How Machine Learning is Transforming the Education Sector

For progress towards human-level Artificial Intelligence, machine learning is critical. The massive adoption of machine learning and Artificial Intelligence (AI) cannot be overlooked. Various sectors are buying into the idea of using machine learning in their operations. Throughout the last decade, machine learning proof of existence and evolution is clear through self-driven vehicles, effective search on the web, practical speech recognition, and the increased understanding of the human genome. The pervasive use of machine learning today is evident as everyday tasks rely on machine learning technology.

Methods of Machine Learning

Machine learning methods are defined by the algorithms involved.

Unsupervised learning

If the machine learning algorithms are unsupervised, the instructors' content is not labeled or classified. The unsupervised algorithm explores the system's means of assuming function to define an unknown structure from uncharacterized data.

Supervised learning

Machine learning is applicable after learning. Fresh data is retrieved and used in predictive analysis. Characterized data is analyzed to produce a function used in predicting results.

Semi-supervised learning

The algorithms fall between unsupervised and supervised algorithms. The training process uses both characterized and uncharacterized data.

Machine Learning in the Education Sector

Machine learning is defined as a branch of Artificial Intelligence (AI) and computer science that embraces statistical technologies, data, and algorithms to emulate human learning. Algorithms are coached on classification and prediction through statistical techniques hence unveiling essential data mining insights. The insights are widely applicable in significant sectors and businesses, resulting in immense growth.

The education sector and learning in general currently focus on feeding students information to retain it. The students' intelligence is later gauged based on their ability to retain information. The major flaw in this mode of learning is students' understanding of information and application in the real world is not examined. With players realizing the toxicity of using this learning model, their search for an efficient model has gotten them to machine learning.

Some of the transformations the education is receiving for using machine learning include:

1. Personalized and customized learning

The conventional learning model focused on a harmonized learning where all students are taught equally. Unlike the traditional method, machine learning offers flexibility and customization. Learners customize their education model to guide their learning in terms of the most suitable learning speed. It is possible to personalize their learning where students are free to choose their preferred subjects, teachers, curriculum standards, and patterns. Personalization and customization mean students design their learning environment to suit their preferences and needs. A student proceeds to the next chapter only after grasping the previous chapter's content. On the other hand, teachers monitor the students individually, giving them an easier way of assisting them in their weak areas.

2. Content analysis

In machine learning, the teacher instructs students using machines. The machines analyze the teachers' content and determine whether the content meets the applicable quality standards. In addition, since the aim is to meet each student's intellectual capacity, the machine checks the content for compliance. Learning analytics powered by machine learning helps teachers learn through countless content pieces, analyze, interpret, make connections, and draw conclusions that make the teaching process easy. Since the model is tailored towards the individual student's needs, understanding is improved, and in turn, their learning progresses on an upward trajectory.

3. Assessment evaluation

Machine learning has revolutionized the grading system. Assessment of exams and assignments through machines is more accurate than when done manually. The process of confirming answer sheets and grading is tedious and prone to human error. While some instances require human assistance, a highly valid and reliable student performance evaluation process is achievable through machine learning.

4. Teachers' and students' efficiency is improved with machine learning.

Traditionally, instructors spent a substantive amount of time in tedious and repetitive tasks such as collecting assignments, checking attendance, etc. Machines through natural language processing have automated the tasks where teachers' schedules are organized daily, reducing the time spent on that. Teachers have enough time to focus on tasks that are more important to learning smoothly. Pre-defined plans available in machine learning are vital in bridging the gap between a student and the instructor.

5. Projective analysis

Biasness in the traditional learning model was and still is a significant drawback. Instructors used to favor good performers but belittled poor performers. The machine learning model gives no room for bias. Prediction of data means past and present data is highlighted to produce desired results. Hence, the teacher will put more effort into the poor performers as the analyzed data give insights into the areas that require attention.

Machine learning is a wave that is gradually transforming the industry at large. Simple tasks such as your recent search on Netflix were enabled by machine learning. With the lessons learned in the last two years, all sectors are gradually shifting to virtual reality; thus, team players in the education sector need to embrace the new technology for optimum results.