Written by: Dave Reid
Primary Source: Green & Write, February 29, 2016
Teacher quality matters. Attracting, hiring, developing and retaining high-quality teachers is considered one of the most impactful ways schools can improve student outcomes.
Proponents of using more and better data to assess teachers before hiring argue this type of detailed information helps districts and principals attract and hire top-tier teacher candidates who are likely to be effective teachers and just as importantly, remain in the classroom.
Photo Courtesy of Enri Endrian
What Companies Are Saying
Companies, such as TeacherMatch, have taken steps towards improving teacher hiring by using predictive analytics. Simply put, predictive analytics is the use of data and statistical algorithms to identify the likelihood of future outcomes based on historical data. In teacher hiring, this historical data includes things from college selectivity and individual cognitive ability to the ability of an individual to persevere through challenges. TeacherMatch argues using this type of information helps districts identify and hire teacher candidates who have the most potential to be effective educators and remain in the profession.
Another company, Hanover Research, argues their data (which uses similar information as TeacherMatch) can be used to quickly identify the “best and brightest” teachers. Additionally, Hanover Research argues using their information reduces the time and cost to hire teachers and can better match teachers to schools where they will be a good fit.
What Does the Research Say?
The above information should be taken with a grain of salt, as these companies have an interest in schools and districts buying in to the idea that predictive analytics will improve teacher hiring and retention. Therefore, it is important to look beyond these claims and see what academic researchers have found when looking at district hiring practices and how districts use information.
Some research has shown the more access to information school leaders have, the more likely they are to hire more effective teachers. Other research is beginning to surface that shows certain characteristics of teacher candidates predict future teacher quality, but these characteristics are only weakly associated with hiring decisions.
In general, it appears if school leaders are given access to information beyond traditional hiring data, (i.e. a teacher candidate’s certification and degree), school leaders do in fact use additional information and make better hiring decisions.
Not a Perfect Solution
While it appears giving districts, schools, and principals access to more complete and better information has the potential to increase the teacher quality pool and attract and keep the best teachers in our nation’s classrooms, it is not a perfect solution.
This type of data can only do so much as to predict who will be effective in the classroom or remain teaching. For example, data that tracks where teacher candidates are trained, whether at a traditional teacher preparation program or an alternative program, can identify some trends of who remains in teaching longer, but even these trends should be looked at in a case by case basis and in a more nuanced fashion that looks at individual stories.
Additionally, it is difficult to conclude with confidence analytic data can predict which teacher candidates will be best suited to teach at specific schools. The fact remains you will not truly know how a teacher responds to an assignment until he or she spends time in their own classroom in that specific context.
Using predictive analytics to inform K-12 education is not new and as more information becomes readily available, it should continue to be used by districts to make the best estimates at the imperfect process of hiring the teachers.
However, there may never be a perfect hiring tool for K-12 districts and administrators. As researchers have noted, there is a certain mystery to good teaching and even if districts and school leaders have access to a broad set of information prior to making hiring decisions, there is no guarantee this information will result in more effective teachers entering and remaining in the classroom.
Experience is ultimately the best measure of a teachers’ effectiveness and this of course is something that predictive analytics cannot measure until teacher candidates spend time in their own classrooms.
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