How Big Data can boost students motivation

How Big Data can boost students motivation

Big data can be an ally against the lack of motivacion of students. ¿How could it help? Through adaptive learning. ¡Check it out!

Mass data storage and analysis can help teachers identify patterns in a class or a student’s learning pace and, thus, adapt the content and methodologies used to each individual case. It is what is commonly known as adaptive learning.

So what exactly is Big Data?

Big Data is a term that has been covered extensively in news cycles around the world for a while now, and presents itself as a panacea of virtually any field’s modern management procedures. In the education sector it might also become every bit as important as it is in other industries, although, as of yet, there is still a long way to go before its potential is realized in its entirety.

A generic definition of Big Data could be:

“the mass data storage of information pertaining to an activity and its subsequent analysis and study in search of patterns that can predict future behaviour”

How can Big Data help improve education?

Big Data can have a huge impact on the education industry, the key being the ability to determine which variables, among all those that modern technology can provide through data mining, are relevant to the analysis, theory development and, ultimately, the improvement of specific areas within the sector.

Big Data deals with the mass data storage of information collected in a given activity and its subsequent analysis and study in search of patterns that may help predict future behavior. But how can Big Data specifically bolster education standards?

The process begins with raw data collection (grades, hours of active study periods, time elapsed in exercise completion, etc.), followed by a categorization process and, finally, an analysis conducive to the establishment of behavioural parameters that can help us predict what might happen in the future and, therefore, remain one step ahead of forseeable problems, able to act promptly when necessary.

Let us consider an example: we know from previously studied data that students who have obtained grades in the 40% (C-) to 60% (B-) range in their first three tests of Chemistry have a high probability of failing their fourth. This pattern observed in the past will then help increase the preparation of this group of students for the next exam.

Advantages of Big Data for publishers

By applying complex analysis models and algorithms to the information gathered, Big Data allows stakeholders in the education sector, from teachers to school administrators to policy makers, to decide the best possible course of action for a specific issue in a particular context. Similarly, publishers and content creators are already among the greatest beneficiaries of this new paradigm, able to determine underperforming and underused materials and, therefore, concentrate resources on those that yield the best results.

Through the analysis of its content use (activities, books, digital resources, etc.), publishers can extrapolate which elements have worked better for which students, based on how many times they have accessed it, how much time they spent with it, or how long it took them to complete specific exercises and coupling that information with test and exam scores.

Additionally, correlations between topics can be drawn – such as when students need to backtrack to previous lessons to fully grasp the one they’re currently on, or the amount of exercises per unit they need to complete before sucessfully approaching a midterm – and these correlations can make a significant difference for publishing companies in the ability to improve their content with the ultimate goal of making it more efficient for school use.

Big Data at school and also at home

Massive data collection and mining techniques are opening a vast range of opportunities for teachers and content providers, and its potential do spur change and progress is immense.

How long did it take a student to perform an exercise? What is the average class fail rate before completing an exercise successfully? Do students who struggle with maths also underachieve in physics? The limitations here lie with people – not technology. It is therefore essential to prioritize the issues to address and define the appropriate variables to achieve success.

At this stage, through Big Data we can already recognize individual patterns and draw conclusions. As such, it will become a useful tool for a model of adaptive learning.

Adaptive Learning

Adaptive Learning stems from a very clear premise: not all students learn the same way. The same content can be difficult, easy, boring or interesting depending on the student which uses it.

To date, the tendency was to treat all students the same way, using the same content. But thanks to technology and data collection it is already possible to determine each student’s own individual characteristics and move towards a more personalized education model in order to prevent student discouragement and alienation.

Not all students in a class learn the same way. With Big Data it is now possible to approach students on a case-by-case basis, thus preventing discouragement and alienation

Technology, big data and proper analysis by the industry professionals are the tools that will enable us to define these patterns in the learning process.

In Blinklearning we are working to provide educators and publishers relevant data to help identify areas for improvement in both editorial content and learning models, with the ultimate goal of leaving no student behind through lack of personalization.

Kenneth Cukier, one of the foremost experts on the subject, explains the concept very well on the video below:

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