Contextual web searches in Facebook using learning materials and discussion messages

Prates, João Carlos; Fritzen, Eduardo; Siqueira, Sean W. M.; Braz, Maria Helena Lima Baptista; Andrade, Leila Nalis Paiva da Silva

doi:10.1016/j.chb.2012.06.002

Cited by 13 publications

(4 citation statements)

References 15 publications

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“…Orlikowski and Scott (2014) Online evaluation Online and traditional valuation is significantly different in performativity, which has organizational implications. Prates et al (2013) Web search engines Social media data are adopted to improve contextual information extraction through web searching. Rapp et al (2013) Contagion effect Positive contagion effects of social media use are found in enhancing brand performance, retailer performance and consumer-retailer loyalty.…”

Section: Recommendationmentioning

confidence: 99%

Technology in the 21st century: New challenges and opportunities

Sheng

Amankwah‐Amoah

Wang

2019

Technological Forecasting and Social Change

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Although big data, big data analytics (BDA) and business intelligence have attracted growing attention of both academics and practitioners, a lack of clarity persists about how BDA has been applied in business and management domains. In reflecting on Professor Ayre's contributions, we want to extend his ideas on technological change by incorporating the discourses around big data, BDA and business intelligence. With this in mind, we integrate the burgeoning but disjointed streams of research on big data, BDA and business intelligence to develop unified frameworks. Our review takes on both technical and managerial perspectives to explore the complex nature of big data, techniques in big data analytics and utilisation of big data in business and management community. The advanced analytics techniques appear pivotal in bridging big data and business intelligence. The study of advanced analytics techniques and their applications in big data analytics led to identification of promising avenues for future research.

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Section: Recommendationmentioning

confidence: 99%

Technology in the 21st century: New challenges and opportunities

Sheng

Amankwah‐Amoah

Wang

2019

Technological Forecasting and Social Change

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“…Recommendation systems in education filed should be personalized by the objectives of the task rather than on the preferences of students (Losada and Martín, 2014). Online social networks allow users to share ideas, activities, events, and interests within their contact network (Prates et al, 2013). This research project will prove the feasibility of using recommendation systems and social media applications in educational environments to deliver a working prototype of a recommender system that will assist the learner, and this implies learning from or reusing previous students querying experience, activity-based context which is based on users actions (Kramár and Bieliková, 2012), use of social media interactions among students.…”

Section: Introductionmentioning

confidence: 96%

A Personalized Learning Recommendation System Architecture for Learning Management System

Syed

Palade

Iqbal

et al. 2017

Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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The information on the web is ever increasing and it is becoming difficult for students to find appropriate information or relevant learning material to satisfy their needs. Technology Enhanced Learning (TEL) is an area which covers all technologies that improve students learning. Effective Personal Learning Recommendation Systems (PLRS) will not only reduce this burden of information overload by recommending the relevant learning material to the students of their interest, but also provide them with "right" information at the "right" time and in the "right" way. In this paper, we first present a detailed analysis of existing TEL recommendation systems and identify the challenges that exist for developing and evaluating the datasets. Then, we propose an architecture for developing a PLRS that aims to support students via a Learning Management System (LMS) to find relevant material in order to enhance student learning experience. Also we proposes a methodology for building our own collaborative dataset via learning management systems (LMS) and educational repositories. This dataset will enhance student learning by recommending learning materials from the former student's competence qualifications. The proposed dataset offer information on the usage of more than 19,296 resources from 628 courses apart from data from social learner networks (forums, blogs, wikis and chats), which constitutes another 3,600 stored files Finally, we also present some future challenges and a roadmap for developing TEL PLRSs.

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“…Devido às crescentes demandas por conhecimento dentro das organizações e uma disponibilidade limitada de recursos e competências para suprir tais demandas, muitos profissionais, tanto da indústria quanto da academia, acabam buscando por conhecimento em fontes externas para resolver os seus problemas [Prates et al, 2013], [West et al, 2014]. Essas fontes externas são muitas vezes os motores de busca da Web, sites ou mesmo comunidades online, onde pessoas visam encontrar soluções para seus problemas diários.…”

Section: Introductionunclassified

Identificando Colegas para Ajudar em Minhas Dúvidas: Um Estudo Empírico em Comunidades de Perguntas e Respostas

Procaci

Siqueira

Andrade

2015

Workshops Do Congresso Brasileiro De Informática Na Educação

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Online communities of questions and answers have become important places for users to exchange information and build knowledge through interactions. People ask questions and want to get answers. Colleagues (other users), called reliable users, who are willing to help and may provide good answers in such communities can support the knowledge building. Therefore, we investigated users' attributes with artificial neural network and clustering algorithm for finding reliable users. The results show that the usage of an artificial neural network is a good approach as around 90% of the users were correctly identified while the clustering algorithm makes easier to find groups of reliable users.Resumo. As comunidades online de perguntas e respostas tornaram-se lugares para usuários construírem novos conhecimentos. As perguntas são feitas e os usuários esperam obter respostas. Colegas (outros usuários) que possam ajudar a prover boas respostas, chamados de usuários confiáveis, podem apoiar esta construção de conhecimento. Assim, investigamos atributos de usuários de comunidades online juntamente com o uso de rede neural artificial e algoritmo de agrupamento para encontrar os usuários confiáveis das comunidades. 90% dos usuários foram corretamente identificados como confiáveis através de rede neural e o algoritmo de agrupamento possibilitou encontrar grupos de usuários confiáveis com mais facilidade.

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Contextual web searches in Facebook using learning materials and discussion messages

Cited by 13 publications

References 15 publications

Technology in the 21st century: New challenges and opportunities

Technology in the 21st century: New challenges and opportunities

A Personalized Learning Recommendation System Architecture for Learning Management System

Identificando Colegas para Ajudar em Minhas Dúvidas: Um Estudo Empírico em Comunidades de Perguntas e Respostas

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