Estimating difficulty of learning activities in design stages: A novel application of Neuroevolution

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Título: Estimating difficulty of learning activities in design stages: A novel application of Neuroevolution
Autor/es: Gallego-Durán, Francisco J.
Director de la investigación: Llorens Largo, Faraón
Centro, Departamento o Servicio: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Palabras clave: Neuroevolution | Machine learning | Learning analytics | Education | Games
Área/s de conocimiento: Ciencia de la Computación e Inteligencia Artificial
Fecha de creación: 2015
Fecha de publicación: 2015
Fecha de lectura: 18-dic-2015
Editor: Universidad de Alicante
Resumen: In every learning or training environment, exercises are the basis for practical learning. Learners need to practice in order to acquire new abilities and perfect those gained previously. However, not every exercise is valid for every learner: learners require exercises that match their ability levels. Hence, difficulty of an exercise could be defined as the amount of effort that a learner requires to successfully complete the exercise (its learning cost). Too high difficulties tend to discourage learners and make them drop out, whereas too low difficulties are perceived as unchallenging, resulting in loss of interest. Correctly estimating difficulties is hard and error-prone problem that tends to be done manually using domain-expert knowledge. Underestimating or overestimating difficulty generates a problem for learners, increasing dropout rates in learning environments. This paper presents a novel approach to improve difficulty estimations by using Neuroevolution. The method is based on measuring the computational cost that Neuroevolution algorithms require to successfully complete a given exercise and establishing similarities with previously gathered information from learners. For specific experiments presented, a game called PLMan has been used. PLMan is a PacMan-like game in which users have to program the Artificial Intelligence of the main character using a Prolog knowledge base. Results show that there exists a correlation between students’ learning costs and those of Neuroevolution. This suggests that the approach is valid, and measured difficulty of Neuroevolution algorithms may be used as estimation for student's difficulty in the proposed environment.
URI: http://hdl.handle.net/10045/53697
Idioma: eng
Tipo: info:eu-repo/semantics/doctoralThesis
Derechos: Licencia Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0
Aparece en las colecciones:Tesis doctorales

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