In this context, performance measures like the time and operations needed as well as the resulting errors were evaluated. Participants were separated into two groups (ie, novices vs experts) based on prior knowledge in process modeling, which is one fundamental pillar of the QuestionSys framework.ResultsStatistical analysis (t tests) revealed that novices showed significant learning effects for errors (P=.04), operations (P<.001), and time (P<.001) from the first to the last use of the configurator. Experts showed significant learning effects for operations (P=.001) and time (P<.001), but not for errors as the experts’ errors were already very low at the first modeling of the data collection instrument. Moreover, regarding the time and operations needed, novices got significantly better at the third modeling task than experts were at the first one (t tests; P<.001 for time and P=.002 for operations). Regarding errors, novices did not get significantly better at working with any of the 10 data collection instruments than experts were at the first modeling task, but novices’ error rates for all 5 data collection instruments at Session 2 were not significantly different anymore from those of experts at the first modeling task. After 7 days of not using the configurator (from Session 1 to Session 2), the experts’ learning effect at the end of Session 1 remained stable at the beginning of Session 2, but the novices’ learning effect at the end of Session 1 showed a significant decay at the beginning of Session 2 regarding time and operations (t tests; P<.001 for time and P=.03 for operations).ConclusionsIn conclusion, novices were able to use the configurator properly and showed fast (but unstable) learning effects, resulting in their performances becoming as good as those of experts (which were already good) after having little experience with the configurator. Following this, researchers and clinicians can use the QuestionSys configurator to develop data collection apps for smart mobile devices on their own.

show abstract

End-User Programming of Mobile Services: Empowering Domain Experts to Implement Mobile Data Collection Applications

Cited by 27 publications

References 13 publications

Jessie

Jessie

Measuring Mental Effort for Creating Mobile Data Collection Applications

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study

Contact Info

Product

Resources

About