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E-learning Management, By: Julissa Arauz - Coggle Diagram
E-learning Management
Instructional Design and Tutor Role
Pedagogical Models
Constructivism
Active learning through projects and case studies
Tutor’s role as facilitator and guide
Reflection on prior experiences
Connectivism
Promotion of virtual communities of practice
Use of collaborative tools (forums, wikis)
Development of skills to manage knowledge networks
Situated Learning
Contextualization in real or simulated scenarios
Performance-based evaluation
Incorporation of authentic elements from professional or social environments
Profile and Needs Analysis
o Demographic dimensions: age, education, experience
o Psychographic dimensions: motivations, learning styles
o Assessment of digital competencies and student autonomy
o Identification of technological and emotional barriers
o Planning of personalized supports and resources
Design of Collaborative Activities
o Clear role assignment (coordinator, researcher, writer)
o Group deliverables with defined evaluation criteria
o Integration of collaborative and communication tools
o Socialization dynamics, debates, and co-construction of knowledge
o Incorporation of gamification elements for motivation
Tutor Functions and Competencies
o Instructional design planning and advising
o Moderation and facilitation of discussions and activities
o Monitoring participation and performance data
o Personalized interventions for at-risk students
o Promotion of positive climate and conflict resolution
Tools
o Synchronous: videoconferences with shared whiteboard, breakout rooms
o Synchronous: live chats for quick doubt clearing
o Asynchronous: thematic and collaborative discussion forums
o Asynchronous: blogs and announcement boards for information dissemination
o Recorded resources: micro-lessons, tutorials, and screencasts
Creation and Selection of Digital Content
Development of Multimedia Resources
Videos
Script and scene planning
Audiovisual quality (audio, lighting, editing)
Inclusion of support materials (slides, subtitles)
Simulations
Design of variables and real system rules
Intuitive interface and immediate feedback
Integration of gamification elements (levels, challenges)
Data recording for performance analysis
Podcast
Clear and conversational script writing
Quality voice recording and sound environment
Post-production with music, effects, and cuts to maintain rhythm
Integration with LMS activities
Didactic Design Principles
o Precise definition of learning objectives and competencies
o Constructive alignment between objectives, activities, and assessments
o Progressive segmentation to avoid cognitive overload
o Use of scaffolding to support gradual learning
o Promotion of interactivity through questions and discussion spaces
Accessibility and Usability
o Compliance with WCAG standards (levels A, AA, AAA)
o Design for diverse sensory, cognitive, and motor abilities
o Intuitive navigation with clear menus and immediate feedback
o Usability testing with real users to detect friction points
o Visual consistency through institutional style guides
Reusability and Technical Standards
o Design of autonomous and self-contained learning objects
o Use of repositories with standardized metadata for easy retrieval
o Packaging and tagging according to SCORM standard
o Interaction and progress tracking via compatible LMS
o Use of xAPI model to record multi-source learning experiences
Editorial Quality and Communication
o Application of rigorous academic style and writing norms
o Use of precise vocabulary and appropriate tone for target audience
o Clear structure with descriptive headings and scannable text
o Close language that promotes a sense of accompaniment
Platforms and Support Technologies
LMS Selection
Functional alignment with training and organizational goals
Enrollment and registration management
Creation and sequencing of training modules
Automatic certificate issuance
Usability for administrators, tutors, and students
Intuitive interface and clear menus
Simple enrollment and assignment submission processes
Accessibility and minimized technological learning curve
Scalability and deployment model
Growth without performance degradation with concurrent users
Cloud (SaaS) and on-premise hosting options
Compatibility with standards and APIs for integration
LMS Architecture and Components
Presentation layer
Responsive web interface adaptable to devices
Control panel and course catalogs
Work areas and report generation
Business logic layer
User authentication and access control
Course flow orchestration and evaluation rules
Analytics engines and alert generation
Data layer
Relational and NoSQL databases
Storage of user info, grades, and interaction logs
Learning Record Store for xAPI and multi-source event tracking
Other components
Multimedia repository
Synchronous and asynchronous communication modules
Integrated or connected authoring tools
Integration of External Tools
Plugins
Addition of modules (advanced quizzes, gamification)
Compatibility and active maintenance
Evaluation of support and community backing
APIs
Data exchange with ERP, CRM, and internal systems
Secure endpoint design and OAuth 2.0 authentication
Logging and transaction traceability
LTI (Learning Tools Interoperability)
Integration of external apps with single sign-on
Sharing of context (user, role, course)
Recording of grades and interactions in LMS or LRS
Information Security and Data Privacy
Infrastructure protection
Firewalls, IDS/IPS, and network segmentation
Constant updates and patching
Periodic penetration testing
Personal data protection
Compliance with regulations (GDPR, local laws)
Informed consent and data minimization
Encryption in transit and at rest (TLS, AES)
Access control and auditing
Role-based access control (RBAC)
Audit logs for event reconstruction
User education on security best practices
Technological Trends
Artificial Intelligence (AI)
Personalization via profiles and recommendation engines
Chatbots and virtual assistants for 24/7 support
Automated evaluation freeing tutors for complex tasks
Learning Analytics
Dashboards monitoring key indicators
Early identification of dropout risk
Evaluation of resource and strategy effectiveness
Adaptive Environments
Dynamic adjustment of content and difficulty
Personalized feedback and reinforcement materials
Formation of intelligent learning communities and group planning
Evaluation and Quality Assurance
Learning Indicators and Evaluation Instruments
Quantitative and qualitative indicators
Module completion rates
Scores in summative assessments
Participation levels in collaborative activities
Evaluation instruments aligned with objectives
Multiple-choice and open-ended questionnaires
Rubrics for projects and multimedia presentations
System logs and interaction metrics
Formative assessments and continuous feedback
Mini-quizzes for immediate self-assessment
Automated reports alerting tutors to error patterns
Timely interventions: tutoring and reinforcement materials
Self-assessment, Peer Assessment, and Digital Portfolios
Self-assessment for reflection and self-regulation
Reflection guides and metacognitive questionnaires
Identification of strengths and improvement areas
Planning personal learning goals
Peer assessment for social learning and pedagogical critique
Shared rubrics for peer evaluation
Constructive feedback and recognition of improvements
Tutor supervision to ensure reliability
Digital portfolios as formative evidence
Centralization of documents, projects, and reflections
Longitudinal tracking of student progress
Use for accreditation and certification
Quality Models and Accreditation in E-learning
International models and structured frameworks
Quality Matters: instructional design and accessibility review
e-xcellence: six quality dimensions and self-assessment
ISO standards and national accreditation frameworks
Audit and reaccreditation processes
Evaluation of policies, competencies, and technology
Continuous content updates and staff training
Strengthening institutional and student trust
Data Analysis and Continuous Improvement
Collection and monitoring of key metrics
Completion rates, participation, and assessment results
Identification of critical points and dropout causes
Use of dashboards for decision-making
Application of PDCA cycle for optimization
Planning based on previous data
Implementation of adjustments and additional training
Impact evaluation and standardization of best practices
Advanced learning analytics techniques
Data mining and learner profile segmentation
Early alerts for personalized tutoring
Automated recommendations and efficient resource allocation
Results Reporting and Decision Making
Integration of quantitative and qualitative data
Performance metrics and student satisfaction
Qualitative analysis from surveys and tutor feedback
Visual presentation via graphs and interactive dashboards
Illustrative narratives to contextualize data
Success stories and identified challenges
Interpretation of causes and learning effects
Concrete recommendations for continuous improvement
Strategic use of reports for management and planning
Rescheduling tutoring sessions and content redesign
Budget optimization based on learning return
Iterative cycle of adjustment and validation
By:
Julissa Arauz