@article {324,
	title = {NEW APPROACHES FOR IMPROVED QUALITY IN EDUCATIONAL ASSESSMENTS: USING AUTOMATED PREDICTIVE SYSTEMS IN READING AND MATHEMATICS},
	journal = {Problems of Education in the 21st Century},
	volume = {17},
	year = {2009},
	month = {November/2009},
	type = {Original Article},
	chapter = {134-151},
	abstract = {Education has been impacted by the shift from an industrial society to an information-based environment. We are now shifting again to an {\textquotedblleft}innovation-based{\textquotedblright} society which requires what Sternberg (2000) calls {\textquotedblleft}successful intelligence{\textquotedblright}. As the practice of educational assessment evolves, developments in cognitive science and psychometrics along with continuing advances in technology lead to new views of the nature and function of assessment (Dochy, Segers \& Cascallar, 2003; Braun, 2005). Mathematics and reading have been highlighted as crucial indicators of quality in education providing essential knowledge tools and constituting the foundations for lifelong learning skills (European Report on the Quality of School Education, 2000). New methodologies and technologies, and the emergence of predictive systems, have focused on the possibility of assessments which use a wide range of data or student productions to evaluate their performance without the need of traditional testing (Boekaerts \& Cascallar, 2006). This article presents the application of educational assessments utilizing neural network predictive systems in two pionneering studies in reading readiness and mathematics performance. It introduces the application of these methodologies in education, and evaluates the results and quality of the predictive systems. Results from these methods achieved excellent levels of predictive classification. Their impact on educational quality and improvement, as well as accountability is highlighted.},
	keywords = {mathematics education, neural networks, predictive systems},
	issn = {1822-7864},
	url = {http://oaji.net/articles/2014/457-1399915068.pdf},
	author = {Mariel  Musso and Eduardo  Cascallar}
}
