On February 14, 2023, a bipartisan group of US legislators introduced the Data Science and Literacy Act with the goal of boosting access to data science education and building “America’s 21st century STEM workforce”. We sat down with guests Zarek Drozda, Anna Bargagliotti, Christine Franklin and Steve Pierson to discuss the news and to hear why data science education is “the new apple pie”.
- Zarek Drozda is director of the Data Science 4 Everyone coalition.
- Anna Bargagliotti is graduate program director and professor of mathematics at the Seaver College of Science and Engineering, Loyola Marymount University.
- Christine Franklin is the American Statistical Association’s K-12 statistical ambassador.
- Steve Pierson is the ASA’s director of science policy.
Check out our full conversation below or on YouTube.
Timestamps
- The state of data science education in the United States (3:31)
- What will be the main impacts of the Data Science and Literacy Act? (9:14)
- Professional development support for teachers and teacher-educators (13:06)
- How much money is needed to deliver data science education? (18:53)
- Developing a data science curriculum (27:09)
- Building confidence in data, statistics, and technology (31:54)
- Learning from, and making connections with, international colleagues (37:03)
Quotes
“Most of our teachers in US schools, math teachers, have not had any formal training in statistics. Or if they have, it’s been maybe one course. They’re very uncomfortable with trying to implement these standards [for data science and statistics education]. And it’s just going to require a tremendous amount of professional development. Sounds easy in theory to deliver professional development, but very difficult in practice.” (10:18)
“We know that in aggregate, between federal, state, private and local funding, we’re going to have to create the necessary resources to make sure that our K-12 public education system can prepare students for a world that’s changing super fast, and the K-12 system moves super slow in how it adapts to new content. And so really it’s both about what can we do to upskill data science, data literacy skills [and] it’s also about how do we help the system adapt faster as new technology comes out and leverage the importance of data in that.” (19:36)
“I think we’ve spoken to some 50 or 60 offices, both on the Senate side and the House side. And this [has been] received really well. We don’t get any pushback on that there is a need for greater data literacy. Here stateside, I’ve been saying it’s kind of like advocating for apple pie. People get it and they resonate.” (21:20)
“As we introduce this bill, I think we should be messaging [that] there’s a economic competition aspect to this; that it’ll be really important for the US to make investments in this area to, frankly, catch up to where I think other international peers are.” (40:34)
“Data tell our stories, and they reflect what’s happening in our world today – much like art around us in some ways. And a way to think about data science education is to think about what we need our data understanding to be at each point in time in our educational career, or in our lives. And it’s not static, it’s an evolving thing. So you have to move with the data that are being collected.” (42:54)
Transcript
This transcript has been produced using speech-to-text transcription software. It has been only lightly edited to correct mistranscriptions and remove repetitions.
Brian Tarran
Hello, and welcome to the Real World Data Science news q&a. I’m Brian Tarran. And I’m joined today by a panel of guests to discuss some promising developments in the United States around data science education. On the show today we have Zarek Drozda, director of the Data Science 4 Everyone coalition, Anna Bargagliotti, Graduate Programme Director and Professor of Mathematics at the Seaver College of Science and Engineering, Loyola Marymount University, Christine Franklin, the American Statistical Association’s K-12 statistical ambassador, and Steve Pearson, who is the ASA’s Director of Science Policy. Welcome all. Thank you for joining us. Steve, I’d like to come to you first, because it was one of your ASA science policy tweets that that first drew my attention to this story. And that specifically was the tweet about the introduction of a new Data Science and Literacy Act in the US House of Representatives. Can you give our viewers an overview of the act and its significance to the data science education landscape, please.
Steve Pierson
Happy to Brian and I also want to credit my colleague, Ed Wu, an ASA science policy fellow who worked a lot on this and championed it. So I see kind of two overarching points here for the bill. One is just to help out those budding efforts around the United States to bring more data science education to students, right, the demand in the jobs is out there. Students should know about these jobs, we want to connect them to the 21st century jobs. So this is a Department of Education programme that helps out those schools, communities that need the help that want the help. We’re not trying to require anything of schools, which already have enough curriculum requirements. So this is a voluntary programme, that I mean, I think it’s developing curriculum, it’s providing professional development. But I think there’s another aspect of this, Brian, which is just kind of the attention that this can bring to these jobs, to the schools to the members of Congress that, you know, data intensive jobs are a great job opportunity in the 21st century, right? You can look at so many places to know that, right? The Bureau of Labor Statistics has both statistician and data scientist as the top 10 jobs in terms of projected growth for the next decade. There’s Glassdoor, there’s many others, so we know that. We want to make sure that today’s students know about those opportunities and are connected to them. But we also want to just kind of diversify the STEM workforce. So there’s components of that in the bill as well. And so we want, we think that, you know, a bill introduced into the US Congress will help bring attention to that, including the members of Congress and others.
Brian Tarran
Excellent. I’ll take a step back briefly to look at the data science education landscape as it is today and Zarek, you helped facilitate a National Academies workshop last September, and one of the aims was indeed to survey that landscape for data science education for the K-12 grades – and for international audiences, correct me if I’m wrong, that’s students aged about five up to 17. Is that correct?
Zarek Drozda
Right, for five to 18 range.
Brian Tarran
So yeah, so how would you summarise that kind of state of data science education in the US right now?
Zarek Drozda
Yeah, well, first focus on the workshop that was facilitated by the National Academies. And that was not the first but definitely the largest to date, in terms of a national convening of the United States for data science and data literacy education. We had 100 plus researchers, programme developers, and higher ed faculty in the room. There were 500 online, it was a big, you know, kind of early stage milestone for billing data science education in the United States. And we had a number of topics ranging from you know, what does this look like a practice? What is the professional development for, for K-12 teachers look like? What are examples both standalone data science courses, and also integration into different existing K-12 subject areas. And it was really a showcase of you know, a lot of the curriculum work that’s been developed over the past 10, 15 years for building your full length data science high school courses, or for building lesson plans or for building, you know, education, classroom specific software for data analysis that students can really get their their head around. And so I think it was a milestone to then this legislation then built off right that Steve was mentioning. I’m glad that you know news of both the National Academies workshop and then the legislation and kind of the growing momentum here in the US generally has made it across the pond. I think, partially our social media game was strong enough, which is exciting to hear. But I think it’s just a testament to you know, the energy for this space is really growing, right? Because it’s, it’s career connected. I think it’s so relevant to so many other emerging technologies, whether it’s artificial intelligence or ChatGPT or cyber. And I think, students, what came through really clearly in the workshop is that students really find this content relevant because of the technology applications. And it seems so, you know, of the moment.
Brian Tarran
Yep. Well, certainly, you talked about jumping across the pond. So two weeks ago now, but it might have been three, I attended a meeting, a discussion around what they refer to as the digital skills gap in the UK. And I left that meeting feeling very much like what they were talking about, defining as digital skills were data science skills. And so when I saw that there was this Data Science and Literacy Act, I thought, well, you know, here’s, here’s something that hopefully, other other places like the UK can learn from. So Anna, you participated didn’t you in the workshop? So do you mind sort of giving us an overview of the data science landscape as it is, as it is now?
Anna Bargagliotti
Absolutely, um, so I think, in the United States, at the moment, I think we’re at a spot where the different states are sort of moving. And in the United States, each state has their own department of education. So they, we are not, they’re not federal standards, they are state standards. And each in a lot of states, those standards are being revised and to include data science standards, and those discussions are moving pretty quickly, with some states already approving, other states implementing this coming fall, for example. And other states that are still in the process of sort of starting that. But it’s, but it’s exciting. The other thing that’s really been happening is trying to understand what the curriculum should what curriculum should look like in K-12, in particular. And the GAISE report, like you mentioned before, has a, lays out a, you know, a nice sort of example of what that should look like at the elementary, middle school and high school levels, and can be used sort of as an anchor point for states looking at what they should be doing. I would say at the university level, at the what we call the 12-16 level in the US, we are pretty good. There are many data science programmes and majors and minors across the United States. And they are quite strong, there’s more and more that pop up. And overall, those majors and minors look pretty similar from university to university, and those students are coming out with very, you know, a good skill set, and they are all finding jobs. As Steve mentioned, the job growth is there. And students are feeling quite prepared when they go to into the job growth. So I think where the university level is, could have been well articulated and well defined at the moment, the K-12 level is still sort of in flux of trying to figure out what should be there. And part of that has to do also with teacher preparation, it’s trying to understand what teachers should have and know in order to teach whatever these data science ideas are that are important for the K-12 level. The GAISE report makes some very, I think, concrete recommendations about what that should be, particularly being anchored in the statistical investigative process or problem solving process and understanding how you can use questioning in that and that being really a skill set that we are trying to promote in K-12 education, that then they use also at the university level if they continue on that way. And then just understanding that there’s different large conceptions of data now. Data are not just numbers, data could be sounds, could be text and whatnot. And these ideas are sort of these data science ideas that we are trying to promote in K-12, as well as at the university level. So I think overall, the landscape is quite good. I mean, we’re moving in these great directions. And I think slowly, we’re getting to some consensus about what that looks like. And we’re seeing that in the states moving forward with different standards.
Brian Tarran
Excellent. And so you mentioned the GAISE report there and that’s the Guidelines for Assessment and Instruction in Statistics Education, and of course that’s something you co-authored with, with Chris Franklin, Chris. Yeah, of course. Yeah. I’d like to bring you into the conversation. Now, Chris. You know, you and I have spoken a number of times over the years about GAISE about statistics, education and statistical literacy and, and the challenges of delivering high quality education in data and statistics at all levels of the curriculum. I wanted to get your impression of what you think the big contributions are that this act will hopefully make towards improvement of the data science education landscape.
Christine Franklin
Well, I was very excited to see this act. And I think one of the big impacts that I see is how it can help our state departments of education actually try to implement standards that we’re seeing put in place right now, in terms of statistics and data science. Teachers, right now, most of our teachers in the US schools, math teachers have not had any formal training in statistics. Or if they have, it’s been maybe one course, they’re very uncomfortable with trying to implement these standards. And it’s just going to require a tremendous amount of professional development. Sounds easy in theory to deliver professional development, but very difficult in practice. And a big part of the difficulty of delivering professional development is funding to, to pay for this. Unfortunately, what I’ve seen is state departments of education will often implement these very nice standards in their curriculum, but then they’ve run out of money, or they don’t have sources of funding to where they can then think about the professional development of the teachers. So it’s not only the professional development of the teachers, but also the curriculum that’s going to support the standards that are put in place. Now, fortunately, ASA, for example, has just a wealth of open source resources that teachers can use. But then how did the teachers know where to get it? How do they know how to implement it in the classroom. So state departments are charged with trying to develop a framework of materials for their teachers. This takes money, this takes expertise. So not only that, but I think this bill can help with funding to allow state departments to do that. But professional development typically has been like week long workshops, day workshops, maybe they go online and do workshops, but really, for professional development to be successful. Teachers need day to day support, which requires funding once again, to provide the resource within schools and school systems to provide more of the day to day support that these teachers need. And I think lastly, one thing that we don’t think a lot about, but I’m hoping this bill will help, is the assessment that goes along with the curriculum that we’re implementing for statistics and data science. Once again, that takes funding that takes manpower support. And I’m really hoping that this bill can be a source that that our state DoE’s can turn to make their standards more successful with implementation.
Brian Tarran
Yeah, Chris, when you were speaking there about providing professional development support for the teachers, it reminded me of when I was a primary school governor here in the UK a few months back, a few years back, sorry. And we would always talk about math education, trying to improve math education, and that I think, the teacher confidence to deliver the math curriculum is always the issue that we run up against. So having support, having resources, having people that can go in and help, that changes the dynamic, I think, for teachers and certainly equips them to, to deliver on, you know, on that vision of, of data science education and statistical literacy for all, which is something that we spoke about before, right?
Christine Franklin
That’s exactly right.
Brian Tarran
That’s, that’s your vision for where we get to as a society?
Christine Franklin
Well, I think one thing, one other thing we need to remember, it’s not just the teachers that need our support. It’s also the teacher educators that are preparing our school level teachers. And we, we need to keep that at the top of the list of priorities because most of our teacher educators recognise that our school level teachers need this support, but they are in a similar situation to where they don’t know exactly what they need to do. And so we’ve got kind of two big spots here that we need to work on for professional development, and it’s gonna take a lot of time and effort.
Brian Tarran
Yeah, well, we’ll come back to that later, if you don’t mind. Zarek, did you want to come in? It looked like you was about to chime in.
Zarek Drozda
Yeah, I was just, I wanted to agree with Chris and second it and expand it because I think it’s professional development for teacher educators and every layer above that, right. It’s every layer above the teachers: teacher educators, it’s the district staff who are implementing and creating these programmes, it’s the state staff we’re creating the standards, it’s state policy makers, it’s federal policymakers – like, you could think about professional development for all those stakeholders. And we know we need to build, you know, better education, right, for every one of those groups that are above the individual educator in a classroom, knowing that the national infrastructure is just supporting the teacher in the individual classroom to do this best at the end of the day. So yeah, we think about that in terms of a, there’s a whole system that needs to move here.
Brian Tarran
I think that’s an important point to note, I think, because I go back to that digital skills workshop I was at and one of the questions that came up from from the chair was, you know, what one thing can I take back to the Secretary of State to say we need to add this to the curriculum, but it’s not one thing is it? It’s, it’s it’s a whole system, as you say, Zarek. I did want to ask you a bit about the organisation that you’re a part of, you have a coalition called Data Science 4 Everyone. To what extent was you involved in the kind of shaping of this, of this bill? And, you know, you obviously, you’ve obviously welcomed it, and you’re excited about the potential, and how much work do you see as being left to do to, to kind of get it over the line and get it into application?
Zarek Drozda
Sure, well, just a very quick background on DS4E. We’re a national initiative and a coalition as you said, based at the University of Chicago here in the States. We’ve been putting together a community of education researchers and K-12, system leaders to advance policy and advance awareness and advance the case making right for why data science and data literacy and statistical acumen is so important in this day and age. And we’re really working across the K-12 system, which is very decentralised in the US, to try to forward those goals. Yeah, as I think about next steps and what’s needed, again, just re-double what Chris said, we need more funding. We did some, to give you an example. So when computer science was being built out as a new school subject in the States, they spent somewhere in the range of three to $4 billion over 15 years to build an entirely new school subject. We’re not necessarily doing that here, right? We’re not necessarily building out a whole new school subject, I think we’re really, at least our group has been much more focused on how can we integrate and upskill teachers in K-12 math, or K-12 science or K-12 social studies, right, and integrate these concepts into the existing K-12 ecosystem, working with the different subject societies. But, you know, this bill is a first really great milestone, but we know now we’re going to have to call on state legislators to pass appropriations at the state level to fund teacher support locally, we’re going to have to, you know, call on schools and districts, right, to help give teachers time to be able to implement these classroom experiences. And we’re going to need more research. Right. So this bill calls for grant programmes to state and local partners to create. But I think we also need more funding for NSF and IES, the two kind of education research bodies in the US at the national level, to fund things like student assessments, or to fund accommodations for students with disabilities to be able access to technology for data science software. There’s a lot more R&D work that also has to happen to bring down the adoption costs over time for doing this type of work and making sure that every student regardless of their background, can benefit from the skill areas, and you know, upskilling in this space. And I think the last thing I would say is that we know we also need to work on teacher confidence, right? I think it’s both teacher confidence with statistics, right, and probabilistic thinking. And it’s also the the confidence of the technology, which is brand new, right? Most classrooms in the US have not been using spreadsheets, even though most workplaces do, let alone, you know, R, Python, SQL, any of the more kind of modern computational tools that are used in modern day statistics. And so we have a lot of work on that front to do as well.
Brian Tarran
Okay. And so, if I’ve understood it correctly, there’s about $50 million over five years that is being asked for in this bill. But that’s, from what you’re saying, am I correct in thinking that’s just a kind of a small part of what’s needed to completely deliver on your goals?
Zarek Drozda
It is a first step. An important one, but a first step.
Brian Tarran
So, longer term, is it– Do you head towards the billions territory, like in the computer science space? Or is it a little less demanding of finances and resources and that, do you think? I know it’s hard to say, to pin these things down, but–
Zarek Drozda
At least from my angle, I’d love the group to jump in here, it’s probably a little bit less demanding. But we know that in aggregate between federal, state, private and local funding, we’re going to have to create the necessary resources to make sure that our K-12 public education system can prepare students for a world that’s changing super fast, and the K-12 system moves super slow, right, in how it adapts to new content. And so really it’s both about, you know, what can we do to upskill data science, data literacy skills. It’s also about how do we help the system just adapt faster as new technology comes out and, you know, leverage the importance of data in that.
Steve Pierson
And Brian, I can jump in a little bit on the price tag, which, when we were shopping around this bill, we didn’t actually include like a cost per year for this, because we know that that can be a very sensitive topic, and we were, we really wanted to have bipartisan introduction. So, and we were fortunate to get it right. But it was the offices who have agreed to kind of consider us that came up with the $10 million. And I’m, you know, I know for a fact that, you know, a few of the offices, at least one of the offices did want significantly more, but this was how we were gonna get bipartisan introduction.
Brian Tarran
Yeah. Okay. And on that point, that bipartisan nature of the bill’s introduction, does that give you as a group hope that it will eventually make it through Congress and become a law, and that there’ll be these resources made available?
Steve Pierson
Yeah, absolutely. And I’ll also just say, Brian, that I think we’ve spoken to some 50 or 60 offices, both on the Senate side and the House side. And this is, it’s received really well, we don’t get any pushback on that, you know, yes, there is a need for greater data literacy. Here stateside, I’ve been saying it’s kind of like advocating for apple pie. Right. This is, people get it and they resonate. And to that point, we only brought this to Representative Stevens, I think it was maybe late October. But they really wanted to move on this, they wanted to wait for the new Congress for bandwidth issues. But, significantly, we’re told that the representative wanted this to be her first bill introduced of the new Congress, and she had many to choose from. So I think that’s really positive. The other thing is, I’ve heard from email, we haven’t had a chance to debrief with the staff yet, because they’re swamped with all kinds of things, but they’re getting a lot of positive feedback from people about this bill. So it really seems to be kind of tapping a nerve. A recognition.
Zarek Drozda
I was just going to add to Steve that when we went– So in advance of that legislation being introduced, we had 15 of the largest math and science and technology education associations supporting the legislation, which was a huge win. It showed, I think, that this data science, data literacy, education is really a collaborative multi-subject effort in the States, which does not happen often, it’s usually very siloed. And I think the other thing I’d say is, in the first 24 hours since the bill was introduced, we had 150-160 additional education leaders and organisations sign on to the letter of support that we were helping circulate between Data Science 4 Everyone and the American Statistical Association. And just to re-emphasise that we saw a lot of energy around this, and bipartisan, right. We’re building support on both sides of the aisle, because it’s, you know, this is apple pie, it’s so evident that every student’s going to need this for the next decade.
Brian Tarran I like this. So data was once the new oil, but now data science education is the new apple pie. I think this is this is great. Anna, you know, assuming that the Data Science and Literacy Act goes through, funds are made available, this work starts in earnest, what sort of timescales are we looking at, do you think, before we start to see the real world impact, you know, in terms of teacher training, student outcomes, and then eventually, obviously, building this workforce, that is so needed, that is equipped with data literacy and data science skills?
Anna Bargagliotti
Yeah, I think I kind of want to say two things to this point. I think post K-12, the college level, we are seeing those outcomes, and they are great. And I think we are really, that is, it feels very good. It feels like we’ve targeted the right things. It feels that students report back that they’re excelling in their jobs and doing great things. So I think that part is sort of, is taken care of. I think at the K-12 level, what’s harder is we’re less nimble. Like Zarek mentioned, K-12 is just a beast to move. It’s very difficult. And we’re in a situation where the target of data science changes every day, truly every day, for two real reasons. One is because the conceptualization of data changes every day. We can imagine today we think of data as text, but in probably a month, there’s some other type of data that we haven’t thought of that will emerge. And so now all of a sudden, you’ve got, you’re trying to teach pillars or concepts in K-12 that are actually a moving target. And then the other big thing that changes pretty much every day is our capabilities for wrangling, visualising access to data, all that stuff is changing. And so at the university level, you’re much more nimble because you’re in these courseworks, and your students are very advanced, and you can kind of move within those, those spaces, and you can change a course at a time. At the K-12 level we’re much more prescribed and harder to move. So I think in terms of timeframe, I think if we focus on the sort of large concepts and the baseline skill sets that we want to graduate students from K-12, I think we can move much quicker than getting into sort of the nitty gritty of a student needs to be able to programme per se, or something like that, like I think more that statistical investigative process, and those questioning, those types of ideas are really the crux of what K-12 looks like that then allows the university level to be more nimble. So for me, the timeframe is I think we have to, we have to think about it differently as we’re never going to arrive. It’s not going to be like, Oh, in five years, this is completed. It’s a, are we reacting in the right way? Or are we sort of ahead of the game. And I think we can get ahead of the game if we go to the concepts and that idea instead of thinking of topics that we’re teaching. And I hope that with this, I have great hope for this bill. I think it’s like everybody said that this is just such a great first drop in the bucket with the apple pie that I think hopefully in the next few years, the states are going to have been moved and then everybody will be doing some type of data science at the state level. And then it’s like Chris mentioned, before moving the professional development, which is a big challenge.
Brian Tarran
When you were speaking, just there, Anna, and you were you were talking about the difference between you what you’re achieving at the college level versus the younger level, it made me think that, you know, this isn’t just about providing a next generation of data science workers, right, it’s about equipping everyone with the skills to be able to exist in a data science world. And this goes to the point that I think Chris and I have spoke about before, right, about being able to be, when you’re confronted with data, being able to ask the right questions about that data. And so I think obviously, that’s, to me, that’s where I think maybe the Guidelines for Assessment and Instruction in Statistics Education seems a promising first step in the development of a data science, data literacy, statistics curricula, do you see that that needing to be – obviously these things are always needing to be revised, right – but do you see that, Chris, as a kind of foundation on which the states can start to build and to move towards this vision that’s laid down in the act?
Christine Franklin
Well, most definitely. And we’ve been real fortunate in the US here that our states right now that are trying to implement more statistics and data science standards, are going to the GAISE II document to use as a guiding document. And ASA has been very fortunate also to where these states, many of them have reached out to us to actually help advise them as they go through the process. I wanted to come in on a follow up with something Anna said in terms of the timeline. As she was speaking, I thought about how when we were sending out our document for review with the GAISE II, the updates, and we sent it out to probably about 20 to 25 different reviewers, we received more feedback than we knew what to do with. But many of the reviewers, very well respected people in the field, came back to us and said, we were being way too ambitious with the GAISE II document that, in fact, so many states here in the US we’re still back trying to implement the recommendations we made in the 2005 GAISE document. And our response was, we can’t be writing for today. What our goal is, is to try to write a document to where 5, 10 years from now, this is where we hope that our K through 12 system will be. And I’ve noticed this in working with other national documents, that I think the tendency oftentimes is to try to write a document for where we are now. And I think that we always need to, in terms of a timeline, think about 10 years down the road when we wrote the document. And it’s hard to be visionary. But as Anna was saying, things are changing so quickly. I mean, the GAISE II document, I think the writers are all saying, oh, we should have included this, we should have included that. Because since its publication in 2020, we’re already seeing needed changes. So I think we’re always going to be catching up to some degree. But as Anna said, I think the goal of these documents needs to be conceptual understanding, it needs to be that role of questioning. I like to say, I just want people to be healthy sceptics to where when they see statistical information, they see data, they immediately start asking questions. They may not know the answers, or know what the answers aresupposed to be, but at least they’re questioning.
Anna Bargagliotti
And I’ll just add one really quick thing, Brian, if that’s okay. Chris and I are about to embark on another ASA initiative, which is the revision of the SET report, which is the Statistics Education for Teachers report that the last publication was, oh my gosh, Chris, I can’t remember even the date–
Christine Franklin
2015.
Anna Bargagliotti
2015. And the the idea of the new report that we will be starting to write this year, along with some wonderful colleagues, is going to be to talk about the teacher preparation aspect of that in light of everything that’s happened with statistics and data science in particular, what should that look like? And how, how could that be? So I just wanted to add that.
Brian Tarran
Excellent. I also wanted to ask, again, maybe this is a question for Zarek. If we’re talking about data science for everyone, what about people like me, who have already already finished their education? I mean, I know you can always learn; every day is an opportunity to learn new things. But, you know, from your coalition’s perspectives, Zarek, what do you want to see happen so that, you know, that we’re not leaving behind big chunks of the population, you know, the older chunks of the population who haven’t had the benefit of going through this education system as it is now, let alone what it might be in five years time, right?
Zarek Drozda
Yeah, Brian, it’s a great question. And I, I just came off a year of serving as a fellow in the US Department of Education. And I had a lot of conversations with the programme officer who is responsible for research, education research and adult education, about that subject. Right. And I think one of the themes that I learned from that work and from that, from those conversations was this idea around fear of technology, as it accelerates. It’s really hard for people to deal with, you know, ChatGPT, DALL-E, AI, neural networks, you know, the list goes like on and on, and it changes every month, as as we’ve discussed here. And I think a big goal from our side is both to build confidence when people are dealing with a deluge of data and a increasing amount of information, right, which we talked about, and it’s also the confidence in the technology tools that are constantly changing. So I think as we think about, you know, a student K-12, we want to get them on a tool, so they can be confident and switch to the next one when it comes out. Because we know that technology integration is just is really critical. And the same thing is true for the adult learners, right, or for the for the folks that are older, there is a wealth of online digital learning, online courses, asynchronous experiences, to learn any coding languages, any programming language, any just software – doesn’t even have to be computation or coding intensive, right, it can just be spreadsheets or, or Tableau or some of the not-too-syntax heavy tools, and there’s so much digital and elearning opportunities for that. If we can build formal exposure into the classroom pathway, and build student confidence to then jump on to those later on. That is really critical. Because if you don’t get an exposure, it’s so hard to take the first step to jump in to the digital training or to you know, go to your employer and say, I want this, you know, this professional development programme, because it’s hard to know where to start. And we also worked on a data literacy training programme for Department of Ed employees while I was there and helping design that experience. You know, for professional or mid career, folks, I think the most important thing was you build confidence and create bitesize first steps to try to like tone down the fear, so people can approach the new world with confidence rather than just responding to it.
Brian Tarran
So I just want to wrap up now, last question for you all, really, maybe if we start with Steve. From a policy perspective, Steve, you know, are there other initiatives on the horizon and things in the pipeline that, you know, people should be watching for in terms of trying to improve data science education across the board in the US, and maybe specifically for the ASA are their areas you’re going to be focusing your efforts and support on.
Steve Pierson
We certainly do want to expand this effort. And we’re trying– In a way, this bill is serving as a way for us to gather that information, because people then know that we’re doing this and they might well hopefully suggest items to, to us. We’ve gotten some I know that, you know, Zarek has a file, I have my own file of what we might want to, how we might want to extend it just with this bill. But yes, we certainly do want to do that. And so for listeners out there that have ideas, please please send them to us. I won’t offer specifics at this point. And I’d love to hear what my colleagues have to say, as well.
Brian Tarran
Does anybody want to jump in on that? Zarek, what’s on your list?
Zarek Drozda
Very short term is just building a Senate version of the legislation, I think, right? We’re going to be working with with the ASA on that, to find champions in that chamber. But then I think I would go back to my call for you know, state legislators need to be thinking about this as well, right, because we know that every state is going to look a little different. We’re in a context right now where locally driven education solutions are going to be really important. And so we’re going to have to build different slightly different flavours of this all around the country. And we’re going to need a lot of work from, I think, state and local champions to fund and help support and build these experiences that are so critical for students across the country.
Brian Tarran
And are there, you know, on the point you mentioned earlier, Zarek, about this news,crossing the pond and reaching me over here in the UK, do you look elsewhere, you know, outside the US and the UK for other good examples of where education systems are starting to integrate data science into the teaching at, you know, the earlier parts of the school curriculum and the school levels.
Anna Bargagliotti
I think Chris can probably jump in even more than me on this one. But definitely in New Zealand. Our colleagues in New Zealand are fantastic. And they’ve been doing K-12 data science and statistics very well for many, many years. And Chris has some very close collaborators. So I’ll hand it over to her to on that. But I thought I’d mention it.
Christine Franklin
Yes, I I think that that’s when you know, when Steve says how can we reach out, I think even beyond policy, a collaboration with international colleagues that have advanced their work in K through 12. I mean, I had the good fortune of having a Fulbright to New Zealand back in 2015. And just the inspiration, the wealth of knowledge that I obtained from there, to bring back to the US with our work here was phenomenal. Plus, we built up collaborations that we are continuing today. We have collaborations with people in the UK, for example, in other countries. I think the other thing I wanted to say besides reaching out internationally is that we as statisticians in the US need to be doing more to help K through 12. And I think about my, you know, my colleagues at the university in the statistics department which I was part of, my colleagues, some of them actually worked with me to reach out to the math educators so that they could try to help with what needed to be done with the preparation of teachers. So I think statisticians need to become more involved, both practising statisticians and academic statisticians, with helping educators at K through 12. And that includes trying to become involved with state departments of education as well, because that’s really where things filter down to the local level in our school districts. So I would like to see somehow a structure put in place to make that happen more. And I’m hoping things such as this bill will bring that awareness to practising statisticians, that this is really important, and you need to become more aware of what’s happening at K through 12, and become involved.
Zarek Drozda
To just add to what Chris is saying, I think it is so important for investment in the K-12. space. And, Brian, I’ll give you a sneak preview of a report that we were collating on international examples of data science and statistics education in K-12, and really serious investments that we’ve seen, I think, and Chris already mentioned, some really great ones that have been long running champions at this internationally. Our recent scan: Israel, their ministry of education is doing a tonne of work for data science, data literacy education. I’ve had so many conversations with them over Zoom. China added a standard semester for big data statistics coding and modelling. And it’s now also in their college entrance exam. There’s examples in Germany, New Zealand, South Korea, Scotland, we’ve we’re continuing to build the list. Frankly, in the UK, right, with core maths, you’re seeing more integration of data and computational thinking into the curricula pathways there. I think in many ways the US is behind, and as we introduce this bill, I think we should be messaging, there’s a economic competition aspect to this, right; that it’ll be really important for the US to make investments in this area to, frankly, catch up to where I think other international peers are.
Brian Tarran
Steve, you wanted to come in on that?
Steve Pierson
I mean, you mentioned like someone coming in mid career, right. And I think that that is really important. But we’ve also kind of been talking about the other ways you can access this, right, in any part of your career, but you can also access data jobs, rural and urban. So we’ve been kind of selling that dimension. But also, you know, just access in terms of diversifying the STEM workforce, we think that’s really important. But it’s also about degree level, right? We did a search for one member of Congress that had a major, I think it was a pharmaceutical in their district. And if you put in data, right, and thousands of jobs pop up for that company. And it’s not just the PhDs, right, it’s more entry level, the people who are what Zarek mentioned in terms of just accreditation, that you can enter a lot of points. But I also want to just make a– point out one part of the bill, which really singles out two-year colleges, which can help the mid career people, the early career people or others, and they face a lot of the same challenges as K through 12. Right, making sure that the instructors are upskilled, that they have a curriculum, but they also need the time to coordinate with their other disciplines that are involved here. They need time to go to that local workforce, what do you need in terms of data science? And for those students that want to go on to a four-year degree, they need to make sure that there’s a smooth pathway for those students. So there are provisions in the bill also for for two year colleges. And those would be my closing comments, Brian.
Brian Tarran
Okay. Anybody else for some closing words? Or should we wrap up on reminding everyone that data science education is the new apple pie?
Anna Bargagliotti
I can close this with something more philosophical, maybe. To me, I think a nice way to think about it or sort of a romantic way to think about it is it’s just data tell our stories, and they reflect what’s happening in our world today, much like art around us in some ways. And a way to think about just data science education is just to think about what we need, what we need our data understanding to be at each point in time in our educational career, or in our lives. And it’s not static, it’s an evolving thing. So you have to move sort of with the data that are being collected.
Brian Tarran
Very good point. Thank you very much, Anna, Steve, Zarek, and Chris, for joining us to talk about the Data Science and Literacy Act. I’m sure there’ll be much more to to follow and update on as this act or bill winds its way through through Congress. So we’ll look forward to hearing more about that in due course. So thank you for joining us today. Thank you to those of you who are watching for joining us. Stay tuned at realworlddatascience.net for more news Q&As.
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- How to cite
- Tarran, Brian. 2023. “US legislators get their data science act together.” Real World Data Science, March 6, 2023. URL