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

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Generic system architecture for context-aware, distributed recommendation

In the existing literature on recommender systems, it is difficult to find an architecture for large-scale implementation. Often, the architectures proposed in papers are specific to an algorithm implementation or a domain. Thus, there is no clear architectural starting point for a new recommender system. This paper presents an architecture blueprint for a context-aware recommender system that provides scalability, availability, and security for its users. The architecture also contributes the dynamic ability to switch between single-device (offline), client-server (online), and fully distributed implementations. From this blueprint, a new recommender system could be built with minimal design and implementation effort regardless of the application.Electrical and Computer Engineerin

External pressures on teaching

[FIRST PARAGRAPHS] The primary role of the PRS-LTSN is to improve the quality of education by encouraging the sharing of good practice and innovation, and the discussion of common problems. However, there are other forces at play, which are pursuing the same end by different means. The purpose of this article is to explain what these forces are, and how the PRS-LTSN can help departments to satisfy their demands. The first set of pressures comes from the Government via the funding councils, namely the requirement for higher education institutions (HEIs) to be publicly accountable for the services they provide with Government funding. The assumption is that the two main activities of HEIs are teaching and research: ● The Research Assessment Exercise2 (RAE) is conducted by the Higher Education Funding Council for England (HEFCE) on behalf of the other funding councils, and research ratings have a major influence on funding. ● The assessment of the quality of teaching and of institutional quality assurance mechanisms is the responsibility of the Quality Assurance Agency (QAA) (see Appendix), which is an independent body funded jointly by the funding councils, Universities UK (UUK) and the Standing Conference of Principals (SCoP). Ratings do not affect funding, except that there is the ultimate sanction of withdrawal of funding for persistently unsatisfactory programmes of study. ● More recently, the Transparency Review commissioned by the funding councils evaluates the extent to which funding for research is actually spent on research, and funding for teaching is actually spent on teaching

Hybrid Collaborative Filtering with Autoencoders

Collaborative Filtering aims at exploiting the feedback of users to provide personalised recommendations. Such algorithms look for latent variables in a large sparse matrix of ratings. They can be enhanced by adding side information to tackle the well-known cold start problem. While Neu-ral Networks have tremendous success in image and speech recognition, they have received less attention in Collaborative Filtering. This is all the more surprising that Neural Networks are able to discover latent variables in large and heterogeneous datasets. In this paper, we introduce a Collaborative Filtering Neural network architecture aka CFN which computes a non-linear Matrix Factorization from sparse rating inputs and side information. We show experimentally on the MovieLens and Douban dataset that CFN outper-forms the state of the art and benefits from side information. We provide an implementation of the algorithm as a reusable plugin for Torch, a popular Neural Network framework

Reflections on the EU objectives in addressing aggressive tax planning and harmful tax practices Final Report. CEPS Report November 2019

This Report analyses the EU’s instruments to tackle aggressive tax planning and harmful tax practices. Based on desk research, interviews with stakeholders and expert assessments, it considers the coherence, relevance, and added value of the EU’s approach. The instruments under analysis are found to be internally coherent and consistent with other EU policies and with the international tax agenda, in particular with the OECD/G20 BEPS framework. The Report also confirms the continued relevance of most of the original needs and problems addressed by the EU’s initiatives in the field of tax avoidance. There is also EU added value in having common EU instruments in the field to bolster coordination and harmonise the implementation of tax measures. One cross-cutting issue identified is the impact of digitalisation on corporate taxation. Against this background, the Report outlines potential improvements to the EU tax strategy such as: making EU tax systems fit for the digital era; leading the international debate on tax avoidance; enabling capacity building in Member States and developing countries; strengthening tax good governance in third countries; ensuring a consistent approach at home and abroad; achieving a level playing field for all companies; and increasing tax certainty and legal certainty

THOR: A Hybrid Recommender System for the Personalized Travel Experience

One of the travelers’ main challenges is that they have to spend a great effort to find and choose the most desired travel offer(s) among a vast list of non-categorized and non-personalized items. Recommendation systems provide an effective way to solve the problem of information overload. In this work, we design and implement “The Hybrid Offer Ranker” (THOR), a hybrid, personalized recommender system for the transportation domain. THOR assigns every traveler a unique contextual preference model built using solely their personal data, which makes the model sensitive to the user’s choices. This model is used to rank travel offers presented to each user according to their personal preferences. We reduce the recommendation problem to one of binary classification that predicts the probability with which the traveler will buy each available travel offer. Travel offers are ranked according to the computed probabilities, hence to the user’s personal preference model. Moreover, to tackle the cold start problem for new users, we apply clustering algorithms to identify groups of travelers with similar profiles and build a preference model for each group. To test the system’s performance, we generate a dataset according to some carefully designed rules. The results of the experiments show that the THOR tool is capable of learning the contextual preferences of each traveler and ranks offers starting from those that have the higher probability of being selected