3 research outputs found
Anotação semântica para recomendação de conteúdos educacionais
Orientador: Julio Cesar dos ReisDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Sistemas de apoio à aprendizagem exploram diversos recursos multimídia para considerar individualidades do aluno bem com diferentes estilos de aprendizagem. Todavia, a crescente quantidade de conteúdos educacionais disponíveis em diferentes formatos e de maneira fragmentada di?culta o acesso e compreensão dos conceitos em estudo. Embora a literatura tenha proposto abordagens para explorar técnicas de recomendação que permitem representação explícita de semântica por meio de artefatos como ontologias, essa linha não foi totalmente explorada e ainda requer muitos esforços de pesquisa. Esta pesquisa objetiva conceber um método de recomendação de conteúdo educacional explorando o uso de anotações semânticas sobre transcrições textuais de videoaulas. As anotações servem como metadados que expressam o signi?cado de trechos das aulas. A técnica de recomendação, como principal contribuição esperada, fundamenta-se nas anotações disponíveis para de?nir estratégias de ranking de conteúdos disponíveis a partir da proximidade semântica dos conceitos combinadas com técnicas de aprendizagem de máquina. A contribuição envolve o desenvolvimento de protótipos funcionais de software para validação experimental com base em conteúdos de videoaulas reais e deve destacar as principais vantagens e limitações da abordagem. Os resultados obtidos permitirão o acesso à recomendações mais adequadas para melhorar o processo de aprendizagem apresentando a possibilidade de uma experiência mais satisfatória pelos alunosAbstract: Learning support systems explore several audio-visual resources to consider individual needs and learning styles aiming to stimulate learning experiences. However, the large amount of online educational content in di?erent formats and the possibility of making them available in a fragmented way turns di?cult the tasks of accessing these resources and understanding the concepts under study. Although literature has proposed approachestoexploreexplicitsemanticrepresentationthroughartifactssuchasontologies in learning support systems, this research line still requires further investigation e?orts. In this MS.c. dissertation, we propose a method for recommending educational content by exploring the use of semantic annotations over textual transcriptions from video lectures. Our investigation addresses the di?culties in extracting entities from natural language texts in subtitles of videos. Our work studies how to re?ne concepts in a domain ontology to support semantic annotation of video lecture subtitles. We report on the design of a video lecture recommendation system which explores the extracted semantic annotations. Our solution explored semantically annotated videos with an ontology in the Computer Science domain. Obtained results indicate our recommendation mechanism is suited to ?lter relevant video content in di?erent use scenariosMestradoCiência da ComputaçãoMestre em Ciência da Computação2017/02325-5; 2018/00313-2FAPES
A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness
People increasingly use videos on the Web as a source for learning. To
support this way of learning, researchers and developers are continuously
developing tools, proposing guidelines, analyzing data, and conducting
experiments. However, it is still not clear what characteristics a video should
have to be an effective learning medium. In this paper, we present a
comprehensive review of 257 articles on video-based learning for the period
from 2016 to 2021. One of the aims of the review is to identify the video
characteristics that have been explored by previous work. Based on our
analysis, we suggest a taxonomy which organizes the video characteristics and
contextual aspects into eight categories: (1) audio features, (2) visual
features, (3) textual features, (4) instructor behavior, (5) learners
activities, (6) interactive features (quizzes, etc.), (7) production style, and
(8) instructional design. Also, we identify four representative research
directions: (1) proposals of tools to support video-based learning, (2) studies
with controlled experiments, (3) data analysis studies, and (4) proposals of
design guidelines for learning videos. We find that the most explored
characteristics are textual features followed by visual features, learner
activities, and interactive features. Text of transcripts, video frames, and
images (figures and illustrations) are most frequently used by tools that
support learning through videos. The learner activity is heavily explored
through log files in data analysis studies, and interactive features have been
frequently scrutinized in controlled experiments. We complement our review by
contrasting research findings that investigate the impact of video
characteristics on the learning effectiveness, report on tasks and technologies
used to develop tools that support learning, and summarize trends of design
guidelines to produce learning video
Artificial Intelligence methodologies to early predict student outcome and enrich learning material
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