With a focus on workforce preparedness, students engage in learning experiences designed to develop real-world data science and AI problem-solving skills. Through course activities, homework and projects, students practice critical thinking and gain hands-on experience with workplace data science workflows. These strategies help foster a sense of ownership over the choices and decisions they make with data.

At DSA, instructors use a variety of strategies, such as inquiry-driven innovation planning to design activities that deepen students’ understanding and application of data science. In planning inquiry-driven innovation, instructors get an opportunity to integrate the other elements of the ADAPT framework, such as the ten common learning elements, to create rich and engaging data science learning experiences. 

Planning Inquiry-Driven Innovation

Instructors may take the following four steps to plan for their inquiry-driven innovation. The steps are also explained in detail in this template.

• Step 1: Identify your focal population
  • Identify the focal population of your innovation work

• Step 2: Identify your intended outcomes 
  • Consider the impact you’d want the focal population to achieve/experience.

• Step 3: Identify your intended innovation 
  • Develop a strategy for achieving the impact you identified for the focal population. Consider the other two aspects of the ADAPT framework (project-based learning and 10 learning elements) as a guide to prepare students for a data science and AI empowered workforce.  

• Step 4: Develop a plan 
  • Develop a plan to implement the innovation with the identified focal population. Keep in mind that the process of innovation can be iterative and instructors can be flexible with their goals as they support students’ learning outcomes. 

Examples of Inquiry-Driven Innovation from ADAPT Instructors