# Assignment Design

**Jump down to:**Begin with outcomes | Student work in teams | Scaffold your assignment | Provide instruction on methods/tools | Provide instruction on analysis and presentation | Model the behaviors you want students to adopt | References

Whether asking students to analyze someone else's presentation of data or to produce original research, several principles can avoid teaching-with-data pitfalls. As you approach your new assignments and course materials, start at the end. What do you want your students to learn by taking this course? Only after you have articulated the goals for your course can you think about how teaching with data can best support those ends.

Next, think about the skills your students have in the area of data analysis. Depending on the background of your students, you may need to organize them into groups to mitigate skills gaps. When you are ready to prepare your assignments, think about "scaffolding" your assignments. This practice of breaking larger projects into smaller parts simultaneously provides students with better preparation and avoids grading nightmares. While it might go without saying, it can't go without doing: provide clear instruction on the methods and tools students will need for the assignment. This instruction should extend to teaching how to present the results of students' data work. Finally, throughout the term be self-aware for opportunities to model what you are teaching in the data assignments. If there is sloppiness in our own presentations of data in lectures we will undermine all of our other efforts.To speed your work, check out the Discipline- and method-specific activity collections or browse activities that emphasize teaching with data in a variety of disciplines

## Begin with explicitly articulated student learning outcomes for your course

What are the 4 to 7 key learning goals for this course? And how does teaching with data support students' achievement of those goals? It is easy to become excited by a new pedagogy when the research suggests it can notably enhance student learning. And when we are excited we want to dive right in. But before jumping in to revising assignments or course modules it is important to step back and identify the ways the new activities will connect with the primary objectives for the course.

This is true for at least two reasons. First, syllabi are very tight. Most faculty have never met a colleague who concluded a term saying, "With two weeks left in the term I had covered everything I felt I needed to cover. The rest of the term was just filler." Across all fields, faculty are keenly aware of disciplinary expectations of content coverage for courses. This sense of obligation is particularly sharp in courses which are pre-requisites for other courses in the curriculum. With so much external pressure, any aspect of a course that doesn't align with primary learning goals must be quickly tossed aside, no matter how much excitement surrounded its initial addition to the syllabus.

Second (and more importantly), innovations in teaching should never be done for the sake of "change." The goal is to teach students more effectively. While teaching with data often achieves that end, it is very time intensive (for both the faculty member and students) and so needs to be used deliberately when and where it is aligned with course goals.

## Consider having students work in teams to mitigate skills gaps

Sometimes teams can allow an instructor to work around variations in student experience with tools or methods. If you survey your class at the beginning of the term to find out who is comfortable with what, you can assign students to teams designed to ensure that each team has an "expert" in each tool and/or method required in the assignment.

If you choose to proceed in this direction, make sure to ask yourself whether you want all of the students to end up equally well-prepared with the required methods and tools. Is it okay if the one student who has experience with this instrument collects the data while others look on (and never learn to do it themselves)? If not, then be sure to build in time for peer instruction.

Read more about cooperative learning

Read more about peer-led team learning

## Consider "scaffolding" your teach-with-data assignment

"Scaffolding" in this context means breaking the work into smaller parts so that you can provide feedback before the final product is complete. For example, in an assignment to find and present the correlation between two student-selected variables in a dataset, students may first be asked to briefly describe the two variables they have selected and what theoretical connections they anticipate. This allows the teacher to correct misunderstandings–about the theory or even about variable definitions–before students put those ideas into the larger report.

Scaffolding allows you to correct fundamental errors before they are inserted into the larger, final product. This serves two purposes. First, it helps students organize their work. Research shows that when students are asked to take on new tasks, they may experience regression in previously mastered skills. This predictably follows from having their attention devoted to the new task at hand. Scaffolding helps students see their work as a series of more manageable pieces.

The second purpose for scaffolding is more pragmatic. Teaching-with-data assignments often involve a complex interaction of tasks. (Indeed, that is often the point!) When students make a fundamental mistake in the first step, the resulting final product can be incredibly difficult to grade. While much of what the students did after making the early error "made sense," the final product may be irreparably damaged. The tension between "what follows makes sense given the error" and "the error leads to a ridiculous end point" can be very difficult to resolve.

Scaffolding also allows you to increase the complexity of assignments over the course of the term. For example, if some skills are not consistently taught in prior classes, then you can use assignments early in the term to teach students the methods you want them to use in the final project.

Of course, scaffolding need not be an either/or proposition. You may choose to scaffold assignments at the beginning of the term and then eliminate this guardrail as students gain confidence and competence. The main point is that instructors must equip students with the skills they need before they take on any new task.

Read more about scaffolding and sequencing

## Provide instruction for the methods/tools students will need

Sometimes this principle can go without saying because the goal of the teaching-with-data activity is to teach students the new method or tool. But often our goal is to get students to wrestle with the data or the ideas behind the data. The analysis tool is just a vehicle. For example, you may ask students to explore the correlations between several variables using Excel. Despite fluently mastering hundreds of apps for the smart phones, a large fraction of students have never used a spreadsheet. Even a "simple" task like plotting the data in a scatter plot can pose significant challenges. Without instruction, students can waste time that was intended for data exploration.

While students often require instruction, this does not necessarily mean you need to provide it personally during class time. Most information technology departments can provide introductions to software tools. And many such tools have online tutorials which can be assigned to students who lack experience. However, the more specific the tool or method is to your discipline the more likely you will have to teach it yourself. Two areas that commonly require teaching: the use of statistical tools and understanding and creating graphs.

## You get what you teach: Provide explicit instruction on data analysis and presentation

To be sure, it is valuable to give students repeated opportunities to engage data, use it in problem solving, and present findings. And if this form of student-engaged practice were ubiquitous, it might be fine to simply provide "another opportunity" for students to get their hands dirty with data. But given that teaching with data is not ubiquitous, students likely need more than opportunity–they need explicit instruction. This is likely true in the large (e.g. "What do you mean by 'testable hypothesis?'") and the small (e.g. "I didn't know I had to label the axes of a graph.").

Providing examples of high quality work will help some students. Better yet, provide a set of examples that demonstrates a range of quality. But most will need you to "walk them through" those examples to help them see what makes good work good. If you have a grading rubric, strongly consider sharing it with your students. As one colleague of mine said about her early (and bumpy) experience as a scholar, "I wasn't producing C work because I wanted to. It was just that no one had shown me how to produce A work!"

**Literature on providing examples**

- The literature on learning shows that students need two or more examples to distinguish surface characteristics from underlying principles–and you probably want them to focus on the principles! See Gick and Holyoak 1983 for the seminal research on the importance of multiple examples.
- Jane Miller's
*Chicago Guide to Writing about Numbers*provides a great model for showing students a set of examples of varying quality.)

## Model the behaviors you want your students to adopt

Let your students see you engage data. Sometimes this may be staged. For example, when presenting a lecture you might show students a plot of the raw data and then "work through" the process of analysis in front of them. But don't be afraid to occasionally let them see you take on real, open-ended problems. Many students find it very powerful to see their teacher in the process, generating hypotheses in "real time." While it may feel risky to teach from material for which we don't know the answers, it teaches students the important lesson (particularly in the sciences) that scholarship is not about mastering a canon. Rather, it is about generating and exploring important new questions for which we do not have clear answers.

Either way, as you model the practices you value, be sure to call students' attention to the moves you are making (and that you wish them to copy).

**References**

Gick, M. L. and Holyoak, K. J. 1983. "Schema induction and analogical transfer," *Cognitive Psychology*, 15(1): 1-38.

Miller, J. E. 2004. *The Chicago Guide to Writing about Numbers: The Effective Presentation of Quantitative Information.* Chicago: University of Chicago Press.