Using Big Data in Education: 9 Ways to Get Ahead of the Curve

Author: | Category: General BI, Industry News | Tags: education, big data, big data in education | Published: 10/7/2015

Using Big Data in Education: 9 Ways to Get Ahead of the Curve
I’m sure you would agree with me when I say that education has dramatically evolved from its old-school confines of bricks, mortar, and the printed page. The proliferation of online courses has removed many barriers that existed. This has enabled a lot more people to gain the necessary knowledge in order to better oneself. From a business analytics standpoint, and what makes this paradigm in education really exciting, is the explosion of data. The possibilities of gaining value from this data are endless; however, I have listed nine ways where I believe big data earns good grades, whether in a traditional setting or in an online classroom. I would love to hear your thoughts.
  1. Personalised learning. By assessing an individual student’s learning style and looking at patterns in data, teachers can adapt their materials or teaching style. Students, too, can work with their teachers to customise their individual learning plans.
  2. College readiness. Schools can look at which students need extra attention to ensure that they’re ready for college. Sources of data could include grades, test scores, attendance records, or other performance-related input.
  3. Implementing intervention programs. These programs can improve performance of students who are struggling in a certain subject area, especially if their performance is affecting their decision to remain in school, or their ability to pass a class or proceed to the next level.
  4. Assessing the value of intervention programs. Once specific programs—such as reading programs—have been up-and-running for a while, data can help assess whether these programs are having a positive impact on student performance.
  5. Digital learning programs; online courses. Colleges and universities can collect data on the effectiveness of, for example, massive open online courses (MOOCs). Although adhering to privacy and confidentiality regulations is obviously essential, instructors can use data from server and browser logs to study how students are learning, and then modify the courses accordingly.
  6. Content performance. Teachers can look at how well a course or topic performs among a large group of learners. Are the stated objectives being achieved, or is the content ineffective in getting students to be engaged and performing well?
  7. Resource playlists. Teachers can use lists that an educational app generates, similar to how recommendation engines give “You might also like” lists of books or films, based on user behavior.
  8. Monitoring resource usage. Teachers can subsequently look at students’ performance on exams, and correlate that data with data on whether students used certain resources. They can gauge how many students watch recommended or assigned videos, and measure whether students watch the entire video, or see at what point they stop watching.
  9. Error analysis. Incorrect answers can be analysed to determine if an error occurred because the student lacked knowledge, or if the question was in some way ineffective or poorly worded.

I want to finish with one question for you to think about and to comment on, if you feel the urge:
Do you see any major barriers before there is mainstream usage of big data across the education sector?

Related resources

  1. Follow Pyramid Analytics on LinkedIn.
  2. Follow @PyramidAnalytics on Twitter.
  3. Follow Pyramid Analytics on SlideShare.

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