Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Personalization via collaboration in web retrieval systems: a context based approach

World Wide Web is a source of information, and searches on the Web can be analyzed to detect patterns in Web users' search behaviors and information needs to effectively handle the users' subsequent needs. The rationale is that the information need of a user at a particular time point occurs in a particular context, and queries are derived from that need. In this paper, we discuss an extension of our personalization approach that was originally developed for a traditional bibliographic retrieval system but has been adapted and extended with a collaborative model for the Web retrieval environment. We start with a brief introduction of our personalization approach in a traditional information retrieval system. Then, based on the differences in the nature of documents, users and search tasks between traditional and Web retrieval environments, we describe our extensions of integrating collaboration in personalization in the Web retrieval environment. The architecture for the extension integrates machine learning techniques for the purpose of better modeling users' search tasks. Finally, a user-oriented evaluation of Web-based adaptive retrieval systems is presented as an important aspect of the overall strategy for personalization

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

Supporting teachers in collaborative student modeling: a framework and an implementation

Collaborative student modeling in adaptive learning environments allows the learners to inspect and modify their own student models. It is often considered as a collaboration between students and the system to promote learners’ reflection and to collaboratively assess the course. When adaptive learning environments are used in the classroom, teachers act as a guide through the learning process. Thus, they need to monitor students’ interactions in order to understand and evaluate their activities. Although, the knowledge gained through this monitorization can be extremely useful to student modeling, collaboration between teachers and the system to achieve this goal has not been considered in the literature. In this paper we present a framework to support teachers in this task. In order to prove the usefulness of this framework we have implemented and evaluated it in an adaptive web-based educational system called PDinamet.Postprint (author's final draft

Models for online, open, flexible and technology enhanced higher education across the globe – a comparative analysis

Digital technology has become near ubiquitous in many countries today or is on a path to reach this state in the near future. Across the globe the share of internet users, for instance, has jumped in the last ten years. In Europe most countries have a share of internet users near to or above 90% in 2016 (last year available for international comparisons), in China the current share is 53%, but this has grown from just 16% in 2007, even in Ethiopia the share has grown from 0.4% to 15.4% in the same period (data from ITU). At the same time expectations of widespread adoption of digital solutions in higher education have been rising. In 2017 the New Media Consortium’s Horizon Report predicted that adaptive learning would take less than a year to be widely adopted (Adams Becker et al., 2017). And projects such as ‘Virtually Inspired’ are showcasing creative examples of how new technologies are already being harnessed to improve the quality of teaching and learning. Furthermore, discussion of the United Nations’ Sustainable Development Goals emphasise the key potentials that digital technology holds for achieving the goals for education in 2030 (UNESCO, 2017). These developments lead university and college leadership to the question of how they should position their institution. What type of digitalisation initiatives can be found practice beyond best practices and future potentials? This is the question that this study attempts to answer. It sets out to analyse how higher education providers from across the world are harnessing digitalisation to improve teaching and learning and learner support and to identify emerging types of practice. For this, it focuses on the dimensions of flexibility of provision (in terms of time, place and pace) and openness of provision (in terms of who has access to learning and support and who is involved in the design of learning provision), as both of these dimensions can significantly benefit from integration of digital solutions. The method of information collation used by the study was a global survey of higher education institutions (HEIs) covering all world continents, more than thirty countries and 69 cases. The survey found that nearly three-quarters of all HEIs have at least one strategic focus and typologies were developed based on this analysis to group HEIs with similar strategic focuses. Overall, the findings suggest that most higher education providers are just at the beginning of developing comprehensive strategies for harnessing digitalisation. For this reason, the authors of this study believe that providers can benefit from the outcomes of this study’s research, as it can be used by university and college leadership for benchmarking similarities and differences and for cooperative peer learning between institutions. The database of cases and the guidelines for reviewing current strategies, which accompany this study, aim to facilitate this learning and evaluation process