Multiple-choice Question Examination Research Articles

The equation for medical multiple-choice question testing time estimation.

Multiple-choice questions (MCQs) offer a suitable means to assess the cognitive domain of learners with a high degree of objectivity. The study's objective is to formulate an equation for determining the ideal timing for MCQ examinations, thereby inspiring the development of a model to estimate the duration of these examinations. The authors generated a specific computer program that integrated with the operating system of the examination. Technical-specific features included the ability to calculate the speed of students taking examinations with images or videos in the questions or options. This bespoke computer program was designed specifically for assessing individual students' MCQ test-taking pace and generating a proctor report in a computer-readable format. Subsequently, data derived from this program underwent regression analysis to determine the speed at which students completed MCQ examinations. The data were collected from a total of 1035 examinees, all of whom were non-native English speakers. The average reading rate was 62.38±20.4 words/min. It was found that the rate decreased significantly in difficult (50.65±6.9 words/min) items compared to easy (82.29±21.3 words/min) and intermediate (60.56±19.1 words/min) items (p<0.001), respectively. The linear regression analysis predicted option selection (words/min) as; 33.92+1.93(%tables/figures)+0.14(%recall)-0.37(%application), r2=0.45, p<0.001. It is not advisable to base the decision solely on reading time or time allocation. Examination administrators are advised to proactively plan ahead, with particular emphasis on establishing a well-defined taxonomy, as it constitutes a fundamental cornerstone in the utilization of the estimation equation.

Medical knowledge of ChatGPT in public health, infectious diseases, COVID-19 pandemic, and vaccines: multiple choice questions examination based performance.

At the beginning of the year 2023, the Chatbot Generative Pre-Trained Transformer (ChatGPT) gained remarkable attention from the public. There is a great discussion about ChatGPT and its knowledge in medical sciences, however, literature is lacking to evaluate the ChatGPT knowledge level in public health. Therefore, this study investigates the knowledge of ChatGPT in public health, infectious diseases, the COVID-19 pandemic, and its vaccines. Multiple Choice Questions (MCQs) bank was established. The question's contents were reviewed and confirmed that the questions were appropriate to the contents. The MCQs were based on the case scenario, with four sub-stems, with a single correct answer. From the MCQs bank, 60 MCQs we selected, 30 MCQs were from public health, and infectious diseases topics, 17 MCQs were from the COVID-19 pandemic, and 13 MCQs were on COVID-19 vaccines. Each MCQ was manually entered, and tasks were given to determine the knowledge level of ChatGPT on MCQs. Out of a total of 60 MCQs in public health, infectious diseases, the COVID-19 pandemic, and vaccines, ChatGPT attempted all the MCQs and obtained 17/30 (56.66%) marks in public health, infectious diseases, 15/17 (88.23%) in COVID-19, and 12/13 (92.30%) marks in COVID-19 vaccines MCQs, with an overall score of 44/60 (73.33%). The observed results of the correct answers in each section were significantly higher (p = 0.001). The ChatGPT obtained satisfactory grades in all three domains of public health, infectious diseases, and COVID-19 pandemic-allied examination. ChatGPT has satisfactory knowledge of public health, infectious diseases, the COVID-19 pandemic, and its vaccines. In future, ChatGPT may assist medical educators, academicians, and healthcare professionals in providing a better understanding of public health, infectious diseases, the COVID-19 pandemic, and vaccines.

Android Quiz Application Development

Android bias and technology have become a fundamental part of our lives. Mobile biases are being used as a means of data exchange, converse, entertainment, texting and other social communication, making computers perform the most complex tasks. In the field of the moment today, it is seen that multitudinous people want to test their memory and general knowledge capability. We help them do this with our small Android app that allows them to check their overall command. This paper presents the main way in the development of a quiz operation for Android using Android Studio. This work deals with the development of an Android-predicated multiple-choice question examination system. This operation is developed for educational purposes, allowing stoner to produce multiple-choice questions for different tests.

A Comparative Study to Observe the Effects of Online Lectures and Flipped Classroom-Assisted Sessions among First-Phase MBBS Students during the COVID-19 Pandemic

Abstract Background: The past decade has encouraged learner-centered approaches which expect students to take greater responsibility for their learning and support each other’s progress. The flipped classroom (FC) is a teaching–learning tool that uses learner-centeredness. The present comparative study was conducted to observe the effects of online lectures and FC-assisted sessions among first-phase MBBS students during the COVID-19 pandemic. Materials and Methods: The present study was conducted after taking institutional ethical clearance and informed consent of the participants. Two hundred first MBBS students participated and were divided into two Groups A and B. Ten topics of neurophysiology were taught to both groups using either FC-assisted self-directed learning (SDL) sessions or online lecture classes. A cross-over of groups was done to prevent any bias. Pretest and posttest scores of all topics were assessed using multiple-choice questions (MCQs) and feedback was taken. Program evaluation was done. Results: There was no significant difference in performance in pretest MCQ examinations between the two groups. Students of both Groups A and B performed significantly better in posttest MCQ examinations as compared to pretest MCQ examinations. However, the posttest results of the FC group were better. Faculty and students provided positive views in support of the sessions. Students were highly satisfied with the program. Conclusions: FC needs to be introduced in medical schools early, to help young medical students to develop SDL skills which will enhance their ability to become lifelong learners and improve the quality of patient care in long run.

ChatGPT performance on multiple choice question examinations in higher education. A pragmatic scoping review

Media coverage suggests that ChatGPT can pass examinations based on multiple choice questions (MCQs), including those used to qualify doctors, lawyers, scientists etc. This poses a potential risk to the integrity of those examinations. We reviewed current research evidence regarding the performance of ChatGPT on MCQ-based examinations in higher education, along with recommendations for how educators might address challenges and benefits arising from these data. 53 studies were included, covering 114 question sets, totalling 49014 MCQs. Free versions of ChatGPT based upon GPT-3/3.5 performed better than random guessing but failed most examinations, performing significantly worse than the average human student. GPT-4 passed most examinations with a performance that was on a par with human subjects. These findings indicate that all summative MCQ-based assessments should be conducted under secure conditions with restricted access to ChatGPT and similar tools, particularly those examinations which assess foundational knowledge.

Performance of ChatGPT and Bard on the official part 1 FRCOphth practice questions

BackgroundChat Generative Pre-trained Transformer (ChatGPT), a large language model by OpenAI, and Bard, Google’s artificial intelligence (AI) chatbot, have been evaluated in various contexts. This study aims to assess these...

Advantages and pitfalls in utilizing artificial intelligence for crafting medical examinations: a medical education pilot study with GPT-4

BackgroundThe task of writing multiple choice question examinations for medical students is complex, timely and requires significant efforts from clinical staff and faculty. Applying artificial intelligence algorithms in this field of medical education may be advisable.MethodsDuring March to April 2023, we utilized GPT-4, an OpenAI application, to write a 210 multi choice questions-MCQs examination based on an existing exam template and thoroughly investigated the output by specialist physicians who were blinded to the source of the questions. Algorithm mistakes and inaccuracies, as identified by specialists were classified as stemming from age, gender or geographical insensitivities.ResultsAfter inputting a detailed prompt, GPT-4 produced the test rapidly and effectively. Only 1 question (0.5%) was defined as false; 15% of questions necessitated revisions. Errors in the AI-generated questions included: the use of outdated or inaccurate terminology, age-sensitive inaccuracies, gender-sensitive inaccuracies, and geographically sensitive inaccuracies. Questions that were disqualified due to flawed methodology basis included elimination-based questions and questions that did not include elements of integrating knowledge with clinical reasoning.ConclusionGPT-4 can be used as an adjunctive tool in creating multi-choice question medical examinations yet rigorous inspection by specialist physicians remains pivotal.

The Scientific Knowledge of Bard and ChatGPT in Endocrinology, Diabetes, and Diabetes Technology: Multiple-Choice Questions Examination-Based Performance.

The present study aimed to investigate the knowledge level of Bard and ChatGPT in the areas of endocrinology, diabetes, and diabetes technology through a multiple-choice question (MCQ) examination format. Initially, a 100-MCQ bank was established based on MCQs in endocrinology, diabetes, and diabetes technology. The MCQs were created from physiology, medical textbooks, and academic examination pools in the areas of endocrinology, diabetes, and diabetes technology and academic examination pools. The study team members analyzed the MCQ contents to ensure that they were related to the endocrinology, diabetes, and diabetes technology. The number of MCQs from endocrinology was 50, and that from diabetes and science technology was also 50. The knowledge level of Google's Bard and ChatGPT was assessed with an MCQ-based examination. In the endocrinology examination section, ChatGPT obtained 29 marks (correct responses) of 50 (58%), and Bard obtained a similar score of 29 of 50 (58%). However, in the diabetes technology examination section, ChatGPT obtained 23 marks of 50 (46%), and Bard obtained 20 marks of 50 (40%). Overall, in the entire three-part examination, ChatGPT obtained 52 marks of 100 (52%), and Bard obtained 49 marks of 100 (49%). ChatGPT obtained slightly more marks than Bard. However, both ChatGPT and Bard did not achieve satisfactory scores in endocrinology or diabetes/technology of at least 60%. The overall MCQ-based performance of ChatGPT was slightly better than that of Google's Bard. However, both ChatGPT and Bard did not achieve appropriate scores in endocrinology and diabetes/diabetes technology. The study indicates that Bard and ChatGPT have the potential to facilitate medical students and faculty in academic medical education settings, but both artificial intelligence tools need more updated information in the fields of endocrinology, diabetes, and diabetes technology.

Direct evaluation of skills, knowledge and behaviours of pharmacists in the Republic of Ireland

The approach to reviewing Continuing Professional Development (CPD) for pharmacists in Ireland has undergone significant change over the past decade. All registered pharmacists must maintain an electronic portfolio of their CPD, which is reviewed periodically. Patient-facing pharmacists may also be randomly selected to participate in Practice Review. This consists of a clinical knowledge multiple choice question (MCQ) examination (Clinical Knowledge Review (CKR)) and interactions with simulated patients centred on pharmaceutical care cases ((Standardised Pharmacy Interactions (SPIs)). The processes underpinning Practice Review are described and a summary of the performance outcomes of pharmacists since the inception of the review process in 2018 are outlined. To date 310 patient-facing pharmacists have participated in Practice Review with the vast majority demonstrating competence. The significance of these results in the context of assessing competence is considered and the merits and limitations of Practice Review are discussed.

A Team-based Learning Approach During Pediatric Clerkship to Promote Clinical Reasoning.

Clinical reasoning skills are essential to medical practice. The problem lies in assuming junior medical students with limited experience develop clinical reasoning and decision-making skills passively and solely through clinical exposure. To prepare learners for independent practice and care for future patients, explicit teaching and assessment of clinical reasoning in low-stakes, collaborative learning environments are needed. The key-feature question (KFQs) format is an assessment approach that focuses on the reasoning and decision-making aspects of medical problems rather than knowledge recall. This report describes the development, implementation, and evaluation of a team-based learning (TBL) approach using KFQs to foster clinical reasoning in the third-year pediatric clerkship at our institution. Over the first 2 years of implementation (2017-18, 2018-19), 278 students participated in TBL sessions. Mean individual student scores significantly improved in the group setting for both academic years (P<.001). Individual scores were moderately positively correlated with their total summative Objective Structured Clinical Examination score (r[275]=0.51; P<.001). The correlation between individual score and their multiple-choice question examination was weaker at 0.29 (P<.001) but remained positive. A TBL session using KFQs to both teach and assess clinical reasoning in clerkship students may help educators identify students with gaps in knowledge and/or reasoning. The next steps include developing and implementing individualized coaching opportunities, and expanding this approach within the undergraduate medical curriculum. Outcome measures to assess clinical reasoning in authentic patient encounters require further research and development.

A Research on Coding Quiz Application

Abstract: Development of android-based Quiz application is mainly required by students and learners to prepare themselves. One of the major goal of our project is to facilitate students in learning, gaining and improving their knowledge skills. Users can prepare for interviews, entrance tests or any other corresponding purposes in a fresh mood and can’t get bored or frustrated due to dullness of application. We designed the application to facilitate the users to be able to take short quizzes using portable devices such as smart phones and tablets. This work deals with development of android-based multiple-choice question examination system namely Quiz. The main goal of the application is to enable users to practice for the subjective tests conducted for admissions and recruitment, with focus on Computer science field.

The pearls and pitfalls of setting high-quality multiple choice questions for clinical medicine.

Multiple choice question (MCQ) examinations have become extremely popular for testing applied knowledge in the basic and clinical sciences. When setting MCQ examinations, assessors need to understand the measures that improve validity and reliability so that the examination accurately reflects the candidate's ability. This continuing medical education unit will cover the essentials of blueprinting an exam, constructing high-quality MCQs and post hoc vetting of the exam. It is hoped that academics involved in assessments use the content provided to improve their skills in setting high-quality MCQs.

Mechanical life support algorithm for emergency management of patient receiving extracorporeal membrane oxygenation.

There are limited practical advanced life support algorithms to aid teams in the management of cardiac arrest in patients on extracorporeal membrane oxygenation (ECMO). In our specialist tertiary referral centre we developed, by iteration, a novel resuscitation algorithm for ECMO emergencies which we validated through simulation and assessment of our multi-disciplinary team. A Mechanical Life Support course was established to provide theoretical and practical education combined with simulation to consolidate knowledge and confidence in algorithm use. We assessed these measures using confidence scoring, a key performance indicator (the time taken to resolve gas line disconnection) and a multiple choice question (MCQ) examination. Following this intervention the median confidence scores increased from 2 (Interquartile range IQR 2, 3) to 4 (IQR 4, 4) out of maximum 5 (n = 53, p < 0.0001). Theoretical knowledge assessed by median MCQ score increased from 8 (6, 9) to 9 (7, 10) out of maximum 11 (n = 53, p0.0001). The use of the ECMO algorithm reduced the time taken by teams in a simulated emergency to identify a gas line disconnection and resolve the problem from median 128s (65, 180) to 44s (31, 59) (n = 36, p 0.001) and by a mean of 81.5s (CI 34, 116, p = 0.001). We present an evidence based practical ECMO resuscitation algorithm that provides guidance to clinical teams responding to cardiac arrest in ECMO patients covering both patient and ECMO troubleshooting.

Grading Multiple Choice Questions Based On Similarity Measure

Recent findings show the poor performances of students in Multiple Choice Questions (MCQ) examinations. This could be attributed to the administration of questions on application of knowledge rather than on fundamental of knowledge. Candidates doing these examinations at times choose options that are close to the actual answer but get zero (0) as the reward. The objective of developing a system that rewards a candidate based on the approximation of an option chosen not on the exactness of the option to the answer was therefore formulated in this study. 500 MCQs with their model answers, students’ answers and scores obtained from two universities in Nigeria were collected. Jaro similarity measure was used to compute the degree of similarity between the model answers and the student answers. Results of the experiment show an average deviation of 13.3 marks. The adoption of this method of grading would be very beneficial in evaluating learners on e-learning platforms. The result is encouraging but could be improved upon with semantic similarity measure hybridized with string similarity.

Are Technical Skills Assessed Using Medical Knowledge Associated with Non-technical Skill Knowledge in Anaesthesia Resident Training?

Objective: We examined the association between midterm examinations and anaesthetists’ non-technical skills (ANTS) knowledge using multiple choice questions (MCQs) for anaesthesia training.&#x0D; Materials and Methods: A prospective cross-sectional study was implemented based on two cohort studies conducted in November 2017 and November 2019 at the university hospital in southern Thailand. Each cohort consisted of MCQs, short answer questions (SAQs), objective structured clinical examinations (OSCEs), and MCQs after ANTS simulation workshops during the midterm examinations. The main exposure variable was the midterm examination (MCQs/ SAQs/ OSCEs) whereas the ANTS MCQs were the outcome. The potential predictors were the residents’ role, sex, PGY (1-3), and competency. Associations between midterm examinations and ANTS MCQ scores were analysed using Pearson’s correlation coefficients (r) and multivariate linear regression analysis, and presented as beta coefficient (β) and 95% confidence limit (CL).&#x0D; Results: Forty-eight anaesthesia residents were recruited for the study. After adjusting for PGY, knowledge score, and attitude evaluated by the staff, OSCE was found to be significantly associated with the pretest ANTS scores (β [95% CL]= 1.02 (0.06, 1.98)) and MCQ scores were significantly associated with the posttest ANTS scores (β [95% CL]= 0.14 (0.04, 0.24)). SAQ scores had negative associations with the pretest (β [95% CL]=-0.11 (-0.21, -0.01)) and posttest (β [95% CL]=-0.16 (-0.27, -0.05)) ANTS scores. It was found that there was a significant relationship between midterm examinations and posttest ANTS scores (r=0.52).&#x0D; Conclusion: Technical skills using OSCE and midterm MCQ examination scores were associated with non-technical skill knowledge in anaesthesia training.

Item Analysis of Multiple Choice and Extended Matching Questions in the Final MBBS Medicine and Therapeutics Examination

Background: Most universities around the world use the multiple-choice question (MCQ) examination format to evaluate medical education. However, the suitability and advantages of traditional MCQs and extended matching questions (EMQs) continue to be debated. Objectives: This study mainly aimed to perform a comprehensive comparative analysis of the performance of the EMQ and traditional MCQ formats in the final MBBS exit examination. Methods: We conducted an item analysis of 80 EMQs, and 200 MCQs administered to 532 examinees across the four campuses of the University of the West Indies during the final MBBS medicine and therapeutics examination of 2019. Exam performance measures included central tendency, item discrimination, reliability, item difficulty, and distractor efficacy. Results: For the 532 students who sat the exam, the highest, lowest, and mean (± SD) scores for the EMQs were 93, 41, and 69.0 (± 9.8), respectively; for the MCQs, the respective values were 82, 41, and 62.7 (± 7.4). The predictive value of the EMQ and MCQ grades individually in the overall failure was 0.67 (95% CI = 0.39, 0.87) and 0.89 (95% CI = 0.65, 0.98), respectively. KR-20 coefficients for the EMQs and MCQs ranged from 0.52 to 0.70 and 0.71 to 0.79, respectively. The proportion of questions with two or more functional distractors was consistently higher for the MCQs than for the EMQs in all four cohorts of students. Conclusions: The MCQs were more predictive of the overall failure and had higher inter-item reliability, making the MCQ format more suitable for high-stakes examinations.

Mechanical life support algorithm developed by simulation for inpatient emergency management of recipients of implantable left ventricular assist devices

BackgroundPublished guidance concerning emergency management of left ventricular assist device (LVAD) recipients is both limited and lacking in consensus which increases the risk of delayed and/or inappropriate actions. MethodsIn our specialist tertiary referral centre we developed, by iteration, a novel in-hospital resuscitation algorithm for LVAD emergencies which we validated through simulation and assessment of our multi-disciplinary team. A Mechanical Life Support course was established to provide theoretical and practical education combined with simulation to consolidate knowledge and confidence in algorithm use. We assessed these measures using confidence scoring, a key performance indicator (the time taken to restart LVAD function) and a multiple-choice question (MCQ) examination. ResultsThe mean baseline staff confidence score in management of LVAD emergencies was 2.4 ± 1.2 out of a maximum of 5 (n = 29). After training with simulation, mean confidence score increased to 3.5 ± 0.8 (n = 13).Clinical personnel who were provided with the novel resuscitation algorithm were able to reduce time taken to restart LVAD function from a mean value of 49 ± 8.2 seconds (pre-training) to 20.4 ± 5 seconds (post-training) (n = 42, p < 0.0001).The Mechanical Life Support course increased mean confidence from 2.5 ± 1.2 to 4 ± 0.6 (n = 44, p < 0.0001) and mean MCQ score from 18.7 ± 3.4 to 22.8 ± 2.6, out of a maximum of 28 (n = 44, p < 0.0001). ConclusionWe present a simplified LVAD Advanced Life Support algorithm to aid the crucial first minutes of resuscitation where basic interventions are likely to be critical in assuring good patient outcomes.

Quiz App Based On Android

Nowdays quizzes are very popular for education purpose as well as entertainment purpose. There are many quiz android apps available for android but they are different from each others in terms of content of the quiz, features of app, target users. Nowdays the gradually increase in number of Android phone users it is also increases the development of applications based on Android. This paper presents about android and the development of earning quiz android based app using android studio with Java as the programming language. This work deals with the development of an Android-based multiple-choice question examination system, namely: Quizo Reward App. Quizo Reward App is developed for practice of different entrance examination purpose with entertainment by providing features like interactive user interface, earn money, Leaderboards by which users can know their rank and wants to improve it. This app contains collection of previous year multiple choice based questions and their answer of different entrance examination.

GARLAQs vs. Multiple Choice Questions: Do First‐Year Medical Students Perform Similarly on their Anatomy Examinations?

PURPOSEMedical students at the Case Western Reserve University School of Medicine are assessed on their knowledge of anatomy using an essay exam that tests clinically relevant anatomy/radiology concepts (Gross Anatomy, Radiology, and Living Anatomy Questions, or GARLAQ). GARLAQs are uniquely designed to facilitate student collaboration, as such these essay questions are given to students months prior to examination. This format has raised concerns about student memorization and a lack of in‐depth understanding of GARLAQ answers. The purpose of the present study is threefold: 1) to examine whether students perform similarly on GARLAQs and Multiple Choice Question (MCQ) examinations, 2) to assess whether students perform similarly to the GARLAQs on MCQs throughout the hierarchy of Bloom’s Taxonomy, 3) to collect qualitative data on student opinions of both assessment formats.METHODSFirst‐year medical students will be assessed during the gastrointestinal unit of their clinical anatomy curriculum. Volunteers will be recruited from a class of 184 students. A crossover study design will be employed, wherein participants will be randomly placed into one of two cohorts: 1) first complete the required GARLAQ exam, second the MCQ exam, or 2) first complete the MCQ exam, second the required GARLAQ exam. A paired t‐test will determine if there is a difference in overall examination performance for each respective student. Each MCQ will be categorized using Bloom’s Taxonomy. A paired t‐test will compare each student’s mean scores by Bloom’s category to their GARLAQ performance. Participants will also complete a post‐test survey in which they will be asked to rate their opinions about contrasting exam modalities on a 5‐point Likert scale.RESULTSData collection is currently ongoing and will be completed in February, 2022CONCLUSIONInformation gained through review of student assessment scores and responses can guide future curricular changes at our institution in a cycle of continuous improvement.

Comparative study between academic performances of traditional versus competency-based medical education M.B.B.S. curriculum students in Pharmacology

Background: The new competency-based undergraduate curriculum (CBME) was implemented in India from the academic year 2019. However, there has been no clear-cut comparison on its effect on the academic performance of the students. Aim and Objective: This study aims to find the comparison between students of traditional and CBME curriculum in the subject of pharmacology based on their academic parameters. It also aims to establish comparison and correlation of academic performance with gender, home place area, and mode of learning used. We also aim to determine whether objective parameters like multiple choice question (MCQ) section of preliminary examination show any correlation with final university examination performance. Materials and Methods: This is a cross-sectional type of study. Seventy-seven students from traditional old curriculum and 98 students from CBME new curriculum batches participated with informed consent. The data for analysis between old and new curriculum batches were done using the mark obtained in final university examination in the subject of pharmacology. Quantitative parameters, that is, total marks overall, total theory marks, total marks in practical and oral viva combined, total marks in internal assessment and MCQ marks of preliminary examination, and qualitative variables such as gender of the student, home place area, that is, rural or urban and mode of learning used (physical or physical and digital) were obtained from the participants. IBM-SPSS version 25 software was used to perform descriptive statistics, independent t-tests, and Pearson’s correlation. Results: The results clearly demonstrate that the old curriculum batch of 2018 performed better than 2019 in overall as well as practical aspects of the subject. Pearson’s correlation between all the quantitative parameters, that is, total marks overall, total theory marks, total marks in practical and oral viva combined, total marks in internal assessment, and MCQ marks of prelims for the batch of 2019 showed positive correlation with each other. Conclusions: This study concludes that the old curriculum batch performed better than new curriculum batch in the subject of pharmacology. MCQ examination held in offline mode helps in better correlation with final university results as compared to online mode.