The perception and attitude of educators regarding differentiated teaching in elementary and junior high schools

In the 21st century, the school classroom encompasses great variance between students. Within this differentiated experience teachers must continuously navigate and conduct their lessons. The aim of the current study was to translate the DI-Quest into Hebrew and validate it in Israel, in order to examine the perceptions and integration of differentiated instruction among teachers in Israel. The research included 221 educators who were asked about five components. The findings show significant relations between teachers’ evolving mindset and their work with flexible groups, evaluating teaching, as well as applying differentiated teaching, reflecting that the higher the mindset (indicating an evolving mindset), the higher the application of differentiated teaching and related practical principles. In summary, educators are required to show great flexibility in order to shape learning while adapting to differentiated teaching. They are expected to exercise professional intuition, not only in the context of an orderly curriculum, but mainly to understand students' development and change while learning

Artificial neural networks based controller for glucose monitoring during clamp test.

Insulin resistance (IR) is one of the most widespread health problems in modern times. The gold standard for quantification of IR is the hyperinsulinemic-euglycemic glucose clamp technique. During the test, a regulated glucose infusion is delivered intravenously to maintain a constant blood glucose concentration. Current control algorithms for regulating this glucose infusion are based on feedback control. These models require frequent sampling of blood, and can only partly capture the complexity associated with regulation of glucose. Here we present an improved clamp control algorithm which is motivated by the stochastic nature of glucose kinetics, while using the minimal need in blood samples required for evaluation of IR. A glucose pump control algorithm, based on artificial neural networks model was developed. The system was trained with a data base collected from 62 rat model experiments, using a back-propagation Levenberg-Marquardt optimization. Genetic algorithm was used to optimize network topology and learning features. The predictive value of the proposed algorithm during the temporal period of interest was significantly improved relative to a feedback control applied at an equivalent low sampling interval. Robustness to noise analysis demonstrates the applicability of the algorithm in realistic situations

Regression analysis between the predicted and desired values of the ANN glucose pump controller.

<p>Performance results of the Test set simulation. <b>A</b>: ANN trained using Levenberg-Marquardt (LM) optimization algorithm, <b>B</b>: ANN trained using Gradient-Descent with momentum and adaptive learning rate algorithm.</p

Glucose pump controller design stage block diagram.

<p>The output of this stage is an ensemble of 50 sets of Artificial Neural Network (ANN) connection weights, created using the Test set of data and the best fit parameters vector.</p

Data Groups Characteristics.

<p>SD - Sprague Dawley, BW - body weight, BPG - average basal plasma glucose concentration.</p

Evaluation of the error in the prediction of glucose infusion rate over different levels of random noise.

<p>Random Gaussian density function noise with zero mean, and variance corresponding to signal to noise ratios (SNR) of 5 dB to 35 dB was added to the input data. The prediction error is expressed in mean ± SEM over 100 simulations.</p

Hyperinsulinemic-euglycemic glucose clamp experiment (HEGC) protocol – Schematic illustration.

<p>Representation of the experimental design of the clamp study: The animals were studied under basal conditions for the first 2 hours and under hyperinsulinemic conditions over the last 2 hours. Period–I is characterized by rapid changes in glucose concentration. Period-II exhibits a near steady state behavior of insulin and plasma glucose concentrations. Circles represent times at which blood samples were taken.</p

Optimized Parameters and Best Performance.

<p>μ₀, μ⁺, μ⁻ - Levenberg-Marquardt optimization learning parameters, RMSE - root mean square error, cc - correlation coefficient.</p

Schematic configuration of the experimental setup.

<p>The system consists of three infusion syringe pumps for [3-³H] glucose, insulin and variable glucose respectively. Arterial catheter is connected to the infusion pumps, and venous catheter is used for manual blood sampling. A closed-loop, computer controlled system is proposed for maintaining plasma glucose concentration within the desired level during HEGC.</p