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Qriyo Develops An Algorithm Inspired from Ancient Indian Art of Kundli Matching - Qriyo (qriyo.com) , an EdTech Startup working in Home Tutor Segment, Develops First of its kind Matchmaking Algorithm for Student - Teacher
Qriyo Develops An Algorithm Inspired from Ancient Indian Art of Kundli Matching

 

NewswireToday - /newswire/ - Jaipur, Rajasthan, India, 2016/06/13 - Qriyo (qriyo.com) , an EdTech Startup working in Home Tutor Segment, Develops First of its kind Matchmaking Algorithm for Student - Teacher.

   
 
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Qriyo, a Jaipur-based EdTech Startup uses Teacher - Student matchmaking to provide the perfect teacher. Mudit and Rishabh Jain, two ex-IITB alumni, co-founded Qriyo. For those wondering what difference does or would Qriyo make, Rishabh has an answer. “ Dry air contains 78.09% nitrogen, 20.95% oxygen, 0.93% argon, 0.039% carbon dioxide. If you change the composition, will it be the same? When there is a perfect composition or match, magic happens. Air is like magic, and we like to think our teacher-student matchmaking algorithm as magic too.”

Research shows 93% of students perform better when given personalized attention. Schools cannot provide the personalized attention in classes packed with students. The ratio of teacher to student is sometimes as high as 1 to 40. A lot of guardians seems to understand this, and they send their kids to private tuitions. Sometimes the ratio goes down but again most of the time the ratio climbs even higher. Asking a teacher to come to home is one solution, but a good teacher is hard to find. The online listing sites provide the number but not the assurance that the teacher will be suitable for their need. Parents have to play a ‘find and verify’ game till they get a perfect match.

Finding a perfect match in the first go, this is where Qriyo steps up. Qriyo has evaluated more than 1400 teachers. When a customer books a course, based on gathered customer information, Qriyo compatibility score or QCS is automatically calculated for every evaluated Guru in Qriyo database and teacher with highest QCS is assigned to him. If the customer likes the teacher, the matchmaking is deemed successful else a failure. The failed match is worked upon so that it won’t fail again, the next time. The matchmaking is done based upon some 25 parameters comprising of subject knowledge, educational background, work experience, personality, behavior, location, etc. Adequate research is done before deciding these parameters. Mudit says through the Matchmaking Algorithm, they are currently achieving 80% First Demo Conversions.

 
 
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Qriyo Develops An Algorithm Inspired from Ancient Indian Art of Kundli Matching

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