Learning and candidate matching

Summary

An education-to-employment platform for technical talent.

The platform needed to personalise learning material and connect students’ demonstrated skills with relevant job opportunities.

Scoring models, learning recommendations and a platform that records student-content interaction and enriches candidate profiles.

Tech Challenge

• The platform has a large number of students that become job candidates after finishing the online school. The main challenge lies in sorting students during the studying cycle to identify best talent.
• Another challenge is to supply students with personalized study program. The program provides curated content such as customized tests and materials based on students' skills level and needs.

Solution

• We created a series of scoring models based on the datasets and additionally publicly available data on prior students’ activity.
• Those models evaluate candidates upon their registration for the online school, recommend the most effective studying roadmaps for a given subject.
• Additionally, via push notifications and recommendations of additional studying material the models optimize the collaboration process to recommend the most efficient learning process.

Outcome

A platform linking personalised learning with richer candidate profiles.

Student-content interaction was recorded to support learning recommendations and enrich candidate profiles.