Abstracts - Earli

M 131 August 2007 14:35 - 15:55Room: KonferenciaExpert Panel DiscussionA predictive systems approach to the assessment of self-regulatedlearning.Chair: Monique Boekaerts, Leiden University, NetherlandsOrganiser: Monique Boekaerts, Leiden University, NetherlandsOrganiser: Eduardo Cascallar, Leiden University; Assessment Group International, BelgiumIn order to coach teachers adequately, researchers need to supply them with the tools that cancapture students’ successive attempts at self-regulation. Ideally, teachers should be able todetermine the zone of proximal development in self-regulation. In order to do that, they need topredict expected outcomes in terms of the self-regulation strategies that their students are capableof using. How can they be coached to achieve this? First, teachers need insight into the logicalstructure of SR development in a domain. Second, they need a set of assessment tools that capturetheir students’ current level of self-regulated learning. Researchers need to provide teachers withthis information and with the necessary assessment tools. Unfortunately neither the informationnor the tools are available at present. Predictive approaches offer the opportunity to model selfregulationin the classroom. They are capable of discovering complex relationships andinteractions in the inputs (predictors) and outcomes. They help us understand complexrelationships across self-regulation components in a systematic fashion. As such, these approacheswill allow researchers to explain and predict learning outcomes, by examining the pattern ofrelations between the different types of self-regulation strategies that students use habitually in agiven domain (automaticity) and the effect of favorable and unfavorable learning conditions ontheir strategy use.First panelist: Monique Boekaerts (Leiden University)Monique Boekaerts, Leiden University, NetherlandsTeachers need tools that can capture students’ successive attempts at self-regulation. These toolsshould be sensitive enough to provide information about the level of self-regulation that thestudents have attained. Teachers need this information in order to feed it back to the students whenthey discuss progress and to determine the zone of proximal development. Researchers need thisinformation as well in order to study the pattern of relations between the different types of selfregulationstrategies that students use habitually in a given domain (automaticity) and the effectthat favorable and unfavorable learning conditions have on their strategy use. Are predictiveapproaches able to examine in detail the multiple elements of the self-regulation model in anintegrated fashion? Are they able to link all the elements in the model to the objectives andexpected outcomes?Second and third panelist: Eduardo Cascallar and Tracy Costigan.Eduardo Cascallar, Assessment Group International; Leiden University, BelgiumTracy Costigan, American Institutes for Reasearch, Washington DC, USAThis presentation will describe the application of neural networks (NNs) in modeling selfregulationin the classroom. This machine-learning technique, developed to mirror human brain– 686 –