Education

Higher Education, Research Foundations & Autonomous Institutes

Digital Solutions in the Education Industry has had a major impact on all aspects and spheres of the education spectrum. Whether it is Creative Skills Development, Augmenting Faculty’s role, Remote education to teach large scale, communication Technology involving International Seminars and Conferences, developing E-Contents using Digital Technology, Adopting Education Data Mining or better Career Predictions & Academic Analytics, Admission and Enrolment Management, Student Management, Behavioral Analytics …just to name a few.

CloudPrisma has adopted the below Framework to help our clients in the Education segment take on the Digital Transformation journey.

Enable Cloud

Understand the client’s current state, business needs, and priorities for a migration to a Digital Infrastructure like the cloud. Create a cloud enablement plan with implementation, transition, and support.

Modernize Applications & Data

Use current processes and associated Applications to collect data. Based on the initial study draw a roadmap for creating new Modern Applications that would:

  • Enhance business processes
  • Are lightweight
  • Solves a specific business problem
  • An architecture that gives a seamless downsize or upsize scalability based on business requirements
  • Infuse better data Infrastructure like big data
  • Streamline data ingestion, transformation, and storage

Gain Insight

  • Visualize and Analyze newly found relationships.
    Example – Which courses are ranks top in terms of student satisfaction?
  • Query and react in real-time
    Examples - Near real-time advising for at-risk students
    Streamline enrollments
  • Create insights which can help different law enforcement branches
    Examples - Budget planning
    One view for High-level officials

Climb AI Ladder

  • Enhance Data-Driven Decision Making at all levels
  • Perform complex Analytics for different branches of business

Recommendations

  • Course and major recommender systems use predictive analytics to identify how students are likely to perform in courses and majors based on their previous academic performance.
  • Using characteristics of students who have enrolled in the past, predictive models can help institutions determine the chances that a student will enroll