An Animated Pedagogical Agent as a Learning Management System manipulating Intelligent Learning Objects

Wilges, Beatriz; Mateus, Gustavo Pereira; Silveira, Rosemary Silva da; Nassar, Sílvia Modesto

doi:10.1109/icalt.2007.53

Cited by 3 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Usually, the nonprofessional athletes rely on online information about training methods and nutritional recommendation and iAPERAS represented a more reliable alternative. In Wilges, et al, the authors tested a framework aiming to implement a set of resources for developing Intelligent Learning Objects (Wilges et al, 2007) which argued that the learning environment implemented accordingly is more flexible and adaptable than current approaches. An environment model coupled with an Animated Pedagogical Agent interacted with two agents of the systems.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of phonetic aware speech recognition through multi-objective evolutionary algorithms

Bird

Wanner

Ekárt

et al. 2020

Expert Systems with Applications

View full text Add to dashboard Cite

Recent advances in the availability of computational resources allow for more sophisticated approaches to speech recognition than ever before. This study considers Artificial Neural Network and Hidden Markov Model methods of classification for Human Speech Recognition through Diphthong Vowel sounds in the English Phonetic Alphabet rather than the classical approach of the classification of whole words and phrases, with a specific focus on both single and multi-objective evolutionary optimisation of bioinspired classification methods. A set of audio clips are recorded by subjects from the United Kingdom and Mexico and the recordings are transformed into a static dataset of statistics by way of their Mel-Frequency Cepstral Coefficients (MFCC) at sliding window length of 200ms as well as a reshaped MFCC timeseries format for forecast-based models. An deep neural network with evolutionary optimised topology achieves 90.77% phoneme classification accuracy in comparison to the best HMM that achieves 86.23% accuracy with 150 hidden units, when only accuracy is considered in a single-objective optimisation approach. The obtained solutions are far more complex than the HMM taking around 248 seconds to train on powerful hardware versus 160 for the HMM. A multi-objective approach is explored due to this. In the multiobjective approaches of scalarisation presented, within which real-time resource usage is also considered towards solution fitness, far more optimal

show abstract

Section: Introductionmentioning

confidence: 99%

Optimisation of phonetic aware speech recognition through multi-objective evolutionary algorithms

Bird

Wanner

Ekárt

et al. 2020

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

IoT and Big Data Analytics in E-Learning

Попчев

Orozova

Stoyanov

2019

2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE)

View full text Add to dashboard Cite

Semantic Modelling for E-Learning Coordination

Elçi

Ertuğrul

Elçi

2016

Advances in Educational Technologies and Instructional Design

View full text Add to dashboard Cite

This chapter reports a metadata model for e-learning coordination based on content semantics grounded on Semantic Web concepts. To start with, e-learning models are surveyed identifying process content, such as phases, activities, data schema, rules and relations, so on, as relevant for a coordination model. The study looks into the mechanism of e-learning environment and how e-learning processes can be classified for purposes of activity coordination. A metadata model for coordination of e-learning is being sought as expressed in Semantic Web concepts and transcribed using the related languages like OWL, RDF, SPARQL and others. A learner centric proposal is advanced which augments the IEEE Learning Technology Standards Committee's industry leader Learning Technology System Architecture. Examples are worked out and future research directions are identified. This is a continuing research project on application of Semantic Web initiative in e-learning; i.e., design and development of markup and annotation tools, relevant ontologies and intelligent agents in meeting learner needs.

show abstract

An Animated Pedagogical Agent as a Learning Management System manipulating Intelligent Learning Objects

Cited by 3 publications

References 2 publications

Optimisation of phonetic aware speech recognition through multi-objective evolutionary algorithms

Optimisation of phonetic aware speech recognition through multi-objective evolutionary algorithms

IoT and Big Data Analytics in E-Learning

Semantic Modelling for E-Learning Coordination

Contact Info

Product

Resources

About