TOWARDS FORMATIVE E-ASSESSMENT IN PROJECT MANAGEMENT THROUGH PERSONALIZED AUTOMATED FEEDBACK

Formative e-assessment is a complex process, in which learners can build their knowledge, fill up their knowledge gaps or increase their learning abilities. The feedback mechanism is considered to be highly important for the formative dimension of e-assessment. Current paper proposes a model for automated feedback in a project management e-assessment environment: the model blends a built-in feedback sheet (a document containing the correct answers) with a recommender engine, which searches the web for references related to the incorrectly answered questions. The feedback model is personalized, because the web search is made taking into account the user profile: the list of concepts which weren’t correctly understood. This list of concepts is mapped on project management domain ontology.e-assessment, project management, automated feedback, ontology, knowledge system

Recommendation system using the k-nearest neighbors and singular value decomposition algorithms

Nowadays, recommendation systems are used successfully to provide items (example: movies, music, books, news, images) tailored to user preferences. Amongst the approaches existing to recommend adequate content, we use the collaborative filtering approach of finding the information that satisfies the user by using the reviews of other users. These reviews are stored in matrices that their sizes increase exponentially to predict whether an item is relevant or not. The evaluation shows that these systems provide unsatisfactory recommendations because of what we call the cold start factor. Our objective is to apply a hybrid approach to improve the quality of our recommendation system. The benefit of this approach is the fact that it does not require a new algorithm for calculating the predictions. We are going to apply two algorithms: k-nearest neighbours (KNN) and the matrix factorization algorithm of collaborative filtering which are based on the method of (singular-value-decomposition). Our combined model has a very high precision and the experiments show that our method can achieve better results

Color Imagery for Destination Recommendation in Regional Tourism

This paper presents a novel recommender service system that considers the image as a uniform representation for tourists’ expectations, destinations, and local tourism SMEs. Images carried by each stakeholder role is modeled and managed by several system modules, and they also evolve to reflect the real time situations of each entity. In addition, the system is dynamic in terms of its emphasis on the dynamic relationships among these roles and entities. When interactions occur, image mixing will be conducted to derive extra image attributes for the adjustments of the images. Besides, since colors can be mapped onto emotions, this paper adopts colors to operate the image matching and mixing process in order to find good matches of destinations for the recommendations meeting the tourists’ emotional needs. Although this image related approach we proposed is used in tourism domain, we believe our method could also contribute to other areas of either practical applications or academic studies

A Comparative Study of Recommendation Systems

Recommendation Systems or Recommender Systems have become widely popular due to surge of information at present time and consumer centric environment. Researchers have looked into a wide range of recommendation systems leveraging a wide range of algorithms. This study investigates three popular recommendation systems in existence, Collaborative Filtering, Content-Based Filtering, and Hybrid recommendation system. The famous MovieLens dataset was utilized for the purpose of this study. The evaluation looked into both quantitative and qualitative aspects of the recommendation systems. We found that from both the perspectives, the hybrid recommendation system performs comparatively better than standalone Collaborative Filtering or Content-Based Filtering recommendation syste

SINVLIO: using semantics and fuzzy logic to provide individual investment portfolio recommendations

Portfolio selection addresses the problem of how to diversify investments in the most efficient and profitable way possible. Portfolio selection is a field of study that has been broached from several perspectives, including, among others, recommender systems. This paper presents SINVLIO (Semantic INVestment portfoLIO), a tool based on semantic technologies and fuzzy logic techniques that recommends investments grounded in both psychological aspects of the investor and traditional financial parameters of the investments. The results are very encouraging and reveal that SINVLIO makes good recommendations, according to the high degree of agreement between SINVLIO and expert recommendationsThis work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the projects SONAR2 (TSI-020100-2008-665) and the Spanish Ministry of Science and Innovation under the project “FINANCIAL LINKED OPEN DATA REASONING AND MANAGEMENT FOR WEB SCIENCE” (TIN2011-27405).Publicad

Sistema de recomendações no SAPO Campus: desenvolvimento e avaliação: estudo de caso do mecanismo implementado no SAPO Campus

Mestrado em Comunicação MultimédiaCom o lançamento e utilização de novas ferramentas para contextos educativos que pretendem realçar a importância da comunicação e colaboração em ambientes de aprendizagem, surgem novos desafios que é necessário investigar para encontrar soluções adequadas. No caso deste estudo, procura-se compreender e encontrar soluções adequadas para a problemática da sobrecarga de informação que pode ocorrer num espaço em que todos os elementos de uma comunidade têm a possibilidade de partilhar conteúdo e comunicar entre si. Nestes contextos, é possível que a informação disponível possa atingir uma quantidade que o utilizador não tem a capacidade de assimilar na sua totalidade e, muitas vezes, originando que perca informação relevante para si. Uma possível solução para este desafio passa pela integração de sistemas de recomendação transversais a essas novas ferramentas, potenciando o seu desenvolvimento baseado no próprio utilizador e também na sua interação com a comunidade, promovendo o desenvolvimento de novas formas de partilha e de interação entre os utilizadores. Neste trabalho apresentam-se as principais etapas da conceção, desenvolvimento e avaliação da integração de um sistema de recomendações numa plataforma social para contexto educativo, o SAPO Campus.With the advent of new educational tools for learning contexts with the purpose of highlighting the importance of communication and collaboration in learning environments, new challenges should be taken into account in order to discover proper solutions. With this investigation, we seek to understand and find a suitable approach to the information overload problem that can occur in places where all community members have the ability to communicate and share content. It is possible, in these contexts, for the available information to reach such an amount that the user will not have the ability to fully assimilate it, which often causes the loss of the track of data, which is importante to the user. A possible solution for this challenge implies the integration of transverse recommendation systems in these tools, enhancing their growth based on the user and his interaction with the community and promoting the emergence of new methods for him to share and interact with others. This study presents the main steps of the design, development and assessment of the process for integrating a recommendation system in SAPO Campus, a social platform for the educational context

Improving customer generation by analysing website visitor behaviour

This dissertation describes the creation of a new integrated Information Technology (IT) system that assisted in the collection of data about the behaviour of website visitors as well as sales and marketing data for those visitors who turned into customers. A key contribution to knowledge was the creation of a method to predict the outcome of visits to a website from visitors’ browsing behaviour. A new Online Tracking Module (OTM) was created that monitored visitors’ behaviour while they browsed websites. When a visitor converted into a customer, then customer and marketing data as well as sales activity was saved in a new Customer Relationship Management (CRM) system that was implemented in this research. The research focused on service websites. The goal of these websites was to promote products and services online and turn enquiries into offline sales. The challenge faced by these websites was to convince as many visitors as possible to enquire. Most websites relied on Search Engine Optimisation (SEO) and Pay Per Click (PPC) advertising for traffic generation. This research used PPC advertising to generate traffic. An important aspect of PPC advertising was landing page optimisation. The aim of landing page optimisation was to increase the number of visitors to a website who completed a specific action on the website. In the case of the websites investigated in this research the action consisted of completing and sending an enquiry form from the websites. The research looked for meaningful commonalities in the data collected by MS CRM and the OTM and combined this with feedback from the collaborating company’s sales team to create two personas for website visitors who had enquired. Techniques for improving landing pages were identified and these led to changes to landing pages. Some of these changes were targeted at a particular visitor persona. The effect of changes made to a landing page was measured by comparing its conversion rate and bounce rate before and after the changes. Behavioural data collected by the OTM was then analysed using a data mining engine to find models that could predict whether a user would convert based on their browsing behaviour. Models were found that could predict the outcome of a visit to a service website.EThOS - Electronic Theses Online ServiceGBUnited Kingdo