Measuring the difficulty of activities for adaptive learning

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Title: Measuring the difficulty of activities for adaptive learning
Authors: Gallego-Durán, Francisco J. | Molina-Carmona, Rafael | Llorens Largo, Faraón
Research Group/s: Informática Industrial e Inteligencia Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Difficulty estimation | Difficulty measure | Learning activity | Adaptive learning
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: Jun-2018
Publisher: Springer Berlin Heidelberg
Citation: Universal Access in the Information Society. 2018, 17(2): 335-348. doi:10.1007/s10209-017-0552-x
Abstract: An effective adaptive learning system would theoretically maintain learners in a permanent state of flow. In this state, learners are completely focused on activities. To attain this state, the difficulty of learning activities must match learners’ skills. To perform this matching, it is essential to define, measure and deeply analyze difficulty. However, very few previous works deal with difficulty in depth. Most commonly, difficulty is defined as a one-dimensional value. This permits ordering activities, but limits the possibilities of deep analysis of activities and learners’ performance. This work proposes a new definition of difficulty and a way to measure it. The proposed definition depends on learners’ progress on activities over time. This expands the concept of difficulty over a two-dimensional space, also making it drawable. The difficulty graphs provide a rich interpretation with insights into the learning process. A practical case is presented: the PLMan learning system. This system is formed by a web application and a game to teach computational logic. The proposed definition is applied in this context. Measures are taken and analyzed using difficulty graphs. Some examples of these analyses are shown to illustrate the benefits of this proposal. Singularities and interesting spots are easily identified in graphs, providing insights in the activities. This new information lets experts adapt the learning system by improving activity classification and assignment. This first step lays solid foundations for automation, making the PLMan learning system fully adaptive.
ISSN: 1615-5289 (Print) | 1615-5297 (Online)
DOI: 10.1007/s10209-017-0552-x
Language: eng
Type: info:eu-repo/semantics/article
Rights: © Springer-Verlag GmbH Germany 2017
Peer Review: si
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Appears in Collections:INV - i3a - Artículos de Revistas
INV - Smart Learning - Artículos de Revistas

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