The Data Series: Part III (Final)
(This is the final part of this series. Each section functions independently, but here are Part I and Part II for the curious.)
According to Dr. Iris Garner, a National Research Consultant, these are the most common data used to assess any individual student’s performance:
Assessment, Attendance, Behavior, Benchmarks, Classwork, Demographics, Grades, Health, Homework, Investment, Leadership, Observation, Participation, Quizzes, Results, Socio-economic, Tests
Dr. Garner clarifies that this list does not cover all data, but when used to coach a teacher, it should be recognized that the above emphasized words are achievement measures. Achievement measures are paramount to a school’s success and must be taken and observed. However, most teachers, especially new ones, need help with the data points that influence their achievement measures. They should not focus too much on the achievement data itself, after all it may only be lower than average due to their lack of experience. Additionally, as mentioned in part II of this series, it is unlikely that they have the training required to do anything with the data.
Leaders should rarely have coaching conversations with achievement data increasing as the only goal. That will likely result in someone concerned more about a specific project or test over enhancing their performance as a whole. Doing that reminds me of the Buddhist concept of mistaking the pointing finger for the moon. Achievement data is most useful when we use it to help point us in the direction of something that is either working or not working. This proved true for my work in the private sector as well. Our team tended to work better if we were working on becoming stronger in general rather than doing one specific project better. Fixing a process has longer lasting results than fixing a project.
Anyway, let’s look at the rest of the data sets that influence student achievement. Teachers either have very little control or variance in attendance, demographics, health, and socio-economic factors. So, the most useful data sets when coaching a teacher are behavior, leadership, observation, and participation. These are all specifically related to the classroom environment; so, when referring to those four data sets, I will use that term. Breaking these factors down tells me that the most successful teachers have positive and engaged classroom environments.
I admit, this is personal to me, but I want more people to realize that the conditions surrounding results are much more important than the results themselves.
If a teacher has a healthy classroom environment and their students’ scores are still low, coaches would then need to begin doing the work of considering problems in the curriculum with the teacher, explain the use of scaffolds to them, or show teachers how to track student classwork differently. We may write off this teacher as ineffective due to test scores alone. However, after regarding the aforementioned data points, this is actually probably a strong teacher who just needs guidance!
In contrast, teachers who do not have stable classroom environments still need coaching, but the coaching would have to be with their context in mind. For instance, if poor teaching practices have left a classroom without any goodwill or respect toward their teacher, mending that relationship should come way before instructional coaching occurs. It would be difficult to implement a tracking system, ask more robust text-dependent questions, or even use more culturally relevant materials effectively without addressing that. No instructional “best practice” is going to repair a social problem. I have noticed that the typical response to teachers who struggle to control their classrooms is to let them fail and fire them instead of supporting them.
Teaching as a profession demands more training, more prestige, and more respectable salaries, but until that happens, it is even more critical that we build capacity. Getting rid of talent before it has been given a chance to improve is not productive.
In my case, as a first-year teacher, I possessed several positive, but less quantifiable qualities like empathy, cultural responsiveness, and informal engagement methods. My students were comfortable, eager to come to class, felt I had their best interests at heart, wanted to impress me, and felt empowered. It seems that I would have been more successful if someone taught me to leverage those qualities. They were acknowledged from time to time, but that was all.
Not all leaders or teammates find praise natural, receiving, or giving. However, there are practical benefits to remembering that humans are not robots. So, whether we like it or not, coaches need to understand that emotions affect performance.
During the past few summers, I worked as a coach to new teachers. As part of the training process, I received research on how to give criticism properly. One of the main things I learned to avoid was giving destructive criticism. An article in the official journal of the International Association of Applied Psychology defines destructive criticism as “negative feedback that is inconsiderate in style and content, which exists at the intersection of performance feedback and interpersonal mistreatment.”
A thorough review of the study revealed some surprising data. For instance, destructive criticism improved the performance of less-competitive people. But, non-competitive people have the potential to vastly outperform their less-competitive counterparts. However, destructive criticism harms relationships, and data suggests it causes competitive workers to perform worse. Theoretically, since competitive people attach their identity to their successes, their performance tends to suffer as they begin to practice self-destructive habits in an effort to improve. And, it is critical to remember that competitive workers want to improve. Thus, non-personal process-based constructive criticism is something they tend to desire.
The school I worked for employed mostly competitive personalities. It is evident through our accomplishments and the way we spoke about our work and students. Most companies aim to hire competent, competitive people. So, it is reasonable to assume that most employees will not benefit from destructive criticism.
I believe that most destructive criticism comes from a place of accidental insensitivity or ignorance, not malice. I think whether a colleague or authority figure is giving feedback, it is imperative to check-in while coaching to see if the person receiving feedback seems upset. I have had to pivot mid-conversation with someone once I realized my feedback was more destructive than constructive. Hurting our teammates or employees is counterproductive! Besides, if they are spiteful, they’ll leave and work that much harder for a new team. Remember, over-reliance, and focus on incorrect or limited data is not helpful.
In conclusion, the information we can glean from data should be subject to the utmost scrutiny. Data processing is a skill that takes time; but, slowing down to do it properly will save more time later. The main takeaways from this part of the series are to consider what data we use in coaching conversations, to be mindful of how we present that information, and to examine the interrelatedness of everything we do. When used correctly, data can be very powerful, and I think we all know what comes with great power.
