Posts tagged ‘data science’

Let’s program in social studies classes: NSF funding for our work in task-specific programming languages

If we want all students to learn computer science (CS for All), we have to go to where the students are. Unfortunately, that’s not computer science class. In most US states, less than 5% of high school students take a course in computer science.

Programming is applicable and useful in many domains today, so one answer is to use programming in science, mathematics, social studies, and other non-CS classes. We take programming to where the students are, and hope to increase their interest and knowledge about CS. I love that idea and have been working towards that goal for the last four years. But it’s a hard sell. I told the story in 2018 (see post here) about how the mathematics teachers rejected our pre-calculus course that integrated computing. How do we help non-CS teachers to see value in computing integrated into their classes?

That’s the question Tammy Shreiner at Grand Valley State and I get three years to explore, thanks to a new grant from the US National Science Foundation in the research strand of the “CS for All” Program. Tammy teaches a course on “Data Literacy for Social Studies Teachers” at GVSU, and she (with her colleague Bradford Dykes) have been building an open educational resource (OER) to support data literacy education in social studies classes. We have been working with her to build usable and useful data visualization tools for her curriculum. Through the grant, we’re going to follow her students for three years: From taking her pre-service class, out into their field experiences, and then into their first classes. At each stage, we’re going to offer mentoring and workshops to encourage teachers to use the things we’ve showed them. In addition, we’ll work on assessments to see if students are really developing skills and positive attitudes about data literacy and programming.

Just a quick glimpse into the possibilities here. AP CS Principles exam-takers are now about 25% female. AP US History is 56% female exam takers. There are fives times as many Black AP US History exam-takers as AP CSP exam-takers. It’s a factor of 14 for Hispanic students. Everyone takes history. Programming activities in a history class reach a far more diverse audience.

I have learned so much in the last couple of years about what prevents teachers from adopting curriculum and technology — it’s way more complicated than just including it in their pre-service classes. Context swamps pre-service teaching. The school the teacher goes to influences what they adopt more than what they learned pre-service. I’ve known Anne Ottenbreit-Leftwich for years for her work in growing CS education in Indiana, but just didn’t realize that she is an expert on technology adoption by teachers — I draw on her papers often now.

Here’s one early thread of this story. Bahare Naimipour, an EER PhD student working with me, is publishing a paper at FIE next month about our early participatory design sessions with pre-service social studies teachers. The two tools that teachers found most interesting were CODAP and Vega-Lite. Vega-Lite is interesting here because it really is programming, but it’s a declarative language with a JSON syntax. The teachers told us that it was powerful, flexible — and “overwhelming.” How could we create a scaffolded path into Vega-Lite?

We’ve been developing a data visualization tool explicitly designed for history inquiry (you may remember seeing it back here). We always show at least two visualizations, because historical problems start from two accounts or two pieces of data that conflict.

As you save graphs in your inquiry to the right, you’re likely going to lose track of what’s what. Click on one of them.

This is a little declarative script, in a Vega-lite-inspired JSON syntax. It’s in a task-specific programming language, but this isn’t a program you write. This is a program the describes the visualization — code as a concise way of describing process.

We now have a second version where you can edit the code, or use the pull-down menus. These are linked representations. Changing the menu changes the code and updates the graph. Changing the code updates the menu and the graph. Now the code is also malleable. Is this enough to draw students and teachers into programming? Does it make Vega-Lite less overwhelming? Does it lead to greater awareness of what programming is, and greater self-efficacy about programming tasks?

We just had our first in-service teacher workshop with these tools in August. One teacher just gushed over them. “These are so great! How did I not know that they existed before?” That’s easy — they didn’t exist six months ago! We’re building things and putting them in front of teachers for feedback as quickly as we can, in a participatory design process. We make lots of mistakes, and we’re trying to document those, too. We’re about applying an HCI process to programming experience design — UX for PX.


If you know a social studies teacher who would want to keep informed about our work and perhaps participate in our workshops, please have them sign up on our mailing list. Thank you!

September 14, 2020 at 7:00 am 14 comments

Data science as a path to integrate computing into K-12 schools and achieve CS for All

My colleague Betsy DiSalvo is part of the team that just released Beats Empire, an educational game for assessing what students understand about middle school computer science and data science https://info.beatsempire.org The game was designed by researchers from Teachers College, Columbia University; Georgia Tech; University of Wisconsin, Madison; SRI International; Digital Promise; and Filament Games in concert with the NYC Dept. of Education. Beats Empire is totally free; it has already won game design awards, and it is currently in use by thousands of students. Jeremy Roschelle was a consultant on the game and he just wrote a CACM Blog post about the reasoning behind the game (see link here).

Beats Empire is an example of an important development in the effort to help more students get the opportunity to participate in computing education. Few students are taking CS classes, even when they’re offered — less than 5% in every state for whom I’ve seen data (see blog post here). If we want students to see and use computing, we’ll need to put them in other classes. Data science fits in well with other classes, especially social studies classes. Bootstrap: Data Science (see link here) is another example of a computing-rich data science curriculum that could fit into a social studies class.

Social studies is where we can reach the more diverse student populations who are not in our CS classes. I’ve written here about my work developing data visualization tools for history classes. For a recent NSF proposal, I looked up the exam participation in the two Advanced Placement exams in computer science (CS Principles and CS A) vs the two AP exams in history (US history and World history). AP CS Principles was 32% female, and AP CS A was 24% female in 2019. In contrast, AP US History was 55% female and AP World History was 56% female. Five times as many Black students took the AP US History exam as took the AP CS Principles exam. Fourteen times as many Hispanic students took the AP US History exam as took the AP CS Principles exam.

Data science may be key to providing CS for All in schools.

April 27, 2020 at 7:00 am Leave a comment

International effort to improve data science in schools

I’ve been involved in this project over the last few months. (Where “involved” means, “a couple of phone conversations, and a set of emails about frameworks, standards, and curricula, and I missed every physical meeting.”) Nick Fisher has drawn together an impressive range of experts and professional societies to back the effort. It’s not clear where it’s going, but it is indicative of a growing worldwide interest in “data science” in schools.

The definition of “data science” is fuzzy for me, almost as fuzzy as the term “computational thinking.”  Does data science include computer science? statistics? probability? I think the answer is “yes” to all of those, but then it might be too big to easily teach in secondary schools. If we’re struggling to teach CS to teachers, how do we teach them CS and statistics and probability?

And if budgets and schedules are are a zero-sum game, what do we give up in order to teach data science?  For example, teacher preparation programs are packed full. What do we not teach in order to teach teachers about data science?

This group of experts knows a lot about what works in data science. Their opinion on what students need to know creates a useful measuring stick with which to look at the several data science classes that are being created (such as Unit 5 in Exploring CS). There’s some talk about this group of experts might develop their own course. I’m not sure that it’s possible to create a course to work internationally — school systems and expectations vary dramatically. But a framework is useful.

The aim of the International Data Science in Schools Project (IDSSP) is to transform the way data science is taught the last two years of secondary school. Its objectives are:

1. To ensure that school children develop a sufficient understanding and appreciation of how data can be acquired and used to make decisions so that they can make informed judgments in their daily lives, as children and then as adults

2. To inspire mathematically able school students to pursue tertiary studies in data science and its related fields, with a view to a career.

“In both cases, we want to teach people how to learn from data,” Dr Fisher said.

Two curriculum frameworks are being created to support development of a pre-calculus course in data science that is rigorous, engaging and accessible to all students, and a joy to teach.

  • Framework 1 (Data Science for students). This framework is designed as the basis for developing a course with a total of some 240 hours of instruction.
  • Framework 2 (Data Science for teachers). As a parallel development, this framework is designed as the basis for guiding the development of teachers from a wide variety of backgrounds (mathematics, computer science, science, economics, …) to teach a data science course well.

Dr Fisher said that the draft frameworks will be published for widespread public consultation in early 2019 before completion by August.

“We envisage the material will be used not just in schools, but also as a valuable source of information for data science courses in community colleges and universities and for private study.” For further information: idssp.info@gmail.com, or visit www.idssp.org

September 17, 2018 at 7:00 am 2 comments


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