Big data meets education

Data analytics is important to improve all sorts of learning and prepare students for its reach

Almost two decades ago, a failing baseball team with an innovative manager and tech-savvy staffer changed the sports world by looking closely at data.  And the shift they made in team management was part of a change in thinking about information that is a growing priority in education today – and experts say it should become and even bigger one.

The team was the Oakland Athletics. They had the worst record in baseball in the late 90s but grew to be one of the most successful teams in just a few years, winning a record 20 consecutive games and recording twice as many wins as losses. The change came about because they began looking at data and made decisions about game strategies and player selection based on it.

The A’s strategy (it became the subject of a popular movie, Moneyball) with analytics have since come to touch nearly everything we do – from the highly successful customer management at Amazon and a grocery store’s layout, to the way a hospital handles infection control or a GPS system routes us around traffic.

And now, advocates say it is something educators should be specifically thinking about more often for two reasons: because it will benefit teaching and school operations, and because their students need to understand it for careers as it touches every facet of their lives.

To improve schools

“I'm coming clean right here, right now,” said Jennifer Morrison as she spelled out the ways that data can improve learning.  “I'm a practicing classroom teacher, and I love data. Data connect me to my students and their learning, push me to high levels of reflection on my practice, and spur me to engage in dialogue with colleagues, students, and parents.”

“The tool most useful for supporting the flexible decision making that teachers need to increase the quality of the learning experience is big data analytics,” an international university says in describing its data it offers. “It gives educators and students an edge in understanding where and how improvements can be made in the learning process.”

A recent study provided details about how decisions at schools could be made more effectively at several levels if data was carefully collected, evaluated and put to use.

Appearing in a chapter of the book Handbook of Contemporary Education Economics, the research  “outlines the importance of using data that are increasingly available to guide decision-making in education institutions, ranging from the federal and state policies at the system level to pedagogical and instructional decisions in schools and classrooms.”

The research shows it can be used for big decisions about policy in the federal government or a school or to gather information about an individual student. For instance, does start time affect student performance, is an attendance improvement program working, how is student success in an advanced class reflective of the curriculum of a lower level course or how is an individual student performing with a certain type of instruction or assessment.

They suggest that in education data is often collected and even analyzed, but its impact is not often enough measured. “Good policy directly based on the data is only sometimes implemented – and too often the policies lapse or shift and the new approaches are not measured,” the researchers said.

The Strategic Data Project at the Center for Education Policy Research is working with about 125 schools to help them use data to make improvements.

Among its success stories are a school district that examined its suspension process based on new and better data, one that evaluated and revised a “summer melt” program – and state education systems in Delaware and California that found that certain untested assessments were a good indicator of student success and teacher effectiveness, and good at spotting students who were struggling.

A report from the U.S. Department of Education spells out in detail the ways in which data can be used for student success:

• When are students ready to move on to the next topic?
• When are students falling behind in a course?
• When is a student at risk for not completing a course?
• What grade is a student likely to get without intervention?
• What is the best next course for a given student?
• Should a student be referred to a counselor for help?

“Educational data mining and learning analytics research are beginning to answer increasingly complex questions about what a student knows and whether a student is engaged,” the report notes. “For example, questions may concern what a short-term boost in performance in reading a word says about overall learning of that word, and whether gaze-tracking machinery can learn to detect student engagement.”

To prepare students.

The significance of teaching students about data science has been spelled out in articles describing how data will be important in a variety of careers.

According to consultant and author Tom Vander Ark,  who writing in the Harvard Business Review once called data science The Sexiest Job in the 21st Century, data analysis will be prominent in careers we wouldn’t expect. “In a few years every biologist will be computational,” he says. “It looks like the same will be true for doctors, mechanics, economists, water managers and soldiers. As every profession becomes computational, wrangling big data sets will become more important.”

He recommends that in the same way schools have integrated writing throughout the curriculum they should promote data analysis because “every problem worth solving has a data set associated with it”. He suggests students work on projects with their communities where they collect and analyze data in connection with others for a joint community project.

When Carthage College decided to offer a special training program for STEM teachers it chose data analytics as the topic for them to learn about.

“We want to help teachers bring current issues such as climate change or vaccination into their classrooms,” says Andrea Henle, a biology professor at the college, who is one of the persons directing the program to help STEM instructors in schools nearby include more attention to data. “These issues often involve interpreting complex data sets, and teachers will be able to improve their students’ ability to engage with this information.”

A study late last year indicated that teaching analytics in life science was important. It suggested instruction in data analytics should teach students about four stages:

• which quantitative, analytical tools to use.
• how to apply these tools
• how to interpret analyzed data in light of a specific question or hypothesis.
• how to effectively communicate results in different platforms.

Written by Jim Paterson, Education World Contributing Writer

Jim Paterson is a writer, contributing to a variety of national publications, most recently specializing in education. During a break from writing for a period, he was the head of a school counseling department. (www.otherperplexity.com)

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