Across all industries, big data is playing an integral role in progress and innovation — from retail and banking to healthcare and communications. Education is no exception. Using the latest technology in big data analysis, educators are pinpointing weaknesses in the education system and identifying ways to improve them. This could mean having a better understanding of students or updating antiquated techniques. One thing is certain: the clock is ticking on yesterday’s chalkboards and report cards. But how exactly does this technology play into the modern classroom?
Tailor-Fit Courses
Big data can be used as a basis for customizing programs for individual students. Every student has different way of learning. Some are visual learners, while others are more auditory or verbally inclined. Therefore, the learning process should be personal, not a “one-size-fits-all” method.
Now, new methods have been developed so that students can learn at their own pace and style. One such approach is blended learning, which is a combination of digital and offline learning. Teach Thought praised its ability to give students the social aspect and supervision of a physically present teacher, coupled with the flexibility of online classes.
More Accurate Grading Methods
Just like cookie-cutter courses, standardized grading systems have also received their fair share of flak over the years. Edtech speaker Catlin Tucker claims traditional grades do not require students to think when they are learning, which in turn stunts their growth. It is also an ineffective means of measuring a student’s level of education. Some students may be more inclined to a quiz format, while others could be visual learners. Leveraging big data helps educators produce a more accurate measurement of how much a student has mastered a class.
Creating New Areas of Learning
Maryville University’s breakdown of their Business Data Analytics curriculum highlights how successful businesses rely on analytics to look for trends and identify opportunities. Netflix, for example, uses big data to predict viewing habits and see which content to create that will drive more customers to their service. Another example is Amazon because it leverages user activity info to make customer relations more effective. This principle can also apply to education, where teachers and administrative leaders must be able to pinpoint industry needs and create a curriculum that meets its demands. Courses like graphic design and multimedia, for example, weren’t available until there became a demand for creatives across a number of industries. Nowadays, traditional courses are being traded in for more specific and timely ones, like retail operations. Once these gaps and demands are identified, it becomes easier to build courses and programs to supplement the business ecosystem. A previous article here on The Data Administration Newsletter also supports this idea. After all, the availability of detailed patterns and trends has been a game changer for organizations across the world.
Efficient and Immediate Feedback
Another advantage of big data is that it allows for much faster feedback, in turn increasing efficiency and cutting costs. It could be as simple as utilizing group forums and online chat groups. By maintaining dynamic feedback between teachers and students, learning is made much more engaging. Some examples include Canvas, a learning platform where students can use audio and video provided by teachers to follow instructions and give feedback. Another is the Google Classroom app, which caters mainly to teachers who are creating lessons plans and communicating with students.
Harmonizing the Liberal Arts and Tech
Contrary to what most people think, the liberal arts doesn’t have to be played down when it comes to the technological revolution. Stanford Literary Lab, for instance, is working towards a digitized database that crunches thousands of pieces of text into digestible pamphlets. Founder Franco Moretti urges academics to focus less on a few exceptional literary works, and highlights the tens of thousands of other books left unexplored. He stresses that people can choose to read as closely as they want, but understanding literary history will require more tools — and that’s where big data comes in. In this case, big data analytics is being used to consolidate large amounts of ideas and information to create a better overall understanding instead of simply relying on a select few books.
