Learning analytics for the prediction of the educational objectives achievement
作者： Manuel Fernandez-Delgado , Manuel Mucientes , Borja Vazquez-Barreiros , Manuel Lama
关键词:
摘要: Prediction of students' performance is one the most explored issues in educational data mining. To predict if students will achieve outcomes subject based on previous results enables teachers to adapt learning design teaching-learning process. However, this adaptation even more relevant we could fulfillment objectives a subject, since should focus resources and activities related those objectives. In paper, present an experiment where support vector machine applied as classifier that predicts different are achieved or not. The inputs problem marks obtained by questionnaires must undertake during course. very good, classifiers achievement with precision over 80%.
sci-hub.se 参考文章(16)
Terence A. Etchells, Alfredo Vellido, Paulo J. G. Lisboa, Àngela Nebot, Francisco Mugica, Learning what is important: feature selection and rule extraction in a virtual course the european symposium on artificial neural networks. pp. 401- 406 ,(2006)
Susan Toohey, Designing Courses for Higher Education ,(1999)
Naeimeh DELAVARI, Somnuk PHON-AMNUAISUK, Mohammad Reza BEIKZADEH, Data Mining Application in Higher Learning Institutions Informatics in education. ,vol. 7, pp. 31- 54 ,(2008) , 10.15388/INFEDU.2008.03
Peter Brusilovsky, Hemanta Nijhavan, A Framework for Adaptive E-Learning Based on Distributed Re-usable Learning Activities E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. ,vol. 2002, pp. 154- 161 ,(2002)
Milos Jovanovic, Milan Vukicevic, Milos Milovanovic, Miroslav Minovic, Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study International Journal of Computational Intelligence Systems. ,vol. 5, pp. 597- 610 ,(2012) , 10.1080/18756891.2012.696923
Daniel Burgos, Colin Tattersall, Rob Koper, How to represent adaptation in e-learning with IMS learning design Interactive Learning Environments. ,vol. 15, pp. 161- 170 ,(2007) , 10.1080/10494820701343736
Nicola Henze, Wolfgang Nejdl, A logical characterization of adaptive educational hypermedia adaptive hypermedia conference. ,vol. 10, pp. 77- 113 ,(2004) , 10.1080/13614560410001728128
Cristóbal Romero, Sebastián Ventura, Educational Data Mining: A Review of the State of the Art systems man and cybernetics. ,vol. 40, pp. 601- 618 ,(2010) , 10.1109/TSMCC.2010.2053532
Ioanna Lykourentzou, George Mpardis, Ioannis Giannoukos, Vassili Loumos, Vassilis Nikolopoulos, Early and dynamic student achievement prediction in e-learning courses using neural networks Journal of the Association for Information Science and Technology. ,vol. 60, pp. 372- 380 ,(2009) , 10.1002/ASI.V60:2
R. Stevens, A. Soller, A. Giordani, L. Gerosa, M. Cooper, C. Cox, Developing a framework for integrating prior problem solving and knowledge sharing histories of a group to predict future group performance collaborative computing. pp. 1651209- ,(2005) , 10.1109/COLCOM.2005.1651209