In any individual math classroom there may be an advanced student, a dyslexic student, and many other students with various levels of proficiency and confidence in the current module of math. How can you customize the educational experience of each of these students to enable the best outcome? McGraw-Hill Education says it believes that small data, which in this case is data generated by how students interact with the curriculum, is the answer.
Stephen Laster joined the company as chief digital officer a little over three years ago to drive its implementation of small data to improve educational outcomes. He's someone who is passionate about education, and someone whose own educational experience was mixed.
"I am very, very dyslexic," he said. "I was in grade school in the 60s and 70s, and it wasn't until I reached 9th grade that I was diagnosed." So although Laster has "a near-perfect visual memory," nobody understood how he learned. Neither was the kind of digital educational experience that McGraw-Hill Education is now offering available.
"I'm super passionate about what we are doing," he said. "This works for all kids of all kinds. It works for advanced students and lets them go at any rate they want. And it works for kids like me who see the world differently."
How does the system do that, and how can it improve educational outcomes for all students? Here's an overview.
When students engage in any course of study, Laster said, they are trying to understand something about a predefined area of knowledge, such as algebra or psychology. In each of these areas there is a set of predefined learning objectives for them to master. Maybe algebra has 400 of these learning objectives. If students become proficient in all 400 objectives, they will have a high level of assurance that they really know the topic, according to Laster.
But mastering each of these 400 objectives will take different forms for each student.
The McGraw-Hill Education system, through its many brands, including Aleks and LearnSmart, takes students through each of these objectives, and then gives them mini-assessments or quizzes along the way. These mini-assessments are designed to measure the student's mastery of the objective. But the assessments also include questions about how confident the students are of the answers they give in those assessments.
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"We instrument it by asking students their level of confidence," Laster said. "We instrument it by understanding where the student is spending their time to acquire the learning. Then we feed this into a set of algorithms and artificial intelligence that helps redefine the map for that particular student and sets them on their way to learn the next most appropriate topic and learning objectives."
The system not only directs a student to the next place to learn, but it also provides insights to the student's teachers about which students may be having issues with what objectives.
"Imagine on a daily basis a teacher being able to walk into a classroom and understanding that these particular five students are having the same problem and just don't get it," he said. "I could pull them out for five to ten minutes of instruction to help clear up their problem. That's the power of small data."
In this case, the small data is information about how the student interacts with the curriculum, whether the student has mastered an objective, and how confident the student is about his or her mastery of the material. But small data can be applied to use-cases across many industries. For instance, a system that tracks inventory at a retail company and sends alerts when stock is low could be considered small data. It only becomes big data when you join it with hundreds of other data streams about marketing, customer preferences, social media sentiments, and more.
Small data is usually defined by the fact that it is not big data. Information is collected for a specific purpose, in this case to improve student outcomes and the curriculum. The information about the student does not include card swipes that track the student across the school campus. It does not include the student's family history, medical records, or retail purchases. It does not include information about all the other websites the student has visited. It only includes data about the student's interaction with the curriculum.
For the student, the process provides a clearer path to mastery.
"Imagine for the student who may have grown up with a fear of mathematics, really being guided to stay on a path of positive progression, so that math becomes not only achievable, but fun, and then that student actually turns into a confident learner," he said.
Beyond that, those students' paths through the curriculum are fed back into the system to drive further improvements into the curriculum.
"That's the beauty of the closed loop," Laster said. "Not only are we able to help evaluate and help students become confident learners. We are also able to get feedback on the learning moment … We have information at a learning-objective level about how things are working that we can use to make things better and more effective."
However, Laster is quick to point out that the goal is to improve educational outcomes, not to put students in some dark room with a computer and no social experiences.
"Education is inherently social. But there aren't enough master teachers to go around, and the model doesn't scale."
Laster came to McGraw-Hill Education to make this kind of closed-loop digital education system a reality after spending much of his career in eLearning and digital education. Before joining McGraw-Hill Education, he served as CIO and CTO of Harvard Business School and a member of the school's administrative leadership team overseeing the school's academic research and administrative computing teams.
He also established and led Intelligent Solutions, a consultancy that helps universities and businesses leverage their digital technology and collaborative tools. He has been a teacher at the undergraduate, graduate, and executive and professional level.
A few months after Laster joined McGraw-Hill Education, the McGraw-Hill group of companies announced plans to sell the education division to private equity firm Apollo Global Management in a deal worth $2.4 billion when it was completed in March 2013. In September 2015 McGraw-Hill filed for an initial public offering to raise $100 million for general corporate purposes. Apollo will retain majority ownership of the company.
In its prospectus, McGraw-Hill Education said its two top competitors in the market are Houghton Mifflin and Pearson Plc. The prospectus also said that the company sells educational materials to 13,000 K-12 school districts. Digital learning has represented a growing share of the company's revenues, according to the prospectus.
Efforts like the one Laster has been driving at McGraw-Hill Education are not new, the CDO told InformationWeek. These efforts have been underway at the company for many years. But the recent widespread access to computers at universities and even more recently in the kindergarten through 12th grade environments has made such closed-loop learning systems more practical for wide distribution.
"This is everywhere now. We have millions and millions of learners using this today at virtually every grade level."
Laster said that the use of small data was a deliberate one by McGraw-Hill Education, in a world where many organizations, such as retailers, are choosing big data instead. Laster explained why small data is the right choice for education.
"Education is a purposeful journey against a domain of knowledge that you are trying to impart on the student," he said. "We believe you should enter that domain with purposeful questions that drive outcomes.
"McGraw-Hill Education has a laser focus on outcomes," Laster said. "We just want to collect the minimum amount of data to get the job done, and that's why we like our approach."
That further drives the McGraw-Hill Education mission.
"We have an obligation to give every child the education they need to make the most of themselves," Laster said.
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