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Optimizing power analysis for randomized experiments: design parameters for student achievement
(2024)
Randomized trials (RTs) are promising methodological tools to inform evidence-based reform to enhance schooling. Establishing a robust knowledge base on how to promote student achievement requires sensitive RT designs demonstrating sufficient statistical power and precision to draw conclusive and correct inferences on the effectiveness of educational programs and innovations. Proper power analysis is therefore an integral component of any informative RT on student achievement. This venture critically hinges on the availability of reasonable input variance design parameters (and their inherent uncertainties) that optimally reflect the realities around the prospective RT—precisely, its target population and outcome, possibly applied covariates, the concrete design as well as the planned analysis. However, existing compilations in this vein show far-reaching shortcomings.
The overarching endeavor of the present doctoral thesis was to substantively expand available resources devoted to tweak the planning of RTs evaluating educational interventions. At the core of this thesis is a systematic analysis of design parameters for student achievement, generating reliable and versatile compendia and developing thorough guidance to support apt power analysis to design strong RTs. To this end, the thesis at hand bundles two complementary studies which capitalize on rich data of several national probability samples from major German longitudinal large-scale assessments.
Study I applied two- and three-level latent (covariate) modeling to analyze design parameters for a wide spectrum of mathematical-scientific, verbal, and domain-general achievement outcomes. Three vital covariate sets were covered comprising (a) pretests, (b) sociodemographic characteristics, and (c) their combination. The accumulated estimates were additionally summarized in terms of normative distributions.
Study II specified (manifest) single-, two-, and three-level models and referred to influential psychometric heuristics to analyze design parameters and develop concise selection guidelines for covariate (a) types of varying bandwidth-fidelity (domain-identical, cross-domain, fluid intelligence pretests; sociodemographic characteristics), (b) combinations quantifying incremental validities, and (c) time lags of 1- to 7-year-lagged pretests scrutinizing validity degradation. The estimates for various mathematical-scientific and verbal achievement outcomes were meta-analytically integrated and employed in precision simulations.
In doing so, Studies I and II addressed essential gaps identified in previous repertoires in six major dimensions: Taken together, this thesis accumulated novel design parameters and deliberate guidance for RT power analysis (1) tailored to four German student (sub)populations across the entire school career from Grade 1 to 12, (2) matched to 21 achievement (sub)domains, (3) adjusted for 11 covariate sets enriched by empirically supported guidelines, (4) adapted to six RT designs, (5) suitable for latent and manifest analysis models, (6) which were cataloged along with quantifications of their associated uncertainties. These resources are complemented by a plethora of illustrative application examples to gently direct psychological and educational researchers through pivotal steps in the process of RT design.
The striking heterogeneity of the design parameter estimates across all these dimensions constitutes the overall, joint key result of Studies I and II. Hence, this work convincingly reinforces calls for a close match between design parameters and the specific peculiarities of the target RT’s research context.
All in all, the present doctoral thesis offers a—so far unique—nuanced and extensive toolkit to optimize power analysis for sound RTs on student achievement in the German (and similar) school context. It is of utmost importance that research does not tire to spawn robust evidence on what actually works to improve schooling. With this in mind, I hope that the emerging compendia and guidance contribute to the quality and rigor of our randomized experiments in psychology and education.
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.
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.
This study investigated the role of medium (face-to-face, cyber) and publicity (public, private) in adolescents' perceptions of severity and coping strategies (i.e., avoidant, ignoring, helplessness, social support seeking, retaliation) for victimization, while accounting for gender and cultural values. There were 3432 adolescents (ages 11-15, 49% girls) in this study; they were from China, Cyprus, the Czech Republic, India, Japan, and the United States. Adolescents completed questionnaires on individualism and collectivism, and ratings of coping strategies and severity for public face-to-face victimization, private face-to-face victimization, public cyber victimization, and private cyber victimization. Findings revealed similarities in adolescents' coping strategies based on perceptions of severity, publicity, and medium for some coping strategies (i.e., social support seeking, retaliation) but differential associations for other coping strategies (i.e., avoidance, helplessness, ignoring). The results of this study are important for prevention and intervention efforts because they underscore the importance of teaching effective coping strategies to adolescents, and to consider how perceptions of severity, publicity, and medium might influence the implementation of these coping strategies.
Teachers' self-efficacy beliefs have been shown to be related to their supportive teaching practices and to adolescents' motivation. Yet, little is known about these relations in elementary school. The present study examined the longitudinal effects of teachers' self-efficacy on student-perceived teacher support and students' mathematics interest with a sample of 2082 students and 133 teachers in third and fourth grade. Results revealed that teachers' self-efficacy was longitudinally related to student-perceived support, which in turn was positively related to students' interest. The findings underline the relevance of teachers' self-efficacy beliefs and teacher support for the development of students' interest in elementary school.
When used in a sensible way, Interactive Whiteboards (IWB) are supposed to motivate and engage students in learning in the classroom. Thereby, they might also stimulate students who are usually more restrained, such as more anxious students. However, the body of research on the impact of IWB lessons is rather small. The present study investigated whether a 45-minute lesson with the IWB compared to a conceptual identical 45-minute lesson without the IWB would support learning and motivation within the subject English as a foreign language for German students. Moreover, the study examined whether the 45-minute lesson with the IWB compared to the 45-minute lesson without the IWB would be better able to counteract the detrimental effects of foreign language classroom anxiety (FLCA). One hundred and two eighth graders from two secondary schools in Germany took part in this study and were either taught with the IWB (condition IWB; n = 53) or without the IWB (condition No-IWB; n = 50). Results showed that students in the IWB condition stated to be higher motivated and performed better in a vocabulary test than their counterparts in the No-IWB condition. FLCA was negatively correlated with performance in the vocabulary test. Other than expected, learning with the IWB did not compensate the detrimental effect of FLCA, meaning that learning with the IWB was more beneficial than learning without the IWB irrespective of a student's FLCA. Implications of the study will be discussed.
Student teachers often struggle to keep track of everything that is happening in the classroom, and particularly to notice and respond when students cause disruptions. The complexity of the classroom environment is a potential contributing factor that has not been empirically tested. In this experimental study, we utilized a virtual reality (VR) classroom to examine whether classroom complexity affects the likelihood of student teachers noticing disruptions and how they react after noticing. Classroom complexity was operationalized as the number of disruptions and the existence of overlapping disruptions (multidimensionality) as well as the existence of parallel teaching tasks (simultaneity). Results showed that student teachers (n = 50) were less likely to notice the scripted disruptions, and also less likely to respond to the disruptions in a comprehensive and effortful manner when facing greater complexity. These results may have implications for both teacher training and the design of VR for training or research purpose. This study contributes to the field from two aspects: 1) it revealed how features of the classroom environment can affect student teachers' noticing of and reaction to disruptions; and 2) it extends the functionality of the VR environment-from a teacher training tool to a testbed of fundamental classroom processes that are difficult to manipulate in real-life.
In this study, we investigated retention intention and job satisfaction of 238 first-year alternatively certified (AC) teachers. Drawing on Organizational Socialization Theory, we tested the hypothesis that AC teacher extraversion and perceived school support are positively related to the two variables and mediated by self-efficacy. To test our hypothesis, we applied structural equation modeling. Our results demonstrate that extraversion and perceived social support are positively related to retention intentions and job satisfaction. In addition, self-efficacy serves as a mediator. The findings could help school administrators to better understand how to support and retain AC teachers and thus address teacher shortages.
Wages and wage dynamics directly affect individuals' and families' daily lives. In this article, we show how major theoretical branches of research on wages and inequality-that is, cumulative advantage (CA), human capital theory, and the lifespan perspective-can be integrated into a coherent statistical framework and analyzed with multilevel dynamic structural equation modeling (DSEM). This opens up a new way to empirically investigate the mechanisms that drive growing inequality over time. We demonstrate the new approach by making use of longitudinal, representative U.S. data (NLSY-79). Analyses revealed fundamental between-person differences in both initial wages and autoregressive wage growth rates across the lifespan. Only 0.5% of the sample experienced a "strict" CA and unbounded wage growth, whereas most individuals revealed logarithmic wage growth over time. Adolescent intelligence and adult educational levels explained substantial heterogeneity in both parameters. We discuss how DSEM may help researchers study CA processes and related developmental dynamics, and we highlight the extensions and limitations of the DSEM framework.
Wie bewerten begabte und leistungsstarke Jugendliche in separaten Spezialklassen ihren Unterricht?
(2022)
Leistungsstarke und besonders begabte Schüler*innen werden im Unterricht oft nicht genügend gefordert. In speziellen Klassen für besonders Leistungsstarke und Begabte kann der Unterricht stärker auf die Lernmöglichkeiten dieser Gruppe zugeschnitten werden. Spezialklassen gelten insgesamt als leistungsförderlich, Studien zur Unterrichtsqualität sind bisher jedoch rar. In dieser Studie wird untersucht, wie Schüler*innen der Leistungs- und Begabungsklassen (LuBK) im Land Brandenburg die Qualität ihres Unterrichts in Deutsch und Mathematik im Vergleich zu Schüler*innen von Regelklassen einschätzen. Die Datenbasis bilden N = 3371 Schüler*innen der 8. und 10. Jahrgangsstufe aus 33 Schulen. Mittels Fragebögen wurden Merkmale der Unterrichtsqualität nach dem QuAIT-Modell erfragt; die Datenanalyse erfolgte mit regressionsanalytischen Mehrebenenmodellen. Die Schüler*innen der LuBK bewerten die Qualität ihres Unterrichts überwiegend positiver als die Schüler*innen der Regelklassen, Defizite zeigen sich jedoch in beiden Klassentypen bei den Qualitätsmerkmalen der inneren Differenzierung und der Mitsprache bei Unterrichtsthemen.
Teacher self-efficacy for classroom management is an important component of teachers' identity with implications for their teaching quality. Theoretically, it has been described that contextual variables play an important role for self-efficacy development and its consequences. However, little is known about the interrelationships of job resources and demands with teacher self-efficacy, and consequences for teachers' professional behaviors. We extend teacher self-efficacy research by drawing on the Job Demands-Resources model in examining contextual influences on developmental dynamics between classroom management self-efficacy and teacher-reported classroom management, from prior to qualifying as a teacher until mid-career. Participants were 395 primary and secondary Australian school teachers. Longitudinal structural equation models showed teachers' classroom management self-efficacy positively related to aspects of their perceived classroom management, particularly during early career. Between early and mid-career, the positive relationship between self-efficacy and classroom management was moderated by early career excessive demands. Implications are outlined for teacher education and school administration.