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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 2024

Journaling and Codesigning Grades as Data

Open to full-time undergraduate students. Non-HCDE majors are welcome to apply.

The objective of this research group is for students to attain a deeper understanding of how they interact with their grades, if they recognize grades as data points, how grades make them feel, and if there are practices around using grades that connect to the broader ideas of critical data literacy.
Students will engage in reflection on how they interpret, interact with, and perceive grades as data throughout the academic quarter. This includes:

  1. Weekly journal entries
  2. Weekly in-person meetings with co-created discussions and activities
  3. Two passes of autoethnographic data analysis on the journal entries
  4. Potential artifact to sum up learning throughout the quarter (zine, skit, grading method, etc.)

This work is part of a larger research project to understand grades and feedback as data. Students will have the choice to consent to additional analysis of their journal entries and discussion/activity data to be analyzed and used for further research outcomes.

Contact Mina (zavarym@uw.edu) with any questions. 

Winter 2024

Food, Caste and Technology in Seattle

Directed Research Group on Critical Caste and Tech Studies, Winter 2024

On February 21, 2023, Seattle City Council passed a law amending anti-discrimination protections in employment, public places, housing, and contracting to include caste as a protected class. After a long and sustained effort of over 20 years of anti-caste organizations in the US, Seattle became the first city in the United States to ban caste discrimination. As a system of oppression through social stratification, caste is assigned at birth, is immutable, and is reinforced through casteist practices. Caste discrimination in the US cannot be understood without an understanding of the caste demographics of the Hindu Indians residing in the US. A significant majority of Hindu Indians residing in the United States identify themselves as belonging to the General or upper caste. Caste is often reinforced through the separation of food and specific food practices which traveled with Indian migrants to other parts of the world, as did the system of caste. Within the upper caste diaspora, the tendency is to treat caste as a thing of the past and align themselves with modern, progressive claims of ‘castelessness’ or being free of the privileges that come with being the dominant castes (Vaghela, 2022). 

In this DRG, we aim to develop a shared understanding of the analytic of caste and caste logics, as they travel with the Indian diaspora. Specifically, in the first half of the quarter, this DRG will involve an introductory dive into existing literature on caste, technology and food. In the second half of the quarter, participants will be actively involved in a small project that aims to provide empirical evidence of caste practices revolving around food in the Seattle, Redmond, and Bellevue area, with a focus on analyzing reviews of Indian restaurants.  

This DRG is open to Masters and PhD students.   

This DRG will be led by PhD students Sayan Bhattacharjee and Priya Dhawka and sponsored by Assistant Professors Sucheta Ghoshal and Sayamindu Dasgupta.  

 

Autumn 2023

Designing a Child-Friendly User Interface to Support AI-Bias Education

We invite 2-6 dedicated Undergraduate or Masters students who have a strong design background and experience with visual prototyping to participate in this 2-credit DRG. 

Background: The focus of this DRG is to design a child-friendly user interface for an existing system that helps children learn about AI bias. Students will brainstorm and implement design ideas to adapt the user interface to suit a classroom environment. As part of this, students will work on iteratively creating wireframes, prototypes, and high-fidelity mockups to effectively communicate design concepts. 

Facilitation: This DRG will be primarily led by PhD student Aayushi Dangol, and advised by Assistant Professor Sayamindu Dasgupta.

Winter 2023

A Systematic Literature Review of Concept Inventories for Introductory Computer Science Education

Introduction/Description
Concept Inventories (CIs)  are a well-documented tool in education research, generally defined as a set of questions which enable educators to identify not only the concepts students are struggling with, but also the specific misconceptions which they hold. This idea originated with the famous Force Concept Inventory in physics, which was first published in 1998 and led to a subsequent revolution in introductory physics education throughout the nation.

Computer science education researchers have been attempting to precipitate a similar revolution over the last 15 years. Efforts are ongoing, but still far from complete. The last thorough review of literature in the space of computer science CIs was conducted in 2014, nearly a decade ago. With many recent advancements in the field, it is important to once again review all the literature in an organized manner. The goal of this DRG is to prepare a systematic literature review of computer science concept inventories and aim for submission to the ACM Conference on International Computing Education Research (ICER 2023).

Objectives and Student Expectations
Students who join the DRG will receive the experience of working on a full research project with a concrete deliverable. DRG members will be expected to do the following:

  • Attend a weekly one-hour meeting to discuss individual and group progress and plan next steps.

  • Contribute to both reading and writing over the course of the literature review.

  • Dedicate at least 5 hours a week to searching, reviewing, and discussing the literature surrounding concept inventories in computer science.

Students will leave the DRG with a strong understanding of how to conduct a systematic literature review and write an academic paper, both essential and valuable research skills. Additionally, students will receive the opportunity to contribute to an academic research paper if interested.

The lead researcher on this project, PhD student Murtaza Ali, is also working on developing a tool which will concretely implement an existing concept inventory in Python and allow students to work through and slowly diminish misconceptions. There is a possibility that dedicated and interested students who show outstanding commitment in the literature review phase of the project will also receive the opportunity to contribute to the development of this tool.

Who Should Join This DRG?

We are looking for 8-10 students (grad or undergrad) who meet the following qualifications:

  • Programming experience equivalent to one introductory course (formally known as CS1). While you will not need to program in this DRG, familiarity with basic computer science concepts is essential for understanding the literature surrounding computer science CIs.

    • Note: Introductory computer science students are especially encouraged to apply, as you can apply your own experiences with misconceptions to the work.

  • The ability to commit to at least 6 hours of work a week (including DRG meetings).

  • Strong reading and writing skills, with the willingness to actively work on improving these skills. It is not strictly necessary that you have experience with research papers specifically–interest in the subject matter is more important.

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.