In this HSA Bulletin
In the Spotlight!
New Call for Content
Video Demo: A-G Checklist for 2022-23
FAQ of the Month
In the Spotlight!
This month we are excited to put the spotlight on a new program and course available in the A-G Course Management Portal (CMP), Explorations in Data Science, from youcubed. Developed by faculty and undergraduates at Stanford University in collaboration with data scientists from across industry, the course gives "students opportunities to be data explorers through active engagement." Students develop their understanding of “data analysis, sampling, correlation/causation, bias and uncertainty, modeling with data, making and evaluating data-based arguments, and the importance of data in society."
We were excited about this new course offering available for schools to add to their A-G lists and asked the course developers to tell us more about the curriculum and what makes it unique. Please keep reading to learn more.
To find the course in the A-G CMP, follow these instructions for adding a program course. Both the program name and course title are listed in the A-G CMP as Explorations in Data Science - Youcubed Adaptable Curriculum.
Please describe the Explorations in Data Science course. Why was it developed? What makes it unique or innovative?
We developed this curriculum to provide an additional advanced mathematics course for high school students. We have received A-G approval of the course, which means students can take it as an alternative to Algebra 2 or in addition to Algebra 2. It is a wonderful prerequisite for AP Statistics or AP Computer Science. It involves topics of Algebra 2 and has mathematics content that is relevant to the 21st century. There is a chapter in the new California Framework devoted to data science.
In this course students learn to understand, question, and represent data through project-based units. The units enable students to be actively engaged data explorers, developing their understanding of data analysis, sampling, correlation/causation, bias and uncertainty, modeling with data, making and evaluating data-based arguments, and the importance of data in society. At the end of the course, students have a portfolio of their data science work to showcase their new knowledge and understanding. The curriculum is adaptable, so teachers can either use the data sets provided or bring in data sets that are most relevant to their own students.
This course provides ways of understanding the data science process: asking questions; gathering and organizing data; modeling, analyzing and synthesizing data; and communicating. Students work through this process in a variety of contexts. They learn by making sense of complex problems, then — through an iterative process of formulation and reformulation — coming to a reasoned argument for the choices they will make. All of the Standards of Mathematical Practice are addressed in this course.
This course turns on the use and application of various technologies. The appropriate and strategic use of these tools is demonstrated and called upon throughout the course. Tools include:
Each tool required is widely accessible and web-based; downloading apps and software is not necessary for the use of this course.
- Common Online Data Analysis Platform (CODAP) for analyzing and visualizing data;
- Google Sheets for analyzing and visualizing large amounts of data (on the order of hundreds of data points);
- Google Data Commons API, a website wherein students will gather, sort, visualize and export country data that is freely available to the public;
- Tableau for analyzing data and creating visuals; and
- Python through Google Colaboratory, as students learn to use coding with larger data sets.
The course gives several opportunities for students to develop their explanatory writing skills across multiple platforms. Communication at every stage of the data science process is key to making sense of a context and its data, interpretation and story. Students will revise and refine their writing using feedback from themselves, their peers and their teachers.
Tell us about developing the curriculum. How did the team organize the work process?
We began by developing standards for the Data Science course by looking through previous data science coursework, the Guidelines for Assessment and Instruction in Statistics Educations (GAISE) Reports and the Common Core State Standards for Mathematics. From there, we organized standards by “big ideas” and created units around them. We chose contexts and designed an authentic project for each unit that was applicable to those ideas. We had input from academics, undergraduates and data scientists in industry, including Amazon, Airbnb and Google. Our work was initially funded by Google Education and the Gates Foundation.
Please describe one or two assignments or activities that you really like or that seem to particularly engage students.
In one project, students consider the probability of songs from different musical genres being played when a playlist is shuffled. They build a class playlist and discuss the theoretical probabilities of each genre. To calculate experimental probabilities, students program their own simulations using block-based coding in Python. In order to prepare students to program their shuffling simulation they build and analyze a series of simpler programs that help them become familiar with the key ideas behind basic programming. In another project, students consider the bias of a published list of best places to live. After unpacking the attributes valued by publishers, students create their own ranking and prioritization. They explore and choose from data available in Google Data Commons to create a list of criteria for what is most important to them in the place(s) they would like to live. They then use those key characteristics along with Data Commons and Google Sheets to gather, organize and prioritize the data to formulate a model. This model generates a ranking of countries or cities that are based on their priorities for best places to live.
What are some of the successes and challenges you’ve faced working with schools to implement the course? In the case of challenges, how did you overcome them?
Students and teachers have been sharing that they find the course interesting, challenging and creative. Here are some of the quotes shared by students:
Implementing the course poses a challenge, requiring a new way of teaching mathematics, one that’s entirely project-based. Many of the teachers are appreciative of the new approach and learning new ways to assess students. And teachers and students alike are learning new technologies. To address these needs, support for assessment and technology are built in to curriculum resources and professional development. We also provide free office hours every other week to support teachers. Here’s what some of the teachers had to say:
- “This is the most interesting course I have taken. A profound analysis of the data feels like deciphering a crime scene for clues. That makes it all the more fun and enjoyable.”
- “When I think of math, I think of simply writing down problems and solving them. But this course has opened my mind. I'm optimistic … math really isn't as bad as I say it is because there's so many different types of it. … It's definitely changed my perspective. This course is so different from a traditional math class. I think anyone in the class would say that. I would never have had a positive outlook on math based on another ‘regular’ math class, so I’m thankful.”
- “From how the grading works to the curriculum and class communication, it’s such a great way of learning. It’s a very enjoyable class.”
- “I am very happy about this approach to assessment. I see students being willing to submit new things for feedback and revision, rather than a once-and-done grading system.”
- “Students need training on providing feedback to each other — I'd love guidance on this. As a math teacher, I have limited experience guiding students on how to critique each other's writing.”
For schools interested in implementing this course, what resources and programming are available to them? How would they seek a partnership?
Our curriculum is free and online at the youcubed website. We provide live, virtual professional development workshops. The first four units are explored and reviewed at a workshop in June followed by two additional days in October for the remaining four units. Information is available on the Professional Development page of the website. We provide more frequent support in the form of office hours every two weeks. You can also sign up for the newsletter to receive regular updates on the data science course.
Youcubed can be reached via email at email@example.com if you have questions or need additional information.