Teachers’ behavior is a key factor that influences students’ motivation. Many theoretical models have tried to explain this influence, with one of the most thoroughly researched being self-determination theory (SDT). We used a Delphi method to create a classification of teacher behaviors consistent with SDT. This is useful because SDT-based interventions have been widely used to improve educational outcomes. However, these interventions contain many components. Reliably classifying and labeling those components is essential for implementation, reproducibility, and evidence synthesis. We used an international expert panel (N = 34) to develop this classification system. We started by identifying behaviors from existing literature, then refined labels, descriptions, and examples using the Delphi panel’s input. Next, the panel of experts iteratively rated the relevance of each behavior to SDT, the psychological need that each behavior influenced, and its likely effect on motivation. To create a mutually exclusive and collectively exhaustive list of behaviors, experts nominated overlapping behaviors that were redundant, and suggested new ones missing from the classification. After three rounds, the expert panel agreed upon 57 teacher motivational behaviors (TMBs) that were consistent with SDT. For most behaviors (77%), experts reached consensus on both the most relevant psychological need and influence on motivation. Our classification system provides a comprehensive list of TMBs and consistent terminology in how those behaviors are labeled. Researchers and practitioners designing interventions could use these behaviors to design interventions, to reproduce interventions, to assess whether these behaviors moderate intervention effects, and could focus new research on areas where experts disagreed.
Teachers’ behaviour is a key factor that influences students’ motivation. Many theoretical models have tried to explain this influence, with one of the most thoroughly researched being self-determination theory (SDT). We used a Delphi method to create a classification of teacher behaviours consistent with SDT. This is useful because SDT-based interventions have been widely used to improve educational outcomes. However, these interventions contain many components. Reliably classifying and labelling those components is essential for implementation, reproducibility, and evidence synthesis. We used an international expert panel (N = 34) to develop this classification system. We started by identifying behaviours from existing literature, then refined labels, descriptions, and examples using the experts’ input. Next, these experts iteratively rated the relevance of each behaviour to SDT, the psychological need that each behaviour influenced, and its likely effect on motivation. To create a mutually exclusive and collectively exhaustive list of behaviours, experts nominated overlapping behaviours that were redundant, and suggested new ones missing from the classification. After three rounds, the expert panel agreed upon 57 teacher motivational behaviours that were consistent with SDT. For most behaviours (77%), experts reached consensus on both the most relevant psychological need and influence on motivation. Our classification system provides a comprehensive list of teacher motivational behaviours and consistent terminology in how those behaviours are labelled. Researchers and practitioners designing interventions could use these behaviours to design interventions, to reproduce interventions, to assess whether these behaviours moderate intervention effects, and could focus new research on areas where experts disagreed. Educational impact and implications statementThe things teachers do in class have an important influence on their students’ motivation, engagement, and learning. This study uses an international expert panel to identify the teacher behaviours most likely to influence motivation—specifically, teacher behaviours that increase the more healthy, autonomous motivation that comes from within students. This list of behaviours, agreed upon by the experts, could be used by teachers trying to improve their practice, policymakers trying to scale interventions, and researchers trying to assess which behaviours best predict student outcomes.
The aim of this study was to evaluate the effect of banana leaf extract on the quality and shelf life of rainbow trout compared to plastic bags at freezing temperature for 40 days. For evaluating this propose, the antioxidant activity of banana leaf extract was assessed. In addition, the shelf life of fish filets was determined by measuring thiobarbituric acid (TBA) and pHof fish. The banana leaves extract showed the highest content of vitamin E (5.8 ± 0.61 mg /g) and carotenoids (12.8 ± 0.1 mg /g). The potential of Cu (II) reduction the extract was 1.76 ± 0.09. The magnitude of modification in TBA and pH of the packed fish with banana leaves were less than the control samples. The present study demonstrated that the use of banana leaf extract will retard lipid oxidation in fish. fillet during freezing storage that may due to its strong antioxidant properties.
Educational psychology usually focuses on explaining phenomena. As a result, researchers seldom explore how well their models predict the outcomes they care about using best-practice approaches to predictive statistics. In this paper, we focus less on explanation and more on prediction, showing how both are important for advancing the field. We apply predictive models to the role of teachers on student engagement, i.e. the thoughts, attitudes, and behaviours, that translate motivation into progress. We integrate the suggestions from four prominent motivational theories (self-determination theory, achievement goal theory, growth mindset theory, and transformational leadership theory), and aim to identify those most critical behaviours for predicting changes in students’ engagement in physical education. Students (N = 1324 all from year 7, 52% girls) from 17 low socio-economic status schools rated their teacher’s demonstration of 71 behaviours in the middle of the school year. We also assessed students’ engagement at the beginning and end of the year. We trained elastic-net regression models on 70% of the data and then assessed their predictive validity on the held-out data (30%). The models showed that teacher behaviours predicted 4.39% of the variance in students’ change in engagement. Some behaviours that were most consistently associated with a positive change in engagement were being good role models (β = 0.046), taking interest in students’ lives outside of class (β = 0.033), and allowing students to make choices (β = 0.029). The influential behaviours did not neatly fit within any single motivational theory. These findings support arguments for integrating different theoretical approaches, and suggest practitioners may want to consider multiple theories when designing interventions. More generally, we argue that researchers in educational psychology should more frequently test how well their models not just explain, but predict the outcomes they care about.
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