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Artful Analytics:
Overview
Overview
For my spring semester Design Studio II course, I developed an informal curriculum called "Artful Analytics." My curriculum consists of a series of 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.
My aim for the curriculum is that an interest in data science will emerge in the process of activating learners’ existing interests in art. To support learners in reaching this goal, I created a series of sessions, each with their own goals that build on each other. 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").
My Challenge
My Challenge
My task was to design a repeatable learning experience related to a personally meaningful topic. I combined two of my passions: data science and arts and crafts. I focused on combining these topics for an elementary-aged learning population because I wished I had been exposed to data science topics at a younger age in a way that was fun and relevant. I also felt that an informal afterschool space was the ideal environment for my design because these settings emphasize freedom, imagination, and exploration in the learning process.
My Design
My Design
Design Goals
Design Goals
Through my design, I wanted to...
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
Learning Objectives
Learners engaging with the curriculum will...
Acquire data-science skills:
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
Cultivate a data-science mindset:
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
Final Materials
Overview
Overview
"Artful Analytics" is an informal curriculum that is made up of six sessions. Each session is intended to be implemented in 60-minutes for learners in 3rd-5th grade and is designed to be facilitated in informal education settings, such as after school programs. Learners share their own data-art throughout the experience. Although each session has its own focus, individual sessions build on each other. Ideally, the same learners will participate in all six sessions.
Session Structure
Session Structure
Sessions share a common structure: exploring data visualization examples for inspiration, practicing creating data visualizations, and sharing these to learn from each others' experiences. Sessions begin with time to explore part of the data-art making process through facilitator modeling or example data-artwork. Then, learners have time to practice applying a data-art making skill that was included in the modeling or example they explored. Practice activities 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
Session Topics and Activities
In the first and second sessions, learners generate food-related data and are introduced to the process of representing data. In the third session, learners create abstract self-portraits full of hidden meanings that can be understood with a legend. In the fourth session, learners interpret a professional artist's data-art. Finally, learners create and present their own data-art in the last two sessions. I also created an extension activity in which learners can explore the concepts of variables and variation as they measure their heart and breathing rates before and after a dance party.
Supports for Sharing Observations and Feedback
Supports for Sharing Observations and Feedback
Design Conjectures & Conjecture Map
Design Conjectures & Conjecture Map
I created conjectures below as a starting point in setting up an experience that would lead to my intended learning outcomes.
Learning Conjecture
Learning Conjecture
How I expect to achieve my learning objectives
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
Engagement Conjecture
How I expect to invite and sustain participation
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
Equity Conjecture
How I expect to 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.
Conjecture Map
Conjecture Map
My Design Process
My Design Process
Research
Research
To get started, I conducted research in three major areas: data science learning, art and making, and elementary math learners.
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.
Design implications: “Data moves” that develop representational competence must be explicitly taught.
Art and Making
The process of multimedia creation enables learners to better engage with subject material, especially complex topics.
Design implications: Data visualizations can provide 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.
Design implications: 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.
Theoretical Lenses
Theoretical Lenses
In addition to conducting my own research, I also incorporated ideas from the learning sciences:
Cognitive Apprenticeship
Cognitive apprenticeship refers to the process by which a novice develops expertise in cognitive skills that requires a learning environment in which thought processes are visible.
How I used this lens: Learners developed data visualization skills through a process of exploration, observation, guided practice, reflection, and feedback. Facilitators modeled and articulated expected practices so that learners were prepared to complete the activities with fading support.
Constructionism
Constructionism is the belief that learning is an active process that occurs through creating personally and socially meaningful artifacts that build relationships between old and new knowledge.
How I used this lens: Handling data in a tangible way made the process of visualizing data more concrete. The data-artwork that learners produced were used as “tools to think with,” supporting their ability to make data-based inferences.
Self-Determination Theory
Self-determination theory posits that people are motivated to learn and persist in the learning process when three needs are fulfilled: autonomy, competency, and belonging.
How I used this lens: Activities were designed to maintain learners’ engagement over time as they connected with their data, each other, and the progress they made. Learners made meaningful choices in their data-art that reflected their own experiences.
Thick Authenticity
Thick authenticity recognizes that the four “kinds” of authenticity (real-world, personal, disciplinary, and assessment) are interdependent and mutually support the learning process.
How I used this lens: The usefulness and relevance of data visualizations to learners’ current lives and possible futures was prioritized in my activities. Sessions included real-world examples that activated learners’ prior experiences with each topic. Activities included opportunities for learners to incorporate their own data and represent themselves in their data-art making.
My Iterations
My 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
FIRST ITERATION
Design features: My first iteration was a group activity where learners could experience data generation and visualization related to something personal to them: their favorite foods. Learners generated individual lists of their favorite foods, categorized the favorite foods across the group, presented their categories and decision-making process, and looked at "creative" examples of visually presenting food-related data for future inspiration.
Testing: This iteration was tested with students in my learning engineering cohort.
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
SECOND ITERATION
Design features: In this iteration, I tested the first two sessions of the curriculum. These were intended to introduce learners to the data-art making process.
Session 1: Learners generated and sorted data about their favorite foods through food preference exploration, generating individual lists of favorite foods, categorizing the foods people listed, and presenting the categories and decision-making process.
Session 2: Learners represented information symbolically using a learner-created code through exploring of ancient and fictional alphabets, practicing visual variations, contributing a unique letter to the class' shared code, and writing their name in code.
Testing: This iteration was tested with about 10 2nd-4th graders at the local Boys & Girls Club.
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
THIRD ITERATION
Design features: Now that learners were familiar with data-art, I wanted to support their "data talk" in the next two sessions.
Session 3: Learners arranged data and made data-based hypotheses based on learners heartbeats and breathing through looking at real-world health data visualizations, measuring their own heart and breathing rates before and after a burst of exercise, and discussing the patterns they noticed across the group's data.
Session 4: Learners created data visualizations that represented themselves and learned about others by creating symbolic self-portraits. To do so, they explored the facilitator's self-portrait, created individual data-art self-portraits, traded self-portraits with a partner to learn something new about them, and then discussed the patterns they noticed across the group's data portraits.
Testing: This iteration was tested with about 10 2nd-4th graders at the local Boys & Girls Club.
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
FOURTH ITERATION
Design features: In the last two sessions of the program, I developed materials to support learners in creating their own data art.
Session 5: Learners interpreted and made sense of a real artist's data-art related to their personal collections (books, mobile apps, clothes). They completed a "data-art scavenger hunt" and participated in a group discussion where each group shared what they learned about the artist from the data-artwork they looked at.
Session 6: Learners created and presented their own personal data-artworks. Learners presented their data-artwork to the group and gave and received peer feedback.
Testing: This iteration was tested with about 10 2nd-4th graders at the local Boys & Girls Club.
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 alloted.
FIFTH ITERATION
FIFTH ITERATION
Based on what I noted with each iteration, I made the necessary improvements to the entire set of sessions. Then, I arranged them in a sequence that supported the learners' data science skill and mindset development, tying everything together to create a cohesive curriculum that can be accessed in the section at the top of the page.
My Critique
My Critique
Overall Strength: Support for Mental Model Building
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
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.
My Insights
My 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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