Purposeful tasks refer to activities that have a clear, meaningful and relevant purpose for the students. These tasks challenge the students to think critically and apply their knowledge and skills to real-world situations, encouraging deeper understanding and engagement with the subject. The goal of these tasks is to help students develop a sense of agency and autonomy as learners, and to inspire them to take ownership of their own education.

There are many different types of purposeful tasks in science education, but some common ones include:

• Laboratory Investigations: Hands-on activities that involve conducting experiments and making observations to test scientific hypotheses. We suggest using the Argument-Driven Inquiry (ADI) method of designing and implementing laboratory investigations.

• Design Challenges: Tasks that involve students in designing and building prototypes or models to solve a scientific problem.

• Research Projects: Projects that require students to conduct independent research, gather and analyze data, and communicate their findings.

• Simulations: Computer or physical simulations that allow students to explore scientific concepts and phenomena in a controlled environment.

• Second-Hand Data Investigations: Tasks that engage students in analyzing second-hand data sources.

• Case Studies: Tasks that involve students in analyzing and solving real-world problems related to science.

• Field Studies: Outdoor activities that involve students in collecting data and making observations in natural settings.

These tasks are designed to provide students with opportunities to apply their scientific ideas through data-driven activities to continue constructing their explanations of the anchoring phenomenon. 

An integral aspect of our approach involves elevating the focus on students working with data throughout their tasks. 

We recognize the paramount importance of empowering students to actively participate in the process of data analysis and interpretation. To further enrich students' engagement with scientific practices, particularly in the realm of data, our emphasis extends to Stage 3 of our planning tool titled Negotiating Ideas and Evidence through Tasks. Here, we strive to cultivate an environment where students not only collect data but also play an active role in deciding what evidence is pertinent to their investigations and whether further data collection is warranted. By integrating science and engineering practices such as planning and carrying out investigations, and crucially, analyzing and interpreting data, we aim to foster a deeper understanding of scientific concepts and phenomena. 

Through hands-on experiences with data, students are empowered to refine their explanations, solve complex problems, and construct meaning from their observations. While not always possible, we seek to integrate authentic data from studies of the phenomenon into the lessons. For example, in our Axial Seamount unit, we took traditional activities that use decontextualized data to calculate the angle of subduction of the Pacific plate, found authentic data from scientific articles of the subduction zone relevant to Axial, and integrated the authentic data into the task. 

By lesson, we mean the activities that occur around one data-driven task. A single lesson could take several days depending on the task. We suggest thinking of a lesson as consisting of three phases:

The goals of launching the task are to:

1. Intellectually frame the task for the students. Frankly, many teachers push back on the idea of starting a task by introducing important ideas as opposed to using the task for students to discover those ideas on their own. We think of it in terms of the diagram below. 

Traditionally, we get students started on a task and then, after they have experienced the task, we introduce the concept to explain the data they saw in the task. In this way, the science idea (e.g., natural selection) is the product, the thing we are aiming at so it makes sense that we end with the product of the task. 

In MBI, we introduce the idea so students have it to reason with throughout the task and then collectively work to make sense of it at the end of the task. Here the product isn't just the science idea, but how the idea builds toward an explanation of the phenomenon. That's the real goal of the task. Just looking at the orange lines in the figure below, you can see that the intellectual work of trying to make sense of the science idea is limited to the few minutes we get at the end of a task during our wrap-up discussion. In MBI, the students have the entire task with the idea to make sense of what they are seeing in the task. That's a very big difference.

It took scientists hundreds of years to discover most of the ideas at play in a science classroom. Our students are not going to do it in an hour-long lab. Instead, we provide them the ideas to reason with as we engage with the data. We hope you see how framing tasks like this is not "giving away the answer", but is instead a way to more fully engage intellectually with the science ideas at play.

2. Provide the necessary instructions. Clearly explain the steps that students will need to follow to complete the task, explain the materials they have to work with, provide any necessary instructions or guidelines, and make note of any safety concerns. Make sure that students understand what they are expected to do and how they will be evaluated. This may include modeling all or part of the task.

Once the students understand the ideas at play in the task and the instructions, they can begin the main task. As in nearly all aspects of an MBI unit, we recommend students engage in tasks as part of their consistent groups. While student groups are working on the task, it is important that the teacher moves around to each group to help push students' use of the science ideas to make sense of the data being generated or analyzed. The questions we ask are called back pocket questions. They are 'back pocket' because they are constructed before the lesson begins and can be written on an index card you can carry around in your back pocket to remember. We recommend preparing three types of questions:

Finally, after the groups have finished the task and cleaned up, we take time for collective sensemaking. Individual groups have been working together to make sense of the data through the lens of the science idea at play. Now it's time to coordinate those ideas across groups so the groups can learn from each other. Our goal here is to help students see broad trends or patterns of data across the groups, to connect these data to the science ideas at play, and to connect the science ideas to the explanation of the phenomenon. There are three parts to this whole class discussion:

Finally, we end each lesson by filling out the summary table. Summary tables are tools that:

1. scaffold the construction of consensus statements about what was learned during the task and how it relates to the phenomenon, and

2. keep track of our new understandings over time as we build an explanation of the anchoring phenomenon. As you use summary tables, we recommend the following:

As you use summary tables, we recommend the following:

Remember, the object of the summary table work is not to fill in the row as quickly and decisively as possible. The purpose is to have a sustained conversation in which students compare and contrast ideas, learn how to build on or critique their peers’ reasoning, and apply new science ideas to events that go beyond the activity itself.