The children are the future, and it should be a common desire of everybody that education is a priority. The more educated children are, the better off the human race will be in the future.
Improving the world’s education means utilising all of the tools available. We need better teachers, more funding, and the latest advances in technology involved. By using big data and new technology, educators and administrators can better focus their time and create tactics to improve education across the board.
Finding Trends in How Students Learn
Every kid is unique. They have their personalities, likes, dislikes, and learning preferences. While teachers do what they can to accommodate every type of learning style, some students will receive less attention than others. Right now, this is a sad truth.
Yet, thanks to surveillance technology like facial recognition cameras and Fitbits, schools could monitor students during the learning process. Within the first few years of education, educators could identify which methods their students learn best with and then create plans to accommodate them. This could include organising classrooms by learning needs, making sure students have access to relevant tools, and making sure teachers are properly trained in matching teaching methods.
To be able to do this, though, schools would need to set up ways to monitor students and find how they learn. A singular test taken in fifth grade isn’t enough. Long-term surveillance is needed to build an accurate profile for each student, and demographics as a whole.
This ideology can also spread to online learning, both at the K-12 and university level. As students study online, either part or full time, teachers can improve their curriculums by tracking things like where students‘ eyes go on a screen or how often they leave a page. The more data instructors can gather, the more tools they have to help their students.
Identifying the Most Effective Strategies
Every teacher is not created equally. In fact, not every teacher is even good. There are excellent, average, and bad teachers in the world. Yet, measuring the effectiveness of each teacher is hard, as the only data measurements are student test scores and grades.
Yet, combined with surveillance technology mentioned above, teachers could get hard data on how effective any specific activity or lecture is. Metrics could include things like how well they kept their student’s attention, how long it took students to start getting correct answers with practice questions, and even identify key moments where students became confused or lost.
Data like this could give teachers direction on how to improve consistently and give administrators metrics to measure performance. Didn’t students pay attention during the specific lesson? Maybe the structure of it needs to be re-worked. Kids didn’t respond well to a certain film? Look for an alternative way to convey the necessary information. Not only could this improve teaching, but it also encourages teachers to focus on how to engage with kids.
Finding Reasons Behind Behavior
By combining behavioural data from schools with outside information, teachers and administrators can get closer to finding out why children behave the ways they do. Instead of simply making assumptions about them, administrators could find patterns in behaviour against data like demographics, geography, family structures, and more.
That way, people can find out why some kids act out while others don’t. Gather enough data and analysts are likely to find trends that administrators and teachers can then take action for. Making assumptions isn’t good, but if educators have hard data to back up a theory, they can set themselves up to make a difference.
Predictive Analytics in Education
The longer you gather data, the more you can do with it. Once you have data spanning years, it’s possible actually to start predicting the future.
Over time, recurring patterns occur in data, and teachers can use this to help their students. Going back to surveillance data in the classroom, teachers might notice that during certain times of the year, students are more prone to distraction. By knowing this kind of information, teachers can create lesson plans to get the most out of the times students are most likely to focus.
For example, maybe the data suggest that during a specific week, students are highly distractible. Instead of diving into a new topic, this time could better be used for review, or activities could be created to help students focus better.
Another aspect is utilising data to help determine which students might struggle in specific subjects. As more and more data is collected on individual and students as a whole, it will be possible to predict where certain students will struggle in school.
The data that could predict this includes how well they pay attention in the different subject matter, performance in similar subjects previously, how students apply the information, and grades. By analysing the histories of students and comparing them to already collected data, educators could find solutions to helping students before they find themselves struggling.
Similarly, with big data, educators could even suggest subjects and classes to students they might enjoy. Instead of meeting with a school counsellor who barely knows a student, they could turn to data to find out what classes are best for students. That way, students can be properly challenged and take classes they actually enjoy.
Guiding Students to Careers
Current career guidance tests aren’t terribly effective. Many simply ask a person how they feel about different subjects and maybe pose a few questions to test their skill. But if educators adopt big data fully into their curriculum, it could be a big help toward guiding students into the right careers for them.
Not only will they have grades to use, but a host of other data. This could include a summary of what subjects they paid the best attention in, which they seemed to enjoy the most, what kind of careers best match their interests and skills, and more. That way, as they make choices for both higher education and careers, they are going off more than just what sounds fun.
This data could also be included with job market data predicting future needs. That way, students can be better educated what to learn and focus on to find success in the job market. No more blind guesses and assumptions ” students could be guided and informed on what jobs might best suit them.
Clearly, there is a place for big data in education. The question is, will schools get the funding they need to implement it? Without methods to accurately measure data and compile it, with professionals to analyse it, big data will never find a place. But if it does, the educational experience could be revolutionised and improved greatly.
