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

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Title: Estimating difficulty of learning activities in design stages: A novel application of Neuroevolution
Authors: Gallego-Durán, Francisco J.
Research Director: Llorens Largo, Faraón
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Neuroevolution | Machine learning | Learning analytics | Education | Games
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Date Created: 2015
Issue Date: 2015
Date of defense: 18-Dec-2015
Publisher: Universidad de Alicante
Abstract: 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.
Language: eng
Type: info:eu-repo/semantics/doctoralThesis
Rights: Licencia Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0
Appears in Collections: Doctoral theses

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