Blanchard, Emmanuel | Under the supervision of : Claude Frasson | Office: 2365 |
Phone: 514.343.5648 | Email: blanchae AT iro.umontreal.ca
My own project explores ways of enhancing the motivation to use Intelligent Tutoring Systems. In this purpose, I am particularly interested in the Self Determination Theory (SDT, a modern theory of motivation), in multi-agents systems and game-like learning environments. In recent works, I also decided to study cross-cultural differences during computer-assisted learning sessions.
Chaffar, Soumaya | Under the supervision of : Claude Frasson | Office: 2346, 2374 |
Phone: 514.343.6111 #3494 | Email: chaffars AT iro.umontreal.ca
I am interested in my research studies to model the learner emotional reactions in distance learning systems. In a first axis, we seek to predict the learner's emotional reaction at a given time of learning. Our approach of prediction is based on the causes which could trigger this emotion and on its determining factors like the personality for example. In a second axis, we plan to develop an emotional strategy which will be used by the tutor in the purpose to intervene in the best time for controling the emotional state of the learner.
Gambs, Sebastien | Under the supervision of : Esma Aimeur & Gilles Brassard | Office: 3350, 3370 |
Phone: 514.343.6111 #3514 | Email: gambsseb AT iro.umontreal.ca
My main research interets lie in everything that is connected to Quantum Information Processing, as well as Machine Learning, and especially the possible encounters between these two domains. In Machine Learning, I am particularly interested in Unsupervised Learning. Moreover, I am a curious guy and I have many other research interets. For instance, I recently worked on the design of a boosting-like algorithm for a distributed and privacy-preserving setting, and also on the anonymous transmission of quantum information. I am also still interested in Intelligent Tutoring Systems, a domain I used to work on during my Master.
Heraz, Alicia | Under the supervision of : Claude Frasson | Office: 2350, 2366 |
Phone: 514.343.6111 #1654 | Email: herazali AT iro.umontreal.ca
I'm working on leaner model and tutorial strategies. I'm interested in the brain wave activity of the learner and in the brain-computer interfaces. In the frame of NORA's project, I suggest a new extension to the learner model: the brain profile of the learner. Besides, I want to test two new kinds of strategies: neurofeedback strategy and neuropedagogical strategy. My aim is to optimize learning conditions as the attention, the interest and the focus and the management of negative emotions as the stress, the anxiety and the hyperactivity.
Ho Thi Thanh, Ai | Under the supervision of : Esma Aimeur | Office: 2350, 2366 |
Phone: 514.343.6111 #1654 | Email: hothitha AT iro.umontreal.ca
Today, Web services have become popular provide a standard way to expose an application's resources to the outside world so that any user can draw on the resources of the application. However, they may suffer from poor performance because a web service only performs the requests it receives without considering its internal execution state, or even questioning if it would be rewarded for performing these requests. Thus, I am interested in using a recommender system to constraint the behaviour of Web services and to regulate the interactions of these Web services with peers, users, and computing resources. The recommender system suggests how a Web service should act and react in a specified situation based on the two main elements: the resource element (the capabilities and limitations of the operation resources), and the user element (user expectations and requirements).
Zouaq, Amal | Under the supervision of : Claude Frasson & Roger NKambou | Office: 2350, 2366 |
Phone: 514.343.6111 #1654 | Email: zouaq AT iro.umontreal.ca
Our research focuses on the whole process of knowledge acquisition and management within a community as well as on knowledge dissemination through an intelligent tutoring system. Our project, 'The Knowledge Puzzle' is an ontology-based platform. It enables the generation of Learning Knowledge Objects from manual and automatic annotations of documents through natural language processing and machine learning. Knowledge is stored within an organizational memory that acts as a knowledge prosthesis for an intelligent tutoring system. We also work on the standardization of our learning knowledge objects.
Benadada, Khadija | Under the supervision of : Claude Frasson | Office: 2346, 2374 |
Phone: 514.343.6111 #3494 | Email: benadadk AT iro.umontreal.ca
Bendakir, Narimel | Under the supervision of : Esma Aimeur | Office: 2346, 2374 |
Phone: 514.343.6111 #3494 | Email: bendakin AT iro.umontreal.ca
Students pursuing higher education degrees are faced with two challenges: a myriad of courses from which to choose, and a lack of knowledge about which courses to follow. In our research project, we have realized RARE, a course Recommender system based on Association RulEs, which was used on real data coming from the Department of Computer Science and Operations Research at the Universit� de Montr�al. Starting from the history of real data, RARE carries out a data mining process and discovers significant rules that associate academic courses that are followed together. These rules are later used to predict recommendations for new students. RARE offers to users the possibility to rate the recommendations and benefits from their opinions, thus leading to an improvement of the rules. Therefore, RARE combines the benefits of both former students� experience and current students� ratings in order to personalize its recommendations.
Chalfoun, Pierre | Under the supervision of : Claude Frasson | Office: 2346, 2374 |
Phone: 514.343.6111 #3494 | Email: chalfoup AT iro.umontreal.ca
Benlizidia, Sihem | Under the supervision of : Esma Aimeur | Office: 2374, 2346 |
Phone: 514.343.6111 #3494 | Email: benlizis AT iro.umontreal.ca
LORESA (Learning Objects REcommender system based on Semantic Annotation) is a system helps recommending learning objects of interest for a user based on his query, described by authoritative metadata and semantic annotations. The semantic annotations are based on marking the content of the object according to a specific ontology. The system permits to a registered user, who wants to include a learning object to the repository, to annotate it semantically. In the next step, the author is asked to evaluate to which extent (a number from 1 to 5, where 1 is for poor link and 5 for excellent link) his content is linked to the ontology concepts used in the semantic annotation he has already done. This step is essential for solving the cold-start problem inherent to the recommender systems. The recommender system uses this data and their relating ratings to recommend to the user a set of the most suiting learning objects to his query. The user is then asked to rate the recommended content based upon the semantic annotation. The given ratings are used for revaluating the objects semantics and improve the recommendations precision.
Kiared, Abou-Sofiane | Under the supervision of : Claude Frasson | Office: -- |
Phone: -- | Email: kiaredas AT iro.umontreal.ca
Lakhlil, Zakia | Under the supervision of : Esma Aimeur | Office: 2346, 2374 |
Phone: 514.343.6111 #3494 | Email: lakhlilz AT iro.umontreal.ca
Our goal is to construct an English test adapted with user profile. So, at first we extract the examinee's proficiency from the case base, by using case-based reasoning techniques. The most informative item near this examinee's proficiency in the item pool is administered to the candidate. After the response of the item is known, the examinee's proficiency will be estimated by item response theory techniques. In the same manner, one other item is presented to the candidate, and another examinee's proficiency is estimated. The two last steps are repeated until a fixed number of items are administered to the examinee or a predefined threshold is reached by type error
Lopes De Menezes, Ilusca | Under the supervision of : Claude Frasson | Office: 2374, 2346 |
Phone: 514.343.6111 #3494 | Email: lopesdei AT iro.umontreal.ca
Changes that are part of our lives make us to review our manner of thinking, acting and communicating, however, the most common reaction is the resistance to change. The reason why this reaction happens is because the changes, occurring independently in their type or their objective are not usually welcomed. The resistance occurs in different forms varying from anxiety and dissatisfaction to the sabotage of new situations. Therefore, it is important to better learn how to handle our resistances. My research interests are focused on the resistance to change in an organization, prioritizing the emotional factors that drive to this resistance.
Mabrouk, Moez | Under the supervision of : Esma Aimeur | Office: 2374, 2346 |
Phone: 514.343.6111 #3494 | Email: mabroukm AT iro.umontreal.ca
Nowadays, many web-based systems have emerged, namely educational ones. Such systems not only offer a learning environment for distance knowledge acquisition but also evaluate the learner to ensure that the knowledge has been well assimilated using tests or quizzes. In an effort for the learner to understand the question at hand, few systems can sometimes propose helps during the test. However, the offered help is not personalized and thus the same for all the learners. We present UMAKE, User Modeling For Adapted Knowledge Evaluation, a system to assist the learner during his auto-evaluation process by recommending him personalized helps to fully understand the questions. UMAKE recommends the most beneficial help based on the learner's learning style (auditory, visual and kinesthetic). In order to do so, UMAKE employs switching, a hybrid recommendation technique based on both collaborative and knowledge-based filtering. Moreover, adaptive hypermedia technologies such as adaptive navigation support and adaptive presentation are also used.
Rakowski, Zbigniew | Under the supervision of : Esma Aimeur & Gilles Brassard | Office: 2350, 2366 |
Phone: 514.343.6111 #1654 | Email: rakowskz AT iro.umontreal.ca
Razaki, Adebola | Under the supervision of : Claude Frasson | Office: 2350, 2366 |
Phone: 514.343.6111 #1654 | Email: razakiry AT iro.umontreal.ca
CAMELEO Project is an attempt to adjoin a culturally sensitive aspect to the mechanics involved in the decision making process of Intelligent Tutoring Systems. The project is based on research from various domains such as those effected on Culturally Responsive Teaching and Constructivism in learning. The system uses the efficiency of a Multi-Agent system at autonomously gathering and processing information in order to establish uniquely identifying cultural profiles based on User-System interaction.
Uwatowenimana, Jeanne d'arc | Under the supervision of : Esma Aimeur | Office: . |
Phone: . | Email: uwatowej AT iro.umontreal.ca
Zhu, Xi Ning | Under the supervision of : Esma Aimeur | Office: . |
Phone: . | Email: zhuxinin AT iro.umontreal.ca
My research joins the Web expression to the artificial intelligence content in order to create an integrated Web site devoted to the said subject by offering some informational resource concerning the artificial intelligence. Its main functions include: texts, books, videos, sources codes, software and data's browsing and downloading, external links and online research, the online editing, uploading tools and an Internet forum, etc. Numerous technologies are applied, which suit to some Web 2.0's characteristics. Beside, I designed and implemented an administration site. Finally, my work integrally recapitulates the Web technologies' progress and systematically resumes the Web development technologies.