Training Course on Learning Analytics for Student Success

Training Course on Learning Analytics for Student Success

Introduction

In today’s data-driven educational environment, learning analytics has emerged as a transformative tool to drive student engagement, enhance academic performance, and inform data-informed decision-making. Training Course on Learning Analytics for Student Success is designed to equip educators, administrators, and educational technologists with the skills and strategies to harness student data for improving learning outcomes. With the surge in AI-powered dashboards, predictive analytics, and personalized interventions, understanding how to utilize data insights is crucial for every modern educational institution.

This course provides a strategic and practical approach to integrating learning analytics into curriculum design, instructional practices, and institutional policies. Participants will explore trending concepts such as predictive learning models, early alert systems, data visualization, student retention strategies, and adaptive learning technologies. Through hands-on modules and real-world case studies, learners will develop actionable plans to drive student success using cutting-edge analytical tools.

Course Objectives

1. Understand the fundamentals of learning analytics and its role in student achievement.
2. Analyze student data patterns to identify at-risk learners.
3. Implement predictive analytics models for academic interventions.
4. Utilize dashboard tools for real-time learning analytics reporting.
5. Design and evaluate personalized learning paths using data.
6. Apply machine learning in tracking student progress.
7. Build an early warning system for dropout prevention.
8. Integrate AI-based tools into instructional strategies.
9. Evaluate the impact of adaptive learning technologies.
10. Explore ethical considerations in student data handling.
11. Use data visualization to improve academic decision-making.
12. Leverage analytics for curriculum enhancement.
13. Create an institutional strategy for scaling learning analytics.

Target Audiences

1. University Administrators
2. Curriculum Designers
3. Higher Education Faculty
4. K-12 Educators
5. Instructional Designers
6. Data Analysts in Education
7. EdTech Developers
8. Policy Makers in Education

Course Duration: 10 days

Course Modules

Module 1: Introduction to Learning Analytics

• Definition and scope of learning analytics
• Historical development and current trends
• Types of educational data
• Benefits of data-informed instruction
• Limitations and challenges
• Case Study: Arizona State University's adaptive analytics system

Module 2: Data Collection Methods in Education

• Sources of student learning data
• Quantitative vs. qualitative data
• Learning Management System (LMS) tracking
• Surveys and feedback forms
• Data warehousing techniques
• Case Study: Canvas LMS insights for behavior prediction

Module 3: Data Cleaning and Preparation

• Importance of clean data
• Standardization techniques
• Handling missing or corrupted data
• Data integration processes
• Tools for cleaning educational datasets
• Case Study: How Georgia Tech optimized data pipelines for learning analysis

Module 4: Predictive Analytics for Student Performance

• Building predictive models
• Interpreting model outputs
• Factors influencing student outcomes
• Risk assessment frameworks
• Machine learning applications
• Case Study: Predictive analytics model in University of Michigan’s retention program

Module 5: Designing Dashboards for Learning Analytics

• Components of an effective dashboard
• Customizing dashboards for different roles
• Real-time vs. static reporting
• Visualization best practices
• Selecting the right dashboard tools
• Case Study: Power BI use in institutional analytics at Purdue University

Module 6: Early Alert and Intervention Systems

• What is an early warning system?
• Identifying risk indicators
• Automating alert mechanisms
• Action planning for interventions
• Measuring intervention effectiveness
• Case Study: Florida Virtual School’s alert system for drop-out prevention

Module 7: Adaptive Learning Technologies

• What is adaptive learning?
• Tools and platforms available
• Personalizing content delivery
• Tracking engagement with adaptive tools
• Analyzing adaptive learning outcomes
• Case Study: Smart Sparrow’s platform success at University of New South Wales

Module 8: Data Ethics and Privacy in Education

• Student data privacy laws (FERPA, GDPR)
• Ethical use of educational data
• Bias in algorithmic decision-making
• Transparency in data analysis
• Informed consent practices
• Case Study: Ethics audit in student analytics at Stanford University

Module 9: Visualizing Data for Decision-Making

• Best practices in data visualization
• Choosing appropriate chart types
• Storytelling with data
• Using heatmaps and correlation matrices
• Interactive visual tools
• Case Study: Tableau use in institutional planning at Duke University

Module 10: Building a Culture of Data-Driven Improvement

• Leadership for data adoption
• Training educators in analytics
• Aligning analytics with goals
• Continuous feedback loops
• Collaboration between departments
• Case Study: Culture shift at University of Central Florida

Module 11: Evaluating Impact of Learning Analytics

• Creating evaluation frameworks
• Measuring ROI on analytics investments
• Indicators of student success
• Conducting analytics audits
• Adapting strategies based on outcomes
• Case Study: Evaluation framework in the University of Maryland system

Module 12: Analytics for Curriculum Design

• Analyzing curriculum effectiveness
• Data-informed course redesign
• Sequencing learning objectives
• Identifying content bottlenecks
• Improving assessment strategies
• Case Study: Curriculum redesign at Open University UK using learning analytics

Module 13: Enhancing Student Engagement with Analytics

• Identifying disengagement signals
• Personalized outreach strategies
• Gamification and engagement metrics
• Communication channels and timing
• Leveraging peer learning analytics
• Case Study: Using analytics to boost engagement in MOOCs (edX)

Module 14: Scaling Learning Analytics Institution-Wide

• Planning for institutional adoption
• Technology infrastructure needs
• Role of institutional research
• Staff training and support systems
• Monitoring implementation progress
• Case Study: Cross-campus rollout at Arizona State University

Module 15: Future of Learning Analytics

• Trends in AI and education
• Predictive vs. prescriptive analytics
• Integrating IoT and edge computing
• Analytics in hybrid learning models
• Preparing for emerging technologies
• Case Study: Using AI to model learning behavior at Carnegie Mellon

Training Methodology

• Interactive lectures with practical demonstrations
• Hands-on lab activities using real tools (e.g., Tableau, Power BI, Python)
• Group projects and team-based analytics challenges
• Real-world case study analysis and discussion
• Reflective journals and formative assessments
• Capstone project with institutional application plan

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LD account, as indicated in the invoice so as to enable us prepare better for you.