Course Overview

Education Data Analytics and Learning Systems is a comprehensive professional training program designed to equip education administrators, policymakers, researchers, monitoring and evaluation specialists, teachers, academic managers, and data analysts with advanced skills in collecting, managing, analyzing, and utilizing educational data to improve learning outcomes and institutional performance. As education systems increasingly adopt Education Data Analytics, Learning Analytics, Educational Data Mining, Student Performance Analytics, Learning Management Systems (LMS), Education Management Information Systems (EMIS), Academic Performance Monitoring, Data-Driven Education, Digital Learning Analytics, and Evidence-Based Educational Planning, there is a growing demand for professionals who can transform educational data into actionable insights that support quality education and continuous improvement. This course provides participants with practical expertise in applying analytics to educational planning, teaching, learning, and institutional management.

The training explores the complete educational analytics lifecycle, including data collection, student performance monitoring, learning assessment, predictive analytics, institutional performance evaluation, dashboard development, and reporting systems. Participants will learn how to analyze student achievement data, attendance records, assessment results, teacher performance indicators, learning management system data, and educational resource utilization metrics. The course combines theoretical foundations with practical applications using real-world educational datasets and learning system scenarios.

Participants will gain hands-on experience in learning analytics, educational data mining, predictive modeling, student retention analysis, educational dashboards, program evaluation, and performance reporting. The course emphasizes evidence-based decision-making, educational quality assurance, student-centered learning, ethical data management, and the integration of technology into education systems. Through practical exercises and case studies, participants will develop confidence in designing and implementing analytics solutions that improve educational outcomes and institutional effectiveness.

The training further addresses emerging trends in education technology, including artificial intelligence in education, adaptive learning systems, personalized learning analytics, educational big data, cloud-based learning platforms, digital assessment systems, intelligent tutoring systems, and learning experience analytics. Participants will develop competencies required to build modern educational data ecosystems that support student success, institutional excellence, and lifelong learning.

Course Objectives

1. Understand the principles and applications of education data analytics and learning systems.

2. Collect, manage, and analyze educational data effectively.

3. Apply learning analytics to improve student performance and engagement.

4. Utilize educational data for planning, monitoring, and evaluation.

5. Develop predictive models for student success and retention.

6. Analyze learning management system and assessment data.

7. Design educational dashboards and performance monitoring systems.

8. Support evidence-based educational policy and decision-making.

9. Apply ethical and responsible practices in educational data management.

10. Leverage emerging technologies to enhance learning and institutional performance.

Organizational Benefits

1. Improved student learning outcomes and academic achievement.

2. Enhanced educational planning and resource allocation.

3. Better monitoring of student performance and engagement.

4. Improved institutional accountability and quality assurance.

5. Increased student retention and completion rates.

6. Enhanced decision-making through real-time educational insights.

7. Improved teacher effectiveness and professional development planning.

8. Better evaluation of educational programs and interventions.

9. Strengthened educational policy development and implementation.

10. Accelerated digital transformation and innovation in education systems.

Target Participants

· Education administrators and school leaders

· University and college management personnel

· Education policymakers and planners

· Teachers and academic coordinators

· Monitoring, Evaluation, Accountability and Learning (MEAL) specialists

· Educational researchers and statisticians

· Learning management system administrators

· Data analysts and information management officers

· Curriculum and assessment specialists

· Development practitioners working in education programs

· Academic faculty and postgraduate students

· Anyone involved in educational planning, management, and analytics

Course Outline

Module 1: Introduction to Education Data Analytics and Learning Systems

1. Fundamentals of educational data analytics

2. Learning analytics concepts and frameworks

3. Education Management Information Systems (EMIS)

4. Data-driven decision-making in education

5. Educational technology ecosystems

6. Emerging trends in education analytics

Case Study:
Developing a data analytics strategy to improve educational performance and institutional effectiveness.

Module 2: Educational Data Collection and Management

1. Educational data sources and types

2. Student information systems

3. Learning management system data collection

4. Data quality assurance and governance

5. Educational database management

6. Data privacy and security in education

Case Study:
Establishing a centralized educational data management system for a school district.

Module 3: Student Performance Analytics

1. Academic achievement measurement

2. Assessment and examination data analysis

3. Attendance and participation analytics

4. Student progression and completion analysis

5. Learning outcome measurement

6. Performance benchmarking techniques

Case Study:
Analyzing student assessment data to identify achievement gaps and intervention needs.

Module 4: Learning Analytics and Student Engagement

1. Learning analytics frameworks

2. Student engagement monitoring techniques

3. Learning behavior analysis

4. Digital learning activity tracking

5. Personalized learning analytics

6. Early warning systems for at-risk students

Case Study:
Using learning analytics to improve student engagement and academic success.

Module 5: Educational Data Mining and Predictive Analytics

1. Educational data mining concepts

2. Predictive modeling in education

3. Student retention and dropout prediction

4. Classification and clustering techniques

5. Academic risk assessment models

6. Data-driven intervention planning

Case Study:
Developing predictive models to identify students at risk of dropping out.

Module 6: Learning Management Systems and Digital Learning Analytics

1. Learning Management System (LMS) analytics

2. E-learning performance monitoring

3. Course engagement analytics

4. Online assessment analytics

5. Learning content effectiveness analysis

6. Digital learning experience evaluation

Case Study:
Evaluating online learning outcomes using LMS-generated analytics data.

Module 7: Institutional Performance and Quality Assurance Analytics

1. Educational performance indicators

2. Institutional effectiveness measurement

3. Accreditation and quality assurance metrics

4. Faculty and staff performance analytics

5. Resource utilization analysis

6. Strategic performance management

Case Study:
Developing institutional performance dashboards for educational leadership.

Module 8: Education Policy and Program Evaluation

1. Educational policy analysis methodologies

2. Program monitoring and evaluation frameworks

3. Impact assessment techniques

4. Cost-effectiveness analysis in education

5. Evidence-based policy development

6. Educational reform evaluation

Case Study:
Evaluating the impact of a national education improvement initiative.

Module 9: Data Visualization and Educational Reporting

1. Educational dashboard development

2. Data visualization best practices

3. Interactive reporting systems

4. KPI monitoring and scorecards

5. Stakeholder communication strategies

6. Executive reporting for education leaders

Case Study:
Creating an educational performance dashboard for school administrators and policymakers.

Module 10: Artificial Intelligence and Emerging Technologies in Education

1. AI applications in education

2. Adaptive learning systems

3. Intelligent tutoring systems

4. Automated assessment technologies

5. Chatbots and virtual learning assistants

6. Future technologies in education analytics

Case Study:
Implementing AI-powered adaptive learning systems to improve student outcomes.

Module 11: Governance, Ethics, and Data Protection in Education

1. Educational data governance frameworks

2. Student data privacy and protection

3. Ethical use of educational analytics

4. Data-sharing policies and procedures

5. Compliance with education regulations

6. Building trust in educational data systems

Case Study:
Developing governance policies for managing sensitive student information.

Module 12: Strategic Education Analytics and Future Learning Systems

1. Building educational analytics strategies

2. Integrated learning intelligence systems

3. Smart campuses and digital education ecosystems

4. Lifelong learning analytics

5. Future trends in education technology and analytics

6. Developing sustainable learning systems

Case Study:
Designing an integrated education data analytics and learning ecosystem that combines student information systems, learning management platforms, predictive analytics, AI-powered learning support, educational performance dashboards, quality assurance frameworks, institutional intelligence systems, and policy evaluation tools to improve student achievement, teaching effectiveness, institutional performance, educational equity, and lifelong learning outcomes.

Essential Information

Our courses are customizable to suit the specific needs of participants.
Participants are required to have proficiency in the English language.
Our training sessions feature comprehensive guidance through presentations, practical exercises, web-based tutorials, and collaborative group activities. Our facilitators boast extensive expertise, each with over a decade of experience.
Upon fulfilling the training requirements, participants will receive a prestigious Global King Project Management certificate.
Training sessions are conducted at various Global King Project Management Centers, including locations in Nairobi, Mombasa, Kigali, Dubai, Lagos, and others.
Organizations sending more than two participants from the same entity are eligible for a generous 20% discount.
The duration of our courses is adaptable, and the curriculum can be adjusted to accommodate any number of days.
To ensure seamless preparation, payment is expected before the commencement of training, facilitated through the Global King Project Management account.
For inquiries, reach out to us via email at training@globalkingprojectmanagement.org or by phone at +254 114 830 889.
Additional amenities such as tablets and laptops are available upon request for an extra fee. The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a certificate of successful completion. Participants are responsible for arranging and covering their travel expenses, including airport transfers, visa applications, dinners, health insurance, and any other personal expenses.

Education Data Analytics and Learning Systems Training Course