Recommendation Engine Using AI/ML
The Recommendation Engine is a new way of finding information. Instead of searching for keywords to find what you need, the engine looks at which recommendations users interact with most. The engine will also factor in who interacts with recommendations more, meaning that if someone spends a lot of time interacting with one particular recommendation, it will be ranked higher than others.
The client wanted to survey graduates and post-graduate students. The objective was to find out the strengths and weaknesses of each student and recommend courses and mentors according to the report. Finally, use data to check if the recommendations did result in improvement in the student or not.
We gathered the academic and vocational data of the students. In addition to that, we had an EQ, IQ, and a few psychological assessments. Based on the above data, we were able to do a behavioral analysis of the students. All the courses were tagged based on their matching attributes and have AI / ML-powered recommendations.
The dropout rate of university students decreased by 50% in one year of implementation of this Recommendation Engine. The performance of the students increased by 15% in every subsequent semester. The mentors were more productive and effective by getting AL/ML suggested batch of students. The mentors were more productive and effective by getting AL/ML suggestions for a batch of students.
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