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E-learning for institutional and continuous education in technical-scientific domains

MIC-2008-Roma. UNIVERSITA’ POLITECNICA DELLE MARCHE. dipartimento di ingegneria informatica, gestionale e dell’automazione. E-learning for institutional and continuous education in technical-scientific domains. Tommaso Leo tommaso.leo@univpm.it. LIST OF TOPICS.

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E-learning for institutional and continuous education in technical-scientific domains

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  1. MIC-2008-Roma UNIVERSITA’ POLITECNICA DELLE MARCHE dipartimento di ingegneria informatica, gestionale e dell’automazione E-learning for institutional and continuous education in technical-scientific domains Tommaso Leo tommaso.leo@univpm.it

  2. LIST OF TOPICS TECHNICAL SCIENTIFIC DOMAIN 1 COMPLEX SYSTEMS 2 3 MATERIAL KNOWLEDGE CHARACTERS OF THE MATERIAL KNOWLEDGE DOMAIN 4 RELEVANT PEDAGOGICAL MODELS 5 DIDACTIC DIMENSIONS OF TEACHING/LEARNING MK 6 RELEVANT DIDACTIC VARIABLES 7 DIDACTIC FUNCTIONALITIES REQUIRED TO A LCMS 8

  3. 1. TECHNICAL SCIENTIFIC DOMAIN Engineering, in particular Electronic and Electrical Engineering, Control Engineering, Industrial Engineering. The key professional activity is aimed at designing and operating man- made systems that can be considered Complex Systems.

  4. 2. COMPLEX SYSTEMS Attributes Descriptive attributes: - High number of components/subsystems; - Integration of different technologies - Integration of different methods - Interaction with human beings/living organisms - Operation dependent on the environment and initial conditions - Complex and articulated design procedures

  5. 2. COMPLEX SYSTEMS Attributes Formal/conceptual attributes - High level of uncertainty/unpredictability - Behaviour and experiments are substantially not reproducible

  6. 3. MATERIAL KNOWLEDGE Roughly speaking, it is the Knowledge relative to the real operation conditions of engineering complex systems.

  7. 3. MATERIAL KNOWLEDGE Attributes - In general the owners are experts - In general it is implicit, even intuitive - In general it is empirical - To communicate MK is an ill defined problem-space - To be communicated , MK implies, at least Case-based reasoning Problem-driven reasoning

  8. 3. MATERIAL KNOWLEDGE Suggestions “experiential paths” by means of social interaction between experts, novices and learning resources

  9. 3. MATERIAL KNOWLEDGE Criteria to select the expert - long history of practice with the relevant problems, at least longer than the novice; - capability of autonomous operation in solving the relevant problems to set up the problem, to select the key elements for the solution, to find the proper coworkers - recognized high level of competence.

  10. 4. CARACHTERS OF THE MATERIAL KNOWLEDGE DOMAIN A practical reference is the environment of a laboratory dealing with Automatic Control Systems, or a Radar settlement for Air Traffic Control.

  11. www.del.univpm.it/it UNIVERSITA’ POLITECNICA DELLE MARCHE PROGETTO TIGER Carla Falsetti David Fabri Sulmana Ramazzotti Tommaso Leo Cira 2005 Tommaso Leo

  12. 7. TELELABORATORIO IMMERSIVO Schema a blocchi Tommaso Leo

  13. 4. CARACHTERS OF THE MATERIAL KNOWLEDGE DOMAIN didactic resources we think about: - logical/formal resources, - resources allowing procedural skills learning, - resources preparing to synthesize heterogeneous competences, even acquired in different lateral contexts and Knowledge domains, - resources aimed to train for team working and/or team leading of heterogeneous components

  14. 5. RELEVANT PEDAGOGICAL MODELS The adoption of number of different pedagogical models/ didactic approaches, properly combined in a suitable mix, is advisable.

  15. 5. RELEVANT PEDAGOGICAL MODELS Pedagogical models/didactic approaches - didactic approaches able to facilitate memory retention, - pedagogical approaches deriving from constructionism, where reflection is dominant, - constructivism, where experiential dimension is prevalent, - pedagogical approaches where cooperative and collaborative work and social tagging is prevalent.

  16. 6. DIDACTIC DIMENSIONS OF TEACHING/LEARNING MK The “ Didactic situation” has to be related to the knowledge domain to be learnt. In our case we can speak about problem solving for unstructured and ill posed problems, even if the problems are based on solid physics and mathematics ground.

  17. 6. DIDACTIC DIMENSIONS OF TEACHING/LEARNING MK Dimensions of the teaching/learning process : - Topics and contents to be learnt ( “ what” dimension) - Didactic and pedagogical approaches (“ how” dimension) - Contents order and availability at the proper time (“ when” dimension) - Connections and relationships among and within the three dimensions above (“ the meanings network”), to attain a comprehensive understanding of the matter.

  18. 6. DIDACTIC DIMENSIONS OF TEACHING/LEARNING MK • Characters of dimensions can be organized w.r.t.: • Learning goals and learning evaluation ( “application” and “ evaluation” level of the Bloom taxonomy). • Learning goals vs. target users: application level for undergraduate students, evaluation level for second and tertiary level students and continuous education; • -The need of embedding learning within the physical and perceivable world (Immersivity, ability to handle physical artefacts and phenomena).

  19. 7. RELEVANT DIDACTIC VARIABLES They are here , meant as “those allowing a modification in the learners’ behaviour”

  20. 7. RELEVANT DIDACTIC VARIABLES • In the “what” and “how” dimensions • Didactic variables relevant for • learning management/organisation-1/2 • a Syllabus i.e. a clear definition of contents and pedagogical modalities, specification of the input competence level, to allow the personalisation of the learning process. • clear definition and specification of the assessment modalities. • implementation of “adaptivity” of contents at the system side;

  21. 7. RELEVANT DIDACTIC VARIABLES • In the “what” and “how” dimensions • Didactic variables relevant for • learning management/organisation -2/2 • implementation of “adaptivity” at the learner model side; • automatic updating of the user profile; • proposing to the user a specific “learning experience”, coherent with his/her current profile and with the current availability of learning resources (both LO and learning artefacts).

  22. 7. RELEVANT DIDACTIC VARIABLES • In the “what” and “how” dimensions • Didactic variables relevant for learning support • availability of learning resources aimed at experiential and collaborative learning. • Depending on the knowledge domain and user target, the proper communication codes have to be defined.

  23. 7. RELEVANT DIDACTIC VARIABLES • In the “when” and “meanings network” dimensions • Didactic variables relevant for the learning management/organisation • adaptivity of contents. Adaptivity is here meant as more or less detailed and more or less in depth presentation, according to the current needs of the user. • Adaptivity of the learning path to the learner needs and attitudes • E portfolio

  24. 7. RELEVANT DIDACTIC VARIABLES In the “when” and “meanings network” dimensions Didactic variables relevant for learning support - number of assessment tools, mainly aimed to formative evaluation. In our case, tests, supervised problem-solving like, would be desirable.

  25. 8. DIDACTIC FUNCTIONALITIES REQUIRED TO A LCMS Moodle seems particularly oriented to a structured path of learning. It would be useful to integrate into Moodle, or to add to Moodle some modules implementing the following functionalities

  26. 8. DIDACTIC FUNCTIONALITIES REQUIRED TO A LCMS • Experiential and collaborative learning through Immersivity: Web services, allowing immersive telelaboratory experiences, - learning and knowledge development monitoring and evaluation: • to strengthen the tools already existing with the elaboration and integration of available data ; • adding automatic, or semi-automatic evaluation of the communicative interactions ; • Would it be advisable to add social network facilities to allow the environment to manage both formal and informal learning?

  27. Credits and Aknowledgements • Graphic editing: Sulmana Ramazzotti Ph D • Many tanks are due for discussion and advice to • Prof. Kinshuk • Prof. Nian Shing Chen • My students of the PhD course in e.-learning

  28. Thank you

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