Teacher! Please Light My Way: Effect of Providing Individual Feedback on Achievement in Social Studies Education

Feedback in education is messages that contain information about how close the student’s current behavior in the learning process is to the expected behavior, what deficiencies and mistakes exist, and how they can be eliminated, and messages that contain information about the last level reached. Feedback guides students’ learning and is an important component of assessment. In this study, the type of individual feedback was discussed. The research aims to determine the effect of the individual feedback provided to the students from the social studies course on their exam success. The research was conducted with three different social studies teachers and their six different students at three different socio-economic levels. After the teachers were given effective feedback training, two students in their classes were asked to give individual feedback on their lessons. At the end of the process, the exam scores of the students before receiving feedback and the exam scores after receiving feedback were compared according to the class averages. In addition, the opinions of the teachers in this process were also taken in the research. As a result of the research, it was determined that the exam scores of the students who received individual feedback increased and there was a higher change in their scores compared to the class average. According to the teachers, individual feedback increased the students’ interest in the lesson. Finally, teachers suggested to their colleagues that they should receive training on feedback and emphasized that every teacher should be good feedback literacy. Based on these results of the study, it is suggested that teachers should be trained about feedback and that their feedback literacy levels should be increased

Virtual testing against experiment for post-buckling behaviour of coldformed steel columns

Cold-formed steel has already started to replace hot rolled companions in some structural applications. Advantages of cold-formed steel originate from its high strength over weight ratio and ease of manufacturing and construction compared to hot rolled heavy sections. Moreover, cold-formed columns have significant post-buckling reserve which has the potential to be exploited in design process. Therefore, it is essential to predict the response of cold-formed columns by means of high fidelity engineering techniques. Herein an in depth study which links experimental testing and non-linear computational capabilities is undertaken to address the failure behaviour of cold-formed columns. Experimental program comprises coupon tests to specify material properties and compression testing of fixed end cold-formed columns. Thereafter, measured material properties are utilized to generate a stress-strain curve for finite element models. Boundary conditions imposed into simulation models in such a way that would represent test conditions. Creating a suitable mesh for different cross sectional dimensions, different shapes of initial imperfections are introduced into models to compare contributions to performance of columns. Predicted collapse loads and modes via finite element models are assessed against test results. Mesh and initial imperfection sensitivities on failure characteristics are discussed. Finally a general assessment is made for the deployed testing and simulation to generate knowledge for the design evaluation of cold-formed steel columns. Key findings and discussions of present study have the potential to lead to develop promising cold-formed steel column virtual test models

Learning where to see : a novel attention model for automated immunohistochemical scoring

Estimatingover-amplification of human epidermal growth factor receptor2 (HER2) on invasive breast cancer (BC) is regarded as a significant predictive and prognostic marker. We propose a novel deep reinforcement learning (DRL) based model that treats immunohistochemical (IHC) scoring of HER2 as a sequential learning task. For a given image tile sampled from multi-resolution giga-pixel whole slide image (WSI), the model learns to sequentially identify some of the diagnostically relevant regions of interest (ROIs) by following a parameterized policy. The selected ROIs are processed by recurrent and residual convolution networks to learn the discriminative features for different HER2 scores and predict the next location, without requiring to process all the subimage patches of a given tile for predicting the HER2 score, mimicking the histopathologist who would not usually analyse every part of the slide at the highest magnification. The proposed model incorporates a task-specific regularization term and inhibition of return mechanism to prevent the model from revisiting the previously attended locations. We evaluated our model on two IHC datasets: a publicly available dataset from the HER2 scoring challenge contest and another dataset consisting of WSIs of gastroenteropancreatic neuroendocrine tumor sections stained with Glo1 marker. We demonstrate that the proposed model out performs other methods based on state-of-the-art deep convolutional networks. To the best of our knowledge, this is the first study using DRL for IHC scoring and could potentially lead to wider use of DRL in the domain of computational pathology reducing the computational burden of the analysis of large multi-gigapixel histology images

Tailoring Compression Performance of Cold-Formed Steel Columns

Since thin-walled structural analysis and design procedures are utilized for cold-formed steel columns, it is first necessary to understand thin plate behavior to employ proper cross sections which will serve under compression actions. As is well known, thin plates without any longitudinally and/or laterally stiffening elements usually are not present in structural applications. These stiffening elements significantly improve local buckling and collapse characteristics of plates, providing optimized solutions in terms of strength and cost. In cold-formed steel industry there exist some tailoring methods for columns to use the cross-section material more effectively. Designing lipped channels instead of plain ones or deploying rack sections can be shown as examples of stiffening and enhancing flange compression performance. Present study offers a novel tailoring technique which has the potential to improve collapse performance of cold-formed steel columns. Considering the manner of stiffening for thin plates, present work assesses cold-formed steel columns which are manufactured using stiffened sheets. Used stiffened sheets are called as checkered sheets which contain small stiffeners on thin plates in a shape of diamond pattern and are generally used to cover stairs and decks in outdoor environments to prevent slip. Aiming at investigating contributions of small stiffeners on compression performance of cold-formed steel columns, an experimental study was undertaken and column specimens were tested to failure. Plain channel test specimens were manufactured using press braking method and boundary conditions of specimens were designed in such a way that would represent fixed ends. Accompanying the experimental program, non-linear finite element simulation works and AISI-2007 method were employed for manufactured columns using equivalent thickness approach. Results imply that with the proper geometrical configurations, reserve of cold-formed steel columns manufactured using checkered sheets offer structural efficiency in satisfying greater compression loadings compared to that of columns manufactured using plain sheets of equivalent thickness. This stiffened sheets concept has the potential to be facilitated in cold-formed steel commercial and residential structures. More efficient sections also can be acquired for design purposes by optimizing those stiffener configurations under compression loadings

Acute-on-chronic Liver Failure: MELD Score 30-day Mortality Predictability and Etiology in a Pakistani Population

Background: Cirrhosis is a pathological condition that ultimately leads to liver failure. Acute on chronic liver failure (ACLF) has a high short term mortality rate. Viral hepatitis is the most common cause of liver failure in our local population. We carried out this study to identity the 30-day mortality and etiology of patients presenting with ACLF using Model for End-Stage Liver Disease (MELD) score predictability. Methodology: This was a descriptive case series, conducted at Sheikh Zayed Hospital, Lahore, Pakistan from January 31, 2018 to July 30, 2018. One hundred and eighty five patients who met the inclusion criteria were enrolled using 95% confidence level and 4% margin of error. Data was entered and analyzed with SPSS version 23.0. Numerical variables including age was presented by Mean ± S.D. Categorical variables i.e. gender, etiology of acute-on-chronic liver failure and 30-day mortality were presented by frequency and percentage. Data was stratified for age, gender, duration of chronic liver disease and MELD grade to address the effect modifiers. Post-stratification chi-square test was calculated using 95% significance (p≤0.05). Results: Majority of the enrolled patients were male (74.6%) while only 25.4% of the patients were female. One hundred and thirty patients (70.3%) had underlying viral hepatitis while twelve patients (6.5%) and forty three patients (23.2%) presented with alcoholic liver disease and drug-induced ACLF, respectively. Eighty patients (43.2%) died within 30 days of admission.The 30-day mortality with respect to MELD grade was statistically significant (p<0.001) with the highest mortality noted in grade-IV and thirty five patients (43.8%) dying within 30 days of admission (p<0.001). Grade-II and III MELD scores also contributed to the 30-day mortality with twenty three patients (28.8%) and nineteen patients (23.8%) dying within 30 days of admission (p<0.001). Conclusion: MELD scores are able to accurately predict the short-term mortality in patients with ACLF and viral hepatitis was the most common etiology in our population. Early detection and use of appropriate prognostic models may alleviate mortality and morbidity in paitents with ACLF

Vision and Learning for Deliberative Monocular Cluttered Flight

Cameras provide a rich source of information while being passive, cheap and lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work we present the first implementation of receding horizon control, which is widely used in ground vehicles, with monocular vision as the only sensing mode for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a number of contributions: novel coupling of perception and control via relevant and diverse, multiple interpretations of the scene around the robot, leveraging recent advances in machine learning to showcase anytime budgeted cost-sensitive feature selection, and fast non-linear regression for monocular depth prediction. We empirically demonstrate the efficacy of our novel pipeline via real world experiments of more than 2 kms through dense trees with a quadrotor built from off-the-shelf parts. Moreover our pipeline is designed to combine information from other modalities like stereo and lidar as well if available