Evaluation of student learning has always been at the core of educational practice. As educators continue to re-evaluate why and how to assess students, there is a growing movement that is using hard data to drive assessment goals and student learning outcomes.
Data-Driven Instruction: A Brief History
But what is data-driven learning, really? In the early 2000s, legislation in the United States forced schools to report on student performance and learning outcomes. The No Child Left Behind Act (NCLB) became the basis for a new program that evaluated schools and alloted federal funding based on evaluation of students’ performance. While arguably well-intentioned, in practice, the drive to improve outcomes resulted in students memorizing content to faithfully replicate in a standardized testing environment. This phenomenon frequently replaced student skill-building and organic learning. It also became a self-fulfilling prophecy which ignored the systemic disadvantages faced by schools in marginalized communities.
Increasingly problematic, the NCLB was replaced in 2015 by the Every Student Succeeds Act (ESSA), which removed much of the federal government’s role in allotting funding for education, and expanded the criteria for determining “success” at the school level. Critically, the ESSA also required evidence-based planning for interventions where marginalized groups are consistently underperforming, such as minority students, English language learners, or low-income communities.
Using Evidence-Based Education in Supporting Student Success
While no government policy on education will be perfect, the NCLB and ESSA have given rise to a new form of data-driven instruction that has changed “focus from ‘what was taught’ to ‘what was learned’”. Instructors who use quantifiable, evidentiary data for measuring student learning are becoming increasingly able to pinpoint the distinctions and turning points that separate students who have difficulty from those who succeed. More importantly, they are finding that they are able to see the reason for these disparities.
Between 2010 and 2015, research on data-driven instruction was focused on using the data that has been mandated by the NCLB in a way that will circumvent the pitfalls set forth by the Act’s legal framework. Paul Bambrick-Santoyo’s 2010 work Driven by Data: A Practical Guide to Instruction gives a blueprint for educators to use data in this way, and is still used in professional development for schools moving towards this approach. Since 2015, researchers have focused on drilling down to the granularities of where students become divided, in order to close achievement gaps in education. Instructors are finding intervention points earlier and more effectively within a student’s learning journey, as shown in this study from Taiwan. In order to do that, they need Big Data.
In the book International Perspectives on School Settings, Education Policy and Digital Strategies: A Transatlantic Discourse in Education Research, an instructional technology team from the University of Twente makes a compelling case for the use of “Big Data” in education settings. In this case, “Big Data” is defined as having the characteristics of:
- Volume: involving large quantities of data
- Variety: a large number of data sources
- Velocity: the data is continuously updated
To obtain this data, instructors can use a multitude of sources, from student evaluations to national test results to online tracking. If students are learning fully online, does that mean more useful data can be extracted?
A scholarly review of research on Big Data in education from the years 2014-2019 concluded that at the very least, a combined online learning approach may in fact be an ideal way to gather data. The contents of the review “give new insight to universities to plan mixed learning programs that combine conventional learning with web-based learning. This permits students to accomplish focused learning outcomes, engrossing exercises at an ideal pace. It can be helpful for teachers to apprehend the ways to gauge students’ learning behaviour and attitude simultaneously and advance teaching strategy accordingly”.
How Crowdmark Supports Data-Driven Instruction
If this is the case, Crowdmark is in the valuable position to provide instructors with data to help drive instruction and predict student outcomes. Instructors are currently using Crowdmark for innovative data analysis, including
- Extracting text entry on student evaluations to analyze responses at the start and end of a course
- Exporting grades from individual assessments for further data processing
- Tracking student outcomes across assessments, with data by question and by assessment
- Using the student results under multiple choice analytics to provide metadata for predictive analytics
Crowdmark also provides data to students in the form of Performance Reports showing their progress across assessments, as well as links to the instructor feedback on their highest- and lowest-scoring questions. As we continue to build analytics across the platform, we would love to hear how you could use data obtained from Crowdmark to inform your teaching practice.
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