The potential concern about poor quality questions was only marginally experienced. If students created poor questions, they were rated as such by fellow students and these questions were then bypassed by students look- ing for better quality questions. This supports the view that students are effective judges of question quality and that there is a willingness to accept the judgements of other students on what is a quality question . Where students indicated the wrong answer to a question, feedback from other students encouraged the question creator to revise and correct the question and once led to a very lively seminar debate.
4. The syllabus of the Computer Based Online Test shall be of the standard of prescribed eligibility criteria, which may consist of 120 multiplechoice objective questions, comprising Part-A and Part-B ; Part-A consisting of 100 questions shall be relevant to the related discipline, covering the syllabus of prescribed eligibility criteria for the post and Part-B consisting of 20 objective MultipleChoice Question (MCQ) shall be from the Quantitative Aptitude (Simple Maths), Data Interpretation, Analytical Reasoning, Logical Reasoning and Simple English of the 12 th Standard and time allotted for the Test will be 2.00 hour. Each correct answer will be awarded 1 mark each and for every wrong answer, 0.25 marks would be deducted. Computer Based Online Test is tentatively to be conducted in the month of April, 2021, for which candidates shall be required to download the Admit Card from the web-site of IWAI only.
As described earlier, statistical analysis of the facility and discrimination attributes of the set of questions contained in the tests may be used to establish the quality of a test. This is an area of functionality that we have not yet implemented. To achieve this we will need to extend the database capabilities to contain a relevant table to store this data and have it captured at the conclusion of each test. A reporting tool will also have to be developed. This capability will most probably be developed within the Access database, as it is not information that needs to be made available to students in an online mode.
Please listen carefully to these instructions before we take a 10-minute break. Everything you placed under your chair at the beginning of the exam must stay there. Leave your shrinkwrapped Section II packet on your desk during the break. You are not allowed to consult teachers, other students, or textbooks about the exam during the break. You may not make phone calls, send text messages, check email, use a social networking site, or access any electronic or communication device. Remember, you are not allowed to discuss the multiple-choice section of this exam. If you do not follow these rules, your score could be canceled. Are there any questions? . . .
For the evaluation, we used DQGen to insert sam- ple questions in an informational text for children, The Germs, which explains the concept of germs and their danger. Of the 18 paragraphs in this text, we rejected one because it was only two sentences long, and DQGen rejected another because the last sentence failed the grammar checker. For each of the other 16 paragraphs, DQGen generated a cloze question with ungrammatical and nonsensical dis- tracters, but it found plausible distracters for only 13 of the questions, which we evaluated as follows. We recruited eight human judges, members of our research team but unfamiliar with DQGen. We asked them to evaluate each question at two levels, using the form illustrated in Figure 2.
Multiple-choicequestions were evaluated according to the Millman checklist. Fourteen indicators were used including: stem clearness, specific objective of the question, negative option for the stem, specific option, contrasting option, positive words in the stem and options, structure of writing of the stem, duplicate option, spelling of stem and option, vertically writing of the options, positivity of the stem and options, use of “all items” and “none of the above” phrases in the options. Therefore, the final score of each question and consequently the total score of the questionnaire of that semester was determined. Students answer sheets were evaluated and the scores of 25% of students with the highest rank and 25% of students with the lowest rank in the exams were collected . Difficulty index and discrimination index were determined for each question. Difficulty coefficient was calculated as the percentage of the total number of people who correctly answered a question divided by the number of examinees and discrimination coefficient was calculated as the highest rank group right choices minus lowest rank group right choices divided by the number of people in a group (highest or lowest) .
Guidelines for Writing MCQs: It is important to use good grammar, punctuation, and spelling consistently and minimize the time required to read each item. The ideal question will be answered by 60-65% of the tested population. In a well-constructed MCQ, unintended cues should be avoided such as making the correct answer longer in length than the distractors. Instruction for answering the MCQ could be common for a set of questions. For example, for the single best response type, the instruction could be: “Select the most appropriate answer and darken the corresponding circle in the answer sheet provided” 14 . A good
A number of test-taking skills resources were available to college students. Some were devoted exclusively test-taking (Sides & Korchek, 1998; Hoefler, 1995). Others devoted a section or entire chapter to strategic test-taking (Beatrice, 1995; Dembo, 2004; Downing, 2005; Ellis, 2006; Ferrett, 1996; Ferrett, 2010; Kanar, 2004; Langan, 2001; Reynolds, 1996; Wong, 2006). Some specifically addressed the learning and assessment needs of nursing students (Alfaro-LeFevre, 1995; Chenovert, 2006; Hoefler, 1995; Katz, 2004; Sides & Korchek, 1998). For this review, a representative, rather than exhaustive, sample of sources was chosen (See Appendix A). The texts represented a 15-year publication range (1995 to 2010) in order to observe if the recommended approaches have changed. The collection was a convenience sample in that all were sources available to faculty, staff and students in a college of nursing. The sources were also chosen because they represented an extensive range of number of editions in print (one to seven). Some, therefore, were first-time publications for the authors, while others had sold multiple thousands of copies over numerous releases of new, updated editions, suggesting that instructors and students had found them to be beneficial over time and continued to adopt and purchase them.
In this paper, we investigate an unsupervised approach to Relation Extraction to be applied in the context of automatic generation of multiple-choicequestions (MCQs). The approach aims to identify the most important semantic relations in a document without assigning explicit labels to them in order to ensure broad coverage, unrestricted to predefined types of relations. The paper examines three different surface pattern types, each implementing different assumptions about linguistic expression of semantic relations between named entities. Our main findings indicate that the approach is capable of achieving high precision rates and its enhancement with linguistic knowledge helps to produce significantly better patterns. The intended application for the method is an e-learning system for automatic assessment of students’ comprehension of training texts; however it can also be applied to other NLP scenarios, where it is necessary to recognise important semantic relations without any prior knowledge as to their types.
For SciQ, we follow the original train/valid/test splits. For MCQL, we randomly divide the dataset into train/valid/test with an approximate ratio of 10:1:1. We convert the dataset to lowercase, filter out the distractors such as “all of them”, “none of them”, “both A and B”, and keep questions with at least one distractor. We use all the keys and dis- tractors in the dataset as candidate distractor set D. Table 1 summarizes the statistics of the two datasets after preprocessing. |D| is the number of candidate distractors. # MCQs is the total num- ber of MCQs. # Train/Valid/Test is the number of questions in each split of the dataset. Avg. # Dis is the average number of distractors per question. 3.2 Experiment Settings
As the cardiology program entails a high number of pre- requisites and conditions, its implementation cannot be expected to be self-directed. Still, nearly 10 years after the program’s initiation, with about 70% of cardiologists and 15% of AOK-insured patients in Baden-Württemberg participating (own calculations, based on participant lists provided to us by AOK Baden-Württemberg, personal communication and ), there was only little insight on its implementation by medical specialists and cooperating GPs. Therefore, and in light of the efforts and costs the program causes for financers and physicians, this study aimed to answer the questions to what extent participat- ing specialists (and GPs where applicable) actually imple- mented the cardiology program, which of the components mentioned they adapted, and which contextual factors in- fluenced the implementation and outcomes from the view of participating physicians.
The instrument used in this study is the questionnaire that was formulated by the researcher in consultation with the supervisor who went through it item after item. The questionnaire was used for data collection because it requires less time, it is less expensive, and can be appropriately used to collect the desired data from the sample. At the beginning of the questionnaire, there was an introductory note stating the research topic and the purpose of the questionnaire. In this note the researcher ended by thanking the respondents for the time spared to provide responses to the questions, and promised to keep their responses confidential and use them strictly for research purposes.
2. Because the multiple-choice sections vary in length, in some cases being longer or shorter than those typical of the Advanced Placement Exams, the multiple-choice sections of this booklet are not ideally administered in a timed situation. A teacher may certainly review the section and set a time he or she considers reasonable in his or her classroom. However, these sections were not written with specific time limits in mind.
Stop working and close your exam booklet. Place it on your desk, face up. . . . If any students used extra paper for a question in the free-response section, have those students staple the extra sheet(s) to the first page corresponding to that question in their exam booklets. Complete an Incident Report. A single Incident Report may be completed for multiple students per exam subject per administration (regular or late testing) as long as all of the required information is provided. Include all exam booklets with extra sheets of paper in an Incident Report return envelope (see page 60 of the 2015-16 AP Coordinator’s Manual for complete details). Then say:
With the adoption of the New York P–12 Common Core Learning Standards (CCLS) in ELA/Literacy and Mathematics, the Board of Regents signaled a shift in both instruction and assessment. Starting in Spring 2013, New York State began administering tests designed to assess student performance in accordance with the instructional shifts and the rigor demanded by the Common Core State Standards (CCSS). To aid in the transition to new assessments, New York State has released a number of resources, including test blueprints and specifications, sample questions, and criteria for writing assessment questions. These resources can be found at http://www.engageny.org/common-core-assessments.