Course Overview

Education Research and Learning Analytics is a dynamic field that combines educational research methodologies, data analytics, learning sciences, and evidence-based decision-making to improve teaching effectiveness, student outcomes, institutional performance, and educational policy development. As educational institutions increasingly adopt digital learning platforms, learning management systems, online assessments, and data-driven educational strategies, there is a growing need for professionals who can analyze educational data, evaluate learning interventions, and generate actionable insights. This comprehensive training course provides participants with practical knowledge and hands-on skills in education research, learning analytics, student performance analysis, educational assessment, data visualization, and academic decision support systems.

The training explores modern education research methodologies and learning analytics frameworks used by schools, universities, training institutions, ministries of education, research organizations, NGOs, and development agencies. Participants will learn how to design educational research studies, collect and manage learning data, analyze student engagement and performance, evaluate educational programs, and use analytics to support teaching and learning improvements. The course combines theoretical foundations with practical applications using real-world educational datasets and learning scenarios.

Participants will gain practical experience in educational data collection, assessment analysis, student success metrics, learning management system analytics, program evaluation, predictive modeling, and educational reporting. The course examines how learning analytics can be used to identify at-risk learners, improve curriculum design, enhance instructional practices, monitor institutional performance, and support evidence-based educational planning. Through practical exercises and relevant case studies, participants will develop confidence in applying analytical techniques to solve educational challenges and improve learning outcomes.

The training further addresses emerging trends in education analytics, including artificial intelligence in education, adaptive learning technologies, educational data mining, predictive student success models, digital learning ecosystems, personalized learning analytics, educational dashboards, and smart learning environments. Participants will develop the competencies required to leverage educational data effectively, enhance institutional performance, and contribute to innovation in teaching, learning, and educational management.

Course Objectives

1.      Understand the principles and applications of education research and learning analytics.

2.      Design and implement educational research studies and evaluations.

3.      Collect, manage, and analyze educational and learning datasets.

4.      Apply quantitative and qualitative research methods in education.

5.      Evaluate student performance, engagement, and learning outcomes.

6.      Utilize learning analytics tools and educational dashboards effectively.

7.      Conduct program evaluation and educational impact assessments.

8.      Develop evidence-based recommendations for educational improvement.

9.      Strengthen data-driven decision-making in educational institutions.

10.  Apply emerging technologies and advanced analytics in educational settings.

Organizational Benefits

1.      Improved student learning outcomes and academic performance.

2.      Enhanced evidence-based educational planning and policy development.

3.      Better identification and support of at-risk learners.

4.      Improved curriculum design and instructional effectiveness.

5.      Enhanced monitoring and evaluation of educational programs.

6.      Increased efficiency in institutional performance management.

7.      Improved resource allocation and educational investments.

8.      Enhanced accountability and reporting systems.

9.      Strengthened educational research and innovation capacity.

10.  Improved institutional competitiveness and educational quality.

Target Participants

·         Education researchers and academic staff

·         School administrators and principals

·         University lecturers and faculty members

·         Curriculum developers and instructional designers

·         Monitoring and Evaluation (M&E) specialists

·         Education policymakers and planners

·         Learning and development professionals

·         Educational technology specialists

·         Data analysts and institutional researchers

·         NGO and development practitioners in education

·         Graduate and postgraduate students in education

·         Training and capacity-building professionals

Course Outline

Module 1: Foundations of Education Research and Learning Analytics

1.      Introduction to education research and learning analytics

2.      Educational research paradigms and methodologies

3.      Learning theories and evidence-based education

4.      Types and sources of educational data

5.      Learning analytics frameworks and applications

6.      Data-driven decision-making in education

Case Study:
Developing an evidence-based strategy to improve student retention and academic performance.

Module 2: Educational Research Design and Data Collection

1.      Formulating educational research questions and objectives

2.      Quantitative and qualitative research designs in education

3.      Survey development and educational assessment tools

4.      Classroom observation and learner feedback methods

5.      Sampling techniques and participant selection

6.      Ethical considerations in educational research

Case Study:
Designing a research study to evaluate the effectiveness of a new teaching methodology.

Module 3: Educational Data Management and Learning Analytics

1.      Educational data collection and management systems

2.      Learning Management System (LMS) data analysis

3.      Student engagement and participation metrics

4.      Academic performance and achievement analytics

5.      Data quality assurance and validation techniques

6.      Educational dashboards and reporting systems

Case Study:
Analyzing learning management system data to identify factors affecting student engagement.

Module 4: Assessment, Evaluation, and Student Success Analytics

1.      Educational assessment principles and practices

2.      Student performance measurement and evaluation

3.      Program and curriculum evaluation methodologies

4.      Predictive analytics for student success and retention

5.      Learning outcome measurement and impact assessment

6.      Educational performance benchmarking

Case Study:
Using assessment and performance data to improve student success and graduation rates.

Module 5: Data Analysis, Visualization, and Reporting

1.      Statistical analysis techniques for educational research

2.      Qualitative analysis and thematic interpretation

3.      Data visualization and educational reporting

4.      Communicating findings to educators and policymakers

5.      Developing evidence-based recommendations

6.      Research dissemination and publication strategies

Case Study:
Creating an educational performance dashboard to support institutional decision-making and strategic planning.

Module 6: Emerging Technologies and Future Trends in Learning Analytics

1.      Artificial intelligence and machine learning in education

2.      Educational data mining and predictive modeling

3.      Adaptive learning systems and personalized education

4.      Digital learning ecosystems and smart classrooms

5.      Learning analytics ethics, privacy, and governance

6.      Future trends in educational research and learning analytics

Case Study:
Designing an integrated education research and learning analytics framework that combines student performance data, learning management systems, predictive analytics, educational dashboards, and evidence-based interventions to improve learning outcomes, institutional effectiveness, and educational policy development.

 

 

 

Essential Information

 

1. Our courses are customizable to suit the specific needs of participants.
2. Participants are required to have proficiency in the English language.
3. 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.
4. Upon fulfilling the training requirements, participants will receive a prestigious Global King Project Management certificate.
5. Training sessions are conducted at various Global King Project Management Centers, including locations in Nairobi, Mombasa, Kigali, Dubai, Lagos, and others.
6. Organizations sending more than two participants from the same entity are eligible for a generous 20% discount.
7. The duration of our courses is adaptable, and the curriculum can be adjusted to accommodate any number of days.
8. To ensure seamless preparation, payment is expected before the commencement of training, facilitated through the Global King Project Management account.
9. For inquiries, reach out to us via email at training@globalkingprojectmanagement.org or by phone at +254 114 830 889.
10. 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.