Machine learning in education

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Machine learning in education

Machine learning is having a dramatic impact on the way software is designed so that it can keep pace with business change. Machine learning is so dramatic because it helps you use data to drive business rules and logic [1].

How does it Work?

Machine learning uses algorithms to build analytical models, helping computers “learn” from data. It can now be applied to huge quantities of data to create exciting new applications.

Machine learning has become one of the most important topics within development organizations that are looking for innovative ways to leverage data assets to help the business gain a new level of understanding. Why add machine learning into the mix? With the appropriate machine learning models, organizations have the ability to continually predict changes in the business so that they are best able to predict what’s next. As data is constantly added, the machine learning models ensure that the solution is constantly updated. The value is straightforward: If you use the most appropriate and constantly changing data sources in the context of machine learning, you have the opportunity to predict the future. [1]

Machine learning in education concerns teaching machines to learn

Machine learning in education concerns teaching machines to learn about something without explicit programming. At the core of machine learning is the idea of modeling and extracting useful information out of data. Societal trends clearly point to data as the resource of the future.

The use of digital books and adaptive learning allows for a much more tailored and customizable learning experience, as teachers now have access to data on what exactly students are consuming, and how they’re coping with the concepts they’re trying to learn.  Armed with this data, teachers can see if a student, or the class as a whole, is not grasping certain ideas or topics. This insight allows teachers to adjust their lessons accordingly and help at-risk students from falling behind or dropping out.

What’s more, the process is not simply one of providing data to teachers.  Machine learning algorithms create predictive learning paths for students while they are studying.  As students go through a course with adaptive learning software, these algorithms can serve up additional content for the student to study if reinforcement is needed or allow the student to move ahead if the subject matter has already been mastered. [2]

Move away from the Traditional Learning

Machine learning moves away from the traditional, one size fits all way of learning that doesn’t really reflect a more complex reality.  Because not only do people learn at different speeds, but they often arrive from different start points and with different backgrounds.

Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process. Machine learning uses a variety of algorithms that iteratively learn from data to improve, describe data, and predict outcomes. As the algorithms ingest training data, it is then possible to produce more precise models based on that data. A machine learning model is the output generated when you train your machine learning algorithm with data. After training, when you provide a model with an input, you will be given an output. For example, a predictive algorithm will create a predictive model. Then, when you provide the predictive model with data, you will receive a prediction based on the data that trained the model. Machine learning is now essential for creating analytics models. [1]

Machine learning will be more included in the education area. Machine learning can help people to learn at different speeds and start the learning for the start point that fit for each student.

References

[1] Judith Hurwitz and Daniel Kirsch (2018) Machine Learning For Dummies, IBM Limited Edition

[2] Adi Gaskell (2016), Machine Learning And The Future Of Education, Forbes

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