2009 Ninth IEEE International Conference on Advanced Learning Technologies
Automatic personalization of learning scenarios using SVM El Amine OURAIBA1, Azeddine CHIKH2, Abdelmalik TALEB-AHMED3,Zeyneb EL YEBDRI4 1 LIUM - IUT de Laval - Université du Maine - France 2 King Saud University Riyadh - Saudi Arabia 3 LAMIH-UMR CNRS-UVHC8530 - Université de Valenciennes et du Hainaut Cambrésis-France 4 Computer science department - Tlemcen university - Algeria 1
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[email protected] Abstract
2. Towards a personalization system of learning scenarios to learners using SVM
This paper describes a proposition for constructing an automatic personalization system based on SVM (Support Vector Machine) method. Our approach helps the learning units designers to select automatically the learning scenarios adapted to learners. In our experimentation, we have used a database that contains information about computer science engineering students of the Tlemcen university and descriptions of learning scenarios. We have implemented our SVM classifier using the open environment ”Weka”. The test results showed an attractive performance. The values of the classification rate, the precision and the recall are very acceptable.
In e-learning the users population is very numerous and heterogeneous; the users have very different profiles, roles and interest centers,…etc. For this reason, the personalization in learning environments, on the larger sense of the word, becomes an indispensable function to the learning process. Thus, the purpose of personalization systems is to provide to users the content and the services which they want, according to their individual characteristics. However, the task of personalization may be treated as an application of Data Mining where it is seen as a holistic process rather than as a separate algorithms or specific types of data [6]. In this process, the artificial intelligence techniques find their place to develop systems of automatic personalization. These systems increase the amount of relevant scenarios collected from different warehouses targeting just scenarios which are really appropriate and suitable to learners. Therefore, an automatic persnalization system of scenarios should contain mainly the profiles of learners, the descriptions of learning scenarios and the techniques to predict relevance (such as artificial intelligence techniques). In addition, information of learning context, which are also very required, are generally integrated into learners and/or learning scenarios models [9].
1. Introduction The integration of technology in learning, needs to address the very important issue of enhancing the teaching and learning process, rather than just being seen as a new flexible delivery medium [8]. Among the constraints of this new trend in education we find the lack of personalization in learning environments [1] [11] [2]. Furthermore, this development constitutes a need for open, flexible and rich learning environments independent of time and place. Flexible in terms of time, place, content, sequence and delivery media and adaptive in terms of matching the characteristics of all the persons using it [5]. In this paper we are interested by the use of the personalization systems in the field of e-learning using the technique of automatic classification "SVM" (Support Vector Machine) [7] [12]. Firstly, we explain the objective and the operation principle of our personalization system. Thus, we construct our SVM classifier by presenting the data invoked with their models and the necessary phases. Finally we finish by discussing the experimentation results. 978-0-7695-3711-5/09 $25.00 © 2009 IEEE DOI 10.1109/ICALT.2009.72
2.1. Objective We are interested by the realization of an automatic personalization system of learning scenarios to learners. The use of our system targets the pedagogical units designers. They might, either begin from a profile of a particular learner to select the relevant learning scenario or begin from description of learning scenario to find the profiles of the interested learners. 183
2.2. Operation principle
2.3. Construction of our SVM classifier
Our personalization system relies on the structure of a matrix called “matrix of relevance” whose rows correspond to the profiles of learners and columns to learning scenarios. The cells of the matrix contain the value “true” or “false” representing the relevance (or not) of each learning scenario for every learner. The functioning of our system springs from artificial intelligence and particularly from automatic classification. This system is based on particular classifier constructed using SVM (Support Vector Machine) [12]. We have used SVM in order to construct a predictive system from couples of the matrix (learners / learning scenarios) and its corresponding classes (“relevant” or “non-relevant”) by estimating a function of prediction which decides the relevance. In other words, the training goal of our SVM classifier [12] [7] is to estimate the function of exact relevance which associates with each couple (profile/scenario) a value (true/false) according to the suitability or not of the scenario to the profile. Mathematically this works like this : F : P × S •{True,False}. Knowing that “P” represents the set of learner profiles inspired from IMS LIP [3] and “S” represents the set of learning scenarios inspired from IMS LD [4]. Clearly for each couple (pi, sj) belonging to P×S, the function of exact relevance F returns the value “true” if the learning scenario `sj` is
2.3.1. The used data The learners data. Amongst the set of characteristics of the IMS LIP learner’s model [3] we have defined in [9] a learner model by retaining the necessary attributes to personalize with their possible values. Thus, according to this learner model we have collected information about computer sciences engineering students of the Tlemcen university in order to create a database of learners profiles. The learning scenarios data. Also from the IMS LD model [4], we have inspired a model which contains the necessaries attributes to personalize with their possible values (table 1). Thus, according to this model we have described some pedagogical scenarios in computer science in order to create a database of learning scenarios descriptions. The matrix of relevance. It contains a set of data built by the confrontation of the information sets collected previously, which corresponds to learners and learning scenarios.
Table 1. Necessary characteristics of learning scenarios to personalize.
relevant for learner `pi` and the value “false” else. The Figure 1 illustrates the personalization steps : Learning Scenario
Learner
Modeling with IMS LD
Modeling with IMS LIP
Learning scenario model (sj )
Representative characteristics
Learner model (pi)
SVM classifier False (Non-Relevance)
True (Relevance) Matrix of relevance
Learning scenarios personalized to learners
Figure 1. Process of personalization.
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approach is that, the collection of information of learners and of learning scenarios is carried out according to the standards (IMS LIP and IMS LD), i.e. in a formal way in order to target the representative characteristics to reach the best personalization. Morever, we used a very robust and powerful technique of the binary classification (SVM) to decide the relevance. According to the experimentation results of the test of our SVM classifier, its performance is very efficient and promising although samples are still insufficient.
2.3.2. Phases The training phase : its objective is the correct adjustment of the necessary parameters for a better performance of the classifier, for example the choice of the kernel type (linear, polynomial, Gaussian,…) [7]. The test phase : its objective is the estimation of the decision classifier thanks to the data set of the test. This set is a collection of couples (inputs, outputs) whose information linked to the outputs won't be used till the classifier evaluation.
4. References
2.3.3. Implementation of our SVM classifier using the open environment “Weka” We have used the algorithm “SMO” (Sequential Minimal Optimization) [10] available in the open environment “Weka” [13] [14] by adjusting its parameters notably the kernel which is chosen to be “polynomial” with an exponent equal to “3”. Also, in order to create our SVM classifier following the two phases (training and test) we have divided our set of data into two parts : 70% of instances for the training of the classifier and 30% for its test.
[1] A. Cristea, “Adaptive Patterns in Authoring of Educational Adaptive Hypermedia”. Educational Technology & Society, 2003, 6 (4) pp 1-5. [2] D.J. Ayersman and A. Minden, “Individual differences, computers and instruction”. Computers in human behaviour, 1995, 11(3-4), pp 371-390. [3] IMS Learner Information Package Specification, 2005, retrieved in 2007 at : http://www.imsproject.org/profiles/ [4] IMS Learning Design Information Model, Version 1.0 Final Specification, 2003, retrieved in 2007 at : http://www.imsglobal.org/learningdesign/ [5] R. Koper and J. Manderveld, "Educational modelling language: modelling reusable, interoperable, rich and personalised units of learning". British Journal of Educational Technology. 2004, Vol 35 No 5, 537–551. [6] B. Mobasher: “Data Mining for Personalization”. In The Adaptive Web: Methods and Strategies of Web Personalization, Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.), Springer, Berlin-Heidelberg, 2007,Vol.4321.pp.90-135 [7] H. Mohamadally, B. Fomani, "SVM : Machines à Vecteurs de Support ou Séparateurs à Vastes Marges", Survey ,Versailles St Quentin, 16 janvier 2006. [8] M. Nichols, “A theory for eLearning”. Educational Technology & Society, 2003, 6(2) pp 1-10. [9] E A. Ouraiba, M.A. Chikh, A. Chikh, "Filtrage Neuronal des Objets d’Apprentissage selon les Profils des Apprenants". In Proceedings of 10 th MCSEAI, Oran, 28–30 April 2008, pp78-83. [10] J. C. Platt, “Sequential minimal optimization: A fast algorithm for training support vector machines,” in Advances in Kernel Method: Support Vector Learning, Scholkopf, Burges, and Smola, Eds. Cambridge, MA: MIT Press, 1998, pp. 185–208. [11] H.Rumetshofer and W.Wöß, “XML-based Adaptation Framework for Psychological-driven E-learning Systems”. Educational Technology&Society, 2003, pp18-29. [12] Vapnik. V, “Statistical Learning Theory”, NY: John Wiley & Sons, New York, 1998. [13] Weka site, visited in 2007 : http://www.cs.waikato.ac.nz/ml/weka [14] Witten. I. H, Frank. E, "Data Mining: Practical machine learning tools and techniques", 2nd Edition, Morgan Kaufmann, San Francisco, 2005.
2.3.4. Evaluation of our SVM classifier After the test phase (in “Weka”) we obtained the following results: ¾ Time taken to build model: 0.94 seconds; Rate of classification : 92.5 % ; ¾ Detailed accuracy by class : ¾ Table 2. Values of evaluation criteria. Precision 0.889 0.935
Recall 0.8 0.967
F-Measure 0.842 0.951
Class Relevance Non-Relevance
According to those results we notice that the criteria of performance and effectiveness of our classifier take the high values. The time taken to classify the instances is small (0.94 seconds), this means a good performance. In addition, the values of the rate of classification, the F-measure, the recall and the precision are very satisfactory, this means a high effectiveness of our classifier.
3. Conclusion In this paper we proposed an approach of construction of a personalization system of learning scenarios to learners according to their profiles. This system facilitates the task for the learning designers on one hand and for the learners during their learning process on other hand. The principal advantage of our
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