Big Data Will Revolutionize Learning

30 Apr

By Mark van Rijmenam

New technologies allow schools, colleges and universities to analyse absolutely everything that happens. From student behaviour, testing results, careers developments of students as well as educational needs based on changing societies. A lot of these data has already been stored and is used for statistical analysis by government agencies such as the National Center for Educational Statistics. With the rise of more and more online education and the development of MOOC’s all the data gets a completely new meaning. Big data allow for very exciting changes in the educational field that will revolutionize the way students learn and teachers teach. To stimulate this trend, the US Department of Education (DOE) was part of a host of agencies to share a $200 million initiative to begin applying big data analytics to their respective functions, as described in a post by James Locus.

Improve student results

The overall goal of big data within the educational system should be to improve student results. Better students are good for society, organisations as well educational institutions. Currently, the answers to assignments and exams are the only measurements on the performance of students. During his or her student life however, every student generates a unique data trail. This data trail can be analysed in real-time to deliver an optimal learning environment for the student as well to gain a better understanding in the individual behaviour of the students.

It is possible to monitor every action of the students. How long they take to answer a question, which sources they use, which questions they skipped, how much research was done, what the relation is to other questions answered, which tips work best for which student, etc. Answers to questions can be checked instantly and automatically (except for essays perhaps) give instant feedback to students.

In addition, big data can help to create groups of students that prosper due to the selection of who is in a group. Students often work in groups where the students are not complimentary to each other. With algorithms it will be possible to determine the strengths and weaknesses of each individual student based on the way a student learned online, how and which questions were answered, the social profile etc. This will create stronger groups that will allow students to have a steeper learning curve and deliver better group results.

[ Full article available at ]

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Posted by on April 30, 2013 in Best Practices


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