1. Representation of expert knowledge for e-learning
By Baba MBAYE
The Singapore Education Technology Conference 2018
Aug 30, 2018

2. Plan Context Problematic Information and knowledge
Trace analysis in e-learning system
Knowledge model approach
Ontologies knowledge representation in e-learning
Layers model of knowledge
Tutor knowledge model
Learner knowledge model
Expert’s knowledge Model
Results
Conclusion
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3. Context Growth in the use of e-learning tools
Led to an exponential increase in the amount of learning resources available online
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4. Problematic
With this increase volume, learners have difficulty in choosing relevant and useful learning resources.
Heterogeneous resources
inconsistency between the resources and the desired skills.
Wasting time
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5. Information and Knowledge
Information is the set of all data that is external to people, communicated orally or published in documents. Where as knowledge is the result of any mental construction internalized by an individual from information he obtains.
In the 1950s, the evolution of cognitive science and computer science led to the creation of knowledge representation systems such as conceptual maps, semantic networks, semantic schema, entity-relationship models, flow models information, object-oriented models, etc.
These systems make a formal representation of the language, often in a graphical form allowing a representation of knowledge hidden in the quantity of information (knowledge level), for example, two texts, one in French and the other in English, translation from one another, will be represented in the same way by a knowledge model grouping the concepts that these texts deal with and the relationships between these concepts.
Clarence Maybee, Jake Carlson, Maribeth Slebodnik, Bert Chapman, “It's in the Syllabus”: Identifying Information Literacy and Data Information Literacy Opportunities Using a Grounded Theory Approach, The Journal of Academic Librarianship, Volume 41, Issue 4, 2015, Pages , ISSN ,
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6. Information and Knowledge
An order describing a work process and a written text describing the same sequence of operations will be represented in a similar way in a knowledge representation system. In this case we speak then of the semantic representation of the document (representation of meaning). The semantic representation is an image of the mental model of knowledge derived from the peculiarities of the format chosen to represent information.
Xiao Li, Peng Wu, Geoffrey Qiping Shen, Xiangyu Wang, Yue Teng, Mapping the knowledge domains of Building Information Modeling (BIM): A bibliometric approach, Automation in Construction, Volume 84, 2017, Pages , ISSN ,
João Sarraipa, Silvia Baldiris, Ramón Fabregat, Ricardo Jardim-Goncalves, Knowledge Representation in Support of Adaptable eLearning Services for All, Procedia Computer Science, Volume 14, 2012,Pages , ISSN ,
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7. Trace analysis in e-learning system
Faced with the large amount of information sources that online training platforms offer to learners, the question arises as to the quality, coherence and relevance of the content of these courses. In fact, for face-to-face learning, teachers adapt most of the time to the levels of comprehension of each student. What is not the case for distance learning, the tutor lose some perception of the activity of the learner. The pedagogical follow-up is considered as an important element to specify the course of the activity.
The interactions between the teacher and his learner are manifold in classroom training, through teaching aids, gestures and words. The teacher can, according to his observations, change the teaching method to adapt it to the different profiles of his learners.
Jermann, P.,Soller, A. et Muehlenbrock, M. (2001). From mirroring to guiding: A review of state of the art technology for supporting collaborative learning. InProceedings of the First European Conference on Computer-Supported Collaborative Learning(p ).
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8. Trace analysis in e-learning system
In the case of distance learning, these observations are deduced from the traces collected. A digital trace is a set of observations about the learner's interaction with a system. The trace is defined as a temporal sequence of observables. Its use differs according to the objectives pursued by each of the agents of the device. Indeed, the learner uses these traces for the purpose of direct reflexive use. These traces allow him to have information on his evolution inthe program to realize. The teacher, for his part, makes an analysis of the traces for an indirect reflexive use: to control the process of acquisition of knowledge, to adapt his interventions to the objectives of observation, to test the efficiency of the pedagogical guidance.
Jermann, P.,Soller, A. et Muehlenbrock, M. (2001). From mirroring to guiding: A review of state of the art technology for supporting collaborative learning. InProceedings of the First European Conference on Computer-Supported Collaborative Learning(p ).
Baudouin, C.; Beney, M. et Chevaillier, P. (2007). Recueil de traces pour le suivi de l'activité d'apprenants en travaux pratiques dans un environnement de réalité virtuelle. STICEF(Numéro spécial Analyse des traces d'interactions dans les EIAH),14.
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9. Trace analysis in e-learning system
The exploitation of these traces offers useful information reflecting the evolution of the interactions between the agents of the learning platform. These traces are often heterogeneous, difficult to exploit and reusable, in online learning systems. Research work has proposed approaches to collect, transform and analyze the trace according to a treatment model. This model allows its comprehension by describing the element of the trace. Several models have been proposed.
Julien Laflaquière, Lotfi Sofiane Settouti, Yannick Prié, Alain Mille. Traces et inscriptions de connaissances. 18e Journées Francophones d’Ingénierie des Connaissances, Jul 2007, Grenoble, France.not specified, 2007.
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10. Knowledge approach model
From a technological point of view, disability is not an identifying characteristic of the learner but rather a failure of the learning environment to meet the needs of the learner. Disability is a mismatch between the demands of the learner and the education offered. so the solution is to model a learning environment that will appropriately transform the content of the platform to create an optimal learning environment for the individual learner.
Virtual education should be a service accessible to all and it must take into account the needs of each student, adapting the processes to cover their needs. It is an idea that was born in line with global, regional and local political actions, which seek an education that responds to immediate social demands that are focused on the goal of a greater number of students receiving an education during more time, counting on an attractive educational offer with a recognized level of quality, equity and inclusion and involving the vast majority of institutions and sectors of society.
Julien Laflaquière, Lotfi Sofiane Settouti, Yannick Prié, Alain Mille. Traces et inscriptions de connaissances. 18e Journées Francophones d’Ingénierie des Connaissances, Jul 2007, Grenoble, France. not specified, 2007.
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11. Knowledge approach model
Approaches that use learning content, such as SCORM or LRS, API (Application Programming Interface) are used for the backup and observation of activities done by learners. This is API trying to create a single resource accessible to all. Often, to ensure accessibility, developers ensure that Web content meets certain accessibility guidelines as accessibility guidelines for W3C, Web Content. However, this approach presents some difficulties when one considers that the main providers of learning scenarios are teachers, who are often experts in supporting the creation of accessible learning resources.
The model that I propose here is the modeling of all the agents intervening in a classical format (face-to-face training). In order to obtain functionalities that will play the roles of agents in the context of face-to-face training. Thus an ontology of the domain and specifications that will be defined.
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12. Ontologies knowledge representation in e-learning system
We used in our modeling an ontology that explicitly represents a knowledge base of learning, facilitating the categorization of its elements and reasoning. To achieve this goal, it is necessary to understand how to organize knowledge related to learning and turn it into appropriate and attractive learning objects. On the other hand, this knowledge is organized to ensure the manipulation of learning objects. Thus, building an ontology to represent learning is an appropriate goal.
Julien Laflaquière, Lotfi Sofiane Settouti, Yannick Prié, Alain Mille. Traces et inscriptions de connaissances. 18e Journées Francophones d’Ingénierie des Connaissances, Jul 2007, Grenoble, France. not specified, 2007.
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13. Layers model of knowledge
Currently, learning situations are most commonly configured by the tutor(expert) without knowing the learners. highlights the method of instructional engineering through which designers can build and maintain a learning system, relying on two main processes: the knowledge extraction and knowledge acquisition. It is a question of determining, with the help of experts of the field, the user tasks and the cases of use and to make evolve them.
Dufresne A., Basque J., Paquete G., Léonard M., Lundgren-Cayrol K., Prom Tep S., « Versun modèle générique d'assistance aux acteurs du téléapprentissage », STICEF, vol. 10, 2003, p
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14. Layers model of knowledge
I share Thorpe's approach, which has drawn attention to the importance of allowing the possibility of creating dynamic dynamics stick closer to the needs of learners. The tutor is an essential component of learning and can be seen as a "conductor" supervising the activities of the learners and directing them in a direction to help them to even knowledge related to the training. Our extra approach therefore the tutor in the process of preparing and adapting learning situations. The system has shed but help to be the relay between generic learning situations guide by the author (pedagogical designer) and learner situation instantiated by learners.
Thorpe M., "Rethinking Learner Support: the challenge of collaborative online learning",Open Learning, vol. 17, n°2, 2002, p
Guéraud V., Cagnat J.M., « Suivi à distance de classe virtuelle active », Conférence TICE 2004, Compiègne, octobre 2004, p
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15. Layers model of knowledge
We present existing tools for preparing and conducting learning sessions, highlighting the role of our tutor support system. It provides guidance to the tutor according to the learning situations to be set up, adapted to each learner and each work group. Our research aims to provide the tutor with all means to give him the opportunity to adapt his pedagogy according to, on the one hand, characteristics of learners and, on the other hand, variables evolving during the training, such as their behavior or knowledge. The system we modeling is to provide answers on what the tutor observes about the learners, to encourage them to intervene according to the appropriate means or to indicate activities to offer to the learners among the variety of activities and situations of Learners designed by the instructional designer.
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16. Tutor knowledge model
We present existing tools for preparing and conducting learning sessions, highlighting the role of our tutor support system. It provides guidance to the tutor according to the learning situations to be set up, adapted to each learner and each work group. Our research aims to provide the tutor with all means to give him the opportunity to adapt his pedagogy according to, on the one hand, characteristics of learners and, on the other hand, variables evolving during the training, such as their behavior or knowledge. The system we modeling is to provide answers on what the tutor observes about the learners, to encourage them to intervene according to the appropriate means or to indicate activities to offer to the learners among the variety of activities and situations of Learners designed by the instructional designer.
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17. Tutor knowledge model
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18. Learner knowledge model
The adaptation of training courses, presentations and content to the needs of learners and the monitoring of their progress on online learning platforms require the collection of data on learners. The so-called relevant data must be treated in such a way as to make sense of the content that will be proposed for the learners concerned. The learner model is a data structure that characterizes the knowledge acquired by the learner. The five main characteristics represented in the user's model are: the learner's object, his knowledge and training, his experiences, preferences or interests. It is a model that provides information to the environment to adapt to each user and updates them explicitly by collecting its resulting traces of its interactions with the environment.
Brusilovsky, P. (2016) Data-Driven Education: Using Learners' Data to Improve Teaching and Learning. 15th International Conference on Web- Based Learning, ICWL 2016, Rome, Italy, October 26–29
Ahn, J.-w., Brusilovsky, P., and Sosnovsky, S. (2006) QuizVIBE: Accessing Educational Objects with Adaptive Relevance-Based Visualization. In: T. C. Reeves and S. F. Yamashita (eds.) Proceedings of World Conference on E-Learning, E-Learn 2006, Honolulu, HI, USA, October 13-17, 2006, AACE, pp
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19. Learner knowledge model
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20. Expert knowledge model
An expert in charge of the evaluation of the quality and the pertinence of the formats of learners is necessary. This experts will be able to detect system malfunctions and to provide improvements. This modeling will be based on the history of the learners' traces and the history of the tutors. In fact, the goal here is to have an evaluation of the adaptation techniques used by the tutors for their learners. See if the release of an A tutor on a learner or group of learners is adequate.
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21. Results Results about relevancy of content of the learning platform
Learner A
Learner B
Learner C
Learner D
Learner E
Learner F
First iteration
13.4%
18.2%
22.7%
15.6%
18.4%
13.8%
Second iteration
37.7%
33.8%
40.3%
21.2%
21.8%
18.6%
Third iteration
51.8%
40.1%
55.2%
35%
26%
38.5%
Fourth iteration
53.2%
46.6%
61.9%
42.3%
35.5%
45.7%
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22. Conclusion
In this article, we present a model and an architecture supporting this method with our system based on written traces. The purpose of this method is to refine the resources proposed by learners on a learning platform. The implementation of a prototype is in progress.
Aug 30, SETC Baba MBAYE