Refine
Has Fulltext
- no (1048) (remove)
Year of publication
Document Type
- Article (738)
- Monograph/Edited Volume (184)
- Doctoral Thesis (67)
- Part of a Book (26)
- Review (22)
- Other (10)
- Habilitation Thesis (1)
Is part of the Bibliography
- yes (1048) (remove)
Keywords
- Professional development (6)
- Instructional quality (5)
- cyberbullying (5)
- Academic self-concept (4)
- Kooperation (4)
- Teacher training (4)
- Continuing education (3)
- Grundschule (3)
- Lehrerbildung (3)
- Lehrerfortbildung (3)
Institute
- Department Erziehungswissenschaft (1048) (remove)
The evaluation of how (human) individuals perceive robots is a central issue to better understand human-robot interaction (HRI). On this topic, promising proposals have emerged. However, present tools are not able to assess a sufficient part of the composite psychological dimensions involved in the evaluation of HRI. Indeed, the percentage of variance explained is often under the recommended threshold for a construct to be valid. In this article, we consolidate the lessons learned from three different studies and propose a further developed questionnaire based on a multicomponent approach of anthropomorphism by adding traits from psychosocial theory about the perception of others and the attribution and deprivation of human characteristics: the de-humanization theory. Among these characteristics, the attribution of agency is of main interest in the field of social robotics as it has been argued that robots could be considered as intentional agents. Factor analyses reveal a four sub-dimensions scale including Sociability, Agency, Animacy, and the Disturbance. We discuss the implication(s) of these dimensions on future perception of and attitudes towards robots.
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.
Background:
Using the internet to search for information or share images about self-harm is an emerging risk among young people. The aims of this study were (a) to analyze the prevalence of different types of self-harm on the internet and differences by sex and age, and (b) to examine the relationship of self-harm on the internet with intrapersonal factors (i.e., depression and anxiety) and interpersonal factors (i.e., family cohesion and social resources).
Method:
The sample consisted of 1,877 adolescents (946 girls) between 12 and 17 years old (Mage = 13.41, SD = 1.25) who completed self-report measures.
Results:
Approximately 11% of the participants had been involved in some type of self-harm on the internet. The prevalence was significantly higher among girls than boys and among adolescents older than 15 years old. Depression and anxiety increased the risk of self-harm on the internet, whereas family cohesion decreased the probability of self-harm on the internet.
Conclusions:
Self-harm on the internet is a relatively widespread phenomenon among Spanish adolescents. Prevention programs should include emotional regulation, coping skills, and resilience to reduce in this behavior.
German secondary education is known for its early, strict selection of students into different schooling tracks based on prior academic performance, based on the assumption that students learn more efficiently when the learning environment is tailored to their individual abilities and needs. While much previous research has shown that entry into tracks is socially selective, less is known whether there are effects of being exposed to a particular school track on educational success and which mechanisms are contributing to these effects. We investigate this question by comparing the learning progress in reading and mathematics of students in the upper and intermediate schooling track over five years of secondary schooling, based on large-scale German-wide longitudinal data (NEPS-SC3). Even when restricting our sample to a group of students with similar preconditions and controlling for skills at the beginning of secondary schooling, we find that the learning progress in the upper track is higher for both domains, suggesting scissor effects of track exposure. It is mainly the average performance level of the class, and to a lesser degree its social background composition, which mediates these effects. In contrast, migration background composition of the class and instructional quality perceived by students hardly contribute to explaining increasing learning gains in the upper track.