Data-Driven or Data-Informed? Thoughts on trust and evaluation in education

Data-informed or data-driven? This is a question I have wrestled with as a school administrator for some time. What I have found is that the usefulness of student data to inform instruction and accountability rests on the level of trust that exists within the school walls.

First there is trust in the data itself. Are the results of these assessment tools reliable (consistency of results administered over time and students) and valid (accuracy in the results of the assessments to measure student learning)? These are good initial inquiries, but should only be a starting point.

Security of student information should also be a priority when electing to house student data with third parties. One question I have started asking vendors that develop modern assessment tools include “Where do you house our student data?”, “What do you do with this data beyond allowing us to organize and analyze it?”, and “Who owns the student data?”. In a commentary for The New York Times, Julia Angwin highlights situations in which the algorithms used to make “data-driven decisions” regarding probability of recidivism in the criminal justice system were too often biased in their results (2016). Could a similar situation happen in education? Relying merely on the output that a computer program produces leads one to question the validity and reliability of this type of data-driven decision making.

A second issue regarding trust in schools related to data is how student learning results are being used as a tool to evaluate teachers and principals. All educators are rightfully skeptical when accountability systems ask for student learning results to be counted toward their performance ratings and, in some cases, level of pay and future employment with an organization.

This is not to suggest that student assessment data should be off the table when conversations occur regarding the effectiveness of a teacher and his or her impact on their students’ learning. The challenge, though, is ensuring that there is a clear correlation between the teacher’s actions and student learning. One model for data-driven decision making “provides a social and technical system to helps schools link summative achievement test data with the kinds of formative data that helps teachers improve student learning across schools” (Halverson et al, 162). Using a systematic approach like this, in which educators are expected to work together using multiple assessments to make instructional decisions, can simultaneously hold educators collectively accountable while ensuring that students are receiving better teaching.

Unfortunately, this is not the reality in many schools. Administrators too often adhere to the “data-driven” mentality with a literal and absolute mindset. Specifically, if something cannot be quantified, such as teacher observations and noncognitive information, school leaders may dismiss these results as less valuable than what a more quantitative tool might offer. Professional trust can tank in these situations.

That is why it is critical that professional development plans provide educators with training to build assessment literacy with every teacher. A faculty should be well versed in the differences between formative and summative assessments, informal and formal measurements, deciding which data points are more reliable than others, and how to triangulate data in order to analyze results and make a more informed decision regarding student learning.

Since analytics requires data analysis, institutions will need to invest in effective training to produce skilled analytics staff. Obtaining or developing skilled staff may present the largest barrier and the greatest cost to any academic analytics initiative (Baer & Campbell, 2012).

Building this assessment literacy can result in a level of trust in oneself as a professional to make informed instructional decisions on behalf of kids. If a faculty can ensure that the data they are using is a) valid and reliable, b) used to improve student learning and instructional practice, and c) considers multiple forms of data used wisely, then I am all for data-driven decision making as a model for school improvement. Trust will rise and student achievement may follow. If not, an unfortunate outcome might be the data cart coming before the pedagogical horse.

References

Angwin, J. (2016). Make Algorithms Accountable. The New York Times. Available: http://www.nytimes.com/2016/08/01/opinion/make-algorithms-accountable.html?_r=0

Baer, L.L. & Campbell, J. (2012). From Metrics to Analytics, Reporting to Action: Analytics’ Role in Changing the Learning Environment. Educause. Available: https://net.educause.edu/ir/library/pdf/pub72034.pdf

Halverson, R., Gregg, J., Prichett, R., & Thomas, C. (2007). The New Instructional Leadership: Creating Data-Driven Instructional Systems in Schools. Journal of School Leadership. Volume 17, pgs 159-194.

This is a reation paper I wrote for a graduate course I am currently taking (Technology and School Leadership). Feel free to respond in the comments to extend this thinking.

Author: Matt Renwick

Matt Renwick is a 17-year public educator who began as a 5th and 6th grade teacher. After seven years of teaching, he served as a dean of students, assistant principal and athletic director before becoming an elementary principal in Wisconsin Rapids. Matt is now an elementary principal for the Mineral Point Unified School District (http://mineralpointschools.org/). Matt tweets @ReadByExample and writes for ASCD (www.ascd.org) and Lead Literacy (www.leadliteracy.com).

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