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Ending the Federal Mandate on Standardized Testing and Utilizing Learning Analytics
Tori Roloff
Minnehaha Academy
Minneapolis, MN

In 2001, Congress passed the No Child Left Behind Act (NCLB), which was widely criticized for its emphasis on high-pressure standardized testing as a means of holding schools accountable for student outcomes (Flinders 1-2). The Every Student Succeeds Act of 2015 (ESSA), which replaced NCLB, lowered the stakes of standardized tests but maintained the federal mandate (Kamenetz). Public schools are currently required to test students once each year in grades 3-8 and once in grades 9-12; in contrast, the high academic achieving country of Finland only uses one standardized test that is taken at age fifteen (Kamenetz; Fritz 8). Although ESSA is an improvement from NCLB, the act does not go far enough to end the environment of over-testing in schools. A possible solution resides in big data, a technology that has augmented the success of major corporations such as Target for years; big data is commonly defined as “datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze” (Levenson et al. 6; Manyika et al. 1). The most cost-effective change to the American K-12 education system is to eliminate the federal mandate on standardized testing and implement big data in schools to guide school decisions and maintain accountability.

The British economist Charles Goodhart developed a maxim that is commonly written as follows: “When a measure becomes a target, it ceases to be a good measure” (qtd. in Elton 121). Likewise, the overemphasis on standardized testing has changed the goal of education from learning to performing (Grinell and Rabin 750). According to Goodhart’s Law, this shift has caused standardized tests to be an ineffective measure of student improvement and teacher accountability (Elton 125). To center the focus of students, teachers, and schools back on learning, the US needs a cost-effective tool that utilizes multiple measures to determine the quality of an education.

Standardized testing not only has become an ineffective measure of achievement, but also carries immense costs. The Brookings Institution estimated the explicit costs of assessments per state to be $1.7 billion per year in 2012 (1). However, far greater than the explicit costs are the implicit costs that plague both students and society. Firstly, “What is tested now determines what is taught” (Flinders 5). Students are not adequately exposed to valuable subjects such as politics because the material is not assessed by standardized tests (Journell 116). Therefore, students are bearing an opportunity cost by preparing for and taking tests instead of learning meaningful material (Grinell and Rabin 756). While students certainly experience the negative effects of testing, “little empirical evidence exists to demonstrate that output guarantee approaches result in positive lifelong benefits to children” (Tienken 7). In the long-run, society carries the cost by producing citizens who are narrowly educated (Grinell and Rabin 756).

Instead of using uniform tests to define schools, big data should be utilized to inform school decisions and hold educators accountable. The use of big data in schools, commonly referred to as learning analytics, can be defined as “the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” (qtd. in Dietz-Uhler and Hurn 17). Learning analytics has the capability to identify cost-effective programs, to personalize the learning experience by informing educators which teaching approaches are most effective with specific types of students, to identify students who might need additional assistance, and to reveal knowledge gaps in a student body (Levenson et al. 12, 13; Reyes 77, 78). Additionally, big data maintains accountability for schools and teachers. Schools can use big data to easily document “what and how their students are learning” (Dietz-Uhler and Hurn 20); likewise, data can reveal which teachers are responsible for positive and negative student outcomes (Levenson et al. 4).

In addition to containing the potential needed to transform education, learning analytics carries few costs. The costs include hiring individuals who have the necessary skills to properly interpret data and an initial fee to implement software that will store and analyze data (Levenson et al. 12). However, data-analyzing software is generally inexpensive and will lead to more cost-effective decisions in the long-run (Levenson et al. 12). The benefits of management technologies far exceed the costs; principally, big data will inform educators “how we help our students succeed” (Dietz-Uhler and Hurn 24). By determining how educators most effectively teach students, learning analytics will enhance student outcomes such as course completion rates, graduation rates, and eventually lifetime earnings for graduates.

The US should eliminate the federal mandate on standardized testing and shift towards a system of decision-making and accountability that is based on the use of big data. Ending the federal mandate will foster healing in the American K-12 education system after it incurred the detrimental effects of excessive testing; moreover, big data will provide a more holistic understanding of the current education landscape than standardized tests are capable of giving. Ultimately, the technology will revolutionize the manner in which politicians form education policies in the future. With NCLB recently being replaced, it is time to end the era of “one-size-fits-all” federal education mandates.

Works Cited

Chingos, Matthew M., and Brookings Institution. "Strength In Numbers: State Spending On K-12 Assessment Systems." Brookings Institution (2012): ERIC. Web. 19 Mar. 2016.

Dietz-Uhler, Beth, and Janet E. Hurn. "Using Learning Analytics To Predict (And Improve) Student Success: A Faculty Perspective." Journal Of Interactive Online Learning 12.1 (2013): 17-26. ERIC. Web. 27 Mar. 2016.

Elton, Lewis. "Goodhart's Law and performance indicators in higher education." Evaluation &  Research in Education 18.1-2 (2004): 120-128. Web. 27 Mar. 2016.

Flinders, David J. "The Failings Of Nclb." Curriculum & Teaching Dialogue 7.1/2 (2005): 1-9. EBSCO MegaFILE. Web. 17 Mar. 2016.

Fritz, Gregory K. "An Educational Comparison Of Finland And The U.S." Brown University Child & Adolescent Behavior Letter 30.3 (2014): 8. EBSCO   MegaFILE. Web. 27 Feb. 2016.

Grinell, Smith, and Colette Rabin. "Modern Education: A Tragedy Of The Commons." Journal Of Curriculum Studies 45.6 (2013): 748-767. EBSCO MegaFILE. Web. 27 Feb. 2016.

Journell, Wayne. "The Influence Of High-Stakes Testing On High School Teachers' Willingness To Incorporate Current Political Events Into The Curriculum." High School Journal 93.3 (2010): 111-125. ERIC. Web. 27 Feb. 2016.

Kamenetz, Anya. "School Testing 2016: Same Tests, Different Stakes." NPR. N.p., 28 Dec. 2015. Web. 19 Mar. 2016.

Levenson, Nathan, et al. "The Promise Of Education Information Systems: How Technology Can Improve School Management And Success." Center For American Progress (2014): ERIC. Web. 23 Mar. 2016.

Manyika, James, et al. "Big Data: The Next Frontier For Innovation, Competition, And Productivity." Big Data: The Next Frontier For Innovation, Competition & Productivity (2011): 1-143. Business Source Premier. Web. 28 Mar. 2016.

Reyes, Jacqueleen. "The Skinny On Big Data In Education: Learning Analytics Simplified." Techtrends: Linking Research & Practice To Improve Learning 59.2 (2015): 75-80. EBSCO MegaFILE. Web. 27 Feb. 2016.

Tienken, Christopher H. "For The Record: What Education Policy Could Be." Kappa Delta Pi Record 48.1 (2012): 7-9. ERIC. Web. 27 Feb. 2016.