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MACHINE LEARNING IN EDUCATION | Education News

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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 as. 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;

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.

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.

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.

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.

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.

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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