Transcript Diapositiva 1

A component to carry out the Logical
Framework Approach in dotLRN
Alberto Bayón
Olga C. Santos
Jesús G. Boticario
aDeNu Research Group http://adenu.ia.uned.es
A component to carry out the LFA in dotLRN
Outline of the Presentation
Objectives
LFA: Logical Framework Approach
Designing and Architecture
Conclusion
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Objective
 Collaborative Learning Environment
 Web-Based Context
 Real Collaboration Among Students
 Intelligence Learning Management System (iLMS)
SERVICES
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
Forums
Surveys
Storage Area
Email

...
ADAPTATION
 Interaction Analysis
 Suggestions Facility
 Improve Effectiveness
 Adapting to Student’ Needs
 Logical Framework Approach
 Collaborative Extension of LFA
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LFA: Logical Framework Approach
WHAT?
WHO?
WHY?
HOW?
Objective Oriented Planning Methodology
International Development Agencies and NGOs
Development Cooperation and International Aid Projects
1. Stakeholder Analysis
2. Problem Analysis
3. Objectives Analysis
4. Alternative Analysis
5. Project Planning Matrix
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CLF Architecture
Collaborative Logical Framework
OpenACS/dotLRN package
 INTERACTION MODULE (I.M.)
I.M.
M.L.M
.
u.m.
Environment to Execute the Course
Sequence of LFA Phases
Collaborative Extension of LFA
 MACHINE LEARNING MODULE (M.L.M.)
User Model Definition (u.m.)
Students Activity Monitoring
Learners Performance Classification
Machine Learning Algorithms
 ADAPTIVE MODULE (A.M.)
A.M.
Adapted Recommendations for All Users
Encourage to Collaborate
Help the Students in their Tasks
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Interaction Module
 IM Implements the Collaborative Extension of LFA
 Defined by aDeNu to Achieve Real Collaboration
 Students Arranged in Small Groups
 Working in Three Ways in All LFA Phases
INDIVIDUALLY
COLLABORATIVELY
IN AGREEMENT
 Work Alone
 Access to Other Solutions
 Moderator Selection
 Answer the Questions
 Rate Colleagues’ Answers
 Create Consensus
 Reveal the Answer
 Post Comments in Forums  Moderator Works
Individually
 Create Versions
 Colleagues Work
Collaboratively
 New Thread in Forum
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Interaction Module
FUNCIONALITY
Course, Phases, Tasks, Answers
CLF package
User Management
Parties, Groups, Permissions
Answer Life Cycle
Workflow
Versions
Content Repository (Revisions)
Threads and Forums
Forums
Rating Facility
Ratings
Questions
Survey
Scheduled tasks
Cronjob
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Machine Learning Module - User Model
.LRN
OpenAcs
TA
M
Active Data
Passive Data
Active Indicators
Passive Indicators
 Data Mining Techniques for Forums, Versions & Ratings
 Active Data: Taken from .LRN/OpenACS Database
 Passive Data: Use of TAM, Web Track Auditing Management Tool
 Indicators Define the Learners’ Profiles
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Active Indicators: Participative, Insightful, Useful,Non-collaborative,
With-Initiative, Communicative
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Passive Indicators: Thinker-out, Unsecure, Gossip, Inspirable, Inspirator, Thorough
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Adaptive Module – Recommender System
Active Indicators
CLASSIFICATION
Passive Indicators
 Periodic Calculation of Learners’ Profiles Using Machine Learning
 Weka Data Mining Provides Classification Algorithms
 A.M. Generates Suggestions (Recommender System Package)
 Recommendations Help the Students to Enhance their Performance
 Suggest rating or reading other answers
 Suggest the communication with a colleague
 Suggest using a specific tool
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A component to carry out the LFA in dotLRN
CONCLUSION
 Achievements
 Implementing All Kind of Collaborative Activities
 Learners Monitoring
 GPL Licence
 Contribution to OpenACS/dotLRN Community
 Future Work
 Integrating TAM, Weka and Recommender System Packages
 Validation Planning Using CLF in a Pilot Course at UNED
 Taking Part in other iLMS aDeNu Projects: Eu4all, Adaptaplan, aTODOS …
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Screenshots
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Screenshots
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Screenshots
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Questions
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A component to carry out the LFA in dotLRN
[email protected]
[email protected]
[email protected]
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States of Answers & Workflow
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CLF Class Diagram
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