High quality multiplechoice items are difficult to construct but easily and reliably scored. Ebel (1986) states that the item difficulty less than 0.20 for an multiplechoice exam indicate the item is a poor item and believes that this level should not be less than 0.40 whereas Mehrens (1991) and Osterhof (1990) set a less restricted criterion and suggest the 0.20 to 0.40 as a sufficient level for an item to be included in a multiplechoiceexams. The internal consistency criterion knows as the Cronbach alpha is another index that is used to judge a multiplechoice test. In this regard, different level for different test purposes has been offered. Linn (1995) states that the value for the internal consistency should be between 0.60 to 0.85 while
Educational evaluation should not be limited to using a test or set of subtests to evaluate students but rather it should carefully employ test or tests that meet some sort of psychometrics characteristics. Under such circumstances, tests may render results that fairly classify student, diagnose their achievement or are used as the criterion to pass or fail students. In this research, all the multiplechoiceexams used at the college of health were evaluated by performing item analysis of each one of them separately. Results of this research showed that the average of item difficulty for the test conducted at the Paramedics College was 0.55. This value is approximately close to what Gronlund ( 1985) recommends and is with the range 0.3 to 0.70 that Nelson ( 2001) suggests. However, 32.2 percent of tests items showed item difficulties over the 0.70 criterion. This condition indicates that some of the test items were relatively difficult. When an item difficulty approaches high value such as some of the items identified in this research, it indicates that either the instructor did not cover the subject matter thoroughly or the student did not show enough interest to study them well. The other index evaluated was the discrimination index. In this research, the average of discrimination index was 0.21. This value is with the range Nelson (2001) has suggested. However, 15,8 percent of items
We use a two-step process to solve these prob- lems, first using a noisy classifier to find relevant passages and showing several options to workers to select from when generating a question. Sec- ond, we use a model trained on real science exam questions to predict good answer distractors given a question and a correct answer. We use these pre- dictions to aid crowd workers in transforming the question produced from the first step into a multi- ple choice question. Thus, with our methodology we leverage existing study texts and science ques- tions to obtain new, relevant questions and plau- sible answer distractors. Consequently, the human intelligence task is shifted away from a purely gen- erative task (which is slow, difficult, expensive and can lack diversity in the outcomes when repeated) and reframed in terms of a selection, modification and validation task (being faster, easier, cheaper and with content variability induced by the sug- gestions provided).
1) The Two-Tier Multiple-�hoice Test (TTM�T): The TTMCT was developed by Dinçol Özgür (2011). It consists of 1� two-tier multiplechoice items related to the “Chemical Bonding” In TTMCT, students are asked, after checking the answer of the question, as a second step of the question, to check the reasons of their choices from again the given alternatives. The test was reviewed by the experts in the field of chemistry education after it had been prepared to ensure its’ content validity. Cronbach’s alpha reliability of the test was found to be 0.8�.
Because our six corpora are of different genres (study guide, teachers guide, dic- tionary, flashcards), domains (science-domain vs. open-domain), and lengths (300 to 17,000 sentences), we implement six separate tf-idf models, each containing documents only from a single corpus. We then combine the two retrieval features from each model (12 features total) into a ranking perceptron (Shen and Joshi 2005; Surdeanu, Ciaramita, and Zaragoza 2011) to learn which knowledge bases are most useful for this task. This ensemble retrieval model produces a single score for each multiplechoice answer candi- date, where the top-scoring answer candidate is selected as the winner. The top-scoring answer justifications from each of the six retrieval models then serve as justifications. Jansen et al. (2014): The best-performing combined lexical semantic (LS) and CR model of Jansen et. al (2014), which was shown to perform well for open domain questions. Similar to the CR model, we adapted this model to our task by including six separate recurrent neural network language models of Mikolov et al. (2013, 2010), each trained on one of the six knowledge bases. Two features that measure the overall and pairwise cosine similarity between a question vector and multiplechoice answer candidate vector are included. The overall similarity is taken to be the cosine similarity of the composite vectors of both the question and answer candidate, obtained by summing the vectors for the individual words within either the question or answer candidate vectors, then renormalizing these composite vectors to unit length. The pairwise similarity is computed as the average pairwise cosine similarity between each word in the question and answer candidate. Two features from each of the six LS models are then combined with the two features from each of the CR models (24 features total), as above, using a ranking perceptron, with the top-scoring answer candidate taken as correct. Because the LS features do not easily lend themselves to constructing answer justifications, no additional human-readable justifications were provided by this model.
In 2006 the medical faculty of Heidelberg, the Charité Berlin and the LMU Munich set up the MAA  to fa- cilitate cooperation and joint problem solving in assess- ment which occur against the backdrop of limited resources. Under the leadership of the Centre of Excel- lence for Medical Assessments, objectives were set out in a cooperation agreement. The item bank contains not only MCQs, but also key features, OSCE, and structured oral exams . Since its inception, 28 medical faculties have joined the Alliance (see Fig. 1):
development team in mind a phased development and adoption strategy has been developed (See figure 2). In the case of organisations adopting the e-Exam system, the rate of progress along the stages depends on how quickly they are able to build capacity in the design and deployment of e-exams. The complex machinery of educational institutions includes strategies, policy, professional development, technology systems, educational practices and traditions. How quickly stakeholders and systems can adjust to facilitate the change will impact progress along the timeline. To assist in this area a loose community of practice (Wenger, 1998) is building around the project with shared network drive (AARNET Cloudstor), a website, user guides, and workshops run for project participants.
My remedy for what I saw as gross ignorance was to change the learning outcomes, so that students were required to demonstrate basic knowledge and conceptual understanding on a variety of topics, as well as producing a competent (but shorter) written assignment. How was the knowledge and understanding to be assessed? Via a multiple-choice test. And it worked, pretty much. The following year, students clearly had more knowledge and understanding. Multiple-choice questions (MCQs) had delivered what I wanted.
Disease monitoring under immunotherapy is in the focus of an at times controversial debate within the MS community. Prevailing questions are: What should we measure? How should we measure? Is there a threshold of acceptable disease activity, or should we be switching therapy at the first sign of any uncontrolled activity? To further set the scene for this discussion, it may be worthwhile to take a look at disease monitoring in the setting of phase III clinical MS trials, and then compare it to the approach in clinical practice. In a clinical phase III trial, patients are routinely seen every 4 weeks to 3 months. Typical outcomes include assessment of relapse rates, disability as measured by multiple sclerosis func- tional composite and EDSS every 3 months by a certified rater, and additionally cranial MRI, as evidenced in the NEDA concept (see above). Yet, in the future, patients reported outcomes (PRO, e.g. MSIS-29), brain atrophy, or cognitive measures like the symbol digit modalities test (SDMT) every 6 months may add to this concept . Along this line, visual quality of life is a potentially under-recognized parameter, which can be quantified using the NEI-VFQ 25 . In addition, efficacy moni- toring in clinical trials is more frequent than expected in clinical practice, and nowadays engages a range of tech- niques aiming at maximising objectivity (e.g. low con- trast visual acuity charts, optical coherence tomography; OCT etc.). For OCT, feasibility of automatic segmenta- tion with manual correction in specific macular areas was shown even in a multi-center setting . In real-life outpatient practice, there is considerably less time (sometimes only a few minutes), and far fewer re- sources to assess the patient for ongoing disease activity. While access to MRI may be good in many health care settings nowadays, the quality of the radiologist and the report may vary. In addition, clinical studies require a quantitative MRI report. Yet, real world MRI reports are often very descriptive without stating clear quantitative results relative to the previous scan.
A shared task is a typical question answer- ing task that aims to test how accurately the participants can answer the question- s in exams. Typically, for each question, there are four candidate answers, and only one of the answers is correct. The exist- ing methods for such a task usually imple- ment a recurrent neural network (RNN) or long short-term memory (LSTM). Howev- er, both RNN and LSTM are biased mod- els in which the words in the tail of a sen- tence are more dominant than the words in the header. In this paper, we propose the use of an attention-based LSTM (AT- LSTM) model for these tasks. By adding an attention mechanism to the standard L- STM, this model can more easily capture long contextual information. Our submis- sion ranked first among 35 teams in terms of the accuracy at the IJCNLP-2017 multi- choice question answering in Exams for all datasets.
Shohamy (1983) studied the effects of different aspects of test method facets on test takers’ performance and have demonstrated that the methods we use to measure language ability influence performance on language tests and Vygotsky (1969) found a relationship between the language of test instructions and test-takers’ performance. Bachman and Palmer(1981a) also found that scores from self-ratings loaded consistently more highly on method factors than on specific trait or ability factors, and that translation and interview measures of reading loaded more heavily on method than on trait factors. In addition to, Bachman and Palmer (1982a) also found that scores from both self-ratings and oral interviews consistently loaded more heavily on test method factors than on specific trait factors. One of such factors, test method facets, is the influence of “test format”. Whether test constructors use “multiple-choice”, “true-false”, “open-ended” or other testing formats in their tests, may influence the test takers' performance (e.g., Alderson, 2000; Bachman & Palmer, 1996; Buck, 2001).
Indicate all of your answers to the multiple-choice questions on the answer sheet. No credit will be given for anything written in this exam booklet, but you may use the booklet for notes or scratch work. After you have decided which of the suggested answers is best, completely fill in the corresponding circle on the answer sheet. Give only one answer to each question. If you change an answer, be sure that the previous mark is erased completely. Here is a sample question and answer.