Improving the expressiveness of black-box models for predicting student performance

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Title: Improving the expressiveness of black-box models for predicting student performance
Authors: Villagrá-Arnedo, Carlos-José | Gallego-Durán, Francisco J. | Llorens Largo, Faraón | Compañ, Patricia | Satorre Cuerda, Rosana | Molina-Carmona, Rafael
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: Black-box models | Prediction | Student performance | Graphical representation
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: Jul-2017
Publisher: Elsevier
Citation: Computers in Human Behavior. 2017, 72: 621-631. doi:10.1016/j.chb.2016.09.001
Abstract: Early prediction systems of student performance can be very useful to guide student learning. For a prediction model to be really useful as an effective aid for learning, it must provide tools to adequately interpret progress, to detect trends and behaviour patterns and to identify the causes of learning problems. White-box and black-box techniques have been described in literature to implement prediction models. White-box techniques require a priori models to explore, which make them easy to interpret but difficult to be generalized and unable to detect unexpected relationships between data. Black-box techniques are easier to generalize and suitable to discover unsuspected relationships but they are cryptic and difficult to be interpreted for most teachers. In this paper a black-box technique is proposed to take advantage of the power and versatility of these methods, while making some decisions about the input data and design of the classifier that provide a rich output data set. A set of graphical tools is also proposed to exploit the output information and provide a meaningful guide to teachers and students. From our experience, a set of tips about how to design a prediction system and the representation of the output information is also provided.
ISSN: 0747-5632 (Print) | 1873-7692 (Online)
DOI: 10.1016/j.chb.2016.09.001
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2016 Elsevier Ltd.
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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