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Machine Learning in Education: Improving Student Outcomes and Educational Access

Machine Learning in Education: Improving Student Outcomes and Educational Access.

We live in a world that places high value on convenience. Comfort is inherently banal of our existence and this can be seen in the ways we have made shifts from building food delivery apps to cutting down time spent walking or driving to a restaurant or the way we communicate and connect with others via social media and smartphones locally and globally instead of relying on in-face communication.

Technology is the crux of our desire to continually attain convenience and it is quickly revolutionizing major scopes of our lives. The transformative benefits of tech can be witnessed in the traditional institutions of schools; that are continually adopting tech to improve processes and make the lives of educators and students more seamless. From storing school records and report cards online to teaching courses online in cases of weather disasters where students cannot attend in person or just as an option for students who would prefer to learn from the convenience of their home.

It also serves as a useful resource in the form of online exams or homework. Technology has continued to serve us but as our desires continually take new turns so does the tech we implement. It is clear we live in fast-paced world and technology is rapidly changing to meet our needs even in school. A form of technological advancement constantly evolving in the education sector is known as Machine Learning.

Machine learning is a branch of Artificial intelligence that aims to make software applications more accurate by using data and algorithms that enable computers to learn from the data to predict outcomes and make decisions. An example of this includes the ability to draw insights and patterns from large datasets in order to make decisions. In the school system we can experience this through graphs that measure performance using grades or a percentile system that ranks a wide array of students using their results. Therefore, Machine Learning is imperative to educational institutions through the various ways it serves them.

How Machine Learning is Beneficial To Education Institutions

  1. Machine Learning Provides Data That Stream-lines Performance

Machine Learning is an important tool that permits you to draw insights and patterns from large datasets in order to make decisions. This is applicable to the school system in the form of graphs used to measure student’s performance, strengths and weaknesses.

Riiid, is an artificial intelligence company changing the test preparation landscape. Riid came up with a way to predict SAT test-takers’ potential scores. This data helps students streamline their studying to aim for better scores and improve overall performance.

  1. Machine Learning Helps Instructors Evaluate Grades better

Besides Online learning allowing students to learn at their own pace. Machine learning upgrades the whole process by granting instructors the autonomy to permit their students to learn work at any time and place while the artificial intelligence picks up data that can be used to create personalized results that can work by helping students learn and report these findings to help students pin-point patterns and use the information for their growth.

  1. Professors can get work done much quickly with the use of an online grading system. Instructors are now able to save time and also promote sustainability by using less paper and physical files
  2. Automated Assistive Technology Defies Learning Limitations

Students of any background can learn without any limitations. The availability of transcriptions, translations and text-to-speech has helped defy the loopholes overlooked by traditional institutions of learning. Students with disabilities and non-english speakers can now be catered to with the help of artificial intelligence.

Machine Learning in Education: Adaptive Learning

Adaptive learning is an approach to education that uses technology to personalize instruction based on each student’s strengths and weaknesses. It relies on machine learning algorithms to analyze data on student performance and adjust instruction accordingly. The algorithms can identify patterns in student responses, predict areas of difficulty, and recommend interventions that will help students succeed. By tailoring instruction to each student’s individual needs, adaptive learning can improve engagement and achievement.

There are many adaptive learning algorithms in use today, ranging from simple decision trees to complex neural networks. Two examples of adaptive learning platforms that have demonstrated success in improving student outcomes are Knewton and Dreambox. Knewton uses machine learning to analyze student data in real-time and make personalized recommendations for further study. Dreambox provides interactive lessons that adapt to each student’s skill level, using a mix of visuals and game-like activities to keep students engaged.

Adaptive learning has the potential to transform education by allowing students to learn at their own pace and receive personalized instruction. By adjusting instruction to meet the needs of each student, adaptive learning can help struggling students catch up while challenging advanced students to reach their full potential. In addition, adaptive learning can provide real-time feedback to students, giving them the opportunity to correct mistakes and reinforce learning as they go. This personalized approach can lead to better academic outcomes for students and improved educational access for underserved populations.

According to MIT Technology Review, “YouTube has used speech-to-text software to automatically caption speech in videos since 2009 (they are used 15 million times a day). Recently it rolled out algorithms that indicate applause, laughter, and music in captions. More sounds could follow, since the underlying software can also identify noises like sighs, barks, and knocks.

The company says user tests indicate that the feature significantly improves the experience of the deaf and hard of hearing (and anyone who needs to keep the volume down). “Machine learning is giving people like me that need accommodation in some situations the same independence as others,” says Liat Kaver, a product manager at YouTube who is deaf.”

This is why it is imperative to shift away from traditional systems and adopt the new forms of tech and artificial intelligence.


The Reformist

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