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It is well-documented that academic achievement is associated with students' self-perceptions of their academic abilities, that is, their academic self-concepts. However, low-achieving students may apply self-protective strategies to maintain a favorable academic self-concept when evaluating their academic abilities. Consequently, the relation between achievement and academic self-concept might not be linear across the entire achievement continuum. Capitalizing on representative data from three large-scale assessments (i.e., TIMSS, PIRLS, PISA; N = 470,804), we conducted an integrative data analysis to address nonlinear trends in the relations between achievement and the corresponding self-concepts in mathematics and the verbal domain across 13 countries and 2 age groups (i.e., elementary and secondary school students). Polynomial and interrupted regression analyses showed nonlinear relations in secondary school students, demonstrating that the relations between achievement and the corresponding self-concepts were weaker for lower achieving students than for higher achieving students. Nonlinear effects were also present in younger students, but the pattern of results was rather heterogeneous. We discuss implications for theory as well as for the assessment and interpretation of self-concept.
Due to the COVID-19 pandemic, all schools in Germany were locked down for several months in 2020. How schools realized teaching during the school lockdown greatly varied from school to school. N = 2,647 parents participated in an online survey and rated the following activities of teachers in mathematics, language arts (German), English, and science / biology during the school lockdown: frequency of sending task assignments, task solutions and requesting for solutions, giving task-related feedback, grading tasks, providing lessons per videoconference, and communicating via telecommunication tools with students and / or parents. Parents also reported student academic outcomes during the school lockdown (child's learning motivation, competent and independent learning, learning progress). Parents further reported student characteristics and social background variables: child's negative emotionality, school engagement, mathematical and language competencies, and child's social and cultural capital. Data were separately analyzed for elementary and secondary schools. In both samples, frequency of student-teacher communication was associated with all academic outcomes, except for learning progress in elementary school. Frequency of parent-teacher communication was associated with motivation and learning progress, but not with competent and independent learning, in both samples. Other distant teaching activities were differentially related to students' academic outcomes in elementary vs. secondary school. School engagement explained most additional variance in all students' outcomes during the school lockdown. Parent's highest school leaving certificate incrementally predicted students' motivation, and competent and independent learning in secondary school, as well as learning progress in elementary school. The variable "child has own bedroom" additionally explained variance in students' competent and independent learning during the school lockdown in both samples. Thus, both teaching activities during the school lockdown as well as children's characteristics and social background were independently important for students' motivation, competent and independent learning, and learning progress. Results are discussed with regard to their practical implications for realizing distant teaching.
Die vorliegende Studie untersucht die Zusammenhänge zwischen integrativem Schulleitungshandeln, das transformationale und instruktionale Komponenten enthält, und der Nutzungshäufigkeit verschiedener Datenquellen durch Lehrkräfte. Die Ergebnisse eines Strukturgleichungsmodells zeigen, dass integrative Führung direkte und indirekte Zusammenhänge mit der Nutzung verschiedener Datenquellen aufweist. Die Effekte scheinen vorwiegend durch die Kooperationsaktivität der Lehrkräfte vermittelt zu sein.
Motivation and Emotion in Learning and Teaching across Educational Contexts brings together current theoretical and methodological perspectives as well as examples of empirical implementations from leading international researchers focusing on the context specificity and situatedness of their core theories in motivation and emotion.
The book is compiled of two main sections. Section I covers theoretical reflections and perspectives on the main theories on emotion and motivation in learning and teaching and their transferability across different educational contexts illustrated with empirical examples. Section II addresses the methodological reflections and perspectives on the methodology that is needed to address the complexity and context specificity of motivation and emotion. In addition to general reflections and perspectives regarding methodology, concrete empirical examples are provided. All cutting-edge chapters include current empirical studies on emotions and motivation in learning and teaching across different contexts (age groups, domains, countries, etc.) making them applicable and relevant to a wide range of contexts and settings.
This high-quality volume with contributions from leading international experts will be an essential resource for researchers, students and teacher trainers interested in the vital role that motivation and emotions can play in education.
In this article, we address the measurement of individualized instruction in the context of regular classroom instruction. Our study assessed instructional practices geared towards individualization in German third grade reading lessons by combining self-report data from 621 students, from their teachers (n = 57), and live obser-vations. We then investigated the reliability of these different approaches to measuring individualization as well as the agreement between them. All three approaches yielded reliable indicators of individualized practices, but not all of them corresponded with each other. We found considerable agreement between students and observers, but neither agreed with teachers' self-reports. Upon closer examination, we found that students' ratings only correlated with teacher ratings that were provided close to the timepoint of interest. This correlation increased when teacher measures were corrected for response tendencies. We conclude with some recommendations for future studies that aim to measure individualized instruction in the classroom.
Science education researchers typically face a trade-off between more quantitatively oriented confirmatory testing of hypotheses, or more qualitatively oriented exploration of novel hypotheses. More recently, open-ended, constructed response items were used to combine both approaches and advance assessment of complex science-related skills and competencies. For example, research in assessing science teachers' noticing and attention to classroom events benefitted from more open-ended response formats because teachers can present their own accounts. Then, open-ended responses are typically analyzed with some form of content analysis. However, language is noisy, ambiguous, and unsegmented and thus open-ended, constructed responses are complex to analyze. Uncovering patterns in these responses would benefit from more principled and systematic analysis tools. Consequently, computer-based methods with the help of machine learning and natural language processing were argued to be promising means to enhance assessment of noticing skills with constructed response formats. In particular, pretrained language models recently advanced the study of linguistic phenomena and thus could well advance assessment of complex constructs through constructed response items. This study examines potentials and challenges of a pretrained language model-based clustering approach to assess preservice physics teachers' attention to classroom events as elicited through open-ended written descriptions. It was examined to what extent the clustering approach could identify meaningful patterns in the constructed responses, and in what ways textual organization of the responses could be analyzed with the clusters. Preservice physics teachers (N = 75) were instructed to describe a standardized, video-recorded teaching situation in physics. The clustering approach was used to group related sentences. Results indicate that the pretrained language model-based clustering approach yields well-interpretable, specific, and robust clusters, which could be mapped to physics-specific and more general contents. Furthermore, the clusters facilitate advanced analysis of the textual organization of the constructed responses. Hence, we argue that machine learning and natural language processing provide science education researchers means to combine exploratory capabilities of qualitative research methods with the systematicity of quantitative methods.
Hate speech has become a widespread phenomenon, however, it remains largely unclear why adolescents engage in it and which factors are associated with their motivations for perpetrating hate speech. To this end, we developed the multidimensional "Motivations for Hate Speech Perpetration Scale" (MHATE) and evaluated the psychometric properties. We also explored the associations between social norms and adolescents' motivations for hate speech perpetration. The sample consisted of 346 adolescents from Switzerland (54.6% boys; Mage=14; SD=0.96) who reported engagement in hate speech as perpetrators. The analyses revealed good psychometric properties for the MHATE, including good internal consistency. The most frequently endorsed subscale was revenge, followed by ideology, group conformity, status enhancement, exhilaration, and power. The results also showed that descriptive norms and peer pressure were related to a wide range of different motivations for perpetrating hate speech. Injunctive norms, however, were only associated with power. In conclusion, findings indicate that hate speech fulfills various functions. We argue that knowing the specific motivations that underlie hate speech could help us derive individually tailored prevention strategies (e.g., anger management, promoting an inclusive classroom climate). Furthermore, we suggest that practitioners working in the field of hate speech prevention give special attention to social norms surrounding adolescents.
An effective handling of heterogeneous groups in classrooms is one of the main challenges that teachers face when planning their instruction. However, including means of adaptive teaching in classrooms also yields the chance to re-conceptualize classroom instruction. Researchers and practitioners often discuss the question of how different ability levels can be considered adequately in teaching and learning. Because motivation is a central source of competence development and self-regulated learning, the current article discusses how teaching can adapt to learners' different motivational states and traits. In a first step, we review theoretical and empirical perspectives on intra- and interindividual motivational differences in students and their relations to other dimensions of classroom heterogeneity such as gender, ethnic background, and socio-economic status. Against this background, we discuss how instruction can adapt effectively to learners' different motivational needs. We introduce a model of adaptive teaching that refers to students' intra- and interindividual motivational differences and derive conclusions for teacher education and instructional practice.