Education Data Science and Learning Analytics Training Course

Course Introduction

Education Data Science And Learning Analytics Training Course provides an advanced framework for applying data science, statistical analysis, machine learning, artificial intelligence, and educational analytics to improve teaching, learning, student support, institutional performance, and education planning. The course enables participants to transform complex educational data into actionable intelligence that supports evidence-based decisions across schools, universities, training institutions, education programmes, and learning platforms.

The programme explores the diverse data generated across modern education ecosystems, including student information systems, learning management systems, assessment platforms, attendance records, academic records, surveys, digital learning environments, library systems, administrative databases, and learner engagement platforms. Participants learn how to collect, integrate, clean, structure, and analyse these datasets while addressing challenges such as missing information, inconsistent records, fragmented systems, data duplication, and differences in measurement practices.

Advanced analytical techniques are applied to understand learner behaviour, academic achievement, engagement, progression, retention, completion, and educational outcomes. Participants explore descriptive analytics, statistical inference, predictive modelling, clustering, classification, time-series analysis, and longitudinal analysis to identify patterns and factors associated with learner success or difficulty. Emphasis is placed on interpreting analytical findings appropriately and distinguishing meaningful relationships from misleading correlations or data artefacts.

Learning analytics forms a central component of the programme, with participants examining how digital learning activity can provide insights into learner participation, engagement, performance, content interaction, assessment behaviour, and learning pathways. The course demonstrates how these insights can support early identification of learners requiring additional assistance, personalized learning approaches, curriculum improvement, academic advising, and targeted interventions while maintaining appropriate ethical and privacy safeguards.

The course also examines artificial intelligence and emerging technologies transforming educational analytics. Participants explore machine learning, natural language processing, generative AI, automated feedback, recommendation systems, adaptive learning, educational data mining, predictive early warning systems, and intelligent tutoring applications. Particular attention is given to model validation, explainability, fairness, algorithmic bias, responsible AI, privacy, and the importance of maintaining meaningful human oversight in educational decision-making.

The programme concludes with integrated education intelligence and decision-support approaches that connect learner analytics with institutional planning, teaching improvement, resource allocation, quality assurance, and education policy. Emerging areas such as multimodal learning analytics, learning experience platforms, digital credentials, educational digital twins, privacy-preserving analytics, learning engineering, and AI-enabled education ecosystems are incorporated to prepare participants for the rapidly evolving data-driven education environment.

Duration

10 days

Who Should Attend

• Education data analysts responsible for analysing learner, institutional, programme, and education-system datasets.

• Learning analytics professionals working with digital learning platforms and student engagement information.

• Teachers and lecturers seeking advanced data-driven approaches to understanding learner performance and engagement.

• Education administrators responsible for institutional performance monitoring, planning, and evidence-based management.

• School and university leaders seeking analytical tools for improving student outcomes and institutional effectiveness.

• Educational researchers conducting quantitative studies involving learner behaviour, achievement, engagement, and progression.

• Monitoring and evaluation professionals assessing education programmes, interventions, projects, and learning outcomes.

• Education policymakers and planners working with large-scale education datasets and performance indicators.

• Data scientists developing predictive, statistical, and machine learning solutions for educational applications.

• Learning management system administrators responsible for extracting and interpreting digital learning data.

• Institutional research professionals analysing enrolment, retention, completion, academic performance, and student success.

• Quality assurance professionals using evidence and analytics to monitor teaching, learning, and institutional performance.

• Curriculum specialists seeking data-driven approaches to evaluating learning content and educational interventions.

• Digital learning and e-learning professionals developing analytics-enabled learning environments and learner support systems.

• Education technology professionals interested in artificial intelligence, predictive analytics, adaptive learning, and intelligent education platforms.

Course Objectives

• Develop advanced capabilities in education data science, learning analytics, statistical analysis, machine learning, and evidence-based educational decision-making.

• Analyse complex educational datasets to identify patterns in learner performance, engagement, attendance, progression, retention, completion, and achievement.

• Design effective workflows for collecting, integrating, cleaning, transforming, and managing data from learning management and education information systems.

• Apply statistical methods to evaluate relationships among learner characteristics, instructional practices, learning environments, educational interventions, and academic outcomes.

• Develop predictive models for identifying learners at risk of disengagement, poor performance, dropout, delayed completion, or other adverse educational outcomes.

• Apply machine learning and educational data mining techniques to discover hidden patterns in learner behaviour and institutional performance.

• Develop learning analytics dashboards and visualization systems that communicate educational insights clearly to teachers, administrators, researchers, and decision-makers.

• Use longitudinal and time-series analysis to understand changes in learner performance, engagement, enrolment, progression, and institutional indicators over time.

• Integrate data from multiple educational systems to create comprehensive analytical views of learners, courses, programmes, institutions, and education systems.

• Evaluate ethical, privacy, fairness, security, and governance considerations associated with using student data and artificial intelligence in education.

• Explore emerging technologies including generative AI, adaptive learning, intelligent tutoring systems, multimodal learning analytics, and educational digital twins.

• Design integrated education data science and learning analytics solutions that translate complex educational data into actionable insights for learner success and institutional improvement.

Comprehensive Course Outline

Module 1: Foundations Of Education Data Science And Learning Analytics

• Principles, scope, applications, and value of data science and learning analytics across modern education environments.

• Descriptive, diagnostic, predictive, and prescriptive analytics for learners, educators, institutions, programmes, and education systems.

• Relationship between educational data, analytical evidence, decision-making, intervention design, and learning improvement.

• Learning analytics lifecycle covering data collection, preparation, analysis, interpretation, action, evaluation, and continuous improvement.

Module 2: Education Data Sources And Digital Learning Ecosystems

• Student information systems, learning management systems, assessment platforms, attendance systems, and academic records.

• Digital learning platforms, discussion forums, educational applications, library systems, student support platforms, and engagement technologies.

• Surveys, questionnaires, institutional research datasets, demographic information, socioeconomic indicators, and administrative education data.

• Emerging sources including learning activity streams, wearable devices, multimodal data, mobile learning, and educational technology platforms.

Module 3: Education Data Engineering And Management

• Data ingestion, extraction, transformation, validation, storage, and processing for educational analytics environments.

• Relational databases, data warehouses, data lakes, analytical repositories, APIs, and integrated education data platforms.

• ETL and ELT pipelines for automating the movement of learner, assessment, engagement, and institutional data.

• Data lineage, metadata, documentation, reproducibility, version control, and scalable architectures for education analytics.

Module 4: Educational Data Quality And Preparation

• Identification and treatment of missing values, duplicate records, inconsistent classifications, invalid observations, and incomplete learner profiles.

• Data cleaning, transformation, normalization, encoding, aggregation, feature engineering, and preparation of educational datasets for analysis.

• Data quality dimensions including accuracy, completeness, consistency, validity, timeliness, uniqueness, and reliability in education information systems.

• Automated validation rules, quality monitoring, exception management, and processes for maintaining trustworthy learning analytics datasets.

Module 5: Statistical Analysis For Education Data

• Descriptive statistics, distributions, measures of central tendency, variability, correlation, probability, and statistical inference.

• Comparative analysis of learner performance across classes, programmes, institutions, demographic groups, and learning environments.

• Regression, hypothesis testing, confidence intervals, effect sizes, and interpretation of relationships within educational datasets.

• Longitudinal and multilevel analysis for examining learner trajectories, classroom effects, institutional differences, and changes over time.

Module 6: Learning Analytics And Learner Behaviour

• Analysis of learner engagement, participation, interaction frequency, content access, assessment activity, and digital learning behaviour.

• Learning pathway analysis for understanding how learners navigate courses, resources, activities, assessments, and digital learning environments.

• Identification of engagement patterns associated with successful learning outcomes, disengagement, delayed progression, or withdrawal.

• Developing learner profiles and analytical indicators while avoiding inappropriate assumptions based solely on behavioural data.

Module 7: Academic Performance And Student Success Analytics

• Analysis of assessment results, grades, progression, completion, retention, graduation, and academic achievement patterns.

• Identifying factors associated with learner success, academic difficulty, course failure, delayed progression, and withdrawal.

• Early warning analytics for identifying learners who may benefit from additional academic, social, or instructional support.

• Designing evidence-based intervention frameworks that connect analytical findings with appropriate learner support strategies.

Module 8: Machine Learning And Educational Data Mining

• Supervised learning techniques for predicting academic performance, retention, completion, engagement, and learner support requirements.

• Unsupervised learning for learner segmentation, clustering, behavioural profiling, and discovery of hidden patterns in education datasets.

• Educational data mining approaches for extracting meaningful patterns from assessments, interactions, learning activities, and institutional records.

• Model evaluation, cross-validation, feature selection, interpretability, calibration, and prevention of overfitting in educational applications.

Module 9: Predictive Analytics And Education Forecasting

• Predictive modelling approaches for enrolment, student demand, academic outcomes, retention, completion, and institutional performance.

• Time-series analysis for forecasting enrolment trends, course demand, attendance, learning activity, and resource requirements.

• Scenario analysis for examining potential changes in learner populations, instructional approaches, interventions, and institutional conditions.

• Communicating forecasts with appropriate uncertainty, assumptions, limitations, confidence ranges, and alternative scenarios.

Module 10: Artificial Intelligence And Intelligent Learning Systems

• Artificial intelligence applications in personalized learning, automated feedback, educational recommendations, assessment, and learner support.

• Natural language processing for analysing written assignments, discussion contributions, feedback, educational content, and qualitative learner information.

• Generative AI and large language models for learning assistance, content generation, tutoring, summarization, and educational knowledge discovery.

• Intelligent tutoring systems, adaptive learning technologies, recommendation engines, and AI-supported instructional decision-making.

Module 11: Education Dashboards And Data Visualization

• Principles of educational data visualization, analytical storytelling, dashboard design, and communication of learning intelligence.

• Interactive dashboards for learner performance, attendance, engagement, retention, progression, completion, and programme monitoring.

• Key performance indicators, alerts, drill-down capabilities, filters, benchmarking, and comparative institutional analytics.

• Designing role-specific dashboards that present relevant evidence to educators, administrators, researchers, students, and institutional leaders.

Module 12: Geospatial And Population Education Analytics

• Geographic analysis of enrolment, education access, learner distribution, institutional coverage, and regional education disparities.

• GIS techniques for mapping schools, universities, learners, educational resources, service areas, and accessibility patterns.

• Integration of demographic, socioeconomic, infrastructure, transport, environmental, and educational information for spatial analysis.

• GeoAI and spatial modelling applications for education planning, facility placement, resource allocation, and access improvement.

Module 13: Curriculum, Teaching And Learning Analytics

• Using learner and assessment data to evaluate curriculum effectiveness, instructional approaches, course design, and learning content.

• Analysis of learning outcomes, competency attainment, assessment difficulty, question performance, and curriculum progression.

• Identifying content areas associated with learner difficulties and opportunities for targeted instructional improvement.

• Evidence-based course redesign, personalized learning pathways, instructional experimentation, and continuous curriculum improvement.

Module 14: Education Data Governance, Privacy And Responsible Analytics

• Education data governance frameworks covering stewardship, ownership, access, retention, quality, accountability, and responsible data use.

• Student privacy, confidentiality, consent, anonymization, pseudonymization, secure data sharing, and appropriate access controls.

• Algorithmic fairness, bias detection, explainability, transparency, and responsible use of predictive models in educational decision-making.

• Ethical considerations surrounding automated learner profiling, surveillance, AI-assisted assessment, behavioural monitoring, and high-impact educational decisions.

Module 15: Emerging Technologies In Education Analytics

• Multimodal learning analytics combining text, audio, video, interaction, assessment, behavioural, and other educational data sources.

• Learning experience platforms, digital credentials, adaptive learning ecosystems, educational digital twins, and intelligent learning environments.

• Privacy-preserving analytics, federated learning, synthetic education data, edge computing, and secure distributed learning analytics.

• Foundation models, AI agents, generative AI, and emerging computational approaches transforming educational data analysis and learning support.

Module 16: Integrated Education Data Science And Learning Analytics Capstone

• Designing an end-to-end education analytics workflow from data acquisition and engineering through modelling, visualization, and decision support.

• Integrating learner, assessment, engagement, demographic, institutional, and programme information into comprehensive analytical solutions.

• Developing a practical learning analytics dashboard, predictive model, early warning system, or institutional decision-support application.

• Evaluating analytical findings, documenting assumptions and limitations, communicating insights, and developing sustainable education data-use strategies.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.