MACHINE LEARNING IN EDUCATION

Dec 17, 2018 by

Review on education and machine learning

Education is a gradual process of knowledge acquisition, values, skills, habits and believes through study or learning facilitation by way instruction or other practical procedure. Education can be achieved in three different settings; formal, informal and non-formal settings which concurrently impacts formative effects on the way of thoughts, feelings and action.

For several years the education systems has been involving teachers gathering and preparing learning materials, manually monitoring and grading students based on the observation and provide a feedback  progress.   This has always been tedious and tiresome with minimal time and ability to identify learners’ weaknesses and areas that need improvements. Thanks to Machine Learning (a branch of artificial intelligence), a methodology of data analysis that automates analytical model building by utilizing data algorithms to establish patterns and make decisions with minimal external interferences. 

Machine learning technology has continuously played a mega role in education revolution. Concurrently, education field is not only evolving using technology and digital resources but also investing in machine learning consulting that captures and maintain gigantic data set  (such as students/teachers demographic data and performance data, admissions and registration data, human resource information etc.) with ideal aim of identifying meaningful patterns and transformed to base structured knowledge for future references. Machine learning technology is therefore used in education sector with the aim of trying to solve numerous problems and formulating policy making decisions.

Machine learning in education field

Machine learning has a wide range of applications in various fields i.e. social networking such as Facebook algorithms, online shopping, Travel such as car driving etc. manufactured by various technology consulting & engineering companies such asSquadex. Our major concentration pinpoints on the use of machine learning consulting in education.  For effective and efficient machine learning consulting work, major three factors should be considered; the inputs and outputs should be well understood and need of reliable experience. Machine learning in education can be applied in several ways;

  • Predict students’ academic demography (Adaptive learning)

Machine learning regression technology has the ability to monitor and predict students’ future performance and establishing weaknesses in each student by ‘learning’ from education data set mine. A teacher can therefore assess an individual or whole class and adjust the pace to deliver according to student(s) progress.  Acquired information gives room to isolate students who require guidance on topics that they have not understood with an aim helping them to improve.

  • Allows teaching to be differentiated and personalized

Previously prior to machine learning  technology invention, educators heavily relied on physical detailed grade books but currently most of them easily access several books and student’s data in one volume. This has lifted off a significant workload on teachers giving them at least enough time to work one on one with their students and able to identify and understand their areas of weakness and able to evaluate possible remedy before they solidify.

  • Merging students with same interests and goals

Similarly, a classroom always compiles learners with diverse goals and interests and some may happen to struggle to fit in on what is provided without the teachers consent. Machine learning consulting system can merge a student interests and goals with data on their learning styles thus shading light on what kind of content and method of presentation a teacher can use on a student to address knowledge gaps.

  • Test students’ progress

Rose Luckin reiterated that random and continuous assessment tests do not necessarily evaluate learners understanding contrary to machine learning that helps minimizing standardized testing. Machine learning assessment gives teachers as well as parents and learners an effective and constant feedback on learners’ progress and specific areas that need more effort to achieve desired goals. This as well helps learning institutions on future planning and projections producing erudite professions to the corporate world.

  • Fair grading & efficiency

The system is human bias free when it comes to students’ assessment and grading. Additionally, raw data evaluation and analysis takes a short period to produce accurate and well informed results to students and teachers as well than in hay days. Machine learning consulting such as Turn It In has enabled minimizing plagiarism thus promoting creative thinking, practical learning, innovation and extensive research all integrated in one pool.

Conclusion

Machine learning technology can be an active driving engine that would propel education sector to a higher notch if the technology will be fully implemented. The technology lifts off previously cumbersome tasks, saves on time on all affiliates, providing room for adaptive learning with efficient and accurate results.

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