Types Of Questions Research Articles

Evaluating text and visual diagnostic capabilities of large language models on questions related to the Breast Imaging Reporting and Data System Atlas 5th edition.

This study aimed to evaluate the performance of large language models (LLMs) and multimodal LLMs in interpreting the Breast Imaging Reporting and Data System (BI-RADS) categories and providing clinical management recommendations for breast radiology in text-based and visual questions. This cross-sectional observational study involved two steps. In the first step, we compared ten LLMs (namely ChatGPT 4o, ChatGPT 4, ChatGPT 3.5, Google Gemini 1.5 Pro, Google Gemini 1.0, Microsoft Copilot, Perplexity, Claude 3.5 Sonnet, Claude 3 Opus, and Claude 3 Opus 200K), general radiologists, and a breast radiologist using 100 text-based multiple-choice questions (MCQs) related to the BI-RADS Atlas 5th edition. In the second step, we assessed the performance of five multimodal LLMs (ChatGPT 4o, ChatGPT 4V, Claude 3.5 Sonnet, Claude 3 Opus, and Google Gemini 1.5 Pro) in assigning BI-RADS categories and providing clinical management recommendations on 100 breast ultrasound images. The comparison of correct answers and accuracy by question types was analyzed using McNemar's and chi-squared tests. Management scores were analyzed using the Kruskal- Wallis and Wilcoxon tests. Claude 3.5 Sonnet achieved the highest accuracy in text-based MCQs (90%), followed by ChatGPT 4o (89%), outperforming all other LLMs and general radiologists (78% and 76%) (P < 0.05), except for the Claude 3 Opus models and the breast radiologist (82%) (P > 0.05). Lower-performing LLMs included Google Gemini 1.0 (61%) and ChatGPT 3.5 (60%). Performance across different categories of showed no significant variation among LLMs or radiologists (P > 0.05). For breast ultrasound images, Claude 3.5 Sonnet achieved 59% accuracy, significantly higher than other multimodal LLMs (P < 0.05). Management recommendations were evaluated using a 3-point Likert scale, with Claude 3.5 Sonnet scoring the highest (mean: 2.12 ± 0.97) (P < 0.05). Accuracy varied significantly across BI-RADS categories, except Claude 3 Opus (P < 0.05). Gemini 1.5 Pro failed to answer any BI-RADS 5 questions correctly. Similarly, ChatGPT 4V failed to answer any BI-RADS 1 questions correctly, making them the least accurate in these categories (P < 0.05). Although LLMs such as Claude 3.5 Sonnet and ChatGPT 4o show promise in text-based BI-RADS assessments, their limitations in visual diagnostics suggest they should be used cautiously and under radiologists' supervision to avoid misdiagnoses. This study demonstrates that while LLMs exhibit strong capabilities in text-based BI-RADS assessments, their visual diagnostic abilities are currently limited, necessitating further development and cautious application in clinical practice.

Gender and ethnicity intersect to reduce participation at a large European hybrid HIV conference

ObjectiveTo evaluate how gender and ethnicity of panel members intersect to effect audience participation at a large European hybrid conference.DesignAn observational cross-sectional study design was used to collect data at...

Performance of GPT-4 with Vision on Text- and Image-based ACR Diagnostic Radiology In-Training Examination Questions.

Background Recent advancements, including image processing capabilities, present new potential applications of large language models such as ChatGPT (OpenAI), a generative pretrained transformer, in radiology. However, baseline performance of ChatGPT in radiology-related tasks is understudied. Purpose To evaluate the performance of GPT-4 with vision (GPT-4V) on radiology in-training examination questions, including those with images, to gauge the model's baseline knowledge in radiology. Materials and Methods In this prospective study, conducted between September 2023 and March 2024, the September 2023 release of GPT-4V was assessed using 386 retired questions (189 image-based and 197 text-only questions) from the American College of Radiology Diagnostic Radiology In-Training Examinations. Nine question pairs were identified as duplicates; only the first instance of each duplicate was considered in ChatGPT's assessment. A subanalysis assessed the impact of different zero-shot prompts on performance. Statistical analysis included χ2 tests of independence to ascertain whether the performance of GPT-4V varied between question types or subspecialty. The McNemar test was used to evaluate performance differences between the prompts, with Benjamin-Hochberg adjustment of the P values conducted to control the false discovery rate (FDR). A P value threshold of less than.05 denoted statistical significance. Results GPT-4V correctly answered 246 (65.3%) of the 377 unique questions, with significantly higher accuracy on text-only questions (81.5%, 159 of 195) than on image-based questions (47.8%, 87 of 182) (χ2 test, P < .001). Subanalysis revealed differences between prompts on text-based questions, where chain-of-thought prompting outperformed long instruction by 6.1% (McNemar, P = .02; FDR = 0.063), basic prompting by 6.8% (P = .009, FDR = 0.044), and the original prompting style by 8.9% (P = .001, FDR = 0.014). No differences were observed between prompts on image-based questions with P values of .27 to >.99. Conclusion While GPT-4V demonstrated a level of competence in text-based questions, it showed deficits interpreting radiologic images. © RSNA, 2024 See also the editorial by Deng in this issue.

The Tale of Designing a Clinical-Cases Manual for Rotations and Mixed Methods Analysis of Students' Participatory Experience in Co-Creation.

The post-pandemic era ignited the concepts of virtual learning, enhancing a strong need for a specific clinical case manual of commonly encountered scenarios in internal medicine. In this article, we describe the process for creating a clinical cases manual which can be followed for any other clinical science. Further, we report the participatory experience of students in the co-creation of manual. The hand-written notes of the teacher during the rotation of internal medicine were improvised, and created into a digital version. The editorial team which included mainly student volunteers participated in digitization and reviewing the content, diagrams and flowcharts. We describe the process of designing the clinical-cases manual for rotations in internal medicine, which includes handwritten notes, review of the notes, formation of editorial team, digitization of text and artwork, editing and book release. It can be replicated for any other clinical sciences in rotations. In addition, the online survey with both quantitative and qualitative type of questions was used to assess the students' participatory experiences in co-creating manual. The clinical-cases manual for rotations in internal medicine was released and received well. The online survey responses revealed that the participating students benefited in terms of professional and personal development. There was an inter-item statistical difference implying that all the participants were agreeing or strongly agreeing to survey questions. All participants agreed on the usefulness of the manual. The main themes identified through qualitative analysis were technical skills acquisition, lifelong learning and teaching, self-assessment, discipline, time management, teamwork and communication skills. The creation of a clinical case manual for rotation, specific to the local needs can be done through a systematic process, that can be enriched by involving students. Encouraging the participation of students in co-creation is an important academic exercise that contributes to professional and personal development.

Development of Mathematics Interactive E-Worksheet

Technology integration has become essential in the rapidly evolving landscape of education of the 21st century. Moreover, the aftermath of the Covid-19 pandemic has accelerated the adoption of digital tools in learning. This article explores the development of interactive e-worksheets in mathematics education using the Wizer.me platform. In Indonesia, the Merdeka Belajar curriculum emphasises digital technology as a fundamental aspect of addressing accessibility, quality, and equality in education. The developed e-worksheets are designed to enhance the traditional worksheet experience by transforming it into an interactive, engaging, and contemporary format. The advantages include cost savings, environmental friendliness, and combining multimedia elements such as videos and animations to boost student motivation. The study investigates the necessity of digitally accessible learning activities, aligning with the demands of future educational needs. Focusing on fractions as a challenging topic for students, this study uses the 4D model (Define, Design, Develop, Disseminate) as the Research and Development (R&amp;amp;D) model. The Wizer.me platform is employed for its user-friendly interface, varied question types, and integrated Google Classroom. The validation process involves expert judgment from mathematics educators and practical testing among junior high school students. The findings indicate that the developed interactive e-worksheets are valid and practical, receiving positive student responses during the limited trial. The article reveals the importance of innovative learning materials and suggests that teachers leverage technology to create engaging and accessible content, fostering a more dynamic learning environment. The study recommends the widespread adoption of interactive e-worksheets to enhance students' motivation and comprehension, particularly in challenging mathematical concepts like fractions.

F-KGQA: A fuzzy question answering system for knowledge graphs

The wide usage of large-scale knowledge graphs (KGs) motivates the development of user-friendly interfaces so that knowledge graphs become more readily accessible to a larger population. Natural language-based question answering (QA) systems are widely investigated and developed in the context of KGs, which can provide users with a natural means to retrieve the information they need from KGs without expecting them to know the query language. It is very common that natural language contains linguistic terms (fuzzy terms), and fuzzy (flexible) query has been widely investigated in the context of databases. This paper contributes a QA system with fuzzy terms over KGs called f-KGQA. f-KGQA can deal with different types of questions, including simple questions, complex questions, and questions with fuzzy terms. More importantly, users are provided with a channel to flexibly define their fuzzy terms based on their understanding. Our experimental results demonstrate the effectiveness and applicability of f-KGQA in handling questions with fuzzy terms.

Investigating Test-Taking Strategies in an English Reading Comprehension Test Used by Secondary High School Students

Reading is a crucial skill that Indonesian learners are expected to master, as outlined in the 2013 Curriculum. Students are required to develop comprehension skills to understand both explicit and implicit information in a text. To assess students’ knowledge, teachers often use tests. During these reading tests, students may employ strategies to aid their comprehension. This study investigates the strategies used by secondary high school students at MA Darul Ihsan Samarinda during an English reading comprehension test, along with the challenges they face when using these strategies. This study employs a qualitative design to examine the strategies utilized by six students: high score, middle score, and lowest score. The data, gathered through the think-aloud method and interviews, is analyzed using thematic analysis. The findings reveal that students employed 23 out of 31 strategies to answer factual questions and 19 strategies for inferential questions. Both the high score and middle score students predominantly used the strategy of reading the text for clues in both types of questions. Conversely, the lowest score student primarily relied on the strategy of rereading the question. Despite the differences, all students utilized a meaning-based text strategy to choose their answers. The middle score and lowest score students frequently faced challenges with time management and complex vocabulary. Additionally, using mixed-method research designs could provide deeper insights into test-taking strategies. This study focused on the strategies used by high school students during reading tests, while other research has examined test-taking strategies in high-stakes language tests

Custom GPTs Enhancing Performance and Evidence Compared with GPT-3.5, GPT-4, and GPT-4o? A Study on the Emergency Medicine Specialist Examination.

Given the widespread application of ChatGPT, we aim to evaluate its proficiency in the emergency medicine specialty written examination. Additionally, we compare the performance of GPT-3.5, GPT-4, GPTs, and GPT-4o. The research seeks to ascertain whether custom GPTs possess the essential capabilities and access to knowledge bases necessary for providing accurate information, and to explore the effectiveness and potential of personalized knowledge bases in supporting the education of medical residents. We evaluated the performance of ChatGPT-3.5, GPT-4, custom GPTs, and GPT-4o on the Emergency Medicine Specialist Examination in Taiwan. Two hundred single-choice exam questions were provided to these AI models, and their responses were recorded. Correct rates were compared among the four models, and the McNemar test was applied to paired model data to determine if there were significant changes in performance. Out of 200 questions, GPT-3.5, GPT-4, custom GPTs, and GPT-4o correctly answered 77, 105, 119, and 138 questions, respectively. GPT-4o demonstrated the highest performance, significantly better than GPT-4, which, in turn, outperformed GPT-3.5, while custom GPTs exhibited superior performance compared to GPT-4 but inferior performance compared to GPT-4o, with all p < 0.05. In the emergency medicine specialty written exam, our findings highlight the value and potential of large language models (LLMs), and highlight their strengths and limitations, especially in question types and image-inclusion capabilities. Not only do GPT-4o and custom GPTs facilitate exam preparation, but they also elevate the evidence level in responses and source accuracy, demonstrating significant potential to transform educational frameworks and clinical practices in medicine.

Antimicrobial stewardship and education in optometry

ABSTRACT Clinical relevance Antimicrobial stewardship aims to improve how antibiotics are prescribed by clinicians and used by patients, to ensure safe appropriate use, and to prevent and contain antimicrobial resistance. Background Australian optometrists can prescribe a range of antimicrobial agents, but to what extent they consider antimicrobial stewardship is not known. This study evaluated what final-year optometry students and optometrists understood about antimicrobial stewardship in eye care and considered relevance to oral prescribing and education. Methods A knowledge, attitude, and practice (KAP) questionnaire was developed comprising four sections: i) demographic questions, ii) 10 multiple choice questions regarding Knowledge about antimicrobial agents and likely pathogens, diagnosis, treatment, and preventing resistance, iii) 10 Likert rating scale questions about thoughts and Attitudes, iv) mix of question types (5× MCQ: course of action, 16 Likert scale: confidence) related to Practice (diagnosis, management, and prescribing). Descriptive analyses were conducted (medians, mean±SD, and distributions). Results Thirteen students and 36 optometrists completed the survey. Both groups had a good understanding of microbial resistance and antibiotic prescribing for ocular conditions; average knowledge scores were 9.2 ± 0.7 and 8.7 ± 1.1 for students and practitioners, respectively. Scores were neutral to agree for questions related to the need for more educational programs. The answers of practice-related MCQs had a dichotomy of answers indicating a diversity of opinion, as can be the case in practice. Average total practice confidence scores were approximately 57/80 for students and 68/80 for optometrists. Optometrists and students were very confident in their ability to conduct an appropriate case history and use their knowledge and clinical skills, including slit lamp anterior eye assessment. The lowest confidence scores were for conducting microbial testing, i.e. taking swabs for pathology. Conclusion Student and optometrist knowledge, attitudes, and practice around antimicrobial stewardship were high and in line with other health professions globally. There is scope for increased stewardship training.

Ways of questioning, ways of gesturing: how teachers elicit different ways of students’ thinking in elementary science classrooms

ABSTRACT Questions and gestures used in pedagogical contexts have each received considerable attention. However, rarely have studies focused on the relationship between questions and gestures and what this relationship may mean for pedagogical practice and the development of higher-level thinking. Drawing on previous research into the cognitive levels of questions and gestures, this study addresses this issue through focusing on the type of gestures used when teachers ask questions in elementary science classrooms. Participants were two Grade One science teachers, and video recordings constitute the major data sources. The study is primarily qualitative, with frequency analyses conducted to analyse the cognitive levels of questions and the cognitive distribution of gestures in the questions. The results show that elementary science classrooms are dominated by low cognitive level questions, which reflects a direct instruction teaching method. However, the use of higher cognitive level questions suggests that teachers use some learner-centred approaches to challenge and encourage students for deeper levels of thinking. Overall, there is a correspondence between the cognitive levels of questions and of gestures, e.g. LCL gestures are most commonly distributed in LCL questions, although variations in the specific use of gestures in each type of question exist. This research contributes to the understanding of the interplay between questions and gestures within the elementary science teaching-learning context, and has implications for science teachers and teacher educators to consider the relationship between cognitive levels of gestures and questions and its role in developing higher-level thinking in elementary science pedagogy.

Projective Psychological Tests from the Point of View of Neural Network Theory of Society

It is shown that the issues raised by the international expert community in the discussion regarding the legality of using projective techniques (in particular, tests such as the Luscher test, the Szondi test and the Dellinger test) can be resolved on the basis of an adequate interpretation of the concept of “archetype” and an analogy with neural networks. At the same time, the interpretation of the concept “archetype” is based on the neural network theory of society, which allows us to remove the difficulties that arise with the traditional interpretation of this concept. It is shown that there is an equivalent scheme for the testing procedure, in which the testing itself can be reduced to the functioning of the external (output) layer of the general neural network, which also includes a neural network formed by the human brain. This approach makes it possible to remove many of the criticisms put forward against the use of projective techniques (even declaring them pseudoscientific), but it implies a significant change in existing approaches to the interpretation of results obtained when using tests of the type in question. Namely, within the framework of the proposed approach, not every possible combination of images on which the test is built makes sense. Such a meaning can be given only to some of them, specifically to those that are most often encountered in experiments. Such combinations, which can be interpreted as basic, are interpreted on the basis of an analogy with images recognized by a neural network. The remaining combinations are treated as images containing errors; they are reduced to the basic ones based on an analogy with the methods used in constructing error-resistant codes.

Nonsteroidal anti-inflammatory drug use by patients: Impact of modular educational training on pharmacists' questioning, counselling and risk assessments

BackgroundNonsteroidal anti-inflammatory drugs (NSAIDs)-related morbidity and mortality can be reduced through medication counselling and risk reduction. ObjectivesThis study evaluated the impact of short online modular NSAID training on the type and quality of questions asked, risk factors assessed, and counselling offered by community pharmacists to NSAID users. MethodsA cross-sectional questionnaire-guided survey conducted in Ibadan, Nigeria, among 87 pharmacists evaluated the frequency of counselling, NSAID risk factor assessment and barriers to risk assessment. Additionally, a before-and-after RCT was used to evaluate the impact of short online modular NSAID training for the intervention group (IG) on the type and quality of the questions asked, counselling provided, and risk assessed by the pharmacists. Eight standardised patients, aged 25–43 years, four at pre- and postintervention, presented four standardised scenarios at community pharmacies [IG, n = 22, control group (CG, n = 30)] to assess these outcomes. The quality of each outcome (questions asked, counselling offered and risk assessed) was classified as poor (0–≤20%), fair (>20–≤40%), moderate (>40 – ≤60%), or optimal (>60–100%). The data are presented with descriptive statistics. ResultsThe community pharmacists reported counselling patients on NSAID precautions (80–86%) and dosages (51–69%). Gastrointestinal bleeding risk was assessed by 61–89% of the pharmacists, and time constraints (39–42%) and patient impatience (47–75%) were some barriers to risk assessment. Online modular educational intervention significantly improved the types and quality of questions asked by pharmacists (CG: poor to fair, 16%–21%; IG: poor to moderate, 14%–45%), NSAID risk factors assessed (CG: poor to poor, 10%–9%; IG: poor to fair, 11%–27%) and counselling offered (CG: poor to poor, 6%–7%; IG: poor to fair, 6%–22%). ConclusionsShort online modular educational training on NSAIDs improved the types and quality of the questions asked, NSAID risk factors assessed, and counselling provided by community pharmacists to patients during consultations.

A DIRECTIVE SPEECH ACT IN THE BLIND SIDE MOVIE BY JOHN LEE HANCOCK

A speech act is an act performed by saying something. This study aims : 1) to find_out_thetypes of locutionary act, 2) to find out the types of perlocutionary act, and 3) to find out thetypes of illocutionary act in The Blind Side movie by John Lee Hancock. Moreover, this studyconcerned on the use of directive speech act. The researcher used qualitative method with thetype of a library research to describe and analyze the utterances that consisted of speech act.The method of collecting data is non-participant observation and documentation. Interactivemodel data of analysis used to analyze the data. The result of this study showed that : 1) therewere 227 utterances consisted of locutionary act which had no types, 2) there are 227utterances consisted of perlocutionary act which had no types and 3) there are 227 utterancesconsisted of illocutionary act which included asserives, declaratives, commisives, expressivesand declaratives. Furthermore, the writer found 131 utterances consisted of directive speechact which had 31 of requirements, 64 of questions, 20 of requestives, 6 of prohibitives, 3 ofpermissives and 7 of advisories. From the result of this research, the questions types inDirective Speech Act was the most dominant used by major and minor character as thespeakers.Keywords : Locutionary Acts, Perlocutionary Acts, Illocutionary Acts, DirectiveSpeech Act, A Qualitative Research

Assessment Study of ChatGPT-3.5's Performance on the Final Polish Medical Examination: Accuracy in Answering 980 Questions.

The use of artificial intelligence (AI) in education is dynamically growing, and models such as ChatGPT show potential in enhancing medical education. In Poland, to obtain a medical diploma, candidates must pass the Medical Final Examination, which consists of 200 questions with one correct answer per question, is administered in Polish, and assesses students' comprehensive medical knowledge and readiness for clinical practice. The aim of this study was to determine how ChatGPT-3.5 handles questions included in this exam. This study considered 980 questions from five examination sessions of the Medical Final Examination conducted by the Medical Examination Center in the years 2022-2024. The analysis included the field of medicine, the difficulty index of the questions, and their type, namely theoretical versus case-study questions. The average correct answer rate achieved by ChatGPT for the five examination sessions hovered around 60% and was lower (p < 0.001) than the average score achieved by the examinees. The lowest percentage of correct answers was in hematology (42.1%), while the highest was in endocrinology (78.6%). The difficulty index of the questions showed a statistically significant correlation with the correctness of the answers (p = 0.04). Questions for which ChatGPT-3.5 provided incorrect answers had a lower (p < 0.001) percentage of correct responses. The type of questions analyzed did not significantly affect the correctness of the answers (p = 0.46). This study indicates that ChatGPT-3.5 can be an effective tool for assisting in passing the final medical exam, but the results should be interpreted cautiously. It is recommended to further verify the correctness of the answers using various AI tools.

Influence of Model Evolution and System Roles on ChatGPT's Performance in Chinese Medical Licensing Exams: Comparative Study.

With the increasing application of large language models like ChatGPT in various industries, its potential in the medical domain, especially in standardized examinations, has become a focal point of research. The aim of this study is to assess the clinical performance of ChatGPT, focusing on its accuracy and reliability in the Chinese National Medical Licensing Examination (CNMLE). The CNMLE 2022 question set, consisting of 500 single-answer multiple choices questions, were reclassified into 15 medical subspecialties. Each question was tested 8 to 12 times in Chinese on the OpenAI platform from April 24 to May 15, 2023. Three key factors were considered: the version of GPT-3.5 and 4.0, the prompt's designation of system roles tailored to medical subspecialties, and repetition for coherence. A passing accuracy threshold was established as 60%. The χ2 tests and κ values were employed to evaluate the model's accuracy and consistency. GPT-4.0 achieved a passing accuracy of 72.7%, which was significantly higher than that of GPT-3.5 (54%; P<.001). The variability rate of repeated responses from GPT-4.0 was lower than that of GPT-3.5 (9% vs 19.5%; P<.001). However, both models showed relatively good response coherence, with κ values of 0.778 and 0.610, respectively. System roles numerically increased accuracy for both GPT-4.0 (0.3%-3.7%) and GPT-3.5 (1.3%-4.5%), and reduced variability by 1.7% and 1.8%, respectively (P>.05). In subgroup analysis, ChatGPT achieved comparable accuracy among different question types (P>.05). GPT-4.0 surpassed the accuracy threshold in 14 of 15 subspecialties, while GPT-3.5 did so in 7 of 15 on the first response. GPT-4.0 passed the CNMLE and outperformed GPT-3.5 in key areas such as accuracy, consistency, and medical subspecialty expertise. Adding a system role insignificantly enhanced the model's reliability and answer coherence. GPT-4.0 showed promising potential in medical education and clinical practice, meriting further study.

Gamification for Studying Mathematics by E-Learning

The difficulties of studying mathematics in this digital age call for solutions that might boost student engagement and motivation. To make learning more dynamic and interesting, it is suggested that a gamification methodology that combines game simulation with gamification is applied. In order to enhance mathematics instruction using an online learning platform. A thorough grasp of the difficulties associated with studying mathematics. To complement the learning objectives for mathematics, the gamification design includes selecting question types, constructing game situations, and producing visual components. This research creates a gamification paradigm using the Quizizz gaming application. The research focuses on the case of IKPIA Perbanas Jakarta for mathematics courses, which has the primary objective of optimizing mathematical learning through the concept of gamification. The researcher used the Dick and Carey development model, which has 10 phases from requirement identification to assessment, to create this learning model. The implementation of the Quizizz application and the gamification approach in an online learning environment occurs at the development stage. Technical configuration, application testing, and modifications based on preliminary feedback are all part of the implementation process. The success of the gamification technique in raising students' motivation and comprehension of the mathematics content was then assessed. Researchers used this helpful gamification to determine suitable designs for delivering learning content. Gamification for mathematics education is also very practical. In education, gamification by Quizizz as an application for gamification only supports but cannot replace the position of teachers and books. Mathematical learning devices with gamification are effective because they have met the classification very well at 79.17% and 20.83%, respectively, at an accuracy rate of 95.83%. Research has limited problems related to being carried out only in the IKPIA Perbanas Jakarta environment, which has students in semester one who have the characteristics of technology generation with the application of gamification that is integrated into the e-learning of Perbanas. The value of this research is the use of gamification for students studying in higher education and integrated into the Learning Management System (LMS).

PENGEMBANGAN INSTRUMEN TES MATEMATIKA BERBASIS HIGHER ORDER THINGKING SKILLS (HOTS) UNTUK SISWA SMP

The aim of this research is to develop a mathematical test instrument based on Higher Order Thingking Skills for high school students. The research methods used in this research are research and development methods or Research and Development. (R&amp;D). The data-gathering instruments for this search are teacher lift, student perception lift, observation sheet, interview guide, and the definition test of Higher Order Thinking Skills. (HOTS). The study was tested on 25 students of the eighth grade of high school, the test was carried out on the material of linear equations and linear inequalities of one variable. The results of the research show that: 1) the test instrument Higher Order Thinking Skills developed is valid and reliable, 2) has an average difficulty level for each type of question, and 3) the test instruments Higher order thinking skills (HOTS) developed fall into the category both in different power tests. So, the tool developed by the researchers deserves to be used as an assessment to measure the Higher Order Thinking Skills (HOTS) test for high school students

Leveraging Large Administrative Data for Special Education Policy Analysis: Recent Contributions and Future Directions

The purpose of this special issue is to explain the advantages of using administrative data generated by states and school districts for research in special education, illustrate their use in special education policy studies, and encourage more such analyses. In this introduction to the special issue, we define and distinguish large administrative data sets, discuss types of special education policy questions that are appropriate for addressing with administrative data, provide a high-level overview of methods that are commonly used with such data, discuss examples of published analyses that use each method, note some of the limitations to using administrative data, and briefly introduce articles in the special issue.

Studying Continuous, Time-varying, and/or Complex Exposures Using Longitudinal Modified Treatment Policies.

This tutorial discusses a methodology for causal inference using longitudinal modified treatment policies. This method facilitates the mathematical formalization, identification, and estimation of many novel parameters and mathematically generalizes many commonly used parameters, such as the average treatment effect. Longitudinal modified treatment policies apply to a wide variety of exposures, including binary, multivariate, and continuous, and can accommodate time-varying treatments and confounders, competing risks, loss to follow-up, as well as survival, binary, or continuous outcomes. Longitudinal modified treatment policies can be seen as an extension of static and dynamic interventions to involve the natural value of treatment and, like dynamic interventions, can be used to define alternative estimands with a positivity assumption that is more likely to be satisfied than estimands corresponding to static interventions. This tutorial aims to illustrate several practical uses of the longitudinal modified treatment policy methodology, including describing different estimation strategies and their corresponding advantages and disadvantages. We provide numerous examples of types of research questions that can be answered using longitudinal modified treatment policies. We go into more depth with one of these examples, specifically, estimating the effect of delaying intubation on critically ill COVID-19 patients' mortality. We demonstrate the use of the open-source R package lmtp to estimate the effects, and we provide code on https://github.com/kathoffman/lmtp-tutorial.

Tindak Tutur dalam Interaksi Penjaja dan Pelanggan: Studi Kasus Pasar Kampung Lalang

The speech acts uttered by the speaker often do not match what is received by the recipient of the language (buyer). This problem of meaning is also an obstacle in speech that is not paid enough attention by speakers, both speakers (sellers) and buyers. Language is used as a forum for dialogue between speakers and speakers in buying and selling transactions. The aim of this research is to describe speech acts in the interaction between vendors and customers at Kampung Lalang Market. The method used is a qualitative descriptive method that focuses on reality as the actual situation in the Kampung Lalang market area. The data collection technique is carried out using recording techniques, namely recording speech and speech activities by speakers in the context of buying and selling, recorded with a device or similar. This process is carried out for use by sellers and customers and can be played or listened to many times, thereby facilitating researchers in examining speech acts. The second technique is the observation technique, namely observing something directly, the interaction between sellers and buyers at Kampung Lalang Market. The speech results found in the interaction between the seller and the customer are locutionary acts with the types of statements, questions and commands. Illocutionary speech acts include directive, commissive, declarative and expressive types, as well as perlocutionary speech acts. In this speech the seller and customer use Karo language.