Intensifying learner engagement and focus by a block mode flipped learning pedagogy

As an instructional pedagogy, the flipped learning model has been celebrated as student-centered, promotes active learning, and encourages differentiated instruction. It is argued that this pedagogy allows more time in class for students to engage in problem-solving activities facilitated by the teacher. Students' access to learning resources before each class is critical for this instructional approach to work. The chance of success can be improved using educational technologies like learning management systems where materials are stored and accessed via the Internet. However, Internet access is not always similar among students. In most developed nations, Internet access in major cities is more readily available than in regional areas. In addition, providing high-quality learning materials and designing engaging activities to complement flipped learning requires considerably more resources. Our contributions are three-fold. First, we review the literature on flipped learning to present its development as a teaching pedagogy and how it has been applied in different disciplinary areas. Next, we review block mode, an alternative approach to organizing the academic calendar and course delivery, notably in higher education. Third, we describe our recent research in exploring the blending of flipped learning and block mode scheduling in delivering short certificate courses at the Melbourne Institute of Technology (MIT) in Australia which the authors have had affiliations. Outcomes of our study revealed that the blending of flipped learning and block mode may lead to higher learner satisfaction that was a result of (a) requiring quality learning materials be available before the beginning of each block, and (b) intensifying student engagement and focus when they study one subject at a time in each block, which in turn creates positive pressure on their self-regulation and time management

MOESM2 of Genome-wide linkage disequilibrium and genetic diversity in five populations of Australian domestic sheep

Additional file 2: Figure S1. Distribution of minor allele frequency (MAF) for each population studied. The percentage of SNP is plotted for each frequency bin. Figure S2. Average D' values for each population

Independent variables and the dependent variable and their classification index levels.

Independent variables and the dependent variable and their classification index levels.</p

MOESM1 of Genome-wide linkage disequilibrium and genetic diversity in five populations of Australian domestic sheep

Additional file 1: Table S1. Summary statistics for the SNPs, average minor allele frequency and heterozygosity. Table S2. Average linkage disequilibrium (r2) between adjacent markers on the autosomes (OAR). Table S3. Average linkage disequilibrium (D’) between adjacent markers on the autosomes (OAR). Table S4. Chromosome-wise average linkage disequilibrium (D’) for each population studied. Table S5. Chromosome-wise average linkage disequilibrium (r2) for each population studied. Table S6. Summary of the chromosome-wise haplotype analysis. Table S7. Range of inbreeding coefficients for each population studied. Table S8. Mean linkage disequilibrium in the five populations at varying map distances. Table S9. List of the genes located in the region between 49.2 and 51.2 Mb on OAR15

Local R2 of GWR shows where the model performed best (Esri ArcGISTM 10.3).

<p>Local R2 of GWR shows where the model performed best (Esri ArcGISTM 10.3).</p

The OLS model predictions of impacts of all human-related practice variables on DB infestation on date palm plantations in the study area.

<p>The prediction model shows the areas at risk of DB based on different human-related practice parameters (Esri ArcGISTM 10.3).</p

Impacts of human-related practices on <i>Ommatissus lybicus</i> infestations of date palm in Oman

<div><p>Date palm cultivation is economically important in the Sultanate of Oman, with significant financial investments coming from both the government and private individuals. However, a widespread Dubas bug (DB) (<i>Ommatissus lybicus</i> Bergevin) infestation has impacted regions including the Middle East, North Africa, Southeast Russia, and Spain, resulting in widespread damages to date palms. In this study, techniques in spatial statistics including ordinary least squares (OLS), geographically weighted regression (GRW), and exploratory regression (ER) were applied to (a) model the correlation between DB infestations and human-related practices that include irrigation methods, row spacing, palm tree density, and management of undercover and intercropped vegetation, and (b) predict the locations of future DB infestations in northern Oman. Firstly, we extracted row spacing and palm tree density information from remote sensed satellite images. Secondly, we collected data on irrigation practices and management by using a simple questionnaire, augmented with spatial data. Thirdly, we conducted our statistical analyses using all possible combinations of values over a given set of candidate variables using the chosen predictive modelling and regression techniques. Lastly, we identified the combination of human-related practices that are most conducive to the survival and spread of DB. Our results show that there was a strong correlation between DB infestations and several human-related practices parameters (<i>R</i>² = 0.70). Variables including palm tree density, spacing between trees (less than 5 x 5 m), insecticide application, date palm and farm service (pruning, dethroning, remove weeds, and thinning), irrigation systems, offshoots removal, fertilisation and labour (non-educated) issues, were all found to significantly influence the degree of DB infestations. This study is expected to help reduce the extent and cost of aerial and ground sprayings, while facilitating the allocation of date palm plantations. An integrated pest management (IPM) system monitoring DB infestations, driven by GIS and remote sensed data collections and spatial statistical models, will allow for an effective DB management program in Oman. This will in turn ensure the competitiveness of Oman in the global date fruits market and help preserve national yields.</p></div

The best fit model variables from OLS exploratory regression and their related VIF values.

<p>The best fit model variables from OLS exploratory regression and their related VIF values.</p

Example of GWR parameters (βs) for (A) density of palm trees per acre, (B) pesticides and (C) flood irrigation system.

The examples show how modelled relationships vary across the study area. All maps are of the same scale (Esri ArcGISTM 10.3).</p

Explanatory regression model variables and the percentage of prototypes in which were found significant.

<p>Explanatory regression model variables and the percentage of prototypes in which were found significant.</p