Teacher Voices

The Motivation: Reasons to teach data science

They enjoyed statistics and believe it is valuable for students to learn.

“I really enjoy statistics…I feel it’s really valuable for a lot of students to at least understand what statistics is…And I [want to help students to] figure out what’s good, what’s bad, and hopefully…let them realize that even though they may hear a study results…they shouldn’t just take it as fact.”

They viewed data science as an opportunity to offer students a math class that was more applicable to their lives.

“For a long time us teachers have been really frustrated that the [math course]…options for juniors… [are] only [useful] for kids that are going into STEM fields. And so that’s…not a very big portion of the kids…so we’ve…been wishing the state would come up with alternative options that weren’t just dumbed-down math and that’s when we heard about this data science class.”

They viewed data science as a growing field with a lot of career possibilities.

“I think data science in general is going to be a really trendy or is a trending career right now, which is kind of amazing. And, what’s more amazing is that you don’t necessarily need a bachelor’s degree for that…So if I can get kids interested now, that’d be good for them if they want to go in that direction.”

The Teaching: Curriculum adaptation

All of the teachers adapted the curriculum. They:

• Added activities that they newly created, had used in other courses, borrowed from other teachers, or found online.
• Spent additional time diving deeper on certain topics.
• Skipped over topics or activities in favor of moving on to other material.
• Adapted projects to align with students’ interests or skill level (e.g. used data about their school rather than curriculum-provided data sets).
• Added foundational topics from other courses, including Statistics and Algebra 2.
• Allowed students to work independently on projects designed for groups.
• Supplemented curriculum-provided assessments with additional assessments.
• Had students complete activities or projects using different technology than what the curriculum recommended.

The Process: Supports and barriers

Professional Development

Support: Most teachers found the curriculum developer-provided PD quite helpful and appreciated having time to learn about the materials and technology and overview the course.

Barrier: Some teachers, particularly those who do not have the statistics or computer science content knowledge, wanted more support on the subject matter content.

Teacher-Facing Materials

Support: Many of the materials enabled teachers who did not have a lot of experience with the content to feel confident teaching the lessons.

Support: Teachers found the curriculum-provided slides very thorough and useful. They noted the slides used language that was very accessible to their students.

Assessment

Support: Some teachers found the detailed curriculum-provided rubrics very helpful when assessing students work.

Barrier: Some teachers found it challenging to assess the different formats of data science student work. For example, some lessons required written assignments, which some teachers have not assigned in other math classes. One teacher in particular talked about the challenge of assessing work for students who had very low writing skills.

“I’ve definitely…done more grading on completion than I would prefer…I would like to do better…giving the feedback to help the responses [but it’s challenging when they] don’t know how to write in [complete] sentences really. It’s hard when there’s things that they should be learning in other classes as well…I shouldn’t have to discuss ‘start with the capital letter’ and ‘write complete thoughts.’”

Another example came from teachers who were accustomed to grading problems with clear correct or incorrect answers while data science often requires more nuanced approaches.

Technology

Support: Most teachers had not used the technology tools and packages before teaching the course. Nonetheless, they were able to use most or all of them and felt they were accessible to students.

Barrier: Sometimes technology did feel like a barrier. Some had challenges figuring out how to use the technology in certain ways or how best to support students when they ran into problems. Additionally, programs could sometimes take a long time to load on student chromebooks and several of the school districts do not allow students to use Google Colab, which was used in some curricula.

Preparation Time

Barrier: All the data science teachers were teaching the course for the first time, which typically requires a lot of preparation. Many of them felt that due to their other classes and responsibilities, they didn’t have enough time to prepare as much as they would have liked.

“I’m literally like two hours ahead of my kids each day… I chose a new curriculum for my calculus class so I’m trying to prep that and prep my data science class in one prep and sometimes it [works] and sometimes it doesn’t.”

School and District Support

Barrier: Many teachers were the only adults in their school advocating for data science and have had a hard time reaching students because other teachers and guidance counselors were not very aware of its existence or its value.

Barrier: In Utah, unless students take data science as an additional math course, they are not able to take data science and remain on the pathway to take calculus their senior year. Many students, teachers, and administrators view calculus as an important class for students who are college bound so they do not encourage students to enroll in data science as a replacement for traditional junior-level math.

Support: Many teachers believe data science prepares students well for advanced statistics and hope that it will soon serve as a prerequisite so that students can take data science as juniors and college-level statistics as seniors.

The Outcomes: Student gains

In addition to other subject matter content, all of the teachers felt that the course increased their students’ data literacy including:

Understanding of Data Tools:

“I have several [students] that have commented that they see things differently now. One told me that… ‘every time I get on a plane, someone’s working in spreadsheets.’.. He goes, ‘I got to know this [data science content]. If I want to run my business, I got to know this.’ They’re learning so much. Like this is the future. This is what’s happening. Even if they’re not proficient at every single project, they’re all… data literate.”

Bias Recognition:

“I think they’re more data literate, like reading a chart or graph, being more aware that it might be biased information…looking at what the source was or how many people are in the population is important…. So I think that’s the biggest thing.”

Inquisitiveness:

“[I see my students have] so many natural conversations, even on their own, and being inquisitive. I was blown away. It reminded me that what I’m doing is actually real, because you kind of lose that in secondary math… And this [class] was so much of them having their own discussions [about data]. And that was really pretty.”

Skepticism and Critical Thinking:

“I think they’ve learned [not to] take things at face value…I’ve been focusing on this a lot because we just did our machine learning here and I’ve got some students who think that machine learning is a Pandora’s box that we never should have opened and …[others say]…you have to at least understand it in order to make sure you’re not taken advantage of by it. So I think that’s very useful for them…they’ve learned how to be more critical thinkers.”

Data Ethics:

“I think what my students found most important was the ethics conversations with all the technology and such. There’s all these issues that are front and center in our world and they now have some of the background [which will] be very valuable.”