Overview

For my spring semester Design Studio II course, I developed an informal curriculum called "Artful Analytics." My curriculum consists of a series of 60-minute art-based sessions for learning about data science topics, particularly data structuring and visualization. I designed it with learners in third through fifth grade who participate in an informal afterschool environment in mind.

In the spirit of combining a scientific discipline (data science) with a creative pursuit, I use the term "data-art" to refer to this process of using techniques from both domains to create a representation ("data-artwork").

Design Challenge

My aim for this curriculum is that an interest in data science will emerge in the process of activating learners’ existing interests in art.

Design Goals

Design an activity structure that introduces learners to a new data-related concept, provides them with opportunities to practice that concept and encounter examples of it in context, and apply it in their own data-artwork
Support learners' abilities to incorporate "data science" talk into their interpretations of symbolic art and to situate themselves within the data collection and visualization process

Learning Objectives

Learners engaging with the curriculum will acquire data-science skills, including the abilities to....

Identify the key components of a data-art visualization (data, categories, variables, visual variations, constants)
Interpret the underlying data in a data-artwork by using a legend/key or other available cues
Create their own rules to creatively represent data that relates to their experience/identity, mapping from their data set to their data-artwork

Learners engaging with the curriculum will cultivate a data-science mindset, characterized by the abilities to...

Recognize the value of the data available in their everyday experiences
Be prepared for future experiences with data in both formal and informal learning settings
Develop confidence in their ability to do data science

Final Materials

Session Structure

Sessions begin with time to explore part of the data-art making process through facilitator modeling or example data-artwork. Then, learners practice applying a data-art making skill that was included in the modeling or example they explored. Practice activities may take place collectively, in small groups, or independently, and the format varies across sessions. Finally, learners share their data-artwork and discuss the observations they made and/or share feedback.

Session Topics and Activities

Sessions 1-2: Learners generate food-related data and are introduced to the process of representing data.

Session 3: Learners create abstract self-portraits full of hidden meanings that can be understood with a legend.

Session 4: Learners interpret a professional artist's data-art.

Sessions 5-6: Learners create and present their own data-art in the last two sessions.

Extension activity: Learners can explore the concepts of variables and variation as they measure their heart and breathing rates before and after a dance party.

Design Conjectures

Learning Conjecture

How will the learning objectives be achieved?

By collecting data from their own lives, creating data-artworks based on these data, and sharing these with others, learners will gain confidence in interacting with data and recognize the value of data representations.

Engagement Conjecture

What will sustain learners' motivation?

By working with data from their own lives, creatively displaying their data, and sharing their data-artwork with others, learners will leverage their existing interests, develop new skills, and discover new things about themselves and their peers through their data-art.

Equity Conjecture

How will this learning experience contribute to a better future?

By exploring data across a variety of personally meaningful topic, learners will be better prepared to interact with data in a way that enables them to make sense of and solve real-world problems.

Background Research

Data Science Learning

A lack of representational competence (i.e., understanding the grammar of visualizations) reduces learners’ ability to make meaningful use of data visualizations. When creating visualizations, learners often rely on the same types of representations, even when they are not the most apt choices.

⚡ The “data moves” that develop representational competence must be explicitly taught.

Art & Making

The process of multimedia creation enables learners to better engage with subject material, especially complex topics.

⚡Learners' data visualizations can serve as boundary objects through which people can productively share ideas and representations.

Elementary Math Learners

There’s a lot of math-phobia to overcome at this age. Additionally, when learners are already interested in and have pre-existing knowledge about the to-be-explored topic, they will be better prepared to explore the topic through related data.

⚡It is critical for learners to experience “math by another name." Uncovering the math in existing interests makes math seem like something learners already do, rather than something inherently challenging.

Influential Ideas from the Learning Sciences

Cognitive Apprenticeship

Process by which a novice develops expertise in cognitive skills that requires a learning environment in which thought processes are visible.

⚡Learners will develop data visualization skills through a process of exploration, observation, guided practice, reflection, and feedback. Facilitators will model and articulate expected practices to prepared learners to complete the activities with fading support.

Constructionism

Belief that learning is an active process that occurs through creating personally and socially meaningful artifacts that build relationships between old and new knowledge.

⚡Handling data in a tangible way will make the process of visualizing data more concrete. The data-artwork that learners produce can be used as “tools to think with,” supporting their ability to make data-based inferences.

Self-Determination Theory

People are motivated to learn and persist in the learning process when three needs are fulfilled: autonomy, competency, and belonging.

⚡Activities will maintain learners’ engagement over time as they connect with their data, each other, and the progress they made. Learners will make meaningful choices in their data-art that reflect their own experiences.

Thick Authenticity

The four “kinds” of authenticity (real-world, personal, disciplinary, and assessment) are interdependent and mutually support the learning process.

⚡The usefulness and relevance of data visualizations to learners’ current lives and possible futures will be prioritized. Sessions will include real-world examples that activate learners’ prior experiences with each topic. Activities will include opportunities for learners to incorporate their own data and represent themselves in their data-art making.

Design Iterations

After testing my initial prototype with my cohort, I facilitated the sessions and activities at a local Boys & Girls Club. Creating the informal curriculum took many weeks, and I was able to reach my final design by using the following design-test-revise cycle:

Try out activity. Each week, I created a new set of activities to test at a local Boys & Girls Club.
Test activity. Feedback from each testing session informed the development of the next set of activities.
Repeat. I repeated this process for six weeks.
Tie it all together. At the end, I linked the activities to package them into a curriculum, revising all materials to better meet my learning objectives and the needs of my target learners.

FIRST ITERATION

What I wanted to learn:

How did learners respond to the data-art process that I developed?
What are they initially imagining about making a meaningful visualization of this group's data?

What I learned:

The learners found food to be an easy topic to generate data around and noted that they already are aware of common ways to categorize foods (e.g., flavor, color, geographic origin, nutritional content).
Learners didn't have enough time or support to consider alternative sorting methods beyond what they what they came up with. They thought that having another round of sorting after seeing the other group's results and looking at the examples might activate their imaginations.

SECOND ITERATION

What I wanted to learn: How my intended learner population respond to my data-art making process?

What I learned:

All the learners were able to contribute something meaningful to the group's data set. They also found the examples inspiring and shared how they activated their prior knowledge and experiences. Together, these interactions suggested that they found this activity to be meaningful and appealing.
Unexpectedly, I ran out of time and wasn't able to facilitate the full activity I had planned. I needed to re-think how I managed the one-hour time block so that the session plan was more "right sized."
Additionally, the secret code activity did not create opportunities for learners to engage with data structuring. It was fun and engaging but its connection to data concepts was tenuous. I realized that I needed to strike a balance between aligning with their interests and achieving the intended learning goals.

THIRD ITERATION

What I wanted to learn: How do the curricular materials support learners in...

Incorporating data-related vocabulary into their data-art interpretations (i.e., articulating patterns they noticed through looking at data)?
Making their own data-art?

What I learned:

Learners needed more explicit practice in incorporating new vocabulary into their discussions; exposure to the terms was not sufficient. I recognized the value of returning to these terms and asking them to apply them in another context in the next session.
Learners had difficulty transitioning from data-art interpretation to creation. They found it challenging to extend the group and practice activities into their own data-art creation and approaching other data-art examples. It might be useful for learners to see someone model their data-art making process before they'd be ready to do it on their own.

FOURTH ITERATION

What I wanted to learn: How did the new curricular materials support learners' interpretation and creation of data-artwork?

What I learned:

The facilitator modeling portion of the activity was beneficial. Learners were able to use the same processes themselves.
Facilitator and staff participation in the data-art presentation process evened the power dynamics of the learning environment.
Learners needed additional time to complete the activity. Between the two planned sessions, a Boys & Girls Club staff member supported an additional session without me to get the learners started in their data-art making process. In the future, I would make this additional session a formal part of the curriculum.
Due to the openness of the assignment, learners were excited to take on a topic that they were personally interested in. Most of the data-art included representations of learners' personal collections. However, some of these collections were very large and the resultant datasets were too large to complete an artwork in the time allotted. Learners would benefit from some simple constraints around the project so it would be more achievable given the time allotted.

Design Critique

Overall Strength: Support for Mental Model Building

I designed the progression of activities so that learners were continuously returning to previously learned concepts and deepen their understanding of them. Each session offered a new topic area for learners to apply their skills and knowledge, so that learners had numerous opportunities to transfer their experiences into new contexts.

Future Improvements: Design Justice

Were I to re-do part of my design process, I would have spent more time getting to know my initial learners. I assumed that middle school aged learners would be more interested in my activities, and designed my first session with them in mind. When I arrived on site, I was met with a room full of 2nd through 5th graders! There was misalignment in what I had created with what my learners were able to engage with successfully. By getting to know my initial learners, I would have expected this younger age group and geared my activities and materials more toward their needs, avoiding needing to learn from their struggles and misunderstandings.

Emergent Insights

1-hour of afterschool programming ≠ 1-hour of learning.

Considering learners’ needs in the afterschool space is critical!
Time within sessions can be a constraint.

Sessions don’t exist in a vacuum.

Time across sessions can be an asset!
I was able to better achieve cohesion and congruity by referencing activities and finishing projects over multiple days.

Using examples and modeling the facilitated the learning process.

Examples and modeling served as approachable entry-points to data-art.
These materials activated curiosity and/or prior experiences and gave learners a starting point.

By using data as an artistic medium, learners noticed new patterns.

Learners discovered new things about themselves and their peers through data
The experience of making art "made the invisible visible" and tangible!

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