Whether AI Makes Standardized Testing More Useful

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Does Education mix well with AI? It may be possible when you consider the current system of standardized testing, making this a case of Industry 4.0 meets Classroom 2.0.

As an educator, it never fails to mystify me how people can speak so poorly of standardized testing. While I can agree that the current system of standardized tests is bulky and has glaring inefficiencies, it’s far from useless.

What’s pointless is all the data we get from it that we don’t have time to consider fully, so when I see the burgeoning field of EdTech, I see solutions to the problems of standardized testing, and a possible future that makes it more relevant to real life. For now, people see the current system as merely setting a minimum for educational standards, but with some help from Industry 4.0, I think that it could be used as a measuring stick to lead students past that minimum.

It all has to do with assessment, which is the lifeblood of progression regarding one’s education.

Giving Education some AI Assistance

Assessment is a big part of the learning process, but it deals with tons and tons of data.

Now, a person can sift through and manually assess that data (I should know, I’ve done it before), but it takes hours, and teachers are already putting in hours evaluating the daily homework of their students. With all of that grading, it’s easy to get exhausted or even burned out with the process.

Ever feeling a sense of responsibility, teachers trudge on anyway because it is so very necessary to student improvement.

Grading the multiple choice sections of a test are easy, and those processes have been automated in various ways for quite a long time. From the raw data that the grading provides teachers can analyze their student’s growth in the long term, and that analysis allows them to target what a student needs specifically.

To that end, I don’t think that it can be said that standardized testing is entirely useless. It may not reflect real life in the way that we wish education would, but it does allow us to learn about our students in a unique way.

But let me clarify something here: there is a ton of mucking about with data and analysis when you use standardized testing as a metric for student performance.

If you’ve paid any attention to AI research, however, you would know that AI programs are the undisputed kings of data analysis, meaning that a few good algorithms could go a long way toward helping standardized testing mean more while requiring less work. We only have to learn how to program them to provide the particular kind of analysis that we need.

As the name implies, these tests are built from the ground up with certain standards in mind. In the U.S. these standards are usually agreed upon on a state-by-state basis, and they are drafted by education specialists in various fields such as History, Language Arts, Science, and Mathematics.

AI analyzes standardized testing data, allows educators to focus elsewhere.Click To Tweet

Once those standards are set, teachers from schools all over the state design their lessons around them. Instead of teaching for a test, educators are teaching the standards. The test is meant to provide data on how well a student can meet those standards.

If you can trust the standards, then you can trust the data you get from a test.

So it may just be my opinion here, but I don’t think that standardized testing is bad. It’s just what we do with it that needs some adjusting. Maybe teachers should spend less time analyzing the data they get from that testing and instead devote more time turning that data into meaningful learning experiences for their students.

With the old model, you see people complain about ‘teaching for the test,’ but I think that AI assistance can fit into a new model where teaching for a test isn’t so bad because the results from that test can lead to better teaching.

Shifting from Traditional Classrooms

You may not have noticed this if you haven’t been to a school for a while, but classrooms have been steadily changing over the years.

Why?

Because of technology, of course.

Blackboards are rare, for one, and many classrooms boast screens that allow them to interact with their computers via a screen at the front of the room. It’s a lot more high-tech than many remember.  Don’t blink, though, because Industry 4.0 is or has already invaded a public school near you, and interactive screens are nowhere near enough.

We’re entering an era of classrooms that are filled with students who are all issued a device which allows them to access class materials, lessons, homework and even testing.

Instead of giving traditional lectures, teachers are creating online lessons, freeing up their class time to helping students as they work through the lessons at their pace. Paper and pencil are quickly becoming obsolete in the modern classroom. That means that we’re coming full circle where classrooms are blended with technology.

From that, every interaction a student has with their lessons is tracked, and that creates a ton of data.

The paradigm of traditional classrooms is quickly fading, and that’s not the most terrible thing.

Going forward, AI will be crucial to making standardized testing more meaningful, and it could eventually even abolish the annual ‘big test’ by constantly substituting the test with all of the data that comes from a digitally enabled classroom. When Will AI fit in the Classroom?

Before AI will be really useful in the day to day business of a classroom, it needs to be taught. Luckily for us, schools across the world have access to software that can organize massive amounts of standardized testing data, so we have a wealth of info that a hungry deep learning algorithm can use.

Just like IBM’s Watson is being used to learn how to diagnose medical patients, an AI can diagnose a student’s deficiencies, and in many cases, a school will have all the raw data such a program would need to know what those deficiencies are.

No Wrong Answers

The real problem lies in the case of written responses, whether they be short answer questions or essay topics. These responses are usually graded using rubrics that are tuned toward the learning standards set for teachers. Because written responses are subjective in nature, the rubric is usually designed to make the grading as fair as possible.

Simply put, there is no exact right or wrong answer, and AI research still hasn’t brought us an algorithm that has completely cracked language recognition.

If we can get a highly effective language recognition algorithm that is programmed to know a certain subject, then AI will have a solid place in the classroom. They won’t be able to completely take over a teacher’s job, mind you, but they will help immensely in grading and targeting a student’s needs.

And from what I see out of AI research every day, we’re close to having that highly effective algorithm. When it does get developed, I wouldn’t be surprised if we see a surge of ‘EdAI’ in the EdTech sector.

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