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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.
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.
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.
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.
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.
Students' achievement emotions are critical in their academic development. Therefore, teachers need to create an emotionally positive learning environment. In the light of this, the present study investigated the connection between students' enjoyment, anxiety, boredom and, in the first case, students' academic self-concept and, in the second, teachers' diagnostic skills. The third part of our study examined whether this link would be moderated by students' academic self-concept. Our sample comprised N = 1803 eighth-grade students who reported their achievement emotions and evaluated the diagnostic skills of both their German and mathematics teachers. Hierarchical models indicated that students experience more enjoyment and less anxiety and boredom if teachers exhibit better diagnostic skills. The role of teachers' diagnostic skills in relation to students' emotions was in part moderated by the students' self-concept. These results are discussed in terms of their implications for effective teaching.
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.
Several studies have revealed that older students in a year group reach higher achievement scores than younger students in that group. But less is known about how students' relative age in class relates to their self-perception of academic achievement, their social acceptance in class and to how teachers judge their abilities. Therefore, we examined relative age effects within class on students' academic self-concept, peer relations, grades, and teachers' secondary school recommendation. Analyses were based on a sample of N = 18,956 German fourth graders, who had never been retained or accelerated. We applied multilevel regression to control for covariates at the individual and classroom level. There were no substantial relative age effects within class across any of the outcomes, except for a small advantage for the youngest in their reading self-concept. Our findings therefore contradict the common assumption that younger students in class are disadvantaged compared to their older classmates.
Over the past few years, studying abroad and other educational international experiences have become increasingly highly regarded. Nevertheless, research shows that only a minority of students actually take part in
academic mobility programs. But what is it that distinguishes those students who take up these international opportunities from those who do not? In this
study we reviewed recent quantitative studies on why (primarily German) students choose to travel abroad or not. This revealed a pattern of predictive factors. These indicate the key role played by students’ personal and social background, as well as previous international travel and the course of studies they are enrolled in. The study then focuses on teaching students. Both facilitating and debilitating factors are discussed and included in a model illustrating the decision-making process these students use. Finally, we discuss the practical implications for ways in which international, studyrelated travel might be increased in the future. We suggest that higher education institutions analyze individual student characteristics, offering differentiated programs to better meet the needs of different groups, thus raising the likelihood of disadvantaged students participating in academic international travel.