1,386 research outputs found
On Recommendation of Learning Objects using Felder-Silverman Learning Style Model
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation
An integrated mobile content recommendation system
Many features have been added to mobile devices to assist the user's information consumption. However, there are limitations due to information overload on the devices, hardware usability and capacity. As a result, content filtering in a mobile recommendation system plays a vital role in the solution to this problem. A system that utilises content filtering can recommend content which matches a user's needs based on user preferences with a higher accuracy rate.
However, mobile content recommendation systems have problems and limitations related to cold start and sparsity. The problems can be viewed as first time connection and first content rating for non-interactive recommendation systems where information is insufficient to predict mobile content which will match with a user's needs. In addition, how to find relevant items for the content recommendation system which are related to a user's profile is also a concern.
An integrated model that combines the user group identification and mobile content filtering for mobile content recommendation was proposed in this study in order to address the current limitations of the mobile content recommendation system. The model enhances the system by finding the relevant content items that match with a user's needs based on the user's profile. A prototype of the client-side user profile modelling is also developed to demonstrate the concept.
The integrated model applies clustering techniques to determine groups of users. The content filtering implemented classification techniques to predict the top content items. After that, an adaptive association rules technique was performed to find relevant content items. These approaches can help to build the integrated model.
Experimental results have demonstrated that the proposed integrated model performs better than the comparable techniques such as association rules and collaborative filtering. These techniques have been used in several recommendation systems. The integrated model performed better in terms of finding relevant content items which obtained higher accuracy rate of content prediction and predicted successful recommended relevant content measured by recommendation metrics. The model also performed better in terms of rules generation and content recommendation generation. Verification of the proposed model was based on real world practical data. A prototype mobile content recommendation system with client-side user profile has been developed to handle the revisiting user issue. In addition, context information, such as time-of-day and time-of-week, could also be used to enhance the system by recommending the related content to users during different time periods.
Finally, it was shown that the proposed method implemented fewer rules to generate recommendation for mobile content users and it took less processing time. This seems to overcome the problems of first time connection and first content rating for non-interactive recommendation systems
Detection of Trending Topic Communities: Bridging Content Creators and Distributors
The rise of a trending topic on Twitter or Facebook leads to the temporal
emergence of a set of users currently interested in that topic. Given the
temporary nature of the links between these users, being able to dynamically
identify communities of users related to this trending topic would allow for a
rapid spread of information. Indeed, individual users inside a community might
receive recommendations of content generated by the other users, or the
community as a whole could receive group recommendations, with new content
related to that trending topic. In this paper, we tackle this challenge, by
identifying coherent topic-dependent user groups, linking those who generate
the content (creators) and those who spread this content, e.g., by
retweeting/reposting it (distributors). This is a novel problem on
group-to-group interactions in the context of recommender systems. Analysis on
real-world Twitter data compare our proposal with a baseline approach that
considers the retweeting activity, and validate it with standard metrics.
Results show the effectiveness of our approach to identify communities
interested in a topic where each includes content creators and content
distributors, facilitating users' interactions and the spread of new
information.Comment: 9 pages, 4 figures, 2 tables, Hypertext 2017 conferenc
Soft Presentation of Hard News? A Content Analysis of Political Facebook Posts
The current media environment is primarily characterised by a large amount of information and, in contrast, rather fragmented audience attention. This is especially true for social media, particularly Facebook, which have become important news sources for many people. Journalists cannot help but publish content on Facebook if they want to reach the part of their audience that mainly—or even only—consumes news there. On Facebook, journalists are at the mercy of the algorithm that determines the visibility of their content. Because user engagement is a crucial factor in the algorithm, concerns have been raised that journalists are abandoning their normative quality standards to make the news as attractive as possible to the audience—at the expense of media performance. A softened presentation of the news, particularly in Facebook posts, may help achieve this aim, but research on this subject is lacking. The present study analyses this practice of softening the news in four German media outlets’ (BILD, FAZ, Der Spiegel, Tagesschau) political Facebook posts. The results show that the overall level of news softening is low to medium. Furthermore, comparing them to website teasers reveals that news softening is only slightly higher on Facebook (mainly BILD and Der Spiegel), and that there are no converging trends between quality or public service media and tabloid media. Exaggerated fears about news softening are therefore unnecessary. Continued analysis of news softening, as well as ongoing adaption of the concept according to dynamic developments, is nevertheless important
Evaluating Variable Length Markov Chain Models for Analysis of User Web Navigation Sessions
Markov models have been widely used to represent and analyse user web
navigation data. In previous work we have proposed a method to dynamically
extend the order of a Markov chain model and a complimentary method for
assessing the predictive power of such a variable length Markov chain. Herein,
we review these two methods and propose a novel method for measuring the
ability of a variable length Markov model to summarise user web navigation
sessions up to a given length. While the summarisation ability of a model is
important to enable the identification of user navigation patterns, the ability
to make predictions is important in order to foresee the next link choice of a
user after following a given trail so as, for example, to personalise a web
site. We present an extensive experimental evaluation providing strong evidence
that prediction accuracy increases linearly with summarisation ability
An adaptable and personalised e-learning system applied to computer science programmes design
With the rapid advances in E-learning systems, personalisation and adaptability have now become important features in the education technology. In this paper, we describe the development of an architecture for A Personalised and Adaptable E-Learning System (APELS) that attempts to contribute to advancements in this field. APELS aims to provide a personalised and adaptable learning environment to users from the freely available resources on the Web. An ontology was employed to model a specific learning subject and to extract the relevant learning resources from the Web based on a learner's model (the learners background, needs and learning styles). The APELS system uses natural language processing techniques to evaluate the content extracted from relevant resources against a set of learning outcomes as defined by standard curricula to enable the appropriate learning of the subject. An application in the computer science field is used to illustrate the working mechanisms of the APELS system and its evaluation based on the ACM/IEEE computing curriculum. An experimental evaluation was conducted with domain experts to evaluate whether APELS can produce the right learning material that suits the learning needs of a learner. The results show that the produced content by APELS is of a good quality and satisfies the learning outcomes for teaching purposes
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