Education | Lucid Sphere AI

Educational Analytics Software Features in High Demand

Automated segmentation of prospects (by demographics, GPA and SAT, educational background, academic interests, extracurricular activities, etc.).
Tracking enrollment KPIs (e.g., acceptance rate, yield rate, diversity metrics, enrollment by program).
AI-powered analysis of prospect applications to streamline the admissions process and identify patterns in applicant data.
Analyzing historical enrollment data to identify enrollment drivers and bottlenecks.
Marketing analytics, including lead scoring and campaign ROI calculations.
Benchmarking enrollment results against peers.
Predictive analytics to forecast enrollment numbers, graduation and drop-out likelihood.

Tracking student performance, attendance, and engagement metrics.
Data segmentation by student, subject, type of assignment, curriculum, and other required parameters.
Analyzing the results of student and parent surveys and their correlation with other metrics (academic outcomes, satisfaction levels, retention rates, etc.)
Tracking student participation in extracurricular activities and collaborative projects to identify areas for targeted support and intervention and enhance students’ social integration and well-being.
Forecasting student performance based on student-specific data and past results for same-segment learners.
AI-powered personalized suggestions on learning activities and resources for students.
Real-time monitoring of class attendance and participation to take timely intervention measures for at-risk students.
Alerting on student performance falling below the pre-set threshold.

Intelligent course scheduling automation and optimization.
Analyzing the impact of learning materials (e.g., textbooks, videos, simulations) and course pathways based on student performance and engagement.
Identifying gaps, overlaps, and other inefficiencies in learning courses.
Tracking the usage of educational materials (e.g., completion rates of learning modules and pathways within a course).
Identifying patterns in course enrollment and student preferences to enable informed creation of course offerings.

Analyzing educators' performance based on student engagement and academic performance metrics; feedback from students and parents.
Tracking the data on educators' qualifications, certifications, and training.
Analyzing educators’ professional development (e.g., participation in workshops and continuing education programs) and assessing its impact on teaching quality.
AI-powered suggestions on optimal training programs and other professional development courses for each educator.
Personnel turnover analytics.
Insights into personnel workload optimization.
Monitoring collaboration among educators to support efficient teamwork and knowledge sharing.
Analyzing the effectiveness of diversity and inclusion promotion practices in HR.

Monitoring and segmenting financial KPIs, e.g., revenue by tuition and non-tuition sources, cost of instruction by student and department, grant award revenue by researcher.
Analyzing fundraising campaign effectiveness (e.g., donor retention rates, donation amounts per donor).
Pricing strategy planning based on the analysis of demand, competitor prices, and pricing modeling results.
Insights into endowment value based on the analysis of donors and gift usage data.
Payroll analytics and benchmarking.
Financial forecasting to support budgeting.

Monitoring utilization rates for classrooms, labs, and other facilities to optimize their usage.
Insights into energy consumption with AI-powered optimization recommendations.
Real-time analysis of data from surveillance cameras.