Perceived learning environment and students' emotional experiences: A multilevel analysis of mathematics classrooms.
TLDR
Heckhausen et al. as discussed by the authors used a multilevel approach to analyse relationships between perceived classroom environments and emotions in mathematics and found that environmental characteristics conveying control and value to the students would be related to their experience of enjoyment, anxiety, anger, and boredom in mathematics.About:
This article is published in Learning and Instruction.The article was published on 2007-10-01 and is currently open access. It has received 396 citations till now. The article focuses on the topics: Multilevel model & Boredom.read more
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Inquiry project-based learning with a partnership of three types of teachers and the school librarian
TL;DR: Examination of the approach showed the collaboration between the four teaching staff to be effective through the support of the school administration, an inquiry learning expert, and parents, on the other hand, the main limitation was the extra workload for the teachers.
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Learning by Preparing to Teach: Fostering Self-Regulatory Processes and Achievement During Complex Mathematics Problem Solving
Abstract: We developed an intervention based on the learning by teaching paradigm to foster self-regulatory processes and better learning outcomes during complex mathematics problem solving in a technology-rich learning environment. Seventy-eight elementary students were randomly assigned to 1 of 2 conditions: learning by preparing to teach, or learning for learning (control condition). Students’ conceptualizations (task definitions) of the problem, self-regulatory processes, and mathematics achievement were then compared across the 2 conditions. To measure task definitions of the mathematics problem, students developed concept maps of the problem using a tablet application. To capture self-regulatory processes, students were asked to think out loud as they solved the problem. Results revealed that students in the learning by preparing to teach intervention developed a more detailed and better-organized concept map of the problem compared with students in the control condition. Students in the learning by preparing to teach intervention also engaged in more metacognitive processing strategies and had higher levels of mathematics problem solving achievement compared with students in the control condition. No differences were found, however, in planning and goal setting or in use of cognitive strategies across the 2 conditions. Implications of this research suggest students’ initial task definitions may be a key factor in differences found when learning by teaching compared with solely learning for learning. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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Is early ability grouping good for high-achieving students' psychosocial development? Effects of the transition into academically selective schools
Michael Becker,Marko Neumann,Julia Tetzner,Susanne Böse,Henrike Knoppick,Kai Maaz,Jürgen Baumert,Rainer Lehmann +7 more
TL;DR: In this paper, the authors investigated the effects of early transition from elementary to secondary schools for high-achieving and gifted students in 5th grade in Berlin, Germany, and found that the transition into academically selective learning environments seemed to come at some cost for psychosocial development.
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Social and Dimensional Comparisons in Math and Verbal Test Anxiety: Within- and Cross-domain Relations with Achievement and the Mediating Role of Academic Self-concept☆
TL;DR: In this paper, the authors used the generalized internal/external frame of reference (GI/E) model to investigate the relation between achievement and test anxiety in German seventh grade students.
References
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Hierarchical Linear Models: Applications and Data Analysis Methods
TL;DR: The Logic of Hierarchical Linear Models (LMLM) as discussed by the authors is a general framework for estimating and hypothesis testing for hierarchical linear models, and it has been used in many applications.
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Hierarchical Linear Models: Applications and Data Analysis Methods.
TL;DR: This chapter discusses Hierarchical Linear Models in Applications, Applications in Organizational Research, and Applications in the Study of Individual Change Applications in Meta-Analysis and Other Cases Where Level-1 Variances are Known.
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Multilevel Analysis: Techniques and Applications
TL;DR: This work focuses on the development of a single model for Multilevel Regression, which has been shown to provide good predictive power in relation to both the number of cases and the severity of the cases.
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An attributional theory of motivation and emotion
TL;DR: For a long time, the authors have had the gnawing desire to convey the broad motivational significance of the attributional conception that I have espoused and to present fully the argument that this framework has earned a rightful place alongside other leading theories of motivation.