Despite the latest technological advances continuously seeping into all areas of life, including education, there are still plenty of places where obsolete methods and outdated technology remain for some educators and educational institutions.
On the other hand, those who did introduce Machine learning and Artificial Intelligence to their educational processes have almost unanimously noted the success this innovation has brought them.
To find out why this is, let’s dig deeper into what machine learning is and how its application in the education sector is transforming the industry.
Machine learning (ML) is one of those terms that everyone has heard of but may have trouble explaining. ML is one of the applications of artificial intelligence technology, as we detail here in our article on the types of AI used for business. If AI is the ability of machines to think as humans and perform tasks simulating their behavioral patterns, then ML is a way of machines learning to think in this way.
ML encompasses the ability to use data analysis techniques to learn and improve upon the knowledge gained. Solutions based on ML algorithms can make decisions without human intervention based on “knowledge” gained from processing (training) datasets related to specific tasks.
Machine learning and education revolve around the same concept — learning. Here’s how this fundamental principle is being applied to revolutionize the industry.
The first example of applying Machine learning in education is adaptive learning, which means the educational methods are tailored to the student’s needs and abilities, instead of them all being put in one basket. The system will recognize if the student is struggling with the material or perhaps that it’s too easy for her — both adjustments can be made at an early stage to prevent performance issues.
Adaptive learning can be either integrated into a supplementary technological solution for a classroom or be a separate educational system. It can also help detect more minor details in class, such as outdated curriculum or uneven distribution of teacher’s attention.
AI and machine learning empower both teachers and education platforms by almost fully automating many repetitive and data-heavy yet time-sensitive tasks, such as scheduling, assignments, and class management. This can give teachers more opportunities to redirect their efforts into those tasks that require human attention. Students get more “automated personalization”— the system adjusts to their progress without really requiring any extra effort. Online and proprietary eLearning platforms can run like a clock with AI and ML.
For educators, freeing up schedules to do more creative teaching work not only solves potential productivity problems. It can also support the teacher’s well-being, as they can dedicate their time to completing more fulfilling tasks, which helps teachers interact with their audience more in-depth, and students become more engaged in the process.
Machine learning in education helps handle immense volumes of data generously accumulated across all educational establishments and eLearning platforms. This data is so immense that a human specialist couldn’t process them in years even if they wanted to. ML is a real-life-saver when it comes to gathering and analyzing such big data rapidly.
As a result, ML allows us to gain insights into valuable patterns that cannot be discovered using only our eyes and brains. These analytics can be used for a number of purposes — to get a fuller picture of educational specifics, optimize underlying processes, and measure absolute and comparative performance indicators.
Predictive analytics generally means gaining practical insights into possible future events with the clear intention of preventing undesirable moments or advancing beneficial ones. For an ML system to successfully do this, it tracks students’ progress, analyses behavioral patterns, and evaluates performances to help students unlock their full potential.
Among the most important insights educators can gain by using machine learning in their work are:
Personalized learning remains one of the best, most valuable practices of using machine learning in the education sector at the moment. Before ML applications, it was nearly impossible to process and adjust learning material to fit each individual in a class unless it were one-on-one private lessons.
Now there’s a possibility of a more targeted approach that allows students or employees — during the onboarding process or qualification training — to follow the material at their own pace or even choose their own course progression and preferences.
Machine learning as a valuable AI branch can help teachers and systems grade assignments faster, more accurately, and even more impartially than a human ever could by eliminating biases that unfortunately still exist in even the finest teachers.
By no means is artificial Intelligence a substitute for a human teacher but it can complement their work by taking care of monotonous parts of the job. Assessment evaluation still requires human input; however, to reduce labor, a special tool takes care of analyzing different formats of written assignments using advanced grading mechanisms.
Machine learning in education and research, despite being a successful novelty, still encounters a few challenges on its path to complete acceptance. The most obvious one that remains is data privacy.
Since machine learning uses various tools to collect and process confidential information about students and also teachers, it raises quite reasonable concerns about its safety. It’s safe to say that ML school methods still require more research to ensure data security at the highest levels.
The same goes for prediction accuracy. While statistics and algorithms are a tremendous help in data analysis and pattern recognition, it’s still not a definitive representation nor a 100% accurate reflection of real-world situations, no matter how logical it may sound.
How students may behave and react in one setting — for example, in an online class or a new learning space — doesn’t necessarily indicate a teacher should expect the same patterns in another environment.
No article can be complete without mentioning a few real-world examples, not only to prove a point but to illustrate some exemplary cases for those thinking about integrating ML into their businesses. Machine learning gave each of these companies below an opportunity to shine in their own niche and help customers achieve excellence.
Grammarly is a grammar checking tool serving as a virtual assistant to manage pretty much all writing scenarios a person may have — be that in school, work, or personal life. The offered services include grammar, spelling, style, punctuation, and plagiarism analyses, the outstanding accuracy of which is possible due to the winning alliance between machine learning and advanced language processing.
Bakpax is an after-class task automation tool for teachers that saves time, allowing them to use it more productively in order to diversify their teaching methods or handle other tasks. The system uses ML to scan and decipher handwriting to automatically grade papers, give feedback, share and pre-make assignments, and digitize all teaching content.
SchooLinks is another exceptional example of using machine learning in education. This platform assists students with achieving “readiness” on their journeys through college and subsequent career paths by providing thorough planning and guidance. SchooLinks helps select a college, internship, and job, set up a portfolio, schedule meetings, and events, and calculate grants and scholarships by analyzing students’ needs and personalities.
While machine learning is not a universal solution in education, it sure brings an essential technology boost that’s necessary to elevate the whole teaching and learning process. We are all now reaping the benefits of big data, and the general consensus is this: the bigger the data we feed our systems, the more accurate predictions they’re able to make. Machine learning helps companies and educational institutions offer services otherwise unavailable due to limited and archaic teaching methods.
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