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Research

Sayamindu Dasgupta's Research Group Archive

The following research group descriptions are archived because they are no longer offered, the faculty member is on sabbatical, or the group is taking a break. Please contact the faculty member or an advisor to learn more about these groups.

Spring 2022

Evaluative Study on Dataland - A System Designed for Novices to Analyze and Visualize Data

In today's increasingly data-driven world, it is important for young people to learn with and about data. However, existing programming and data analytics systems are not designed with the consideration of young people’s competencies and interests. How can we design to support young people to ask and answer questions with data in creative, engaging, and personally empowering ways? 

To answer this question, we have developed a visual block-based programming system - “Dataland” - for novices to analyze and visualize data. More information about the system can be found here: https://learning-with-data.github.io/. In this directed research group (DRG), we will: (1) conduct internal testing sessions on Dataland and address any issues, and (2) run research workshops with 8-17 year olds to evaluate Dataland. 

This DRG will be led by Dr. Sayamindu Dasgupta and PhD student Regina Cheng. The group will be run for 3-6 dedicated undergraduate students. The course will provide 2-5 HCDE 496 course credits, with the expectation that students will spend approximately 3 hours per week per credit. This DRG will be hybrid with a combination of virtual and in-person meetings. We will meet at a time that is convenient for all the students. 

Prerequisites: Strong speaking, reading and writing skills in the English language, a computer you can use during the project, ability to attend team meetings through an online conferencing platform or in-person, and a commitment to high-quality research are required. Willingness to work both in a team and independently is required. We strongly prefer candidates with passion and experience in usability testing, interview, and qualitative methods. Students do not need to have any prior experience in data science and programming.