1,225 research outputs found
Second Screen User Profiling and Multi-level Smart Recommendations in the context of Social TVs
In the context of Social TV, the increasing popularity of first and second
screen users, interacting and posting content online, illustrates new business
opportunities and related technical challenges, in order to enrich user
experience on such environments. SAM (Socializing Around Media) project uses
Social Media-connected infrastructure to deal with the aforementioned
challenges, providing intelligent user context management models and mechanisms
capturing social patterns, to apply collaborative filtering techniques and
personalized recommendations towards this direction. This paper presents the
Context Management mechanism of SAM, running in a Social TV environment to
provide smart recommendations for first and second screen content. Work
presented is evaluated using real movie rating dataset found online, to
validate the SAM's approach in terms of effectiveness as well as efficiency.Comment: In: Wu TT., Gennari R., Huang YM., Xie H., Cao Y. (eds) Emerging
Technologies for Education. SETE 201
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
ALBERT-Based Personalized Educational Recommender System: Enhancing Students’ Learning Outcomes in Online Learning
Online learners must navigate vast educational resources to find materials that meet their needs. This study introduces an ALBERT-based personalized educational recommender system to improve student learning. ALBERT (A Lite BERT), an optimized variant of the BERT algorithm, captures contextualized word representations and understands the semantic meaning of learning resources, student profiles, and interactions. This study evaluates the ALBERT-based recommender system’s personalized learning recommendations. To assess learning outcomes, a diverse group of students from different educational domains is evaluated. Before and after the recommender system, academic performance, knowledge retention, and engagement are assessed. User satisfaction surveys assess recommendation quality, relevance, and user experience. The recommender system uses ALBERT’s model optimization to improve recommendation accuracy, learner engagement, and personalized learning. The evaluation shows the ALBERT-based personalized recommender system improves online learning outcomes. System-generated recommendations boost student engagement, knowledge retention, and academic performance. User satisfaction surveys show that the ALBERT-based system meets learners’ needs by providing relevant and high-quality recommendations. This research shows how advanced deep learning algorithms like ALBERT can improve personalized online learning. ALBERT’s optimized training and inference speeds up the recommender system’s scalability. This empowers learners to access tailored and high-quality educational resources, maximizing their learning outcomes and potential in online learning
Review of Current Student-Monitoring Techniques used in eLearning-Focused recommender Systems and Learning analytics. The Experience API & LIME model Case Study
Recommender systems require input information in
order to properly operate and deliver content or behaviour
suggestions to end users. eLearning scenarios are no exception.
Users are current students and recommendations can be built
upon paths (both formal and informal), relationships, behaviours,
friends, followers, actions, grades, tutor interaction, etc. A
recommender system must somehow retrieve, categorize and
work with all these details. There are several ways to do so: from
raw and inelegant database access to more curated web APIs or
even via HTML scrapping. New server-centric user-action
logging and monitoring standard technologies have been
presented in past years by several groups, organizations and
standard bodies. The Experience API (xAPI), detailed in this
article, is one of these. In the first part of this paper we analyse
current learner-monitoring techniques as an initialization phase
for eLearning recommender systems. We next review
standardization efforts in this area; finally, we focus on xAPI and
the potential interaction with the LIME model, which will be also
summarized below
IKHarvester - Informal eLearning with semantic web harvesting
Only recently, researchers and practitioners alike have begun to fully understand the potential of eLearning and have concentrated on new tools and technologies for creating, capturing and distributing knowledge. In order to support and extend those solutions we propose the idea of incorporating the informal knowledge into Learning Management Systems. Contributing to the body of research, problems of existing eLearning technologies are documented highlighting areas of definite improvement. Finally, semantic Web harvesting technology as a solution is explored in the form of the knowledge acquisition tool called IKHarvester
A Revision Control System for Image Editing in Collaborative Multimedia Design
Revision control is a vital component in the collaborative development of
artifacts such as software code and multimedia. While revision control has been
widely deployed for text files, very few attempts to control the versioning of
binary files can be found in the literature. This can be inconvenient for
graphics applications that use a significant amount of binary data, such as
images, videos, meshes, and animations. Existing strategies such as storing
whole files for individual revisions or simple binary deltas, respectively
consume significant storage and obscure semantic information. To overcome these
limitations, in this paper we present a revision control system for digital
images that stores revisions in form of graphs. Besides, being integrated with
Git, our revision control system also facilitates artistic creation processes
in common image editing and digital painting workflows. A preliminary user
study demonstrates the usability of the proposed system.Comment: pp. 512-517 (6 pages
Adaptive intelligent personalised learning (AIPL) environment
As individuals the ideal learning scenario would be a learning environment tailored just for how we like to learn, personalised to our requirements. This has previously been almost inconceivable given the complexities of learning, the constraints within the environments in which we teach, and the need for global repositories of knowledge to facilitate this process. Whilst it is still not necessarily achievable in its full sense this research project represents a path towards this ideal.In this thesis, findings from research into the development of a model (the Adaptive Intelligent Personalised Learning (AIPL)), the creation of a prototype implementation of a system designed around this model (the AIPL environment) and the construction of a suite of intelligent algorithms (Personalised Adaptive Filtering System (PAFS)) for personalised learning are presented and evaluated. A mixed methods approach is used in the evaluation of the AIPL environment. The AIPL model is built on the premise of an ideal system being one which does not just consider the individual but also considers groupings of likeminded individuals and their power to influence learner choice. The results show that: (1) There is a positive correlation for using group-learning-paradigms. (2) Using personalisation as a learning aid can help to facilitate individual learning and encourage learning on-line. (3) Using learning styles as a way of identifying and categorising the individuals can improve their on-line learning experience. (4) Using Adaptive Information Retrieval techniques linked to group-learning-paradigms can reduce and improve the problem of mis-matching. A number of approaches for further work to extend and expand upon the work presented are highlighted at the end of the Thesis
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