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Edwin Montoya

Researcher at EAFIT University

Publications -  16
Citations -  76

Edwin Montoya is an academic researcher from EAFIT University. The author has contributed to research in topics: Computer science & Context (language use). The author has an hindex of 2, co-authored 12 publications receiving 20 citations.

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Journal ArticleDOI

Comparison and Evaluation of Different Methods for the Feature Extraction from Educational Contents

TL;DR: The experiments show that according to the type of content and metric, the performance of the feature extraction methods is very different; in some cases are better than the others, and in other cases is the inverse.
Journal ArticleDOI

Affective recommender systems in the educational field. A systematic literature review

TL;DR: In this work, a systematic literature review of affective recommender systems in learning environments is performed to explore the state of the art of the influence of emotions in the educational field, especially in contentRecommender systems.
Journal ArticleDOI

Autonomous recommender system architecture for virtual learning environments

TL;DR: A hybrid recommendation model is described that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used.
Journal ArticleDOI

An automatic approach of audio feature engineering for the extraction, analysis and selection of descriptors

TL;DR: In this article, a hybrid scheme of extraction of audio descriptors based on different principles is proposed for the analysis and selection of these descriptors in a given audio context, which is tested on grouping tasks and compared to previous works on audio classification problems, with encouraging results.
Book ChapterDOI

Traceability Analysis of Patterns Using Clustering Techniques

TL;DR: In this article, the traceability of the knowledge evolution is analyzed by analyzing the capabilities of the techniques to identify the patterns that represent the common information in datasets, and the evolution of their characteristics over time is analyzed.