Eduardo Cascallar

Predicting Mathematical Performance: The Effect of Cognitive Processes and Self-Regulation Factors

A substantial number of research studies have investigated the separate influence of working memory, attention, motivation, and learning strategies on mathematical performance and self-regulation in general. There is still little understanding of their impact on performance when taken together, understanding their interactions, and how much each of them contributes to the prediction of mathematical performance. With the emergence of new methodologies and technologies, such as the modelling with predictive systems, it is now possible to study these effects with approaches which use a wide range of data, including student characteristics, to estimate future performance without the need of traditional testing (Boekaerts and Cascallar, 2006). This research examines the different cognitive patterns and complex relations between cognitive variables, motivation, and background variables associated with different levels of mathematical performance using artificial neural networks (ANNs). A sample of 800 entering university students was used to develop three ANN models to identify the expected future level of performance in a mathematics test. These ANN models achieved high degree of precision in the correct classification of future levels of performance, showing differences in the pattern of relative predictive weight amongst those variables. The impact on educational quality, improvement, and accountability is highlighted.

Looking at learning approaches from the angle of student profiles

This study starts with investigating the relation of perceived workload, motivation for learning and working memory capacity (WMC) with students’ approaches to learning. Secondly, this study investigates if differences exist between different student profiles concerning their approach to the learning and the influence of workloads thereon. Results show a relation for workload and motivation but not for WMC. By means of a cluster analysis, three student profiles were identified based on WMC and motivation. Students characterised by high WMC and average motivation scored higher on surface approaches and lower on deep approaches than students with high autonomous motivation. These latter students also score higher on deep approaches than students characterised by low WMC. Finally, it was found that all student profiles responded the same to the influence of workload. In contrast with prior research, deep approaches were higher when the workload was higher.

Self-Regulated Learning and the Understanding of Complex Outcomes

Monique Boekaerts 1 and Mariel Musso 2, 3, 4 and Eduardo C. Cascallar 2, 4 1, Center for the Study of Learning and Instruction, Leiden University, The Netherlands 2, Centre for Research on Teaching and Training, Katholieke Universiteit Leuven, Belgium 3, Universidad Argentina de la Empresa, Buenos Aires, Argentina 4, Assessment Group International, USA/Europe, Brussels, BelgiumReceived 29 November 2012; Accepted 29 November 2012This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.There is ample evidence that the study of self-regulation and self-regulated learning (SRL) in particular is of significant importance in education and in the understanding of the variables that influence learning. In this context, the role of assessment is central to the current work in the field of self-regulation research, to the conceptualizations derived from empirical work, and to the operationalisation of its concepts in individual and classroom implementations (E. C. Cascallar and M. Boekaerts, 2006). Self-regulation is a complex construct to define and to operationalise. The various conceptualizations of self-regulation all presuppose a detailed accounting of many different components, each of them represented by a variety of proxy variables which can be measured to establish the appropriate level at which the individual or group in question is functioning or performing. These assessments involve an evaluative process in order to estimate the level of performance, achievement, or functioning, through a consistent methodology that rigorously estimates and scales individual or group results based on predetermined standards or on normative performance data. In recent years technology has had an enormous impact in improving both the quality and the utility of assessment. New technology-driven infrastructures have contributed to the quality of assessment systems. These technologies, and the conceptual advances they have enabled, have been instrumental in increasing the potential for new feasible designs of instruments, programs, and applications. In the field of SRL, the use of these technologies, new statistical and modeling methodologies, and conceptual advances in the understanding of self-regulated learning, have all contributed to advances that have enriched and also changed this field of study. There is an extensive literature on SRL and its interactions with several environmental and student characteristics. Although several theoretical models have been developed, different authors have focused on several different dimensions or components. While most of them agree that SRL is a complex and dynamic interaction of cognitive, affective, social, and volitional processes in the service of ones own goals, the field is still lacking a unified perspective on these complex phenomena. Definitions of SRL as a relatively stable individual inclination have been shifting to other definitions of SRL as a complex process in situated learning conditions. Papers in this special issue share this last perspective considering multiple processes and their interrelations between student and task/context. Six papers contrast different theoretical and empirical frameworks and collectively show how new methodologies can address the complexities of the interactions of the variables involved. These papers either contribute to a better prediction and understanding of learning outcomes, or they focus on new integrative conceptualizations of the field. Thus, the purpose of this special issue is to consider new methodological and conceptual developments in the understanding of self-regulated learning in different domains such as: academic success, mathematical performance, and successful professional development.T. J. …