Instrument analysis of the effect of project-based learning models on learning outcomes on the basic competencies of commercial hair trimming

This study was used to determine the feasibility of the instruments before being used for further research. This study calculates the results of the validation of three validators who are experts in their fields using arithmetic averages, the validated instruments are: (1) the syllabus gets a score of 92.67%; (2) Learning Implementation Plan (RPP) scored 94.4%; (3) The Knowledge Assessment Instrument received a score of 93.36%; (4) The skill assessment instrument got a score of 93.31%; (5) The attitude assessment instrument got a score of 96.89%; (6) The Project Based Learning module received a score of 93.1%. In addition, this study also calculates the validity of the items, the differentiability of the items, the level of difficulty of the items and the reliability test of the items. This question consists of 30 items that have been tested on 15 students to find out the results of the calculation: (7) the validity of the items obtained a significance level value (ɑ) 0.05; (8) the level of difficulty of the items obtained a percentage of 16.67 for the easy question category, 70% for the medium question category, and 13.33% for the difficult question category; (9) The differentiating power of the items has very good criteria; and (10) the reliability of the items got a score of 0.753. From the calculation results that have been mentioned, it can be stated that the instrument of the influence of project-based learning models on learning outcomes on the basic competencies of commercial hair trimming is declared feasible and can be used for further research.

Goal-Driven Visual Question Generation from Radiology Images

Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.

Goal-Driven Visual Question Generation from Radiology Images

Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.

ANALISIS KARAKTERISTIK STIMULUS DAN LEVEL KOGNITIF SOAL UM-PTKIN TAHUN 2019

PTKIN plays a role in improving human resources, so a selection system is needed which can select students according to their competence. UM-PTKIN is one of a system for selecting prospective students. In the selection system, the questions being tested require the ability to think from low to a high level of prospective students so that knowing the characteristics of the questions being tested is one of the keys to the success of prospective students in facing selection. This study aims to classify the characteristics of the UM-PTKIN questions based on the form of the question stimulus and the cognitive level of the questions based on the revised Bloom`s Taxonomy. This type of research is qualitative research using content or document analysis techniques based on the characteristics of the stimulus and the cognitive level of questions. The data collection techniques in this study used non-test techniques and focus group discussion (FGD) using questionnaires and FGD guidelines. The results showed 1) mathematics questions on UM-PTKIN basic ability test in 2019 with the 1911 script code contains 3 questions in the form of image stimulus, 1 question in the form of graphical stimulus and tables, 3 questions in the form of stimulus formulas, 2 questions in the form of stimulus for mathematical equations, 4 questions in the form of stimulus examples and 13 questions in the form of case fragment stimulus; 2) The questions contained 26% in the LOTS question category, 30% with the MOST question category and 44% with the HOTS question category with the overall distribution of the question material, the material of the flat shape was the material that appears the most.

Reading Comprehension Test and Its Challenges In Students’ Perspective

There are two objectives of this research were first, to find the most difficult question category that students faced in reading test, and last, to investigate the causes of students' difficulties. This research used a qualitative method and it was conducted at the first semester students of c class English Department of UNTIRTA year 2019. The descriptive method was used to expose the result of this research. The data were collected through documents of reading comprehension final test first semester and an interview with the students. The document was used to find out the most difficult question category, and the result after analyzing the document is the students got difficulties in answering the question about vocabulary mastery. It is also supported by the result of interviewing the students. There were 17 students from 20 students or 85% felt that the vocabulary question category in part B was difficult. Then, the interview was also used to investigate the causes of the student's difficulties in reading test. The result got after analysis, the causes of the students got difficulties and confused to answer vocabulary question because they were lack of vocabulary mastery, and their reading habit was poor. So, it indicates that students’ capability of guessing meaning some unfamiliar vocabulary or words in the current context needs to be improved.

Computer Based Test Using the Fisher-Yates Shuffle and Smith Waterman Algorithm

Tests are used to determine a person’s level of understanding of a subject. The inhibiting factors in tests are less varied questions, questions with insufficient difficulty, subjective assessments, and the length of time in their correction. This research aimed to develop a Computer Based Test (CBT) application. The type of questions in this CBT are multiple choice and essays. This CBT employs categorization of questions, randomization of the questions, and automatic assessment. Questions were categorized manually based on Bloom’s Taxonomy of a lecture. Then the randomization process was carried out using the Fisher-Yates Shuffle algorithm for each question category. The Smith Waterman algorithm was used to automatically assess the essay-type questions. The steps of the Smith Waterman algorithm were preprocessing, data comparison using Smith Waterman, and percentage similarities conversion to test scores. The results of the study showed that the CBT application was able to randomize questions using the Fisher-Yates Shuffle algorithm and automatically assess answers using the Smith Waterman algorithm. RMSE was used to measure of the accuracy of the Smith Waterman algorithm: a value of 1.86 was obtained. Keywords: Computer based test, assessment, Fisher-Yates Shuffle, Smith Waterman

Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach (Preprint)

BACKGROUND COVID-19 is still rampant all over the world. Until now, the COVID-19 vaccine is the most promising measure to subdue contagion and achieve herd immunity. However, public vaccination intention is suboptimal. A clear division lies between medical professionals and laypeople. While most professionals eagerly promote the vaccination campaign, some laypeople exude suspicion, hesitancy, and even opposition toward COVID-19 vaccines. OBJECTIVE This study aims to employ a text mining approach to examine expression differences and thematic disparities between the professionals and laypeople within the COVID-19 vaccine context. METHODS We collected 3196 answers under 65 filtered questions concerning the COVID-19 vaccine from the China-based question and answer forum Zhihu. The questions were classified into 5 categories depending on their contents and description: adverse reactions, vaccination, vaccine effectiveness, social implications of vaccine, and vaccine development. Respondents were also manually coded into two groups: professional and laypeople. Automated text analysis was performed to calculate fundamental expression characteristics of the 2 groups, including answer length, attitude distribution, and high-frequency words. Furthermore, structural topic modeling (STM), as a cutting-edge branch in the topic modeling family, was used to extract topics under each question category, and thematic disparities were evaluated between the 2 groups. RESULTS Laypeople are more prevailing in the COVID-19 vaccine–related discussion. Regarding differences in expression characteristics, the professionals posted longer answers and showed a conservative stance toward vaccine effectiveness than did laypeople. Laypeople mentioned countries more frequently, while professionals were inclined to raise medical jargon. STM discloses prominent topics under each question category. Statistical analysis revealed that laypeople preferred the “safety of Chinese-made vaccine” topic and other vaccine-related issues in other countries. However, the professionals paid more attention to medical principles and professional standards underlying the COVID-19 vaccine. With respect to topics associated with the social implications of vaccines, the 2 groups showed no significant difference. CONCLUSIONS Our findings indicate that laypeople and professionals share some common grounds but also hold divergent focuses toward the COVID-19 vaccine issue. These incongruities can be summarized as “qualitatively different” in perspective rather than “quantitatively different” in scientific knowledge. Among those questions closely associated with medical expertise, the “qualitatively different” characteristic is quite conspicuous. This study boosts the current understanding of how the public perceives the COVID-19 vaccine, in a more nuanced way. Web-based question and answer forums are a bonanza for examining perception discrepancies among various identities. STM further exhibits unique strengths over the traditional topic modeling method in statistically testing the topic preference of diverse groups. Public health practitioners should be keenly aware of the cognitive differences between professionals and laypeople, and pay special attention to the topics with significant inconsistency across groups to build consensus and promote vaccination effectively.

Faculty Versus Resident Self-Assessment Using Pathology Milestones: How Aligned Are We?

Competent physicians must be able to self-assess skill level; however, previous studies suggest that medical trainees may not accurately self-assess. We utilized Pathology Milestones (PM) data to determine whether there were discrepancies in self- versus Clinical Competency Committee (CCC) ratings by sex, program year (PGY), time of evaluation, and question category (Patient Care, Medical Knowledge, Systems-Based Practice [SBP], Practice-Based Learning and Improvement [PBL], Professionalism [PRO], and Interpersonal and Communication Skills) and Residency In-Service Examination (RISE) score. We completed retrospective analyses of PM evaluation scores from 2016 to 2019 (n = 23 residents) 2 times per year. Discrepancies in evaluation scores were calculated by subtracting CCC scores from resident self-evaluation scores. There was no significant difference in discrepancy scores between male versus female residents (P = .94). Discrepancy scores among all PGYs were significantly different (P < .0001), with PGY1 tending to overrate the most, followed by PGY2. PGY3 and PGY4 underrated themselves on average compared to CCC ratings, with PGY4 having significantly lower self-ratings than CCC compared to any other PGY. In January, residents underscored themselves and in July residents overscored themselves compared to CCC (P < .0001 for both). Question types resulted in variable discrepancy scores, with SBP significantly lower than and PRO significantly higher than all other categories (P < .05 for both). Increases in RISE score correlated to increases in self- and CCC-scoring. These discrepancies can help trainees improve self-assessment. Discrepancies indicate potential areas for amelioration, such as curriculum adjustments or Milestone’s verbiage.

Customer Satisfaction with Services at the Ward Offices of Osaka City Government

Using an online survey, this study investigated determining factors related to customer satisfaction with counter services at ward offices (<i>Kuyakusho</i>) in Osaka City, Japan, focusing on direct experiences at these service counters. During a two-day survey period, responses from 400 women, aged 30–59 years, who had visited a ward office over a one-month period were collected. The questionnaire comprised three categories of multiple-choice questions: A) hardware (e.g., physical aspects), B) software (e.g., staff responses), and C) services (e.g., administrative services). Principal component analysis and multiple regression analysis were conducted on each question category concerning various aspects of public service provision. The regression analysis indicated that group C (service delivery quality) had the strongest influence on the dependent variables (ZY1), followed by group B and group A. Adjusted <i>R</i>² value is.60.<br>