My recent publications

A Novel Collaborative Filtering Approach Based on the Opposite Neighbors’ Preferences

Published in Embedded Systems and Artificial Intelligence, Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1076), 2020

Abstract : Collaborative filtering (CF) has become an effective way to predict useful items. It is the most widespread recommendation technique. It relies on users who share similar tastes and preferences to suggest the items that they might be interested in. Despite its simplicity and justifiability, the collaborative filtering approach experiences many problems, including sparsity, gray sheep and scalability. These problems lead to deteriorating the accuracy of the obtained results. In this work, we present a novel collaborative filtering approach based on the opposite preferences of users. We focus on enhancing the accuracy of predictions and dealing with gray sheep problem by inferring new similar neighbors based on users who have dissimilar tastes and preferences. For instance, if a user X is dissimilar to a user Y then the user ┐X is similar to the user Y. The Experimental results performed on two datasets including MovieLens and FilmTrust show that our approach outperforms several baseline recommendation techniques. Read more

Recommended citation: El Fazziki A., El Aissaoui O., El Madani El Alami Y., Benbrahim M., El Allioui Y. (2020) A Novel Collaborative Filtering Approach Based on the Opposite Neighbors’ Preferences. In: Bhateja V., Satapathy S., Satori H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore https://doi.org/10.1007/978-981-15-0947-6_77

Mining Learners’ Behaviors: An Approach Based on Educational Data Mining Techniques

Published in Embedded Systems and Artificial Intelligence, Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1076), 2020

Abstract : Educational data mining is a research field that aims to apply data mining techniques in educational environments. Many data mining techniques such as clustering, classification, and prediction can be performed on educational data in order to analyze the learner behaviors. In this work, we have used the clustering and classification techniques to predict the learners’ learning styles. The students’ behaviors while using the e-learning system have been captured from the log file and given as an input of a clustering algorithm to group them into 16 clusters. The resulted clusters were labeled with learning styles combinations based on the Felder and Silverman learning style model. Then the labeled behaviors were given as input to four classifiers: naive Bayes, Cart, Id3, and C4.5 to compare their performance in predicting students’ learning styles. The four classifiers were performed using Weka data mining tool, and the obtained results showed that Id3 yielded better results than the other classifiers. Read more

Recommended citation: El Aissaoui O., El Alami El Madani Y., Oughdir L., Dakkak A., El Allioui Y. (2020) Mining Learners’ Behaviors: An Approach Based on Educational Data Mining Techniques. In: Bhateja V., Satapathy S., Satori H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore https://doi.org/10.1007/978-981-15-0947-6_62

Improving Collaborative Filtering Approach by Leveraging Opposite Users

Published in Advanced Intelligent Systems for Sustainable Development, Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1102), 2020

Abstract : Collaborative filtering is a widely used recommendation approach that aims to predict for a target user the most appropriate items. This approach uses the ratings given by users who share similar tastes and preferences to predict ratings for items that haven’t been rated yet. Despite its simplicity and justifiability, CF approach stills suffering from several drawbacks and problems, including sparsity, gray sheep and scalability. These problems affect the accuracy of the obtained results. In this work, we present a novel collaborative filtering approach based on the opposite preferences of users. We focus on enhancing the accuracy of predictions and dealing with gray sheep problem by inferring new similar neighbors based on users who have dissimilar tastes and preferences. For instance, if a user X is dissimilar to a user Y then the user ┐X is similar to the user Y. The Experimental results performed on two datasets including MovieLens and FilmTrust show that our approach outperforms several baseline recommendation techniques. Read more

Recommended citation: El Fazziki A., El Madani El Alami Y., El Aissaoui O., El Allioui Y., Benbrahim M. (2020) Improving Collaborative Filtering Approach by Leveraging Opposite Users. In: Ezziyyani M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Advances in Intelligent Systems and Computing, vol 1102. Springer, Cham. https://doi.org/10.1007/978-3-030-36653-7_14

A Multiple Linear Regression-Based Approach to Predict Student Performance

Published in Embedded Systems and Artificial Intelligence, Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1076), 2020

Abstract : Predicting students’ academic outcome is useful for any educational institution that aims to ameliorate students’ performance. Based on the resulted predictions, educators can provide support to students at risk of failure. Data mining and machine learning techniques were widely used to predict students’ performance. This process called Educational data mining. In this work, we have proposed a methodology to build a student’ performance prediction model using a supervised machine learning technique which is the multiple linear regression (MLR). Our methodology consists of three major steps, the first step aims to analyze and preprocess the students’ attributes/variables using a set of statistical analysis methods, and then the second step consists in selecting the most important variables using different methods. The third step aims to construct different MLR models based on the selected variables and compare their performance using the k-fold cross-validation technique. The obtained results show that the model built using the variables selected from the Multivariate Adaptive Regression Splines method (MARS), outperforms the other constructed models. Read more

Recommended citation: El Aissaoui O., El Alami El Madani Y., Oughdir L., Dakkak A., El Allioui Y. (2020) A Multiple Linear Regression-Based Approach to Predict Student Performance. In: Ezziyyani M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Advances in Intelligent Systems and Computing, vol 1102. Springer, Cham. https://doi.org/10.1007/978-3-030-36653-7_2

A new collaborative approach to solve the gray-sheep users problem in recommender systems

Published in IEEE, 2019

Abstract : Recommender systems aim to help users to find items that fit their requirements and preferences. In that field, the collaborative filtering (CF) approach is considered as a widely used one. There are two main approaches for CF: memory-based and model-based. Both of the two approaches are based on the use of users’ ratings to predict the top-N recommendation for the active user. Despite its simplicity and efficiency, The CF approach stills suffer from many drawbacks including sparsity, gray sheep and scalability. The aim of this work is to deal with the gray sheep problem, by proposing a novel collaborative filtering approach. This novel approach aims to enhance the accuracy of prediction by turning the users whose preferences disagree with the target user, into new similar neighbors. For instance, if a user X is dissimilar to a user Y then the user ¬ X is similar to the user Y. To evaluate the performance of the proposed approach, we have used two datasets including MovieLens and FilmTrust. The Experimental results show that our approach outperforms many traditional recommendation techniques. Read more

Recommended citation: A. E. Fazziki, O. E. Aissaoui, Y. E. M. E. Alami, Y. E. Allioui and M. Benbrahim, "A new collaborative approach to solve the gray-sheep users problem in recommender systems," 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS), Marrakech, Morocco, 2019, pp. 1-4, doi: 10.1109/ICDS47004.2019.8942256. http://doi.org/10.1109/ICDS47004.2019.8942256

A Hybrid Machine Learning Approach to Predict Learning Styles in Adaptive E-Learning System

Published in Advanced Intelligent Systems for Sustainable Development, Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 915), 2019

Abstract : The increasing use of E-learning environments by learners makes it indispensable to implement adaptive e-learning systems (AeS). The AeS have to take into account the learners’ learning styles to provide convenient contents and enhance the learning process. Learning styles refer to the preferred way in which an individual learns best. The traditional methods detecting learning styles (using questionnaires) present many limits, as: (1) the time-consuming process of filling in the questionnaire and (2) producing inaccurate results because students aren’t always aware of their own learning preferences. Thus in this paper we have proposed an approach for detecting learning styles automatically, based on Felder and Silverman learning style model (FSLSM) and using machine learning algorithms. The proposed approach is composed of two parts: The first part aims to extract the learners’ sequences from the log file, and then using an unsupervised algorithm (K-means) in order to group them into sixteen clusters according to the FSLSM, and the second part consists in using a supervised algorithm (Naive Bayes) to predict the learning style for a new sequence or a new learner. To perform our approach, we used a real dataset extracted from an e-learning system’s log file. In order to evaluate the performance, we used the confusion matrix technique. The obtained results demonstrate that our approach yields excellent results. Read more

Recommended citation: El Aissaoui O., El Madani El Alami Y., Oughdir L., El Allioui Y. (2019) A Hybrid Machine Learning Approach to Predict Learning Styles in Adaptive E-Learning System. In: Ezziyyani M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-11928-7_70

Advanced prediction of learner’s profile based on Felder-Silverman learning styles using web usage mining approach and fuzzy c-means algorithm

Published in International Journal of Computer Aided Engineering and Technology (IJCAET), 2019

Abstract : Automatic prediction of learner s profile is an important requirement for personalised e-learning. This can be provided based on the learning behaviours of the learners. In this work, the learning behaviour is captured using the web usage mining technique, preprocessed and converted into the XML format based on sequences of accessing contents. These sequences are mapped to the eight categories of Felder-Silverman learning style model (FSLSM) using fuzzy c-means (FCM) algorithm. A gravitational search based back propagation neural network (GSBPNN) algorithm is used for the prediction of learning styles of a new learner. In this algorithm, the neural network approach is modified by calculating the weights using gravitational search algorithm. The accuracy of the prediction model is compared with the basic back propagation neural network (BPNN) algorithm. The result shows that the captured data is labelled as per FSLSM and the accuracy is more in GSBPNN as compare to BPNN. Read more

Recommended citation: Allioui, Y. El. (2019). Advanced prediction of learner’s profile based on Felder-Silverman learning styles using web usage mining approach and fuzzy c-means algorithm. International Journal of Computer Aided Engineering and Technology, 11(4/5), 495. https://doi.org/10.1504/IJCAET.2019.100447. https://doi.org/10.1504/IJCAET.2019.100447

Combining supervised and unsupervised machine learning algorithms to predict the learners’ learning styles

Published in Procedia Computer Science, 2019

Abstract : The implementation of an efficient adaptive e-learning system requires the construction of an effective student model that represents the student’s characteristics, among those characteristics, there is the learning style that refers to the way in which a student prefers to learn. Knowing learning styles helps adaptive E-learning systems to improve the learning process by providing customized materials to students. In this work, we have proposed an approach to identify the learning style automatically based on the existing learners’ behaviors and using web usage mining techniques and machine learning algorithms. The web usage mining techniques were used to pre-process the log file extracted from the E-learning environment and capture the learners’ sequences. The captured learners’ sequences were given as an input to the K-modes clustering algorithm to group them into 16 learning style combinations based on the Felder and Silverman learning style model. Then the naive Bayes classifier was used to predict the learning style of a student in real time. To perform our approach, we used a real dataset extracted from an e-learning system’s log file, and in order to evaluate the performance of the used classifier, the confusion matrix method was used. The obtained results demonstrate that our approach yields excellent results. Read more

Recommended citation: AISSAOUI, O. EL, EL MADANI, Y. E. A., OUGHDIR, L., & ALLIOUI, Y. EL. (2019). Combining supervised and unsupervised machine learning algorithms to predict the learners’ learning styles. Procedia Computer Science, 148, 87–96. https://doi.org/10.1016/J.PROCS.2019.01.012. https://doi.org/10.1016/j.procs.2019.01.012

A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments

Published in Education and Information Technologies, 2018

Abstract : Adaptive E-learning platforms provide personalized learning process relying mainly on learning styles. The traditional approach to find learning styles depends on asking learners to self-evaluate their own attitudes and behaviors through surveys and questionnaires. This approach presents several weaknesses including the lack of self-awareness of learners of their own preferences. Furthermore, the vast majority of learners experience boredom when they are asked to fill out the corresponding questionnaire. Besides that, traditional approach assumes that learning styles are fixed, and cannot change over time. In this paper, we propose a generic approach for detecting learning styles automatically according to a given learning styles model. In fact, our approach does not depend on a specific LSM. This work consists of two major steps. First, we extract learning sequences from learners log files using web usage mining techniques. Second, we classify the extracted learners’ sequences according to a specific learning style model using clustering algorithms. To perform our approach we use Felder-Silverman Model as LSM and Fuzzy C-Means as a clustering algorithm. We have conducted an experimental study using a real-world dataset. The obtained results show that our approach outperforms traditional approach and provides promising results. Read more

Recommended citation: El Aissaoui, O., El Alami El Madani, Y., Oughdir, L. et al. A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments. Educ Inf Technol 24, 1943–1959 (2019). https://doi.org/10.1007/s10639-018-9820-5. https://doi.org/10.1007/s10639-018-9820-5

Integrating web usage mining for an automatic learner profile detection: A learning styles-based approach

Published in IEEE, 2018

Abstract : With the technological revolution of Internet and the information overload, adaptive E-learning has become the promising solution for educational institutions since it enhances students’ learning process according to many factors such as their learning styles. Learning styles are a criteria of great import in E-learning environment because they can help the system to effectively personalize students’ learning process. Generally, the traditional way of detecting students’ learning style is based on asking students to fill out a questionnaire. However, using this static technique presents many problems. Some of these problems include the lack of self-awareness of students of their learning preferences. In addition, almost all students are bored when they are asked to fill out a questionnaire. Thus, in this work, we present an automatic approach for detecting students’ learning style based on web usage mining. It consists in classifying students’ log files according to a specific learning style model (Felder and Silverman model) using clustering algorithms (K-means algorithm). In order to test the efficiency of our work, we use a real-world dataset gathered from an E-learning system. Experimental results show that our approach provide promising results. Read more

Recommended citation: O. El Aissaoui, Y. El Madani El Alami, L. Oughdir and Y. El Allioui, "Integrating web usage mining for an automatic learner profile detection: A learning styles-based approach," 2018 International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, 2018, pp. 1-6, doi: 10.1109/ISACV.2018.8354021. http://doi.org/10.1109/ISACV.2018.8354021

Youssouf EL ALLIOUI

My recent publications