Utilização de Enriquecimento Semântico para a Recomendação Automática de Videoaulas no Moodle

Considering that videos are highly attractive to students and that it is possible to aggregate several information that identifies a video in a satisfactory way, a solution has been developed to carry out the automatic recommendation of educational videos to the teacher, who in turn can make them available to the students of a certain class, initially in the platform Moodle.The solution uses as a source a video previously processed, in order to obtain the main terms (indexing system) present in the audio of the video and the relationship (recommendation) of that video with the other of the repository. An experiment was carried out that confirmed the correctness and feasibility of use for the recommendation of educational videos in the Moodle platform.

[1]  Erik Duval,et al.  Quantitative Analysis of Learning Object Repositories , 2008, IEEE Transactions on Learning Technologies.

[2]  Mohamed Elemam Shehab,et al.  Personalized E-learning recommendation model based on psychological type and learning style models , 2015, 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS).

[3]  Franca Garzotto,et al.  Content-Based Video Recommendation System Based on Stylistic Visual Features , 2016, Journal on Data Semantics.

[4]  Päivi Rasi,et al.  Student-generated instructional videos facilitate learning through positive emotions , 2017 .

[5]  Cynthia J. Brame,et al.  Effective Educational Videos: Principles and Guidelines for Maximizing Student Learning from Video Content , 2016, CBE life sciences education.

[6]  Luís Carlos Costa Fonseca,et al.  Recomendando Objetos de Aprendizagem a partir das hashtags postadas no Moodle , 2013 .

[7]  Yves Raimond,et al.  Automated interlinking of speech radio archives , 2012, LDOW.

[8]  Carla Limongelli,et al.  A recommendation module to help teachers build courses through the Moodle Learning Management System , 2016, New Rev. Hypermedia Multim..

[9]  Wladmir Cardoso Brandão,et al.  Automatic Content Recommendation and Aggregation According to SCORM , 2017, Informatics Educ..

[10]  Michail N. Giannakos,et al.  Exploring the relationship between video lecture usage patterns and students' attitudes , 2016, Br. J. Educ. Technol..

[11]  E. Costa,et al.  Sistemas de Recomendação de Recursos Educacionais: conceitos, técnicas e aplicações , 2013 .

[12]  L. Abeysekera,et al.  Motivation and cognitive load in the flipped classroom: definition, rationale and a call for research , 2015 .

[13]  Cidcley Teixeira de Souza,et al.  Repositórios de Objetos de Aprendizagem - características, classificações, limitações e tendências , 2017 .

[14]  Eduardo Barrére,et al.  Ampliação das Possibilidades de Gamificação no Moodle , 2017 .

[15]  Priscilla Do Nascimento,et al.  Recomendação de Objetos de Aprendizagem baseada em Modelos de Estilos de Aprendizagem: Uma Revisão Sistemática da Literatura , 2017 .

[16]  Ralph Cafolla,et al.  Project MERLOT: Bringing Peer Review to Web-Based Educational Resources , 2006 .

[17]  Cristian Cechinel Empirical foundations for automated quality assessment of learning objects inside repositories , 2012 .

[18]  Vesna Damnjanovic,et al.  Factors affecting the effectiveness and use of Moodle: students' perception , 2015, Interact. Learn. Environ..

[19]  Vinicius H. Ferreira,et al.  LORSys – Um Sistema de Recomendação de Objetos de Aprendizagem SCORM , 2010 .