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Background
Building on the Realistic Accuracy Model, this paper explores whether it is easier for teachers to assess the achievement of some students than others. Accordingly, we suggest that certain individual characteristics of students, such as extraversion, academic self-efficacy, and conscientiousness, may guide teachers' evaluations of student achievement, resulting in more appropriate judgements and a stronger alignment of assigned grades with students' actual achievement level (as measured using standardized tests).
Aims
We examine whether extraversion, academic self-efficacy, and conscientiousness moderate the relations between teacher-assigned grades and students' standardized test scores in mathematics.
Sample
This study uses a representative sample of N = 5,919 seventh-grade students in Germany (48.8% girls; mean age: M = 12.5, SD = 0.62) who participated in a national, large-scale assessment focusing on students' academic development.
Methods
We specified structural equation models to examine the inter-relations of teacher-assigned grades with students' standardized test scores in mathematics, Big Five personality traits, and academic self-efficacy, while controlling for students' socioeconomic status, gender, and age.
Results
The correlation between teacher-assigned grades and standardized test scores in mathematics was r = .40. Teacher-assigned grades more closely related to standardized test scores when students reported higher levels of conscientiousness (beta = .05, p = .002). Students' extraversion and academic self-efficacy did not moderate the relationship between teacher-assigned grades and standardized test scores.
Conclusions
Our findings indicate that students' conscientiousness is a personality trait that seems to be important when it comes to how closely mathematics teachers align their grades to standardized test scores.
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.
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.
Der Kooperation von Lehrkräften wird für die Bewältigung der komplexen Anforderungen des Schulalltags großes Potenzial zugeschrieben. Dennoch ist Kooperation in vielen Lehrkräftekollegien nicht selbstverständlich. Auf Basis einer Befragung von N = 489 Grundschullehrkräften untersucht dieser Beitrag in einem querschnittlichen Design die kollegiale Kooperation in Schulen in Deutschland. Mit einer Regression wurde unter Berücksichtigung der Mehrebenenstruktur der Daten geprüft, in welchem Ausmaß personale, kompetenzbezogene und institutionelle Merkmale die Umsetzung verschiedener Kooperationsformen wahrscheinlicher machen. Die Ergebnisse zeigen, dass die Kooperationsform „Austausch“ in der Arbeit der Lehrkräfte ausgeprägt wahrgenommen wurde, die Kooperationsform „Kokonstruktion“ weniger. Zudem zeigen sich Gemeinsamkeiten, aber auch Unterschiede in den begünstigenden Faktoren. Während sich für beide Kooperationsformen die Wahrnehmung kollektiver Selbstwirksamkeit und das Zusammenspiel zwischen organisatorischen und räumlichen Rahmenbedingungen als prädiktiv erwiesen, spielte der Enthusiasmus lediglich für den Austausch und die Unterrichtserfahrung nur für die Kokonstruktion eine Rolle.
We present the first systematic literature review on stress and burnout in K-12 teachers during the COVID-19 pandemic. Based on a systematic literature search, we identified 17 studies that included 9,874 K-12 teachers from around the world. These studies showed some indication that burnout did increase during the COVID-19 pandemic. There were, however, almost no differences in the levels of stress and burnout experienced by K-12 teachers compared to individuals employed in other occupational fields. School principals' leadership styles emerged as an organizational characteristic that is highly relevant for K-12 teachers' levels of stress and burnout. Individual teacher characteristics associated with burnout were K-12 teachers' personality, self-efficacy in online teaching, and perceived vulnerability to COVID-19. In order to reduce stress, there was an indication that stress-management training in combination with training in technology use for teaching may be superior to stress-management training alone. Future research needs to adopt more longitudinal designs and examine the interplay between individual and organizational characteristics in the development of teacher stress and burnout during the COVID-19 pandemic and beyond.
er vorliegende Beitrag beschäftigt sich mit den Publikationen, die in der Zeitschrift für Erziehungswissenschaft (ZfE) in den Jahren 1998–2017 veröffentlicht wurden. Angesichts der Veränderungen in der erziehungswissenschaftlichen Forschungslandschaft in der jüngeren Vergangenheit untersuchen wir, inwiefern sich eine veränderte Schwerpunktsetzung auch in den Beiträgen der ZfE nachweisen lassen. Dazu führen wir zunächst eine quantitative Textanalyse durch und identifizieren die häufigsten sowie die charakteristischen Bigramme (Zweiwortsequenzen) in vier aufeinanderfolgenden Fünfjahres-Abschnitten (1998–2002, 2003–2007, 2008–2012, 2013–2017). Zudem prüfen wir, inwiefern bestimmte Wortstämme (bspw. „erziehungswissenschaft“, „bildungsforsch“, „didakt“) über die Jahre hinweg häufiger auftreten. Schließlich erstellen wir mit dem Textmining Tool Leximancer™ concept maps, die Hinweise auf die semantische Struktur der Themengebiete und Schlüsselkonzepte geben. Die Ergebnisse deuten darauf hin, dass im gesamten Zeitraum mehrheitlich Beiträge mit empirischem Fokus publiziert wurden, ein inhaltlicher Fokus auf sozialen Aspekten von Bildung lag und die Beschäftigung mit der allgemeinen Erziehungswissenschaft abnahm.
Teachers' attitudes toward inclusion are frequently cited as being an important predictor of how successfully a given inclusive school system is implemented. At the same time, beliefs about the nature of teaching and learning are discussed as a possible predictor of attitudes toward inclusion. However, more recent research emphasizes the need of considering implicit processes, such as automatic evaluations, when describing attitudes and beliefs. Previous evidence on the association of attitudes toward inclusion and beliefs about teaching and learning is solely based on explicit reports. Therefore, this study aims to examine the relationship between attitudes toward inclusion, beliefs about teaching and learning, and the subsequent automatic evaluations of pre-service teachers (N = 197). The results revealed differences between pre-service teachers' explicit attitudes/beliefs and their subsequent automatic evaluations. Differences in the relationship between attitudes toward inclusion and beliefs about teaching and learning occur when teachers focus either on explicit measures or automatic evaluations. These differences might be due to different facets of the same attitude object being represented. Relying solely on either explicit measures or automatic evaluations at the exclusion of the other might lead to erroneous assumptions about the relation of attitudes toward inclusion and beliefs about teaching and learning.
Previous research has identified students' personality traits, especially conscientiousness, as highly relevant predictors of academic success. Less is known about the role of Big Five personality traits in students when it comes to teachers' decisions about students' educational trajectories and whether personality traits differentially affect these decisions by teachers in different grade levels. This study examines to what extent students' Big Five personality traits affect teacher decisions on grade retention, looking at two cohorts of 12,146 ninth-grade and 6002 seventh-grade students from the German National Educational Panel Study. In both grade levels, multilevel logistic mediation models show that students' conscientiousness indirectly predicts grade retention through the assignment of grades by teachers. In the ninth-grade sample, students' conscientiousness was additionally a direct predictor of retention, distinct from teacher-assigned grades. We discuss potential underlying mechanisms and explore whether teachers base their decisions on different indicators when retaining seventh-grade students or ninth-grade students.
Die Fähigkeit zu beraten gilt als ein wichtiger Aspekt professioneller Kompetenz von Lehrkräften. Lehrveranstaltungskonzepte, die theoretisches Beratungswissen vermitteln und gleichzeitig praktische Erfahrungen im Beraten ermöglichen, sind daher hochrelevant für die Entwicklung berufsspezifischer Fähigkeiten. Angelehnt an ein vierdimensionales Modell der Beratungskompetenz wurde an der Universität Potsdam ein Seminarkonzept für angehende Lehrkräfte entwickelt. Es bietet Lerngelegenheiten, um Beratungswissen zu Kommunikations-, Diagnostik-, Problemlöse- und Bewältigungs-Skills zu erwerben und dieses Wissen in konstruierten Beratungssituationen im Seminar anzuwenden, die klassisch für die berufliche Schulpraxis sind. Darüber hinaus wurden die Lehramtsstudierenden motiviert, spezifische Beratungskompetenzen – konkret das aktive Zuhören – im Rahmen der Schulpraktischen Übungen anzuwenden. Erste Erkenntnisse der Analyse der durchgeführten Unterrichtsstunden werden dargestellt.
Reflecting in written form on one's teaching enactments has been considered a facilitator for teachers' professional growth in university-based preservice teacher education. Writing a structured reflection can be facilitated through external feedback. However, researchers noted that feedback in preservice teacher education often relies on holistic, rather than more content-based, analytic feedback because educators oftentimes lack resources (e.g., time) to provide more analytic feedback. To overcome this impediment to feedback for written reflection, advances in computer technology can be of use. Hence, this study sought to utilize techniques of natural language processing and machine learning to train a computer-based classifier that classifies preservice physics teachers' written reflections on their teaching enactments in a German university teacher education program. To do so, a reflection model was adapted to physics education. It was then tested to what extent the computer-based classifier could accurately classify the elements of the reflection model in segments of preservice physics teachers' written reflections. Multinomial logistic regression using word count as a predictor was found to yield acceptable average human-computer agreement (F1-score on held-out test dataset of 0.56) so that it might fuel further development towards an automated feedback tool that supplements existing holistic feedback for written reflections with data-based, analytic feedback.