**1. Introduction**

Currently, technology is everywhere including the education sector, where it has proven to be of great importance for realizing the learning outcomes for students. Education is no longer just the teaching of text or requiring the student to memorize manuscripts. The instructional process, both inside and outside of the classroom, has become an activity with measurable goals and results. Over time, educational techniques have turned out to be a dynamic part of the inputs and outputs of the learning process. Moreover, these practices have grown into a vital part that plays a significant role in broadening the advancement of the components of the learning system, upgrading the rudiments of the curriculum, and making both more effective and resourceful.

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

These components are used in the process of planning, implementing, evaluating, following-up and developing objectives [1]. Machine learning has become a new frontier for higher education. Being one of the strongest newer technologies, machine learning plays the main rules in artificial intelligent and human interaction. Machine learning is the innovative tool being used to combat cancer, climate change, and even terrorism [2]. It is the new infrastructure for everything. Consequently, machine learning helps computers to find hidden insights without being programmed to do so. Moreover, machine learning works as a good predictive.

**2.1. Machine learning**

Currently, education and learning remains largely focused on feeding students with information and hoping that it is retained. Accordingly, a student's intelligence is assessed by testing their ability to recall information previously taught. The problem is that this model ignores examining how well the students understand the information and how they apply it in real-life situations. This model has proven to be toxic over the years. More schools and education centers have begun to realize how use of machine learning can make work more efficient and easier and have started to adopt technology at an increasing rate. Indeed, machine learning can accommodate all kinds of students. In the long run, machine learning is bound to produce the following advantages:

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*Customized and personalized learning* – Machine learning is flexible enough to cater to all students regardless of their learning speeds. By making use of algorithms that learn how the student consumes information, machine learning allows the learner to move ahead only after they have truly grasped the previous content. This process ensures that no student is overlooked or left behind. This is true even if they are the only one in class that has not yet understood the content. The machine learning system also allows teachers to individually monitor student and help them those areas where they are deficient. This contrasts with the traditional educational method, which focuses on a one-size-fits-all management where everyone in class is taught the same way. This type of learning can be found in the EdTech and MagicBox learning systems [3]. *Analytics of content*—Refers to a machine learning system where teachers instruct students by using machines. The machines are used to analyze the information teachers are using to teach and to determine whether the quality of the content meets the applicable standards. The machines are also used to help determine if the content taught to the students complies with the intellectual ability of each student. Since students are taught in accordance to their

*Grading*—Machine learning systems are used to reduce the amount of time needed to grade student work. In addition, machines are used to increase the efficiency and accountability of the grading system. The system still allows for the larger portion of the grading to be performed by teachers. However, machines aid in the analysis of student information such as

*Simplification of tedious tasks*—In the traditional method of learning, teachers spend a substantial amount of time in repetitive and tedious tasks, such as taking class attendance or gathering of class assignments. Machines can be used to automate these tasks and reduce the time or need for teachers to do them. Accordingly, teachers will have more time to focus on more important tasks such as making sure that their students fully understand the learning material. *Students' progress*—By using machines, the teachers can monitor each student on a personal level and evaluate their learning progress, individually. Machines can also provide additional learning patterns of the students, which help teachers to determine the best ways of teaching the students. As the above information makes clear, using machine learning in teaching brings numerous advantages to the table. It is therefore advisable for every school to adopt these types of learning platforms, such as the EdTech revolution program. With this, learning becomes easier, more

individual needs, their learning progress and understanding improve.

in the detection of plagiarism or cheating.

