ML in Education: Voice Recognition for Language Learning

Machine Learning use in
Education
All rights reserved. AI.Business Owner: http://ai.business

USE CASE – Duolingo:
Voice recognition for language learning
Personalized, fun, universally accessible language learning

Duolingo - Voice recognition for
language learning: EFFECTS OF USAGE
• Predicts your word strength;
• Figures out which sentences will help you best
practice your weakest words/skills;
• Recommends immersion practice documents
(translations) based on your progress;
• Estimates the quality of a translation-in-progress.
Source: https://www.duolingo.com/comment/776000

USE CASE – Plexuss: College
comparison and recruitment platform
Plexuss.com facilitates contact
between universities and future
students, and aims to help students
make an informed decision when it
comes to choosing the right university.

Plexuss - College comparison and
recruitment platform: EFFECTS OF USAGE
• Allows users to take a virtual tour of their selected campuses, compare colleges, and
live chat with their choice of universities.
• Includes a college ranking system, which collates data from trustworthy sources
including Forbes, Reuters and Shanghai Ranking.
• Compares data using a variety of criteria like in- and out-of-state tuition, acceptance
rates, college endowment funds, or more advanced search criteria such as student-to-
faculty ratios, SAT score percentiles, Environmental sustainability policies.
• Colleges no longer have to send out expensive and time-consuming recruitment
information packs, and are instead able to easily view candidate profiles through the
website.
Source: 1) https://plexuss.com/ ; 2)http://startupbeat.com/2015/05/27/u-s-college-comparison-and-recruitment-
website-plexuss-com-now-out-of-beta-testing/

USE CASE – ITS: Intelligent Tutoring
System

Intelligent Tutoring System:
EFFECTS OF USAGE
• Aims to provide immediate and customized instruction or feedback to learners, usually
without intervention from a human teacher.
• Used in both formal education and professional settings.
• Aims to solve the problem of over-dependency of students over teachers for quality
education.
• Once an ITS was created, teachers could copy it and modify it for future use.
• Useful when large groups need to be tutored simultaneously or many replicated tutoring
efforts are needed (in technical training situations such as training of military recruits and
high school mathematics).
• Have been constructed to help students learn geography, circuits, medical diagnosis,
computer programming, mathematics, physics, genetics, chemistry, etc.
Source: https://en.wikipedia.org/wiki/Intelligent_tutoring_system

USE CASE – Recognition Apps
Let your smartphone to
decode the world for you.

Recognition Apps: EFFECTS OF USAGE
• As more schools bring tablets into the classroom, educators are finding that apps
are game changers.
• This generates excitement and motivates students.
• Some of the most powerful education apps are used for teaching reading and
supporting differentiation for students with disabilities (especially the ones using
speech and text recognition).
• Enhances hands-on science activities, and also could substitutes school’s existing
materials.
Source:
1) http://classtechtips.com/2013/01/23/rock-and-mineral-identifier/
2) http://www.scholastic.com/teachers/article/50-fab-apps-teachers

USE CASE – Woogie: Educational
companion

Woogie - Educational companion:
EFFECTS OF USAGE
• Able to detect, read, process and understand the human language. Also, it will have the capability
of converting text to speech and speech to text.
• An app is designed for parents only so that they can connect with their kids through the
companion.
• Able to play radio stations, podcasts and shows according to the user’s age. Also, it will play music
on request or based on learning algorithms.
• Helps the child to memorize different information from multiple areas. It does that based on
interactivity.
• Acknowledges the kid’s presence in the room and reacts according to this. Also, will control smart
home appliances like room lights or sound volume.
• Keeps the child up-to-date on the information he shows interest on. For example, if the child has a
favorite artist, the companion can provide useful news about that artist.
Source: http://hiwoogie.com/

USE CASE – Learning analytics
"an educational
application of web
analytics aimed at
learner profiling, a
process of gathering
and analyzing details
of individual student
interactions in online
learning activities."
(The 2016 Horizon
Report)

Learning analytics: EFFECTS OF USAGE
• Students are often direct consumers of learning analytics, particularly through
dashboards that support the development of self-regulated learning and insight into
one’s own learning
• Assists students in course selection. For example, Degree Compass pairs current
students with the courses that best fit their talents and program of study for upcoming
semesters
• Advisors can use this system to identify the students who are at the highest risk of
failure.
• Provides a broad range of insight into course materials, student engagement, and
student performance.
Source:
1) https://library.educause.edu/topics/teaching-and-learning/learning-analytics
2) https://library.educause.edu/~/media/files/library/2015/10/ewg1510-pdf.pdf

USE CASE – Personal trainer
Helping exercisers improve
their form by giving fitness
advice with machine learning

Personal trainer: EFFECTS OF USAGE
• Helps people exercise in proper form.
• Increase the effectiveness of their
workouts.
• Helps avoiding injury risk.
Source: http://cs229.stanford.edu/proj2015/183_poster.pdf

USE CASE – Viper: Plagiarism checking
Universities could check students' essays for
plagiarism. Also useful to help students avoid
plagiarism before they hand in their work.

Viper - Plagiarism checking:
EFFECTS OF USAGE
• Easy to use side by side comparison
• Fast, 100% accurate reports
• Over 10 billion resources scanned
• Free (also a paid option, for higher speed on large
amount of data)
Source: . http://www.scanmyessay.com/universities.php

USE CASE – Automated Essay Grading
Within a single prompt, the models are able to make
predictions that closely match those made by human
graders.

Automated Essay Grading:
EFFECTS OF USAGE
• For essays of intermediate writing level (7-10th grades) and given
enough human graded training examples for a writing prompt, the
system can automate the grading process for that prompt with
fairly good accuracy.
• Using machine learning to assess human writing can potentially
make quality education more accessible.
Source:
http://cs229.stanford.edu/proj2014/Alex%20Adamson,%20Andrew%20Lamb,%20Ralph%20Ma,%2
0Automated%20Essay%20Grading.pdf

USE CASE – Better Reading Levels
Measuring the
reading difficulty of
a particular text is a
common and salient
problem in the
educational world,
particularly with
respect to new
/struggling readers.

Better Reading Levels:
EFFECTS OF USAGE
• Uses machine learning to reproduce the results of the Lexile Reading Measure (the
most popular metric for reading difficulty).
• Focuses on four features:
– sentence length,
– paragraph length,
– word length and
– difficulty of vocabulary.
Source:
http://cs229.stanford.edu/proj2014/AdamGall,Better%20Reading%20Levels%20through%20Machine%2
0Learning.pdf

ML in Education: Voice Recognition for Language Learning

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