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Education Analytics
A 360-Degree Overview
In data analytics since 1989, ScienceSoft builds custom analytics solutions that help educational institutions enhance instructional processes and outcomes, increase enrollment, improve budget allocation, optimize fundraising strategies, and more.
Contributors
Head of Data Analytics Department, ScienceSoft
Business Analyst, ScienceSoft
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Education analytics is a way to use historical and real-time data to get a comprehensive view of enrollment, learning outcomes, attendance, educators’ qualifications and performance, financial management, facility usage, and other aspects of educational institutions.
- Implementation time: 2–6 months for an MVP.
- Costs: $30,000–$600,000, depending on the solution's scope. Use our free online calculator to get a tailored ballpark estimate.
- Core integrations: student information system, ERP, CRM.
- Average annual ROI: 300% with a payback period of 4 months.
Educational Analytics Software Features in High Demand
Enrollment analytics
- 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.
Student body analytics
- 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.
Curriculum analytics
- 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.
Educator analytics
- 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.
Facilities analytics
- 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.
Core Integrations for Education Analytics Software
- Student information system (SIS) – to get accurate enrollment forecasts, assess curriculum efficiency, and optimize course scheduling.
- Learning management system (LMS) — to get forecasts on students' performance and timely detect at-risk students; to get personalized recommendations for academic performance improvement.
- CRM — to analyze relationships with prospects, students, parents, alumni, faculty members, and donors and get personalization and optimization insights for engagement, marketing, and fundraising campaigns.
- HR management system — to analyze the performance of educators, perform employee turnover analysis, optimize educators’ schedules and workload, and enhance the efficiency of continuing education and collaboration.
- Financial management software (e.g., accounting software, development office systems, grants and research administration systems) — to get insights for informed financial management decisions, including budgeting, fundraising activities, and endowment management.
How to Develop a Custom Analytics Solution for an Educational Institution
Education analytics software development is the process of delivering case-specific analytics capabilities to enable data-driven decision-making for educational institutions. Drawing on 35 years of experience implementing custom analytics solutions, ScienceSoft’s consultants summarize the four key steps of the process and share our best practices in developing analytics software for the education industry.
1.
Business analysis and requirement engineering
The purpose of this step is to translate your business goals into clear, actionable software requirements. Business analysts conduct interviews with multiple stakeholders, including CIOs/CTOs, school administrators, and educators, document the requirements, and prioritize them for implementation.
If the target system is highly complex, ScienceSoft recommends starting with a proof of concept (PoC) to verify solution feasibility. We can also go for an MVP to gather early user feedback and adjust the software accordingly.
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2.
Technical design
During this stage, a software architect decides on integrations, architecture components, and techs that will enable the required analytics processes. Many analytics components can be built using available platforms (e.g., Azure Synapse Analytics, Amazon Redshift, Google BigQuery) to reduce project timelines and costs.
At this step, ScienceSoft compares the applicable cloud services and technologies across such parameters as performance, integration capabilities, pricing structure, and scalability. Based on the comparison results, we choose the techs that will ensure an optimal cost-to-benefit ratio of the solution and make it easy to upgrade in the future. Data security controls and data management procedures are also determined at this stage.
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3.
UI/UX design
To ensure smooth user adoption and convenient data exploration, it's a good practice to tailor analytics dashboards to specific user roles. E.g., educators are likely to benefit from a detailed representation of student performance data, while administrators may need a comprehensive view of the institution’s performance with easy access to details by faculty, educator, etc.
Following our UX/UI design best practices, we often audit the software systems our customers have in use (e.g., CRM, SIS) to include familiar tools, visuals, and colors and promote easy navigation in the new solution.
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4.
Development, QA, and deployment
We usually recommend shifting QA left and conducting testing in parallel with development to prevent avoidable QA issues early on, enable smooth collaboration between development and QA teams, and ensure zero severe defects in production.
To optimize development costs, ScienceSoft keeps the balance between code reuse (e.g., by utilizing ready third-party components and microservices) and custom code creation. Together with efficient CI/CD design, well-established DevOps practices, and feasible QA automation, such an approach helps us reduce development costs by up to 78%.
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Cost & ROI of Analytics in Education
The cost of education analytics development can range from $30,000 to $600,000, depending on the solution's scope.
The exact figures are largely influenced by the organizational complexity of an institution (e.g., the number of departments and faculty, geo distribution, stakeholder landscape, curriculum diversity), the type of systems to be integrated (custom, OOTB, or legacy software), the number of user roles, the need for ML/AI features and big data techs, and more.
On average, implementing analytics in education brings an annual ROI of 300%, driven by improved student experience and engagement, increased productivity of academic teams, and enhanced efficiency of fundraising campaigns.
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|
Basic solution |
Medium-complexity solution |
Advanced solution |
|---|---|---|---|
|
Data complexity
|
Structured data (e.g., XLSX, relational databases, XML). |
Structured and semi-structured data (e.g., JSON files). |
Structured, semi-structured, and unstructured data (e.g., DOCX, PDF, JPG, JPEG, MP3, MP4, HTML files). |
|
Data processing frequency
|
Batch (e.g., every 24 hours) |
Batch |
Batch and real-time E.g., for data from surveillance cameras. |
|
Analytics scope
|
Basic analytics features (e.g., revenue and expense tracking and financial indicators calculation; tracking students’ grades and attendance; monitoring employee turnover rates and recruitment costs; tracking enrollment numbers). |
Advanced analytics features (e.g., budget variance, cost-benefit, scenario analysis with insights into budget allocation optimization; identifying at-risk students, insights into talent acquisition and retention strategies, staffing needs forecasting; identifying trends in enrollment dynamics). |
Comprehensive analytics across multiple stakeholders (e.g., students, parents, staff, donors, alumni) and geographically distributed units with granular and general data views. |
|
AI and machine learning
|
|
Simple, public-available AI models (e.g., for students’ sentiment analysis). |
Custom AI models (e.g., for personalized student-specific learning recommendations). |
|
Reporting and visualization
|
Via market-available tools like Power BI, Tableau, Looker. |
Via market-available tools like Power BI, Tableau, Looker. |
Via market-available tools like Power BI, Tableau, Looker, and custom data visualization modules. |
|
Cost
|
$30K–$60K |
$60K–$200K |
$200K–$600K |
60% of Educational Institutions' CIOs Make Data Analytics a Strategic Priority
The Survey of Campus Chief Technology/Information Officers conducted by Hanover Research features feedback from 175 CIOs/CTOs at public, private nonprofit, and for-profit higher education institutions in the US. It provides the following insights about how educational institutions use data:
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