e-learning at Macerata UniversityPresentation Transcript
Macerata, where is?
E-Learning at Macerata University
WHO we are, WHAT we do
Focus areas, research areas
ILE, Intelligent Learning Environment
Aims and objectives
Structure and technologies
E-Learning at Macerata University CELFI e-Learning centre provides resources and technologies supporting personalised, online/blended learning activities. 4 Faculties 17 Degree Programmes 1069 students 5 Postgraduate Masters 3 Postgraduate Courses 1420 students 2 Lifelong learning courses 1800 students Ph.D. programme in e-Learning and Knowledge Management 25 students Faculty of Law Faculty of Humanities and Philosophy Faculty of Educational Science Faculty of Political Science The department of Educational Science is responsible for research and development of advanced learning content.
Support centres CAIM - Centre for Informatics and Multimedia supports ICT-based learning through the implementation and maintenance of dedicated services and equipment providing a technological infrastructure to students, researchers, administrative and teaching staff. CELFI - e-Learning Centre supports and coordinates the progressive methodological and technical strengthening of teaching and learning processes based on ICT and multimedia. The Centre serves as a strategic resource for the integration of e-Learning technologies both in teacher didactics and in student activities. It manages e-Learning events and distance/frontal courses jointly with academic departments. CIEM Centre for Informatics, E-Learning and Multimedia 35 people 7 laboratories 410 workstations structured into two organisations
CELFI focus areas
Strategic, pedagogic and organisational modelling of university’s online learning;
Teacher and online tutor training;
Faculties’ online learning environment setup;
Pedagogical models, research and organisation of national and international conferences;
Coordination and development of international project.
CELFI activity AREAS
CELFI and Ph.D. r esearch areas
Models for e-Learning;
Online environment design and e-Learning tools;
Knowledge Management and Ontologies for e-Learning;
Semantic web and e-Learning; (mash up)
Multimedia and interactive video streaming;
Our didactical approach enables a flexible, non-neutral pedagogical model
Real world complexity and class heterogeneity (cultural, motivational, etc.);
Different learning styles of students;
Different motivation and awareness;
Different teacher educational philosophies;
The importance of personal reflection and awareness of own identity (individual activities next to personal spaces) (PLEs);
To relate Formal and Informal education;
To make technology and didactics interacting.
Intersubjectivity and collaborative knowledge construction (Wenger, 1998; Jonassen, 1999; Bereiter, 2002; Brown, 2005);
It allows, at design phase, to overcome restrictive approaches and to set out for a situated design that is connected with the experience ; this happens while keeping:
an internal coherence and a similar structure across the various parts that soon become familiar to students;
autonomy of single levels that, even though they present the same structure, consist of specific branches.
An approach where teacher-student-contest relations allow for the creation of an environment, and the environment represent a snapshot of the community and its developed, shared knowledge;
An environment that expresses relations developed between its components.
Significant e-tutor presence
Each course is supervised by a professor and managed by an online tutor possessing subject-matter and pedagogical competences; usual ratio e-tutor to students is 1/25;
E-tutors’ tasks can be defined as follows:
Help and guidance: they operate on all subjects and support students in cross-areas;
Subject-matter: they posses specific subject competences as well as generic relational ones.
E-tutors must attend a three-month training to gain relational competences focused on management of online student groups.
E-tutors’ tasks, 1/2
Student guidance and assistance;
Help students within specific subject-matter support and guidance;
Constantly monitor student activities and progress;
Support teachers with course activity design;
Organise and develop study resources.
E-tutors’ tasks, 2/2
E-tutors are asked a great commitment towards:
Student guidance and assistance in order to facilitate the creation of learning communities;
Constant monitoring of student activities through the analysis of platform and specific activity logs.
E-tutors enable the individualisation of learning paths and foster the creation of learning communities also through:
Interpretation of data from platform and activity logs;
Definition of different strategies that can be applied based on multiple indicators.
A learning entity is represented by a single student as well as a group or class;
In order to develop a community each module includes:
A preliminary welcoming activity and continuous support allowing students’ positioning and personalisation of his learning paths;
Collaborative activities that reveal the students’ personal experiences and allow to negotiate shared cognitive domains;
Design activity and project work (in team).
Personal reflection and awareness activities facilitating the creation of dynamic professional identities.
The three macro dimensions :
Collaborative and regulative dimension;
Personal and reflexive dimension.
Each dimension is characterized by:
Different learning aims and objective typology;
Own activities and tools;
Specific monitoring & assessment tools.
Online Learning Environment The conceptual artifacts (materials) are “ Boundary Objects ” connecting the three dimensions Self-Directed - Directed from Others Group - Individual 1 Instructional Activities 2 Collective and regulative activities 3 Reflective and personal activities Materials Materials Materials
1 st – Instructional dimension
Knowledge and skills acquisition;
Media redundant materials; learning object; instructional delivery;
Objective assessment (test).
2 nd – Collaborative and regulative dimension
Knowledge building; problem-solving; activation of regolative processes;
Activities such as knowledge sharing and negotiation, collaborative projects, case study, open-end problems; problem posing and decision making;
Tools for qualitative analysis, Latent Semantic Analysis and Social Network Analysis.
3 rd – Personal dimension and e-Portfolio
Competence acquisition; identity awareness; reflection on own professional and personal identity;
Personal selection and narration (blog); reflection tool; reflexive path; learning experience criss-crossings;
Online Learning Environment 3 Self-Directed - Directed from Others Community - Personal Instructional Delivery, Test 1 Community of Practice, Knowledge 2 Management (elicitation, collaboration, regulation) Personal Learning Space, ePortfolio (identity, trajectories, learning, awareness) Communities of Practice Individual in the Community Tools for reflection and individual writing are predominant Tools for research, organization and collaboration are predominant Tools for selection, sharing and negotiation are predominant Materials are predominant Individual Learning with support Self-directed peer group Individual and Informal education Class and Formal education Reflection and Individual activities are predominant The attention to content and discipline is predominant
Coherence and processes of cross design between pedagogical and technological approaches
CELFI uses different environments to suit different needs of online learning:
Graduate courses: a custom-designed version of U-Portal jointly developed with the University of Udine. This infrastructure has been successfully used and regularly updated for four years.
Post-grad courses: an in-house developed, ASP-based environment constantly undergoing modifications and improvements, used for the last four years also.
Since last year other LMS platforms (Moodle, Dokeos, etc.) have been tested and experimented for minor or external courses
Graduated, U-Portal based courses:
Degree programmes in Educational Sciences http://celfi.unimc.it/sdf/ (1)
Master degree in “Educational Design”, attended by 196 teachers from all across Italy (drop-outs: 2) during 2007-2008 http://zope.unimc.it/master/progetta (2)
Master degree in “Open Distance Learning”, running since 2005 and attended by approx. 20 students every year http://celfi.unimc.it/odl/ (3)
Specialisation, Moodle-based courses:
Specialisation course for Online Tutors, attended by 70 grad students during 2007-2008 http://celfi.unimc.it/moodle (4)
Specialisation course for the Ministry of Health attended by 1007 health-workers http://www.eduiss.it/fad/ (5)
Different technologies, same model
We keep the same pedagogical approach in each learning environment we deploy; for this reason every LMS includes:
Modular structure, easily editable by teachers and tutors;
Dispositifs for negotiation featuring indented forums;
Dispositifs for collaborative writing;
Dispositifs for learning path reflection;
Dispositifs for the construction of maps and patchworks;
e-Portfolio for personal reflection, awareness and learning path documentation.
Personal page, blog Self introduction Personal blog
Learning unit entry page Activity introduction Study materials Forum discussion Suggested activities
Map, aggregation tool Simply drag & drop study materials to create node elements into map and document personal learning paths showing the activities carried out.
Student activity monitoring Every activity / action is logged for group readings / writings. SNA graphs are dynamically generated.
Increase of enrolments: 100% within last three years;
Increase of teacher involvement (30% - 70% dependent on the faculty) with online learning and great impact of acquired knowledge on face-to-face teaching activity;
Overall quality improvement through certification by EU-funded UNIQUe accreditation;
Increase of incoming requests for the provision of Lifelong Learning by Italian Ministries (Health, Internal Affairs);
Participation in European Initiatives involved with e-Learning (e.g. Streaming Media Training ).
Future challenges, 1/2
University’s 3-year development plan includes:
Online students enrolment increase from 9% to 15%;
Increase e-Course offering and number of faculties involved in online learning;
Improve overall teaching quality of online learning;
Enhance integration of ICT with face-to-face learning;
Expand participation in international project aimed at technological and pedagogical research focused on e-Learning and Knowledge Management.
Future challenges, 2/2
In order to achieve the University’s goals we have identified three objectives:
Personalisation of LMS and individualisation of learning paths;
Improve competences of teachers and e-tutors;
Flexibility and autopoiesis of the learning environment.
Autopoiesis literally means "auto (self)-creation" (from the Greek: auto – αυτό for self- and poiesis – ποίησις for creation or production), and expresses a fundamental dialectic between structure and function. (Source: Wikipedia)
I.L.E. Intelligent Learning Environment
In 2007 a new learning envinronment project was initiated in order to improve quality of online learning while supporting professors and e-tutors in their day-to-day activities
From today’s ITS…
Intelligent Tutoring System (ITS) models and experiences:
Dick and Carey (1990);
Beck and Stern (1996);
ANDES, VanLehn (2005);
Baghera, Webber (2005);
ITS systems are subject matter oriented e and foresee an very specialized subject specific, rigid interaction;
Need for general purpose, subject matter independent implementation, aimed primarily as a professional support tool for professors and e-tutors. It should relief e-tutors from 1 st level learning entity support activities.
… to ILE
The Intelligent Learning Environment is based on a pedagogical didactical domain;
Semantic indicators supply a mapping of addressed content, issues discussed or elaborated;
Provide in real time a mapping of the elaborated activity, the acquired knowhow of the learning entity (student, group or community);
Supply a mapping of established relations in a given community;
Suggests appropriate didactical strategies to professors and/or e-tutors to individualise and personalise given learning paths;
Constant monitoring of learning entities (LE) (student, group or community).
Communicate with a given LMS platform via Agents according to predefined rules and actions;
Agents with “profile based subject matter“ expert knowledge;
Relief e-tutors from 1 st level “subject matter“ tutoring activity;
Support students / work groups (LE) with 1 st level, subject matter tutoring;
Interpret the “tutoring role” according to:
Knowledge / subject matter profile;
Pedagogical profile in conjunction with the LE profile ;
ILE – AI engine input:
LMS tracking data;
LSA, or any type of interpretable system, external input;
Manual intervention from Experts, Tutors etc.
ILE tutoring concept
Base parameters for monitoring:
Profile of learning entities;
Advanced activity logging / tracking;
Use semiotic-semantic indicators for interaction analysis;
Use of tagging and LSA;
Use indicators of the Social Network Analysis (SNA).
Base of didactical knowhow:
Wide range of models and learning dispositifs;
Models and dispositifs for collaborative knowledge construction;
Models and dispositifs for reflection and awareness of learning.
1 st level student support: reduction of e-tutor load 2 nd level student support: relieve of e-tutor load
Evaluation of LMS platform for ILE
Requested characteristic for the LMS:
Adequate tools for group activities;
State of the art usability and ease of use for authors;
Clear mapping of learning path structure based on XML;
Developed for scalability, portability, interoperability;
Open source/architecture for future or specific case oriented extensions.
OLAT Version 6 from the University of Zurich was selected as LMS platform for the ILE Project
ILE Architecture / Concept OLAT LMS - System Video Conference Subsystem OpenMeeting AI – ILE Subsystem LMS Authentication AI Interaction Agent AI Tutor Interface AI Knowledge Base LMS DB
LMS – ILE Environment LMS – ILE Environment Global Architecture of the ILE Platform
LMS – ILE Environment LMS – ILE Environment
OLAT Java based LMS performing functions like:
Student , Teacher, Authors administration
General access control
Global community functions like Forum, Wiki, Blog
Extended tailored functions like e-Portfolio etc.
AI coaching / tutoring Engine
Learning entity Agent (real-time student / group profiler)
Curriculum Subagent (generating and adapting learner curriculum)
Knowledge base Agent (subject matter related)
Coach / Tutoring Subagent
Subject matter profiler
Presentation Agent (Dynamic session initiator)
Coaching/Tutoring administrator Agent
AIML / UML / SOAP utilities
AI ILE Authoring tools
Global Knowledgebase management utilities
Standalone e-Course development environment
AI simulation / Test utilities
OLAT – ILE integration OLAT – ILE INTEGRATION ILE-AI Push/Pull Servlet ILE-AI LMS Communicator AI - Engine
LMS – ILE design issues LMS – ILE Design issues
Static Design strives for a simple, elegant, efficient solution to a single situation.
Outside of that situation the design is useless
Adaptation strives to just survive in a constantly changing situation.
Adaptation is continuously making design decisions
Requirements Design Implement Test Water Fall Design Process Impl Impl Adaptive Control Loop Implementation Loads Capacity Cost QoS Adaptive Control Policy Conditions Conditions
AI – ILE “the Agent” Plug-ins Blackboard (Method for inter Agent Communication) Agent Publish (Push) Subscribe (Pull) Message Queue
AI – ILE Agent Framework System specific Variable Boundary Cougaar Agent Reference Model Framework Infrastructure Application domain specific component servic e BB Behavior effecter coordinator sensor component service servic e component library Agent BB Behavior effecter coordinator sensor component component service servic e library Agent Abstracted Environment
AI - Architectural Mapping Concept Sensor-Based Control Loop Model-Based Control Loop Cognitive Control Loop Model Policy Situation inference rules days to minutes secs to msecs Network Disk management plane data plane Sensor/ Activity Proxy Agents Real-time Optimizer Agents processing status coordination resource status coordination resource trends coordination Cognitive Learner Agents processing. trends coordination Situation Predictor Agents processing pattern coordination resource pattern coordination Sensor/Activity Proxy Agents Processing Units CPU
AI - Architectural Mapping
Transitioning of control loops human to automation
Functional modules (oval shaped)
Underlying distributed environment
Sensor / Activity to control loop coordination
Evolving degree of human involvement
ILE – AI Environment Concept Agent Agent Coordination Artifact (CA) Agent Agent Shared state Defines roles Role-players
Coordination Artifacts (CA’s)
Are first-class entities in MAS
Define explicit roles for role-players (Profiles)
Offer shared state between the role-player & the CA
Coordinate behavior among role-players
Designed and implemented as distributed, scalable implementation
Summary ILE Platform
Considered as a “Intelligent Support Tool” for Tutors and Learning Entities;
Act’s according to predefined profiles;
Relief Tutors from 1 st level “ subject matter “ tutoring activity;
Support students / work groups (LE) with 1 st level subject matter tutoring;
1 st level tutoring is proposed in “friendly suggestion mode”, logged in the LE course activity log files;
Leaves Authors the “didactical Freedom”;
Allows a “tailored, adaptable learning path”;
Proposed ILE-AI actions are always confirmed and initiated by the “human tutor”.
Thank you! Prof. Pier Giuseppe Rossi, director [email_address] Ing. Attilio Pedrazzoli, PhD professor [email_address]