How do algorithms influence teaching and bridge the students’ knowledge gap

Education | Sectors   |   
Published March 2, 2018   |   

The essence of education traditionally has involved the transition of accumulated knowledge to younger generations for most of its history. However, as the working routine changes from repetitive work to the knowledge-based activity, the requirements put to the quality of education have changed. So, how are big data and the resulting algorithms influencing teaching and helping students learn?

It can analyze how they’re doing

Because the data sets of student learning are so diverse, very slight nuances about how students are doing can be teased out. In this way, Arizona State is analyzing the keystrokes of the students using their devices to measure how well they are progressing, how they are struggling and what their weak and strong points are.

This, in turn, means that they can step in and help students long before they themselves might even be aware they’re in trouble.

Personalized programs

For the longest time, education assumed that one size fits all. It doesn’t matter how strong or weak a student’s skill is, it is better to put them among people of their own age and let them absorb whatever is being taught in that year.

Of course, this wasn’t just down to convention. It was also related to the difficulties of tracking what every student in a school needed and creating individual programs that best suited their learning styles.

Big data is changing that. As we gather more and more information about students, we don’t only get a better idea of how they’re doing, but can adjust the syllabus to better suit their learning needs. Even better, as this is automatic, this can be done for every student without overtaxing the teachers’ capability.

Evaluation without bias

Another way in which these evaluations differ is that they are no longer witnessed through the prism of a teacher’s likes and dislikes. For the longest time, we’ve known that teachers favor some students over others. For example, teachers tend to give higher grades to more attractive students. This does not happen consciously and instead is the result of how we’re put together, which makes it an incredibly difficult problem to tackle.

Big data offers a way out. After all, a computer does not recognize a student by their race, sex, or visual appeal. Similarly, big data can consider a test in absolute isolation – not giving the benefit of the doubt to students that have done better on previous exams. This creates an equal playing field where we are judged based on how we’re performing instead of all the external factors that surround it but should be irrelevant.

It can boost engagement

By exploring the numbers produced by 100s of thousands of students working on software, it will become far easier to know what is interesting to students and what is not.

Big data will then give a possibility of customization the learning experience to make what students are learning directly relevant to them. Even better, the technology will be able to analyze future lessons and use what it has learned from the student in previous encounters to modify the material as well as predict how hard the student will find it and how much time they will need.

It will even be able to conclude when students should take breaks and when they’re best served to study alone or in a group.

Fitting the right personalities together

Big data will make group projects as productive as possible. Right now, students are often grouped based on where they’re sitting in the class or who are their friends. The thing is, though likability is certainly a useful factor in deciding who to work with, it is certainly not the only way to do so.

A much better idea is to find which students are going to be the most useful to each other and group based on that. This will avoid the popularity contest whereby students that everybody wants to work with have too many choices while less popular students have too few.

Similarly, because students are grouped based on who they will work best with, class engagement will rise and struggling students can be brought back up to speed by who they work with rather than the teacher alone.

Last words

As we pay more attention to engaging all the stages of one’s memorization process, we’re going to see a revolution the learning curriculum is adopted on the fly based on a student’s energy levels, current interests and even how well they are able to focus.

Thus, having a good idea of what each student is capable of in turn will mean they can be advised in one of the most difficult decisions we all have to make, what we will do after school. In this way, big data will not just revolutionize the classroom, but even the choices we will make afterwards. That’s exciting (and perhaps a little bit scary).