Top 5 Ways Machine Learning Will Transform Education In Near Future
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Technology is changing the way we live, work, and play, and the educational field is no exception. Machine learning will transform the education sector as fully as it will most others, and it’s up to today’s generation of educators and students to plan effective ways to deploy machine learning to make the transition smooth and effective. If you are in school now or work in the education field, it’s imperative to understand the changes that are coming your way soon. In this article, we’ll take a look at five ways that machine learning will improve education in the coming years.
1. The personalization of learning
Artificial intelligence can help to track student progress
and individualize the learning process, freeing instructors to spend more time interacting with students rather than strategizing what to teach them. AI systems can identify students’ strengths and weaknesses, as well as areas where students need additional assistance and customize the learning experience to help students with what they need. AI systems can optimize the curriculum by generating a specific set of learning goals and objectives for each student based on past practice and predicted future results. Online courses are particularly beneficial in terms of utilizing machine learning to track and quantify student experiences in order to customize the learning environment to student needs.
2. Machine assistance in grading and evaluating work
In today's digital age, many college students are turning to external professional assistance with paper writing
in order to complete courses more quickly and efficiently. Machine assistance and AI can help educators discover when college students have used professional help on their academic papers by comparing their research papers or essays to a database of previously submitted papers or even using AI to compare a student’s writing style over time to that of a specific paper or essay to evaluate whether it is likely that the student wrote a particular suspect paper. This assistance, some of which is already available through plagiarism-detection software, can take some of the guesswork out of grading and provide a foundation for educators to discuss with students potential problems with their writing.
3. Evaluating and predicting student performance
will soon be able to process the vast amount of data collected on students during a course in order to evaluate a student’s performance and predict future results. Already, some learning management systems can predict a student’s likely grade on an essay based on the student’s past performance and the class average for an assignment. Soon, these computerized tracking programs will be able to collect enough data to predict when students are likely to have trouble on an assignment and connect those students preemptively with the resources they will need to succeed. In this brave new world of predictive grading, data is king and doing well becomes a process of building a track record of success across one’s entire educational career.
4. Machine grading of student work
Everyone is familiar with machine grading of multiple-choice questions, especially the old type of scanning bubble sheet where a number two pencil is required. However, in the near future, machines will be able to grade student essays, “reading” their paragraphs and evaluating whether they met the requirements of assignments. Already, computers are being used to grade
some standardized test essays, and soon the process may be expanded to longer, more complex papers. This, of course, runs the risk that students will be able to game the system by using keywords and disconnected facts because machines won’t be able to evaluate style and coherence. However, this can be alleviated if a human is employed to review and confirm machine grades.
5. Predicting and planning for future careers
Many older people remember taking paper-based aptitude tests that would help to recommend a future career. However, today’s data-driven environment offers a wider range of information to help guide similar recommendations today. Soon, machine learning will allow educators to process students’ data from a range of inputs, including grades, writing samples, aptitude tests, recommendations, and more in order to develop a customized career path recommendation that takes into account a 360-degree overview of a student’s life, strengths, weaknesses, and goals.
We are only at the beginning of a great leap forward in the power of machine learning to transform the educational
experience. Over the coming years, we can expect current trends to accelerate and machine learning to hake a growing role in shaping future students’ educations.
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