Business Intelligence in Education and Learning Analytics Training Course: Transforming Data into Educational Insights

Introduction

Educational institutions today generate massive amounts of data from student performance, learning management systems, attendance records, assessments, and administrative operations. Business Intelligence (BI) allows educators, administrators, and policy makers to transform this data into actionable insights that improve learning outcomes, optimize resource allocation, and support strategic decision-making. By leveraging predictive analytics, interactive dashboards, and advanced visualizations, educational organizations can monitor student engagement, identify learning gaps, and implement data-driven interventions effectively.

This training course equips participants with practical skills to apply Business Intelligence in educational and learning analytics contexts. Through real-world case studies, hands-on exercises, and BI platform integration, learners will explore student performance analysis, predictive modeling for academic success, curriculum optimization, and interactive reporting. By the end of the course, participants will be able to design and deploy BI solutions that enhance teaching effectiveness, improve learning outcomes, and inform evidence-based educational strategies.

Duration: 10 Days

Target Audience

  • Educational administrators and managers
  • Academic analysts and researchers
  • Learning and development professionals
  • Business Intelligence professionals in the education sector
  • Policy makers and decision-makers in educational institutions

10 Objectives

  1. Understand the fundamentals of education analytics and BI
  2. Explore data sources for student performance, engagement, and institutional operations
  3. Design data models and reporting frameworks for learning analytics
  4. Implement ETL/ELT pipelines for educational datasets
  5. Apply predictive analytics for student success and curriculum optimization
  6. Develop and deploy interactive dashboards for monitoring learning outcomes
  7. Measure key performance indicators in academic and operational contexts
  8. Ensure data quality, governance, and compliance in educational analytics
  9. Examine best practices and case studies in learning analytics
  10. Deliver actionable insights to support data-driven educational decision-making

15 Course Modules

Module 1: Introduction to BI in Education

  • Overview of BI applications in education
  • Benefits for teaching, learning, and institutional decision-making
  • Key components of education BI systems
  • Emerging trends in learning analytics
  • Evidence-based education strategies

Module 2: Educational Data Sources

  • Student information systems and academic records
  • Learning management systems (LMS) and digital platforms
  • Assessment and evaluation data
  • Attendance and behavioral data
  • Data quality and integration challenges

Module 3: Data Modeling for Learning Analytics

  • Star and snowflake schemas for educational data
  • Fact and dimension tables for academic and operational metrics
  • Designing models for KPIs and dashboards
  • Normalization vs denormalization
  • Optimizing models for analysis efficiency

Module 4: ETL/ELT Processes in Education BI

  • Extracting data from LMS, SIS, and other systems
  • Transforming datasets for analysis readiness
  • Loading into data warehouses or BI platforms
  • Automating ETL/ELT workflows
  • Monitoring and troubleshooting pipelines

Module 5: Student Performance Analytics

  • Monitoring academic performance and progression
  • Identifying at-risk students
  • Benchmarking against academic standards
  • Predictive modeling for student outcomes
  • Dashboard visualization for performance insights

Module 6: Curriculum and Course Analytics

  • Evaluating course effectiveness
  • Tracking learning outcomes across programs
  • Identifying curriculum gaps
  • Assessing instructional methods
  • Reporting for academic planning

Module 7: Engagement and Retention Analytics

  • Analyzing student participation and engagement
  • Identifying factors affecting retention
  • Predictive modeling for dropouts
  • Student satisfaction and feedback analysis
  • Dashboard reporting for engagement strategies

Module 8: Learning Resource and Faculty Analytics

  • Monitoring usage of learning resources
  • Evaluating faculty performance
  • Teaching effectiveness metrics
  • Resource allocation analysis
  • Reporting and visualizations for faculty and administration

Module 9: Assessment and Examination Analytics

  • Analyzing test and exam results
  • Standardized assessment performance
  • Item analysis and question effectiveness
  • Trends in academic achievement
  • Interactive dashboards for assessments

Module 10: Predictive Analytics in Education

  • Forecasting student performance and outcomes
  • Early intervention modeling
  • Scenario analysis for academic planning
  • Prescriptive analytics for curriculum optimization
  • Integrating predictive insights into dashboards

Module 11: Real-Time Education Analytics

  • Monitoring live LMS and classroom data
  • Event-driven alerts for student engagement
  • Low-latency dashboards for academic interventions
  • Automated reporting of critical metrics
  • Supporting timely decision-making

Module 12: Advanced Analytics and Machine Learning

  • Clustering and segmentation of students
  • Performance prediction using ML models
  • Learning pattern recognition
  • Recommender systems for personalized learning
  • Case studies in advanced education analytics

Module 13: Cloud and Hybrid BI Architectures in Education

  • Cloud-based BI solutions for educational institutions
  • Hybrid deployments for secure student data
  • Security, privacy, and compliance considerations
  • Scalability for large educational datasets
  • Platform selection and deployment best practices

Module 14: Data Governance and Compliance

  • Ensuring data quality, accuracy, and integrity
  • Privacy and regulatory compliance (FERPA, GDPR, etc.)
  • Access control and auditing
  • Governance frameworks for education BI
  • Best practices for reporting and monitoring

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport Pick Up is provided by the institute. Accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Business Intelligence In Education And Learning Analytics Training Course: Transforming Data Into Educational Insights in Niger
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