Teacher Moneyball: Can big data and predictive analytics help find the next generation of star educators?

The teaching profession is facing a supply and demand challenge: retirement of baby-boomer educators is on the rise, while enrollment in teacher education programs is on the decline;  job satisfaction among teachers is dipping, with many new teachers leaving the classroom in their first three to five years.  Add these trends up and all signs point to a growing national need for teachers over the next several years. And in fact, at the beginning of this current school year, districts around the country reported significant teacher shortages. 

Rather than aggressively recruit more people in general to become teachers (see efforts such as www.Teach.Org ), the solution to rising demand may rest in targeted strategies that identify the strongest future educators—those most likely to be effective in the classroom and most likely to stick around.  In this way, teacher preparation programs can help schools retain and stabilize their workforce, while reducing constant hiring needs.  In other words, we should focus less on increasing the overall supply of potential teachers, and more on selecting and preparing the potentially most effective, resilient teachers.  Without this type of selectivity, federal, state education agencies and local school districts risk spending significant money to continuously recruit, induct and train a “revolving door” of teachers who enter and leave the profession. 

In the era of big data, could teacher preparation programs not develop more targeted teacher recruitment and screening strategies to find the strongest, highest potential candidates?  Perhaps the best place to turn for insight is the sports world.  In his best-selling book, Moneyball, Michael Lewis tells the true story of how Billy Beane and his cash-strapped Oakland Athletics major league baseball team developed a resourceful and innovative approach of using predictive analytics to identify high potential, but undervalued (read: non-sought after) professional baseball players.  Annually one of the lowest budgeted teams in the major leagues, Beane and the Athletics have used this approach to great success, fielding highly competitive teams year-after-year, and routinely outperforming higher payroll teams.   

An intriguing notion is whether the same approach – predictive analytics—could be used to identify high potential performers in other fields, such as education.  In other words, can we isolate the key traits of effective teachers and then assess the prevalence of these traits in prospective teachers to forecast who the next star educators will be?   

The education literature is inundated with research and articles on the qualities and characteristics of effective teachers.  From a quick online scan of this literature, a short—by no means exhaustive—list of themes emerge:

●   Positive disposition – effective teachers exhibit optimism, energy, enthusiasm, a joy of teaching, self-confidence, appreciation and praise for students, and sense of humor.

●   High standards /expectations – effective teachers start with the assumption that all students can be successful, impose no limits on learning, challenge their students, and encourage students to give their best effort.

●   Organization and clarity – effective teachers purposefully plan and plan far ahead, pay attention to detail, deliver well-structured and paced lessons, review and return student work promptly, explains objectives, rules, expectations and content clearly, making difficult concepts easier to understand.

●   Self-critical / reflective /problem-solvers – effective teachers continually self-exam their practice and style, asking how they can improve, and what went well or not so well with lessons and assignments. They look for answers in student data and work, and are open-minded, welcoming constructive feedback from supervisors and colleagues. They embrace setbacks and problems as solvable challenges, and seek out solutions. 

Thus, following the Oakland Athletics example, could teacher education programs develop reliable ways to assess whether prospective teachers / student teachers demonstrate these and other key attributes? For example, as part of an application process, might a teacher education program have candidates teach a mini-lesson, receive critical feedback, and re-teach the same lesson again to observe how they respond to and incorporate feedback?  Or might they ask candidates to read and respond to a challenging case-study or even a series of Lumosity like exercises to gauge their analytical and problem-solving skills? Or have candidates complete a personality test to screen for a positive disposition? 

Of course, these attributes—like most human personality traits—are not easily measured, especially intangible qualities, such as a positive disposition.  Developing valid, reliable and unbiased aptitude and performance assessments is an ongoing challenge.  And there is a dizzying array of social scientists, conferences and companies racing to create the next predictive metrics and software for everything from student behavior, to fitness, to financial stock performance.  Yet, in the case of identifying new, high-potential teachers, this pursuit would be worthwhile if it could help to select great future educators.  No other factor is more important to student success than highly skilled teachers – and this has been reliably and validly measured. Moreover, it could save education agencies and schools both the time and money they now spend on the “revolving door” of teachers.