Teaching Visualization with Design Studies for Social Good
By: Uzma Haque Syeda
Tags: Design Studies, Social Good, Service Learning, Pedagogy, Data Visualization

As shared through course feedback surveys, the students liked the Service-Learning component of the technical course. Their comments echoed the following themes:

It provides real-world data science experience.

“In the classroom, we are often given clean, groomed data to work with, but through service learning, I was able to learn how to work with messy, real data that serves a real purpose.”

It provides professional experience to students.

“I had a chance to develop professionally and collaborate on a project on a team.” “I understand how to communicate with people in my community more.”

It Impacts students on a personal level.

“This experience definitely made the data feel a lot more personal.” “The power of being connected to an individual’s needs was powerful.”

Students make a positive impact on the community.

“This makes accurately portraying their data even more important to me, because I now know how important they are to the community and how much these visualizations can help them understand where they can improve.”

To learn more about Service-Learning and for resources on how to implement it into your teaching, please visit our website. More information and thoughts on Visualization for Social Good can be found in this medium post by Michael Correll.

How does DSLM handle typical design study pitfalls?

A typical design study can fall into many pitfalls as mentioned by Sedlmair and colleagues in their paper. However, the systematic layout and the supervision of a visualization expert in DSLM greatly reduces the chances of many of these pitfalls from occurring.

Pitfalls identified by Sedlmair and colleagues in their paper. The red ticks represent the pitfalls that DSLM handles through its systematic structure.

For example, DSLM prevents pitfall #1 (premature advance: jumping forward over stages) from happening in the first place as students cannot jump to another step without completing the previous ones and running it by their instructors.

Likewise, in the winnowing stage, the process of identifying the domain partners by the S-L organizers and the instructor goes through several winnowing stages, so the chances of having a premature collaboration are reduced to a great extent (pitfall #3). The systematic structure and regular feedback sessions from the instructors and the collaborators ensure that the students are on the right track, taking care of pitfalls 18 (learning the domain language too little/too much), 19 (too little abstraction), 20 (premature design commitment), and 22 (non-rapid prototyping).

For a more detailed discussion about how DSLM reduces the chances of regular design study pitfalls from occurring, we refer you to our paper.

Evaluation of DSLM with S-L:

We implemented DSLM with S-L across 5 semesters of visualization courses (3 undergraduate and 2 graduate courses). During this time, students worked with 7 different partner organizations, many of whom were recurring partners across different semesters.

Every semester, students and partners were asked open response questions on their learning experience with DSLM and S-L through a voluntary online survey form. We also interviewed three partners about their thoughts on the whole process. All of these responses were open-coded and 4 high-level themes were identified in both the student and partner responses using thematic analysis techniques. We list the themes with supporting quotes below:

Feedback from Students

Better Learning of the curriculum

“The service helped in applying course concepts in a real-world setting. We were solving real problems using what we learned in class.” “The experience and course concepts were directly linked.”

Impact on the community

“My service has made me interested in the idea of using data science skills to promote social Good.” “It helped me see the greater picture of what my skills can be used for.”

Efficiency in project completion

“This model not only gave me good grades, but I never got flustered…”

Amongst the positive comments, we also received some constructive criticisms. One such theme was as follows:

Disconnect between S-L Goals and Personal Goals

“A more technical project would have been in sync with what we learned in the class and hence a better application of the concepts.”

Feedback from Community Partners

Effectiveness of the course

“The students bring great technologies and fresh eyes to our challenges.”, “…it brought out trends in our data in ways we had not seen before.”

Success of the projects

“The quality matches the standards we display on our organization website” “It helped us understand the data we have.”

Learning experience for the partners

“I would change the goals if the students feel they can do a better job of something else and present a case for it.”

Overall, both students and partners expressed positive thoughts on the DSLM implementation with S-L. We also received a few comments for future improvements. For more details and results of our framework’s evaluation, please see our paper.

If you are interested in adopting DSLM in your course, please take note of the following!

Over the duration of the 5 semesters of DSLM implementation with S-L, we learned some valuable insights and reflected on what we could have done better. Therefore, if DSLM seems like something you want to try out, here are a few suggestions from us:

Implement DSLM with S-L

As we mentioned above, S-L brings bonus benefits to the process! So, why miss out?

Choose a domain problem that is easy to understand.

Remember that you have a tight timeline, so choose a domain problem that will not require students to do extensive background reading or training with domain experts. In our case, this was relatively easy as most non-profit community organizations focus on relatable topics such as education, volunteerism, neighborhood improvement, etc.

Work with pre-curated and “clean” data.

Along the lines of the previous suggestion, it is best to choose a domain partner who can provide “relatively clean data” that will not be a time sink for students to refine. It is important to note that, even “relatively clean data” will need a lot of processing as well but aim for a balance where this does not become excessive.

Structure and limit the amount of iteration

Design studies are iterative and lengthy. To provide iteration, but limit its duration, a pre-set number of feedback sessions with the collaborators should be determined at the beginning of the semester. This might hamper the full potential of the design study, but is a necessary compromise for an accelerated timeline.

Setting the stage right and plan ahead

If you are not using a Service-Learning facility to identify domain partners, make sure you are doing it way ahead of the semester. This will provide ample time to make sure that both you and the domain collaborator are on the same page.

Adjust pre-requisites to adapt to different semester lengths.

If you have a long semester, then students can have fewer prerequisites (prior skills) as there will be enough time for the instructor to teach and students develop skills. If you have a shorter semester, then increase the prerequisites so that students are adequately prepared and technically well equipped to tackle the problems.

Set realistic expectations and have signed contracts

It is very important for partners to understand that the student projects might end up not meeting the level of sophistication they are looking for. Likewise, it is also imperative for students to understand that these are real stakeholders and they need to have a quality deliverable by the end of the semester. For this purpose, a signed contract agreed upon between the students, instructor, and organization is essential.

Assign two groups to the same organization if possible

Another strategy to help alleviate the possible failure of a project is to assign at least two project groups to each organization.

Have more tightly defined guidelines.

This will ensure a more streamlined project execution. Some partners expressed that they prefer more straightforward deliverables and that graduate students tend to make more exploratory visualizations. We attribute this to the fact that graduate students were less strictly guided in each step owing to their expertise compared to the undergraduates. However, in future implementations, we will make sure that all projects are strictly guided irrespective of their levels.

Define clear Intellectual Property (“IP”) and data use guidelines.

Each partner organization in our implementation was required to prepare necessary privacy and/or data use consent forms for their collaborating student groups. This is very important to make sure that the final visualizations created by the students are used under fair policy.

Maintain effective communication

Although a no-brainer, we feel that a reminder to maintain effective communication both with students and community partners is absolutely essential for a successful implementation of DSLM.

Give your students a taste of real visualization research!

Design studies are a canonical form of research in the visualization field. The goal of DSLM is to empower students to step into the shoes of visualization researchers, and with S-L create a positive impact in the community. Even though it is a simplified version of an actual design study, it exposes them to the process rather than a lack of design study experience as is the case in current visualization courses.

We also look forward to future work implementing DSLM in large (50+ students) classrooms, as well as apply DSLM for expedited successful research projects (e.g., semester-long internships).

Data visualization has the power to produce social impact and positive change, and students have great potential to contribute to it as budding visualization researchers. Don’t you agree?