Abstracts - Earli

E 2429 August 2007 14:30 - 16:30Room: 0.83 EötvösSymposiumTechnology-based assessments of learning strategies and selfregulationof learningChair: Joachim Wirth, Duisburg-Essen University, GermanyOrganiser: Joachim Wirth, Duisburg-Essen University, GermanyDiscussant: Sarah Manlove, Universiteit Twente, NetherlandsMany aspects of the use of learning strategies as well as of the self-regulation of learning are notreadily observable. Therefore, one challenge in studying self-regulated learning is to find ways todocument these aspects. Questionnaires and other widely used self-report measures regard selfregulatedand strategic learning as a trait that can be assessed independently from a specificlearning situation. But inconsistent and low correlations with learning outcome indicate a lowvalidity of these measures. Technology-based assessments regard self-regulated and strategiclearning as an event and therefore record behavioral data online while a specific learning process.Several studies using these kinds of technology-based online measures demonstrate their moderateto high validity. Thus, it seems that technology-based measures are promising new approaches onthe assessment of learning strategies and the self-regulation of learning. The symposium presentsnew developments in the field of technology-based online measures including computer-based aswell as video-based methods. Chances and challenges will be discussed concerning theirimplications on models and theory of self-regulated learning and concerning practical issues of theassessment and the support of self-regulated and strategic learning.What happens during the learning process? A study with 11th graders in self-regulated learning,their motivation and their learning strategiesAnita Püttmann, University of Frankfurt, GermanyRegina Vollmeyer, University of Frankfurt, GermanyThe cognitive-motivational process model (Vollmeyer & Rheinberg, 1998) assumes that initialmotivation affects performance via the mediator strategies - besides the mediators motivationalstate and functional state during the learning. However, there is a discussion as how to measurestrategy. Self-reported measures and objective measures do not predict performance in the sameway. Therefore in this study we tried to find several objective indicators for strategy. Whilestudents were learning using a pc-based physic program initial motivation, motivation and flowwhile learning, and learning strategies were measured. Participants were 32 high-school students(15 female, 17 male), average age 16.25. As the first aim of the study was to test several strategyindicators we ran a factor analysis which revealed two factors that could be interpreted asqualitative and quantitative clusters. However, only the qualitative cluster, and especially "timespent on animations" influenced performance. A second aim was to test the cognitive-motivationalmodel. As the correlation between the applied strategies, motivational variables and performance,demonstrated gender differences we ran a path analysis. This path analysis ends up the attempt tounderstand what happens during the learning process.– 313 –