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Overview and details of the sessions of this conference. Please select a date or room to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
Session Overview
Session
PAP-19: Math and Science
Time: Thursday, 30/Aug/2012: 9:00am - 10:30am
Session Chair: Narciss Susanne, University of Dresden
Location: 457
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Presentations

How Well do Motivation and Academic Achievement Predict Course Preferences?

Cathy Tran1, AnneMarie M. Conley1, Stuart Karabenick2

1University of California, Irvine, United States of America; 2University of Michigan, United States of America

This study explores the influence of motivation and academic achievement on student course preferences. We focus on two motivation components: expectancy and value, which highlight the perceived competence to be able to do the task (expectancy) and reasons for wanting to do the task (value). Seventh to ninth grade students (N = 2,424) in an urban school district reported their expectancy and task values for mathematics and their preference for math class (relative to science and English) at the beginning and at the end of the school year. Our research extends the work on early adolescent expectancies and task values by looking at groups of students with different initial preferences for math class as well as by focusing on the understudied populations of Vietnamese and Hispanic students. Results indicate that for students who preferred a non-math class most in the fall, increases in self-reported math expectancy and task value during the school year independently increased their probability of switching their preference to math class at the end of the year. For students who had a preference for math class at the outset, increases in math expectancy and task value independently increased their probability of still having a preference for math class at the end of the year. Changes in math achievement scores during the school year did not significantly influence student class preferences at the end of the school year for both groups.

Predicting long-term growth in adolescents' mathematics achievement: It is not how smart you are, but how motivated you are and how you study that is important.

Kou Murayama1, Reinhard Pekrun1, Stephanie Lichtenfeld1, Rudolf vom Hofe2

1University of Munich, Germany; 2University of Bielefeld

This research examined how adolescents' motivation (perceived control, intrinsic motivation, and extrinsic motivation), cognitive learning strategies (elaborative and surface strategies), and intelligence jointly predict long-term growth in math achievement over five years. Using longitudinal data from six annual waves (grades 5 through 10; N=3,530), latent growth curve modeling was employed to analyze growth in math achievement. Results showed that the initial level of math achievement was strongly predicted by intelligence, with motivation and cognitive strategies having additional effects. In contrast, intelligence had no effect on the growth of achievement over years, whereas motivation and learning strategies were predictors of growth. These findings highlight the importance of motivation and learning strategies in facilitating adolescents' development of mathematical competencies over time.

Investigating the impact of perceived competence on student behaviour through logfile analyses

Narciss Susanne1, Schnaubert Lenka1, Eichelmann Anaj1, Andres Eric2, Goguadze George2

1Technische Universitaet Dresden, Germany; 2DFKI, CelTech, Germany

This paper aims at illustrating how logfile analyses can be used for investigating how motivational factors such as perceived competence influence students' behaviour within web-based multi-trial error-correction-tasks. Data for these logfile-analyses were collected in a pre-test-treatment-post-test study with 159 students (mean age 12 years, 80male). Participants of this study worked on tasks-with-typical errors (TWTE) during the treatment. TWTE are specifically designed multi-trial-learning-tasks which contain one (or several) specific task-requirement(s) and one (or several) typical error(s) related to these task requirements. Students were asked to detect and correct the errors. They had at least three attempts to correct an error. All 1855 logfiles of the TWTE students worked on were included in the analyses. These logfiles were analyzed with regard to behavioural traces related to successful and unsuccessful attempts to solve a TWTE. In particular, we compared the behaviour of students with low vs. high perceptions of competence subsequent to an unsuccessful attempt (= failure). In line with research regarding the influence of perceived competence on performance and motivation, our findings suggest that students with a low perceived competence tend to perform poorer and specifically skip trials more often after a failure. Results from such logfile analyses provide a basis for developing remedial adaptation strategies for web-based learning environments.


 
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