International effort to improve data science in schools
September 17, 2018 at 7:00 am 2 comments
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
Entry filed under: Uncategorized. Tags: computer science, computing education, data science.
Computing Ed Research - Guzdial's Take 
1.
gasstationwithoutpumps | September 17, 2018 at 12:51 pm
Things looked good until I got to the “240 hours of instruction”. That is two full-year high-school courses—not going to happen except in an insignificant number of schools.
Upgrading the AP statistics classes to data science would be a hard enough task—and that is only about 120 hours of instruction. Even that would be a small effect (about 216,000 students took the AP Stats exam in 2017).
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Alan Fekete | September 22, 2018 at 3:06 am
[I am a member of the IDSSP team, but this comment is based more on my experiences developing a Data Science major for undergraduates] I’d like to respond to Mark’s question about “Does data science include computer science? statistics? probability?”. While there are many different definitions of Data Science, I would say that Data Science includes some, but not all, of Computer Science, and it includes some, but not all, of Statistics. There is the old joke that a Data Scientist is someone who knows more about computing than a statistician, and more about statistics than a computer scientist. For example, I don’t think a Data Scientist needs to know how a compiler works, or how an operating system is structured, or about planning and knowledge representation in AI, or graphics. Similarly, at our institution, the Data Science major has been designed to include the practical and computational parts of Statistics, but not the probability theory subjects.
Thus I think its reasonable to teach data science in a similar amount of time to what one would use to teach computer science; there are some topics that are in both, and some extra topics, but also plenty that are left out.