Teachers’ Moral Evaluation of Students in an Inclusive Secondary School: A Study of Minority Students’ Behaviour and School Performance

This study aims to analyze the issue of morality in a teaching and learning set up. After discussion and answering the question “Is it ever the case that teachers hold students morally blameworthy or praiseworthy for factors that are known to be beyond their control?” the study concludes that teachers hold students to be morally blameworthy or praiseworthy for factors that are beyond their control, because they do not fully comprehend their lack of control over their situation, which is still bad. The study also found that most teachers do not have a clear cross-cultural knowledge of minority students’ background causing a moral judgement dilemma of students’ behaviours and actions. A critical look at other variables that may affect students’ learning is recommended by this study.Keywords: minority students, blameworthy, praiseworthy, knowledge, moral judgement

Dropout, a basic and effective regularization method for a deep learning model: a case study

Deep learning is based on a network of artificial neurons inspired by the human brain. This network is made up of tens or even hundreds of "layers" of neurons. The fields of application of deep learning are indeed multiple; Agriculture is one of those fields in which deep learning is used in various agricultural problems (disease detection, pest detection, and weed identification). A major problem with deep learning is how to create a model that works well, not only on the learning set but also on the validation set. Many approaches used in neural networks are explicitly designed to reduce overfit, possibly at the expense of increasing validation accuracy and training accuracy. In this paper, a basic technique (dropout) is proposed to minimize overfit, we integrated it into a convolutional neural network model to classify weed species and see how it impacts performance, a complementary solution (exponential linear units) are proposed to optimize the obtained results. The results showed that these proposed solutions are practical and highly accurate, enabling us to adopt them in deep learning models.

The Design of Local Wisdom-Based Science Learning on Pressure Materials in Junior High School

The lesson plan is an important aspect that determines the success of learning in the classroom. Teachers must design learning that can relate the material being studied to the daily lives of students, for example, is through integrated local wisdom into science learning. The study aimed to develop a science learning set of local wisdom based on pressure materials in Junior High Schools. The research was research and development and used a 4D development model (Define, Design, Development, and Disseminate). The subjects were material experts, media experts, and science teachers of junior high school. The instrument was a validation sheet of the learning set. Qualitative data were analyzed descriptively, and quantitative data were analyzed using descriptive statistics. This research produces a science learning set of local wisdom-based on pressure materials in junior high schools, consisting of syllabus, lesson plans, assessments, student worksheets, teaching materials, remedial and enrichment programs. The results of expert validation obtained a score of 89.60 with an excellent category. The results of the science teacher assessment were 92.38 with excellent category. In sum, the science learning set of local wisdom based on pressure material in junior high schools was valid and feasible to improve the understanding of science concepts.

Educational Analysis of Young EFL Learners during COVID-19 in Saudi Arabia: Rooting Its Effect

This study was undertaken to pave the way for an enlightened understanding of the effects and reflections of COVID-19 with respect to the educational landscape in terms of distance and online learning set up/learning resources, professional development, management and parental involvement. The researcher employs a mixed-methods approach by utilizing Creswell's [1] explanatory sequential design. There are 100 respondents selected for purposive sampling, which included teachers, students, parents, and school admin staff. The data was collected from 5 cities located in different regions of Saudi Arabia. Based on the results, the study shows that distance and online learning set-up really have a positive impact on students in the Saudi Community. These kind of educational endeavors develop skills for independent learning and they become well-equipped with online learning techniques and styles. It is quite obvious that students hold positive views towards online classes. However, teachers need to enhance their skills when it comes to instructional tools, especially in using computers which is necessary in an online class. Though the school management provides a relevant answer to the queries of students, parents and teachers, the school management should provide support to authorized bodies or the community. In this study, parental intervention was not significant and useful for the students’ assessments. However, parents expect a high standard of performance from their children. It can be revealed in this part that parental intervention is not useful for students’ assessment. In doing so, parents should also limit setting a standard of performance for their children. This might affect the emotional performance of a child. Aside from material support, parents should continue to provide emotional support to their children, especially in these trying times.

Using an implementation science approach to build leader facilitation capability in healthcare: a novel approach for enhancing action learning set facilitation

PurposeThe study describes the implementation of a novel strategy, entitled the Action Learning Set Facilitation Model, to develop internal facilitation capability to lead change. The Model incorporated the Novice-Experienced-Expert pathway, a facilitation development approach underpinning the integrated-Promoting Action on Research Implementation in Health Services Implementation Framework, with action learning methodology.Design/methodology/approachA mixed-methods descriptive approach reports the results of 22 interviews, 182 Action Learning Sets and 159 post program survey data sets to explore facilitator experiences, strengths and potential application of the Model.FindingsAt program completion, five novice (of 174) and one experienced (of 27) facilitator transitioned to the next facilitation level. The three groups of facilitators described positive change in confidence and facilitation skill, and experience of action learning sets. Inconsistencies between self-report competence and observed practice amongst novices was reported. Novices had decreasing exposure to the Model due to factors related to ongoing organisational change. Internal facilitators were considered trusted and credible facilitators.Research limitations/implicationsThere are practical and resource implications in investing in internal facilitation capability, noting proposed and real benefits of similar development programs may be compromised during, or as a consequence of organisational change. Further research describing application of the facilitation model, strategies to enhance multisystemic support for programs and evaluation support are suggested.Practical implicationsThe Action Learning Set Facilitation Model offers promise in developing internal facilitation capability supporting change in organisations. Critical success factors include building broad scale internal capability, stable leadership and longitudinal support to embed practice.Originality/valueThis is the first application of the facilitation component of the integrated-Promoting Action on Research Implementation in Health Services implementation framework embedded to action learning sets as an implementation science strategy for leader development supporting organisational change.

Adverse Selection, Heterogeneous Beliefs, and Evolutionary Learning

We relax the common assumption of homogeneous beliefs in principal-agent relationships with adverse selection. Principals are competitors in the product market and write contracts also on the base of an expected aggregate. The model is a version of a cobweb model. In an evolutionary learning set-up, which is imitative, principals can have different beliefs about the distribution of agents’ types in the population. The resulting nonlinear dynamic system is studied. Convergence to a uniform belief depends on the relative size of the bias in beliefs.

Learning Set Formation and Reversal Learning in Mice During High-Throughput Home-Cage-Based Olfactory Discrimination

Rodent behavioral tasks are crucial to understanding the nature and underlying biology of cognition and cognitive deficits observed in psychiatric and neurological pathologies. Olfaction, as the primary sensory modality in rodents, is widely used to investigate cognition in rodents. In recent years, automation of olfactory tasks has made it possible to conduct olfactory experiments in a time- and labor-efficient manner while also minimizing experimenter-induced variability. In this study, we bring automation to the next level in two ways: First, by incorporating a radio frequency identification-based sorter that automatically isolates individuals for the experimental session. Thus, we can not only test animals during defined experimental sessions throughout the day but also prevent cagemate interference during task performance. Second, by implementing software that advances individuals to the next test stage as soon as performance criteria are reached. Thus, we can prevent overtraining, a known confounder especially in cognitive flexibility tasks. With this system in hand, we trained mice on a series of four odor pair discrimination tasks as well as their respective reversals. Due to performance-based advancement, mice normally advanced to the next stage in less than a day. Over the series of subsequent odor pair discriminations, the number of errors to criterion decreased significantly, thus indicating the formation of a learning set. As expected, errors to criterion were higher during reversals. Our results confirm that the system allows investigating higher-order cognitive functions such as learning set formation (which is understudied in mice) and reversal learning (which is a measure of cognitive flexibility and impaired in many clinical populations). Therefore, our system will facilitate investigations into the nature of cognition and cognitive deficits in pathological conditions by providing a high-throughput and labor-efficient experimental approach without the risks of overtraining or cagemate interference.

The Yield Curve as a Leading Indicator: Accuracy and Timing of a Parsimonious Forecasting Model

Previous studies have shown that the treasury yield curve, T, forecasts upcoming recessions when it obtains a negative value. In this paper, we try to improve the yield curve model while keeping its parsimony. First, we show that adding the federal funds rate, FF, to the model, GDP = f(T,FF), gives seven months vs. five months warning time, and it gives a higher prediction skill for the recessions in the out-of-sample test set. Second, we find that including the quadratic term of the yield curve and the federal funds rate improves the prediction of the 1990 recession, but not the other recessions in the period 1977 to 2019. Third, the T caused a pronounced false peak in GDP for the test set. Restricting the learning set to periods where T and FF were leading the GDP in the learning set did not improve the forecast. In general, recessions are predicted better than the general movement in the economy. A “horse race” between GDP = f(T,FF) and the Michigan consumer sentiment index suggests that the first beats the latter by being a leading index for the observed GDP for more months (50% vs. 6%) during the first test year.