The Objectives of the 2017 conference were:
- To connect educators, curriculum developers, and software developers around the topic of bringing about more data science education.
- To put forward suggestions for needed technology and curriculum innovations to encourage learners to work with data.
- To form and strengthen collaborations, leading to new proposals and projects that bring about more learner engagement with data.
Conference strands
The conference had two strands, Teaching & Learning, and Technology, described below.
Teaching & Learning
Designed for those thinking about curriculum development, this strand addressed the pedagogical challenges and opportunities associated with making use of data technologies in educational settings. Sessions included data-driven learning experiences, discussion of lessons learned by curriculum designers, and hearing perspectives on how to apply these experiences and lessons.
Technology
Designed especially for those with programming experience, this strand focused on software development. Participants built relevant, timely skills that were leveraged immediately for creating a web app.
| Wednesday, February 15 | |
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| 5:00 pm – 6:00 pm |
Registration and Socializing |
| 6:00 pm – 7:00 pm |
Dinner |
| 7:00 pm – 8:30 pm |
Panel Presentation and Discussion with Participants. Panelists presented perspectives on issues confronting educators wishing to increase learner engagement with data. How do we integrate acquisition of data science knowledge and skills with curricula in grades K–14 and in informal learning situations? What are the most important experiences for learners to have with data? What are characteristics of the technology learners need for this to take place?
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| Thursday, February 16 | ||
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Teaching & Learning
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Technology
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| 8:00 – 8:30 am |
Registration and Socializing
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| 8:30 – 9:00 am | Welcome and Overview of Conference
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| 9:00 – 10:15 am | Illustrations of Data Science Integration in Subject Matter Teaching. Try out online materials from projects that have integrated technology for data exploration and analysis into learning STEM content. Join a discussion group led by one of the developers to experience the results of their work and learn about issues they faced relating to pedagogy, content development, and working with teachers.
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Connecting Technologies I. Data comes from somewhere, whether it’s a text file, a large database, a simulation, or collaborative data gathering. In workshop format you’ll learn how to use CODAP to visualize data from a source technology of your choice, working with other participants to plan and execute a demonstration of a new CODAP plugin. Novices will work by modifying a simple example while those with software development expertise can start from scratch or from an existing data source application.
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| 10:15 – 10:45 am | Software demos and networking opportunities Demos from 10:20 – 10:40
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| 10:45 am – 12:00 pm |
Innovations Needed to Support Data Intensive Curriculum Development I.
This first of a two-part session will focus on the question: “What does it mean to be data literate in the age of big data?” Sub-questions might be: What are the overlaps and differences between “traditional” statistics and data science? What kinds of thinking skills are critical to data science/data literacy?
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Data Technology Integration with Online Curricula Learners’ interactions with data can be integrated with learning subject matter concepts. View and experience different ways to bring this about. Discuss technological hurdles and user experience issues. Develop wish lists of desired capabilities.
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| 12:00 pm – 1:30 pm | Lunch
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| 1:45 – 3:00 pm |
Reports from the Trenches. Sample work from projects that are currently developing data intensive curriculum materials including Building Models from the Concord Consortium, ESTEEM from North Carolina State University (NCSU), Zoom In Science from EDC, Terra Populus from the Minnesota Population Center, and Ocean Tracks from EDC.
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Connecting Technologies II. Continuation of morning session with special attention to how CODAP lets a plugin know that the user has done something of interest such as select data.
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| 3:00 – 3:45 pm | Software demos and networking opportunities Demos from 3:10 to 3:30
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3:45 – 5:00 pm |
Innovations Needed in Data Intensive Curriculum Development II. This session will build on our “answers” to the first question in Session 1 and will focus on the follow-up question: “How can we support students in becoming data literate?” A sub-question might be: In what ways can/should data science be integrated into a variety of disciplinary topics?
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Using Simulations and Modeling Environments as Data Sources. Discuss the unique challenges of using simulated and model-based data sources with data analysis platforms. These challenges can be both technical and pedagogical. How do the technical demands on the system interfere with or support an activity’s learning goals? Discuss obstacles that exist from a design perspective (user facing) and performance perspective (underlying computational limitation). Developers of the Molecular Workbench, Sage Modeler, Desmos, and statistical simulation scenarios, as well as learning scientists and designers of learning environments will facilitate discussions around these issues.
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| 5:00 – 6:00 pm |
Unconference Preparation Come up with your own ideas for the next morning’s unconference. At an unconference participants collaboratively decide on breakout session topics, volunteer to moderate a session, self-assemble panels of experts, serve as recorders and report back in wrap-up sessions. Descriptions of session topics are presented aloud and posted for sign-up.
Socializing |
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| Evening |
Self-organized dinner groups.
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| Friday, February 17 | ||
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Teaching & Learning
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Technology
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| 8:30 – 9:45 am |
Unconference Participant-organized small-group rounds, 25 minutes each round. Proposals for sessions will be announced at the end the day on Thursday.
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| 9:45 – 10:30 am | Software demos and networking opportunities Demos from 10:00 to 10:20
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| 10:30 – 11:45 am |
Data Games and Data Science Games. Play data games and data science games, hear short presentations of the creation process, and discuss strategies for using this genre of game in curriculum materials.
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Open-Source Opportunities. In this session we’ll be talking about all kinds of open source scenarios as they relate to Data Science Education Technology. We’ll look at open data, open source software, and open educational resources and think together about how they can be used well for data science education. Our goal will to be to come up with “guiding beacons” to help build communities, encourage collaboration and dramatically enhance learners’ experiences working with data.
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| 11:45 am – 1:15 pm | Lunch
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| 1:45 – 3:00 pm | Novel Educational Opportunities for Data Exploration. Join a small group discussion. What untapped opportunities exist for accessing compelling data? What are the characteristics of a data set that make it suitable and accessible for relative novices? How can we work together—in schools, in informal educational settings, in our communities—to build a data literate society?
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DSET Design. Brainstorm plans for the technologies necessary for data science education, what it looks like, and how to get it developed.
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| 3:00 – 3:45 pm | Software demos and networking opportunities Demos from 3:10 to 3:30
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3:45 – 5:00 pm |
Final Session for All—DSET Community Building Presentations
DSET Community Building.
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| 5:00 pm |
Conference ends |
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