Mohammed Hassan

Recommending Learning Peers for Collaborative Learning through Social Network Sites

With advances in social network sites and easy access to internet services, many learners depend on suggestions from other people on the internet for easy access to very essential information concerning learning materials, and also to collaborate with each other in order to exchange ideas. Current recommender systems for learning focus on recommending a sequence of learning materials based on learners similarities or similarities between the new learning objects and the ones the user is already familiar with in the past. Many learners prefer collaborative learning than learning on their own or in the classroom, but the major difficulty in engaging in an online collaborative learning is how to get a suitable collaborating partners(learning peers). This paper proposed a recommendation system that can search social network sites to find and recommend learning peers to the user based on their post, comment, and common friends on the social network.

Learning system and analysis of learning style for African and Asian students

Many factors can hinder learning process especially in the classroom, but the greatest among all is the students learning preferences. This research work implemented a fuzzy-like mobile-based learning system that can be used to determine the learning preferences of engineering students based on their responses in answering 55 questions with multiple choice answers on the systems questionnaire. The system will automatically categorizes between Active-Reflective, Sensory-Intuitive, Visual-Verbal, Sequential-Global, and Social-Emotional learners. The system is tested with some data collected from students in various schools and Universities in Africa and Asia, to analyze their learning styles. A total of 83 students were examined at each of the two continents. Furthermore, we explained how the system is designed and implemented, different systems module, Analysis of the result obtained, overview of mobile learning, learning style index, and the extended learning style index. Finally, future research directions are proposed.

Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems

Accuracy improvement is among the primary key research focuses in the area of recommender systems. Traditionally, recommender systems work on two sets of entities, Users and Items, to estimate a single rating that represents a user’s acceptance of an item. This technique was later extended to multi-criteria recommender systems that use an overall rating from multi-criteria ratings to estimate the degree of acceptance by users for items. The primary concern that is still open to the recommender systems community is to find suitable optimization algorithms that can explore the relationships between multiple ratings to compute an overall rating. One of the approaches for doing this is to assume that the overall rating as an aggregation of multiple criteria ratings. Given this assumption, this paper proposed using feed-forward neural networks to predict the overall rating. Five powerful training algorithms have been tested, and the results of their performance are analyzed and presented in this paper.

Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems

We often make decisions on the things we like, dislike, or even don’t care about. However, taking the right decisions becomes relatively difficult from a variety of items from different sources. Recommender systems are intelligent decision support software tools that help users to discover items that might be of interest to them. Various techniques and approaches have been applied to design and implement such systems to generate credible recommendations to users. A multi-criteria recommendation technique is an extended approach for modeling user’s preferences based on several characteristics of the items. This research presents genetic algorithm-based approaches for predicting user preferences in multi-criteria recommendation problems. Three genetic algorithms’ methods, namely standard genetic algorithm, adaptive genetic algorithm, and multi-heuristic genetic algorithms are used to conduct the experiments using a multi-criteria dataset for movies recommendation. The empirical results of the comparative analysis of their performance are presented in this study.

Artificial Neural Networks and Particle Swarm Optimization Algorithms for Preference Prediction in Multi-Criteria Recommender Systems

Recommender systems are powerful online tools that help to overcome problems of information overload. They make personalized recommendations to online users using various data mining and filtering techniques. However, most of the existing recommender systems use a single rating to represent the preference of user on an item. These techniques have several limitations as the preference of the user towards items may depend on several attributes of the items. Multi-criteria recommender systems extend the single rating recommendation techniques to incorporate multiple criteria ratings for improving recommendation accuracy. However, modeling the criteria ratings in multi-criteria recommender systems to determine the overall preferences of users has been considered as one of the major challenges in multi-criteria recommender systems. In other words, how to additionally take the multi-criteria rating information into account during the recommendation process is one of the problems of multi-criteria recommender systems. This article presents a methodological framework that trains artificial neural networks with particle swarm optimization algorithms and uses the neural networks for integrating the multi-criteria rating information and determining the preferences of users. The proposed neural network-based multi-criteria recommender system is integrated with k-nearest neighborhood collaborative filtering for predicting unknown criteria ratings. The proposed approach has been tested with a multi-criteria dataset for recommending movies to users. The empirical results of the study show that the proposed model has a higher prediction accuracy than the corresponding traditional recommendation technique and other multi-criteria recommender systems.

Smart media-based context-aware recommender systems for learning: A conceptual framework

Modern technologies have been greatly employed to support teachers and learners for facilitating teaching and learning processes. Recommender systems (RSs) for technology-enhanced learning (TEL) are among those new technologies that have been researched extensively within the past few years. This is because RSs for TEL are intelligent decision support systems that assist internet users in finding suitable learning objects that might match their preferences on the kinds of materials they could require to enhanced their learning activities. However, most of the existing RSs for learning used traditional techniques (2-dimensional user × item, techniques) to recommend learning objects to users without considering the contexts in which the recommendation should be made. Those contexts could be the geographical locations, the level of education, the time of the day or week, their learning preferences, and so on. This paper proposed a conceptual framework of smart media-based context-aware RSs for learning that could consider the learning preferences of users as a context for making accurate and usable recommendations. The proposed system was designed to run on smart devices for learners to test and know their learning styles and receive learning object recommendations according to their learning preferences. The paper contains the conceptualization of the framework and the details of the design and implementation procedure.