Implementation of Guided Inquiry Strategy Combined with Scientific Student Worksheets on the Structure and Function of Plant Tissues to Improve Learning Outcomes

This study aims to determine the application of a guided inquiry strategy combined with scientific student worksheets to improve student learning. The research method used is True experimental using pretest-posttest control group design. The population in this study were students of class XI MIPA MA Negeri 1 Pidie. The number of samples consisted of 132 people, and each class consisted of 33 students. The research instrument used is test questions. The data analysis technique of learning outcomes is through the N-Gain test and the independent sample t-test. The N-gain test of learning outcomes obtained that the average of the experimental class was higher than the control class, where each class was classified as a medium improvement criterion. The independent sample t-test showed a significant average difference between the learning outcomes of the experimental class and the control class, namely tcount > ttable (65.8 > 1.65). The percentage of N-Gain learning outcomes in the experimental class is 77.73% in the high category. This study concludes that there is an increase in student learning outcomes on the material structure and function of plant tissue at MAN 1 Pidie in the experimental class by applying the guided inquiry strategy combined with scientific Student Worksheets

Multi-Fidelity Surrogate-Based Optimization of Transonic and Supersonic Axial Turbine Profiles

Automated Fluid-dynamic Shape Optimization plays a key role in the design of turbomachinery and typically combines Computational Fluid Dynamics (CFD) solvers, parametrization techniques and numerical optimization methods, generally categorized as either direct or surrogate-based (SBO) ones. Here, a particular focus is given to SBO exploiting surrogate models constructed from low-fidelity models, often referred to as variable or multi-fidelity optimization. This study presents a multi-fidelity SBO approach for the optimization of a supersonic turbine cascade operating with an organic fluid and of the transonic LS89 high-pressure turbine vane. A cokriging method is used to simultaneously take into account quantities of interest (QoI) coming from models of different fidelities providing a global surrogate model. A classic Bayesian global optimization method permits to iteratively select promising designs. It relies on the maximization of the so-called Expected Improvement criterion. A geometrical parametrization technique based on B-splines is considered to describe the profile geometry. The total pressure loss coefficient is minimized while the mass flow rate is constrained. For both the application cases, the optimization study reveals a speed-up of 3 to 5 times in the convergence process with respect to classic optimization frameworks based on a single fidelity, while providing similar improvements in terms of fitness functions.

A systematic review and meta-analysis of the relationship between brain data and the outcome in Disorders of Consciousness

A systematic search revealed 68 empirical studies of neurophysiological (EEG, ERP, fMRI, PET) variables as potential outcome predictors in patients with Disorders of Consciousness (diagnoses Unresponsive Wakefulness Syndrome [UWS] and Minimally Conscious State [MCS]). Data of 47 publications could be presented in a quantitative manner and systematically reviewed. Insufficient power and the lack of an appropriate description of patient selection each characterized about a half of all publications. In more than 80% studies, neurologists who evaluated the patients’ outcome were familiar with the results of neurophysiological tests conducted before, and may, therefore, have been influenced by this knowledge. In most subsamples of data sets effect size significantly correlated with its standard error, indicating publication bias toward positive results. Neurophysiological data predicted the transition from UWS to MCS substantially better than they predicted the recovery of consciousness (i.e., the transition from UWS or MCS to exit-MCS). A meta-analysis was carried out for predictor groups including at least three independent studies with N > 10 per predictor per improvement criterion (i.e., transition to MCS versus recovery). Oscillatory EEG responses were the only predictor group whose effect attained significance for both improvement criteria. Other perspective variables, whose true prognostic value should be explored in future studies, are sleep spindles in the EEG and the somatosensory cortical response N20. Contrary to what could be expected on the basis of neuroscience theory, the poorest prognostic effects were shown for fMRI responses to stimulation and for the ERP component P300. The meta-analytic results should be regarded as preliminary given the presence of numerous biases in the data.

Automated Generation of Course Improvement Plans Using Expert System

To satisfy ABET's continuous improvement criterion, an instructor, teaching a course suggests, at the end of the course, an improvement plan to be implemented when the same course is taught next time. Preparation of such a course improvement plan may be mandatory if a pre-specified target level of students' learning is not attained. Since, manual preparation of a course improvement plan is difficult, an idea of generating it using an expert system is presented. The objective is to make the task of improvement plan preparation easier and enjoyable. The proposed expert system has a set of remedies and a set of rules in a data base. A web-based interface queries the instructor about teaching and assessment tools used in the course. The inference engine selects the most appropriate remedy based on instructor's preferences. A cloud implementation of the expert system has been used to test it for a course.