Improving the expressiveness of black-box models for predicting student performance.
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 lea...
| Publicado en: | Computers in Human Behavior Vol. 72; pp. 621 - 632 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
| Publicado: |
Elsevier B.V.
Jul2017
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=122721694&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 122721694 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07475632 JC4 jtl: Computers in Human Behavior issn: 07475632 maglogo: N pubinfo: dt: Jul2017 vid: 72 pid: 2410 pub: Elsevier B.V. artinfo: ui: 122721694 10.1016/j.chb.2016.09.001 ppf: 621 ppct: 11 formats: tig: atl: Improving the expressiveness of black-box models for predicting student performance. aug: au: Villagrá-Arnedo, Carlos J. Gallego-Durán, Francisco J. Llorens-Largo, Faraón Compañ-Rosique, Patricia Satorre-Cuerda, Rosana Molina-Carmona, Rafael affil: Department of Computer Science and Artificial Intelligence, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 Alicante, Spain su: Educational tests & measurements Forecasting Teachers Computer graphics Learning strategies sug: subj: Educational tests & measurements Forecasting Teachers Computer graphics Learning strategies keyword: Black-box models Graphical representation Prediction Student performance Black-box models Graphical representation Prediction Student performance ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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