Embedding collaborative data dialogs into assessment cycles

Personal project coordinators are most likely preparing for moderation of projects or have already gone through the process.

Moderating or standardizing assessment of student work is a valuable strategy for groups of teachers wanting to improve the learning process. The goal of the process is to decide the level of achievement using the criteria for the work being discussed, and then arriving at a consensus on these levels.

Before this decision is made, it helps to hold a dialog around the descriptions of success – in MYP, the criteria.

Two ways of talking – the difference between dialog and discussion

Adaptive Schools^SM suggests two ways of talking in groups.

Dialog is a conversation which functions as a way to create shared understanding. The product of a dialog is understanding.

Discussion is a conversation, which functions as a way to arrive at a decision through collaboration. The product of a discussion is a decision.

It makes sense to hold a dialog and arrive at shared understanding, before launching into a discussion for the purpose of decision-making.

Let’s look at the example of a Personal project moderation session. Beginning with a dialog, the group may achieve the following:

• Clarity on criteria, descriptors, and task specific clarifications
• Shared understanding of criteria, process, and task
• Shared understanding of why the moderation process is key to the personal project cycle
• Shared understanding of how each subject group’s criteria align with the personal project criteria
• Shared understanding on how each instructional thread in the entire programme enhances the knowledge, conceptual understanding, skills and dispositions of students toward success in the personal project

As the group reaches shared clarity on these fine points of the task being moderated, individuals may reach a readiness to begin discussion using specific student samples on the table. This is where discussion begins specifically for the group to decide the levels of achievement for each sample.

What are collaborative data dialogs?

In the course of an academic year, groups might gather for the purpose of informing instruction. Dialog around units of inquiry, tasks, ATL skills, and other points of learning might be on the agenda as the groups aim to use collaboration to create an effective and meaningful assessment cycle.

For example, a Language and literature team I worked with in a large school (2000+ students) met every fortnight to hold dialogs around student work. The purpose of these meetings was to use what we learned from student work to improve upon our writing instruction.

Typically, one of us would bring an anonymized sample of student work (removing the name of the student), giving copies of the sample to everyone in the group. Each teacher would read and mark the sample using a specific set of descriptors from the criteria, which corresponded to what was taught to the students for the particular task. These were the task specific clarifications for the task from which teachers had a student sample.

After marking the student sample individually, we used the task specific clarifications to dialog on our understanding of criteria and the developed task specific clarifications.

The purpose for the meeting guided our dialog. A couple of examples are in the table below.

Some advantages of a dialog around a sample of student work were:

• We understood how the instruction supported student achievement
• We gained clarity on how task specific clarifications helped students learn and succeed in tasks
• We gained clarity on using criteria in our subject
• We increased craft knowledge in planning, teaching and assessing writing
• Our collaborative meetings focused on evidence of achievement, not only our opinions about our students’ abilities
• Our collaborative meetings resulted in collegial and personal efficacy toward our common goals
• We developed shared strategies for improving writing

How do collaborative data dialogs work?

For collaborative data dialogs to work, first of all, the group must have trust. Bringing a sample from the classroom and showing it to colleagues suggests an element of vulnerability. If the group has little relational trust, this sharing of classroom samples might be uncomfortable at best, possibly threatening in the extreme.

A collaborative group needs to maximize their time by using a protocol. An example of a protocol for examining student work is the Tuning Protocol. (Feel free to download the Tuning Protocol from the link. The attribution and source is on the document.)

Using protocols means that the group is focused around the meeting purpose (shared understanding) and does not get sidetracked by other agenda. For example, that group I worked with made sure the facilitator for the meeting (we took turns) published the meeting agenda at least a week before the meeting took place.

This agenda would clearly state who was facilitating, who was timing our meeting, the protocol we would use, and send the copies of the TSC and the student sample along with the agenda to give everyone time to read it.

During the meeting, we started with going over the protocol to make sure we followed it. We would allow a few minutes of clarifying about the process, then when everyone was clear about what to do, we would begin.

It was a skill that we learned and rehearsed of using data when talking about learning, by always (a) going back to the documentation, e.g. the criteria and (b) referring to the sample to justify our opinions on achievement. We recognized that opinions without guidance on shared goals (criteria) nor evidence (specific citations from the work sample) were simply perceptions and may not have value toward the descriptive feedback we sought from the process of reaching shared understanding.

How do collaborative data dialogs inform instruction?

Data dialogs inform instruction in several ways. Some of the ways for that Language and literature group were the following.

• As we reached more clarity about descriptions of quality in the subject criteria, we also improved how we wrote task specific clarifications.
• Because we had more understanding of descriptions of quality, we were able to draw out specific skills that would help our students get to those levels of quality in performance.
• We also had a running record of skills taught within grade levels and increase coherence of writing instruction, vertically.
• We also shared strategies that had worked, and so each one of us grew our repertoire of approaches to teaching.

One of our products as a collaborative group was a clear assessment cycle for writing.

The process of holding data dialogs holds some pressure points for teachers. As mentioned earlier, it might be uncomfortable for teachers to openly share classroom products like task specific clarifications, task sheets, or student samples.

Another pressure point might be that some individuals are uncomfortable with dialog. If the meeting does not have the purpose of arriving at a grade for the task, which happens in a discussion, it can be difficult for some people to accept that something was actually accomplished during the collaborative time.

Like many ‘soft skills,’ collaborative dialog is a set of skills that learners acquire and rehearse before it becomes second-nature in practice.

We can address these pressure points.

Groups can set goals not only for the technical work, like how to use criteria to unpack skills in instruction, but also set group goals for collaboration skills. Adaptive Schools^SM has norms of collaboration that groups can learn, rehearse and develop as they get better at collaboration.

Data dialogs give schools ways to intentionally feedforward into instruction from examination of student work. Not only does collaborative data dialog strengthen approaches to teaching, but it also augments approaches to learning in specific and coherent ways.

Reference
Garmston, R. J., & Wellman, B. M. (2014). The adaptive school: Developing and facilitating collaborative groups(2nd ed.). Norwood, MA: Christopher-Gordon.

Featured photo by Zane Lee on Unsplash