Is it possible for professors to take advantage of AI to improve their performance? Can professors reliably measure what material a certain student has mastered at a certain point in the semester, and use that information to record how effective the teaching has been? Let’s take a look into how machine learning and AI are slowly making their way into the classroom and improving the quality of classes and professors’ performance alike.

The amount of knowledge that a student has learned at a certain point is referred to as their “knowledge state." Understanding and recording the personalized knowledge state of each student is critical to the effectiveness of any machine learning or AI technology.

How to identify a student’s knowledge level

Assessment is typically the method used to measure how much each student knows at a certain point in the semester. Everyone is familiar with midterm assessments and the like; these assessments can consist of either tests and quizzes in class or homework assignments. One approach is to carry out a quiz of 10 questions every week, where five questions are based on information learned earlier on in the course, and the other five are based on the previous week’s lectures. This way a consistent evaluation of the student’s knowledge is being gathered week by week. Progression in students (or lack thereof) will be easy to identify using this method.

While this is a tried and true method, it still requires a lot of manual labor as hundreds of quiz questions will have to be corrected and sifted through by hand. Then to try to analyze the results and spot trends in the data is quite difficult, given the volume of questions. Teachers that are effective must have lots of skills: grading homework, giving engaging lectures and encouraging development. Teachers are not data scientists, though, and should not be asked or expected to be.

This is where machine learning and AI can lend a helping hand. Machine learning can be taught to recognize patterns in the data, which means that it would be able to identify each student’s knowledge state from their test performances over periods of time. It is easy to say that it would also be much faster and much more accurate than an overworked teacher.

Can AI help even further?

Systems can be developed using this data analyzing and machine learning principle in order to not only identify results and present data, but to help the instructor or teacher to deliver more personalized guidance to those who need it. It keeps both the student and teacher in the loop as they both know exactly where the student is with regard to their studies at a particular point in the semester. Furthermore, the information from the whole class can be combined to see which weeks were more effective than others, and what topics students might be weakest in.

This type of machine learning system would consist of a tool that reads the quizzes, assessments or homework assignments that are provided to the class, along with the topics that have been covered so far in the semester. It would then pool the data, analyze it using a unique machine learning algorithm, and present the information to the teacher. It may put the information forward in three categories: homework and quiz topics that the class as a whole has struggled with, learning topics that the class as a whole has not mastered yet, and the individual performance of each student.

One of the key challenges will be to ensure that the machine learning algorithm can correctly identify which homework and quiz question are relevant to which learning topic. Natural-language processing and open-source libraries will be valuable resources in ensuring the system works effectively.

Case study

Such a tool was developed by Jon Reifschneider, who is the director of the Master of Engineering in AI for Product Innovation program at Duke University. He called it the “Intelligent Classroom Assistant” tool, and was created for the “Sourcing Data for Analytics” course that he taught at Duke. He decided to trial his new tool on his students and analyze the results.

What Reifschneider focused on most was the tool’s ability to identify and map homework and quiz questions to their learning topic counterpart. This was an essential part of the functionality of this system. He found that of the 85 questions answered, the tool was able to map out the correct question to learning topics about 82% of the time, based on the 20 learning topics covered in the course. Not perfect, but enough to make the tool useful to Reifschneider.

Using the data from the tool, he was able to spend more time on topics that the tool identified that students were struggling with. He was also able to give a more personalized one-to-one student analysis which ensured that the tutoring sessions were focused on topics or areas where the students needed more help.

Machine learning tools will undoubtedly become more popular in the classroom as time goes on. Utilizing the speed and convenience of computing power can only help teachers and professors focus on delivering the course as effectively and engagingly as possible. This will not only improve the quality of lectures and classes moving forward but will also enable teachers to provide personalized teaching at a greater scale, ensuring every student has their needs accounted for. Where teachers are able to teach at a higher level, students are able to learn more.

What do you think about the integration of machine learning and AI into the classroom? Do you think it will improve teaching standards as the article claims? Engineering360 would love to hear your thoughts in the comments below!

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