IDS: Introduction to Data Science
At-A-Glance
IDS has online lessons available to teachers who complete the professional development. A clickable PDF is available to all. Lessons are paired with a technology suite of data collection tools and data analysis labs.
IDS uses R, through the interface of
.
IDS provides alignment information for the Common Core State Standards for mathematics as well as the California Common Core Standards.
IDS provides teachers with periodic rubric based scoring guides.
IDS suggests a first-year Algebra course as a prerequisite
A PDF of teaching materials is free and an online version is available to teachers who complete the PD. The technology package costs $5 per student and $13 per teacher per month.
IDS has a two-year professional development process that includes 9 days of PD in the first year and then 4 days in the second year. The first year is focused on implementation and costs $5,500 per participant. The second year focuses on deeper data analysis and costs $2,818 per participant. PD is complimentary for 2 administrators or counselors.
Key Features:
- Inquiry-based philosophy
- Student data collection campaigns
- Detailed teacher guidance
Instructional Practices:
IDS is explicit about its inquiry-based philosophy and embraces the BSCS 5-E model (engage, explore, explain, elaborate and evaluate) across its lessons. Its student-centered approach is clear in that it has data “campaigns” during which students develop data sets themselves using a participatory data collection process. Students use these data sets as well as existing large data sets throughout the curriculum. The lessons focus on finding meaning in data and they use a range of media to engage students including articles in magazines or news outlets, hands-on manipulatives such as dice or marbles, and other materials such as index cards and poster boards.
Many lessons explicitly build on the lessons immediately prior as they cluster around conceptual themes such as visualization and experiments as well as overarching topics such as garbage and water. Students are expected to do projects, engage with data science creatively and to think about and tell data stories. Additionally, the lessons give very specific teacher guidance for facilitating collaboration, and communication with student group work being the predominant classroom organizational structure. Students have opportunities to self-assess and other assessments are rubric based.
Lesson Structure:
IDS has four units with theme-based sections in those units and then lessons within each section. Each section has an overview of the “enduring understandings” for the theme, approaches for engaging students, section learning objectives, language objectives and the data file or data collection used. Then, each daily lesson within the section begins with the lesson objective, the materials needed for the lesson, the “essential concepts” and the vocabulary.
Each lesson has detailed step-by-step teacher guidance with periodic teacher notes focused on topics to review. Lessons also provide teachers with specific stage-setting introductions, questions and prompts to support students in the learning process. Lessons use a combination of whole class, team and paired work with much of the lesson time spent with students working in teams. Students are expected to play particular roles in their teams and at the end of each class, one team of students reports out on the most important topics of the day. Students are also expected to engage in regular communication and critical thinking as they move through inquiry-based lessons.
Design Features:
Overall IDS is organized to highlight features of data science and then addresses programming and statistics through that lens. The curriculum is available as both, an online curriculum available to teachers who complete the IDS professional development and a downloadable pdf available to anybody. In addition to downloadable student worksheet pdfs for each lesson, IDS also includes lab response sheets and data collection organizers as downloadable pdfs. Some lessons are unplugged. The lessons are sequenced so that they focus on learning data science and statistics concepts which lead to labs where they engage in data analysis and visualization. The lessons use R through RStudio to answer questions and document answers in their data journals. All computer based assignments are intended to be completed in class so that no student is limited by their access to technology.
Another distinguishing characteristic of IDS is the use of student-collected data. In addition to large authentic data sets accompanying the lessons, students work with data generated during class time or with data collected as part of data “campaigns.” These campaigns include students engaging in participatory sensing which entails using their mobile devices to gather data as human sensors.
Teacher Support and Resource Materials:
IDS provides teachers with supports such as lists of goals and objectives, weekly course outlines, and technology requirements and resources. Additionally, IDS has educative materials for teachers. For example, materials include content on the purpose of the study team structure, how to organize and facilitate study teams and the inquiry-based philosophy. There is also detailed guidance on students’ roles on their teams and examples of what to listen for to ensure they are in fact playing these roles as taught. Other teacher resources provide specific strategies for facilitating discussion, reflection, summarizing and sharing out. IDS also includes a weekly schedule to aid in pacing.
IDS provides support for teachers throughout the year. Support includes a “ticketing system” through which teachers can get quick responses to their questions.