Wrong Options Research Articles

APPLICATION OF BRANCHES IN MICROSOFT FORMS DOCUMENTS AS FORCED REPLACEMENT OF SIMULATION PRACTICAL-ORIENTED TASKS, WHILE STUDYING CLINICAL DISCIPLINES IN THE CONDITIONS OF THE CONTINUING COVID-19 PANDEMIC

Sorry to state that optimistic forecasts for a quick overcoming of the COVID-19 pandemic, unfortunately, did not come true. Accepting modern challenges, the higher education sector, like other areas of human life, continues to adapt and optimize work to the new conditions of a changing difficult, unpredictable environment. While theoretical medical disciplines are likely to experience this re-equipment with greater ease, practice-oriented subjects are in a situation of crisis in the quality of vocational education.
 Results. In such circumstances, simulation systems, which are designed to create a safe environment, where, without risks to the patient, the future specialist can work out typical clinical situations, can be useful. Creating the comprehensive models requires considerable effort and highly specialized knowledge of working with specific software. However, if your aim is to create short cases that can be directly integrated in the form of multiple learning tasks to Microsoft Teams - you should pay attention to the well-known Forms tool from the Office365 cloud service package, which is available by subscription in many schools of this country.
 On purpose of such task formation, we will be able to create branches, providing the use of optional choice questions. When using this function, we get the opportunity to redirect the respondent to different fragments of the form, depending on the chosen answer. Wrong options should be left as well among the proposed, but at the end a result of their choice brief comments, should be made, explaining their absurdity, which would be available for review in case of completion of the task with an erroneous result. The result of such work will be a tree of steps, each of which brings the respondent closer to the case designed by the architect.
 Based on a system of diagnostic and treatment protocols, it is possible to calculate and formulate algorithms of action in certain clinical circumstances and, finally, create a Microsoft Forms document. This form, provided a combination of text and graphic data, allows you to place the respondent in a professional situation easily. Such system gives the opportunity to make typical correct decisions or to make wrong steps, which brings to the circumstances an element of simulation training. Thanks to the built-in assessment system, it is possible to combine learning processes with ongoing and final monitoring of the quality of learning, to stimulate maximizing the result. Upon reaching a positive outcome, form an opinion about the assimilation of the element, or redirect to additional study of certain fragments of topics, in case of formation of the wrong network of steps. To simplify learning, some cases can be prepared with one pass option, which will allow you to practice skills of emergency situations, or tasks limited to a standardized step-by-step solution.
 Conclusions. The Microsoft Forms cloud application can be used to create short clinical cases. Requires a combination of different types of input data and the ability to create branches to use it as a simulation learning tool. As a result, a decision tree is formed, which can be focused on assessing the acquisition of skills, and used to grade respondents, or to develop standardized solutions in typical clinical situations. The outlined system has its drawbacks, but it is necessary to take into account the general availability and ease of configuration.

Advanced Trauma Care for Nurses (ATCN): A Single-Center Analysis of Trauma Nurses Knowledge Gaps.

Trauma is a global cause of death and disability, and trauma systems are not well developed in low- and middle-income countries. Training of nurses in trauma care is of utmost importance to improve the organization and delivery of trauma care. This study aimed to identify common knowledge gaps and develop study aids to improve nurses' performance taking the Advanced Trauma Care for Nurses (ATCN) course. This is a descriptive, single-center study of the multiple-choice final examinations of the ATCN course conducted over 1 year in the All India Institute of Medical Sciences, Rishikesh, Level I trauma center. The questions missed by candidates were compiled, and the wrong options were tabulated. The most commonly missed questions were identified, and the most commonly marked wrong option was analyzed vis-a-vis the correct answer. Each error was classified into either a theoretical error or a practice-based error. Ninety-six nurses attended 6 courses from June 2019 to June 2020. Of the theoretical-based questions, the top 3 categories of most missed questions were geriatric trauma (n = 13; 81.2%), massive transfusion (n = 35; 72.9%), and traumatic brain injury (n = 35; 72.9%). Of the practice-based questions, the top 3 categories of most missed questions were dislocated extremity management (n = 54; 79.4%), basic airway (n = 31; 64.5%), and shock management (n = 30; 62.5%). Periodic identification and categorization of the ATCN course examination's most frequent knowledge gaps allow instructors to develop teaching aids to enhance instructor teaching and improve trauma nurses' knowledge.

Learning to Classify the Wrong Answers for Multiple Choice Question Answering (Student Abstract)

Multiple-Choice Question Answering (MCQA) is the most challenging area of Machine Reading Comprehension (MRC) and Question Answering (QA), since it not only requires natural language understanding, but also problem-solving techniques. We propose a novel method, Wrong Answer Ensemble (WAE), which can be applied to various MCQA tasks easily. To improve performance of MCQA tasks, humans intuitively exclude unlikely options to solve the MCQA problem. Mimicking this strategy, we train our model with the wrong answer loss and correct answer loss to generalize the features of our model, and exclude likely but wrong options. An experiment on a dialogue-based examination dataset shows the effectiveness of our approach. Our method improves the results on a fine-tuned transformer by 2.7%.

The relationship between brain reaction and English reading tests for non-native English speakers

This research analyzed the brain activity of non-native English speakers while engaged in English reading tests. The brain wave event-related potentials (ERPs) of participants were used to analyze the difference between making correct and incorrect choices on English reading test items. Three English reading tests of differing levels were designed and 20 participants, 10 males and 10 females whose ages ranged from 20 to 24, voluntarily participated in the experiment. Experimental results were analyzed by performing independent t-tests on the ERPs of participants for gender, difficulty level, and correct versus wrong options. Participants who chose incorrect options elicited a larger N600, verifying results found in the literature. Another interesting result was found: For incorrectly answered items, different areas of brain showing a significant difference in ERPs between the chosen and non-chosen options corresponded to gender differences; for males, this area was located in the right hemisphere whereas for females, it was located in the left. Experimental results imply that non-native English speaking males and females employ different areas of the brain to comprehend the meaning of difficult items.

E-Admission System

Directorate of technical education (DTE) conducts online admissions for engineering colleges but they don't have software to help students to select college and branch. They give data in the form of tables in pdf file and a student has to go through it. Many students don't understand the data and many parents who come from rural area and uneducated but want their ward to study in engineering fail to fill options form. Whatever may be the CET score they try to fill best colleges known. Hence, our proposed computer aided system will help the students to get the list of all colleges in which they could get the admission at the click of a button. The students only have to enter their marks of CET, AIEEE etc. With this software, the students can very easily obtain the list of colleges even branch wise and category wise. This will not only make the admission process easy but also minimizes stress of students .The main objective of our system is to make right choice of colleges, so that maximum of the students get selected in the first attempt itself. Keywords: DTE, CET, AIEEE, C4.5 algorithm, Centralized data handling I. Introduction After XII th students desiring to take admission in professional colleges like engineering face lots of problems. Admissions to engineering colleges in the state of Maharashtra or any state is based upon common entrance test (CET) and since more than 1.5lakh seats are to be allotted in more than 200 engineering colleges and over 35 different branches of engineering , for students belong to many categories like open, home university, outside home university, reserved category(SC,ST, OBC etc) the problem becomes more serious and students fail to understand which college they are likely to get admission even after going through cut-off data of previous years. Many students fill wrong options and fail in getting admission. To minimize the stress of students we come up with an idea of this computer aided method which will help the students to get the list of all colleges in which they could get the admission at the click of a button making, the admission process fast and easy.

Acceleration of Reinforcement Learning with Incomplete Prior Information

Reinforcement learning is applicable to complex or unknown problems because the solution search process is done by trial-and-error. However, the calculation time for the trial-and-error search becomes larger as the scale of the problem increases. Therefore, in order to decrease calculation time, some methods have been proposed using the prior information on the problem. This paper improves a previously proposed method utilizing options as prior information. In order to increase the learning speed even with wrong options, methods for option correction by forgetting the policy and extending initiation sets are proposed.

A Study of Answer Changing Behavior in MC Tests: The Effect of the Academic Course, Field of Study, Gender and Teachers’ Attitude

This study investigated the answer changing behavior of the Iranian university students and its relationship to the academic course, gender, and field of study. 362 students at the University of Isfahan took part in the study. 76 English language teachers were also surveyed for their attitude toward answer changing on MC tests. The results indicated that 42.11% of the teachers had a negative attitude toward answer changing as they believed that the first answer selected is intuitively the best one and should not be changed. The results also indicated that answer changing would basically have positive effects on the total test scores. It was found that 63.09% of the answer changes were beneficial (from Wrong to Right choices). In contrast, 15.18% of the answer changes were negative (from Right to Wrong options), and 21.73% had no effect on the test scores (from Wrong to Wrong options). As for the effect of gender, no significant difference was found between males and females. Concerning the effect of academic course, the only significant difference lay in the number of Right to Wrong changes. Finally, it was found that field of study would make a difference only when Right to Wrong changes were considered.

Pursuing the wrong options? Adjustment costs and the relationship between uncertainty and capital accumulation

Abel and Eberly (1999) prove that uncertainty has an ambiguous effect on long run capital accumulation in a real options model. We show that, with adjustment costs quadratic in investment, more uncertainty reduces capital and this effect may be large.

The Effect of Changing the Position of the Strong Distractor, when Wrong Options were Removed, on Difficulty Index and Discrimination Index of the Multiple Choice Questions in Physics

The Effect of Changing the Position of the Strong Distractor, when Wrong Options were Removed, on Difficulty Index and Discrimination Index of the Multiple Choice Questions in Physics