aDeNu Research Group A component to carry out the Logical Framework Approach in dotLRN Alberto Bayón Olga C. Santos Jesús G. Boticario
aDeNu Research Group 2 Objectives A component to carry out the LFA in dotLRN Outline of the Presentation LFA: Logical Framework Approach Designing and Architecture Conclusion
aDeNu Research Group 3 Collaborative Learning Environment Web-Based Context Real Collaboration Among Students Intelligence Learning Management System (iLMS) Objective SERVICES Forums Surveys Storage Area ... ADAPTATION Interaction Analysis Suggestions Facility Improve Effectiveness Adapting to Student’ Needs Logical Framework Approach Collaborative Extension of LFA
aDeNu Research Group 4 LFA: Logical Framework Approach Objective Oriented Planning Methodology International Development Agencies and NGOs Development Cooperation and International Aid Projects WHAT? WHO? WHY? HOW? 1.Stakeholder Analysis 2.Problem Analysis 3.Objectives Analysis 4.Alternative Analysis 5.Project Planning Matrix
aDeNu Research Group 5 Collaborative Logical Framework OpenACS/dotLRN package CLF Architecture INTERACTION MODULE (I.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.) Adapted Recommendations for All Users Encourage to Collaborate Help the Students in their Tasks I.M. M.L.M. u.m. A.M.
aDeNu Research Group 6 Interaction Module INDIVIDUALLY Work Alone Answer the Questions Reveal the Answer New Thread in Forum 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 COLLABORATIVELY Access to Other Solutions Rate Colleagues’ Answers Post Comments in Forums Create Versions IN AGREEMENT Moderator Selection Create Consensus Moderator Works Individually Colleagues Work Collaboratively
aDeNu Research Group 7 Course, Phases, Tasks, Answers User Management Answer Life Cycle Versions Threads and Forums Rating Facility Questions Scheduled tasks Interaction Module FUNCIONALITY CLF package Parties, Groups, Permissions Workflow Content Repository (Revisions) Forums Ratings Survey Cronjob
aDeNu Research Group 8 Machine Learning Module - User Model 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 Active Indicators: Participative, Insightful, Useful,Non-collaborative, With-Initiative, Communicative Passive Indicators: Thinker-out, Unsecure, Gossip, Inspirable, Inspirator, Thorough Active Indicators Passive Indicators Passive Data TA M Active Data OpenAcs.LRN
aDeNu Research Group 9 Adaptive Module – Recommender System 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 Active Indicators Passive Indicators CLASSIFICATION
aDeNu Research Group 10 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 …
aDeNu Research Group 11 Screenshots
aDeNu Research Group 12 Screenshots
aDeNu Research Group 13 Screenshots
aDeNu Research Group 14 Questions
aDeNu Research Group 15 A component to carry out the LFA in dotLRN
aDeNu Research Group 16 States of Answers & Workflow
aDeNu Research Group 17 CLF Class Diagram