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TU Berlin

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Dr. -Ing. Babak Naderi

Lupe

Research Interests:

  • Subjective quality assessment
  • Speech Quality Assessment in Crowdsourcing
  • Motivation, Workload, and Performance in Crowdsourcing
  • Statistical Modeling, field data and applied statistics
  • Speech Enhancement
  • Text Complexity and Simplification

Biography:

Babak Naderi has obtain his Dr.-Ing degree (PhD) on the basis of his thesis with a title of Motivation of Workers on Microtask Crowdsourcing Platforms in September 2017. Babak has Master's degree in Geodesy and Geoinformation Science form the Technical University Berlin with a thesis on "Monte Carlo Localization for Pedestrian Indoor Navigation Using a Map Aided Movement Model". He has also a Bachelor's degree in Software Engineering.

Since August 2012, Babak Naderi is working as a research scientist at the Quality and Usability Lab of  TU-Berlin.

2013-2015 Babak was awarded with an BMBF funded Education program for future IT and Development Leadership involving Bosch, Datev, Deutsche Telekom AG, Holtzbrinck, SAP, Scheer Group, Siemens, and Software AG  amongst highly ranked academic institution (Softwarecampus). He was taking part by leading CrowdMAQA project.

Within dissertation, Babak studies the motivation of crowdworkers in details. He has developed the Crowdwork Motivation Scale for measuring general motivation based on the Self-Determination Theory of Motivation. The scale has been validated within several studies. In addition, he has studied factors influencing the motivation, and influence of different motivation type on the quality of outcomes. Models for predicting task selection strategy of workers are developed, including models for automatically predicting expected workload associated to a task from its design, task acceptance and performance. 

Beside others research activities, Babak is actively working on the standardization of methods for speech quality assessment in crowdsourcing environment in the P.CROWD work program of Study Group 12 in ITU-T Standardization Sector.

Reviewed for WWW, CHI, ICASSP, CSCW, MMSys, PQS, HCOMP, ICWE, QoMEX, International Journal of Human-Computer Studies, Computer Networks, Behaviour & Information Technology, Quality and User Experience.

 

Selected talks:

  • "Motivation of Crowd Workers, does it matter?",Schloss Dagstuhl, Evaluation in the Crowd: Crowdsourcing and Human-Centred Experiments, November 2015.
  • "Motivation and Quality Assessment in Online Paid Crowdsourcing Micro-task Platforms",Schloss Dagstuhl, Crowdsourcing: From Theory to Practice and Long-Term Perspectives, September 2013.

 

Office Hours: On Appointment

 

Adresse:

Quality and Usability Lab

Technische Universität Berlin
Ernst-Reuter-Platz 7
D-10587 Berlin

Tel.:+49 (30) 8353-54221
Fax: +49 (30) 8353-58409

babak.naderi[at]tu-berlin.de

Publications

Personalized Motivation-supportive Messages for Increasing Participation in Crowd-civic Systems
Citation key grau2018a
Author Grau, Paul and Naderi, Babak and Kim, Juho
Title of Book Proceedings of the ACM on Human-Computer Interaction
Year 2018
DOI 10.1145/3274329
Month nov
Note electronic
Publisher ACM
Series CSCW
How Published Fullpaper
Abstract In crowd-civic systems, citizens form groups and work towards shared goals, such as discovering social issues or reforming official policies. Unfortunately, many real-world systems have been unsuccessful in continually motivating large numbers of citizens to participate voluntarily, despite various approaches such as gamification and persuasion techniques. In this paper, we examine the influence of personalized messages designed to support motivation as asserted by the Self-Determination Theory (SDT). We designed a crowd-civic platform for collecting community issues with personalized motivation-supportive messages and conducted two studies: a pair-comparison experiment with 150 participants on Amazon’s Mechanical Turk and a live deployment study with 120 university members. Results of the pair-comparison study indicate applicability of SDT’s perspective in crowd-civic systems. While applying it in the live system surfaced several challenges, including recruiting participants without interfering with general motivations, the collected data exhibited similar promising trends.
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