Answer Patterns Research Articles

Exploring Chemists’ Understandings of the Nature of Science with their Levels of Expertise

Several Nature of Science (NOS) studies have been conducted on different categories of professionals and different age groups. Even though NOS is considered a structural facet of scientific literacy, not much research has been conducted on the NOS ideas of scientists and science professionals. This cross-sectional study investigated the nature of science (NOS) views of 40 participants who have or had exposure to chemistry research using a qualitative comparative analysis approach, in one chemistry department in a large Midwest R-1 university. The participants' chemistry research exposure varied from no research to 31 years of experience. Undergraduates, graduate students, postgraduate researchers, faculty, and scientists were recruited for this study to examine how their NOS understanding develops in relationship to their research exposure. These categories of participants demonstrated different and unique patterns of answers related to aspects of NOS. Science content knowledge and research exposure indeed enhanced science learners'/researchers’ NOS understanding, but NOS understanding is not developed in direct proportion to the amount of science content knowledge or research experience that an individual gained. It was observed that professionals with the same expertise level responded to NOS questions similarly, and professionals with different levels of expertise answered NOS questions in significantly different ways.

An inductive bias from quantum mechanics: learning order effects with non-commuting measurements

There are two major approaches to building good machine learning algorithms: feeding lots of data into large models or picking a model class with an “inductive bias” that suits the structure of the data. When taking the second approach as a starting point to design quantum algorithms for machine learning, it is important to understand how mathematical structures in quantum mechanics can lead to useful inductive biases in quantum models. In this work, we bring a collection of theoretical evidence from the quantum cognition literature to the field of quantum machine learning to investigate how non-commutativity of quantum observables can help to learn data with “order effects,” such as the changes in human answering patterns when swapping the order of questions in a survey. We design a multi-task learning setting in which a generative quantum model consisting of sequential learnable measurements can be adapted to a given task — or question order — by changing the order of observables, and we provide artificial datasets inspired by human psychology to carry out our investigation. Our first experimental simulations show that in some cases the quantum model learns more non-commutativity as the amount of order effect present in the data is increased and that the quantum model can learn to generate better samples for unseen question orders when trained on others — both signs that the model architecture suits the task.

Evaluating the Factors Affecting the Involvement of Shariah Committees in Risk Management Practices in Islamic Banks: Qualitative Research in the Kingdom of Saudi Arabia

Purpose: This study aims to add to the existing literature on Islamic finance by explaining the ways in which Shariah committees can improve and strengthen their influence over Islamic banks’ risk management practises. This research attempts to break down the complexities of Saudi Arabia’s risk management and identify the crucial role that Shariah committees play in ensuring compliance with Shariah law. Design of study: A qualitative research method was adopted conducting semi-structured interviews. Purposive sampling was used to choose 34 individuals. The data were analyzed using thematic analysis, with the use of NVivo software, which made it easier to identify patterns in the Arabic answers. Following the ethical norms established by RMIT University, obtaining participants’ explicit consent, and guaranteeing anonymity are all of the top priority. Findings: Several factors determine Saudi Islamic Bank Shariah Committee risk management. An effective regulatory framework, Shariah committee-bank leadership communication, and learning of Islamic financial ideas can aid in Shariah compliance. However, interpreting complex financial services within Shariah, inconsistencies between Islamic principles and standard risk management methodologies, and a shortage of competent Shariah scholars with banking and risk management knowledge may slow development. Research is needed to understand Saudi procedures. Originality: The current research comprehensively documented potential factors that facilitate or resist the involvement of Shariah Committees in practices of risk management in Saudi Arabia’s Islamic banks.

Overcoming the Limitations of Learning-Based VQA for Counting Questions with Zero-Shot Learning

Visual question answering (VQA) research has garnered increasing attention in recent years. It is considered a visual Turing test because it requires a computer to respond to textual questions based on an image. Expertise in computer vision, natural language processing, knowledge understanding, and reasoning is required to solve the problem of VQA. Most techniques employed for VQA consist of models that are developed to learn the combination of image and question features along with the expected answer. The techniques chosen for image and question feature extraction and combining the features change with each model. This method of teaching a model of the question–answer pattern is ineffective for queries that involve counting and reasoning. This approach also requires considerable resources and large datasets for the training. The general VQA datasets feature a restricted number of items as responses to counting questions ([Formula: see text]), and the distribution of the answers is not uniform. To investigate these issues in VQA, we created synthetic datasets that could be modified to adjust the number of objects in the image and the amount of occlusion. Specifically, a zero-shot learning VQA system was devised for counting-related questions that provide answers by analyzing the output of an object detector and the query keywords. Using synthetic datasets, our model generated 100% correct results. Testing on the benchmark datasets task directed image understanding challenge (TDIUC) and TallyQA-simple indicated that the proposed model matched the performance of the learning-based baseline models. This methodology can be used efficiently for counting VQA questions confined to certain domains when the number of items to be counted is significant.

O-248 Novel early cleavage parameters improve embryo selection and reach ploidy predictive values comparable to artificial intelligence

Abstract Study question Can we improve embryo selection and ploidy prediction using novel morphokinetic parameters in blastocyst development, and obtain results comparable to artificial intelligence? Summary answer Time and pattern of novel parameter st2 are related to ploidy. Including them in embryo evaluation improves ploidy prediction, reaching predictive values comparable to AI. What is known already Time-lapse has allowed us to detect more morphokinetic parameters and improve embryo grading systems. However, ploidy prediction still relies on invasive techniques to obtain a reliable result. Artificial intelligence is promising and can already predict blastulation or implantation with good results, but not ploidy yet. Moreover, AI presents some other limitations now, including the black box behind its decisions, which doesn’t allow clinicians to know what parameters are being considered the most. Improving the manual evaluation tools used by embryologists can help improve clinical outcomes until AI is fully introduced in IVF. Study design, size, duration A single center retrospective study was performed. Data included a total of 608 blastocyst from 106 treatments between January 2018 and July 2022. Participants/materials, setting, methods 160 treatments of patients for preimplantation genetic testing cycles were studied. Embryos were cultured in time-lapse incubators and were studied for both morphologic and kinetic parameters. 608 of the embryos developed to good quality blastocyst and were able to be biopsied, and therefore analyzed by PGT for aneuploidies on Day5 o Day6. Annotation of several morphokinetic characteristics was performed on all analyzed embryos. The study was blinded for ploidy and all clinical outcomes. Main results and the role of chance Improved embryo grading systems used by embryologist can reach high predictive values comparable to artificial intelligence. We present a new parameter, “st2, start of t2”, which refers to the first cytoplasmic movements detected at the beginning of the first cell cleavage, as highly implicated in ploidy status. Earlier st2 is associated to better euploidy rates (p < 0.001). Not only the time, but also the phenotype of the movements appears as related to ploidy. Embryos showing none or un-patterned random cytoplasmic movements show a marked tendency towards aneuploidy. Contrary, circular waves prior to cell division are highly associated with euploidy (90% of the cases) (p < 0.0001). The implication of the described parameters was also confirmed by logistic regression, with a receiver operating curve (ROC) value of 0.69 for ploidy prediction (95% confidence interval (CI), 0.62 to 0.76). We processed raw time-lapse images with an automatic annotation software using artificial intelligence and obtained a ploidy predictive value of 0.64. This means that using the optimal parameters in manual embryo evaluation, we are able to improve our grading systems and reach ploidy predictive values comparable than with AI. Limitations, reasons for caution This is a retrospective study. The ploidy predictive potential is now being tested on a prospective study. Wider implications of the findings Optimizing the indicators to select the most suitable blastocyst, like including st2, can help reducing the time until the pregnancy of an euploid baby, avoiding invasive and expensive methods. Moreover, by combining AI with the novel parameter st2 we could improve the potential of the ploidy predictions. Trial registration number not applicable

P-047 Sperm moving pattern analysis with artificial intelligence solution predicts aneuploidy ratios of zygotes

Abstract Study question Can an analysis of sperm movements with artificial intelligence predict aneuploidy ratios of zygotes? Summary answer And sperm movement patterns analyzed by AI diagnosis predicted the aneuploidy ratios of zygotes. What is known already Sperm motilities present a specific pattern like strength, strength with curving, curving only and none progressive et al. Sperm movement patterns are determined by the sperm neck protein which is the centromere protein. Also, this centromere protein of the sperms was involved in the chromosome segregation in the zygote. Study design, size, duration A total of 8,000 clinical participants were recruited by CHA Infertility Center Seoul Station and 5 videos were taken from the semen sample of each participant. Machine learning with sperm moving clip for the diagnosis algorithm predicted aneuploidy ratios of fertilized embryos. Participants/materials, setting, methods We used YOLO_v8 and EfficientNet algorithm on the extracted sperm movement trajectory image to classify sperm into classes according to their motion pattern. This information was combined with the age of the patient to predict the possibility of infertility by analyzing the association with aneuploidy ratios of zygotes using deep learning model. Main results and the role of chance Deep learning AI algorithm utilized diagnosis for unexplained male infertility. No strength moving sperm pattern has shown association with male origin aneuploidy after fertilization. Especially high ratios of cycle moving sperm significantly increased the aneuploidy ratios in the zygote. But paternal aging has no correlation with aneuploidy ratios in the embryo. AI diagnosis have a 0.9 accuracy score. It is expected that the AI solution generated through this system can help overcome the problem of finding the cause of male infertility by predicting aneuploidy ratios of zygotes. Limitations, reasons for caution This study is designed for and performed with a minimum number of sperm move data. For clinical application, further mass data was set-up to increase accuracy of diagnosis. Wider implications of the findings Our AI solution was constructed to predict the possibility of aneuploidy ratios in the zygote by analyzing and classifying the movement patterns of sperm. Therefore, sperm movement-based AI diagnosis solution can be applied to define unexplained male infertility. Trial registration number non-clinical trials

Identification of Misconceptions and Causes of Misconceptions in Stoichiometry Material Using Four Tier Multiple Choice (4TMC)

Stoichiometry is one of the materials in chemistry subjects in high school that contains basic concepts to understand other chemical concepts. At the same time, a few class X students need help understanding and even experience misconceptions. This study aims to identify subconcepts that experience misconceptions, the percentage of students who experience misconceptions, and the causes of misconceptions that occur in students. The research method used is the quantitative descriptive method. The sample of this study was students of grade X MIPA at SMA N 1 Jetis, which was taken based on purposive sampling techniques. The research instrument used was the Four Tier Multiple Choice (4TMC) diagnostic test instrument. The pattern of answers, reasons, and confidence rating from the Four Tier Multiple Choice (4TMC) test results are then processed and analyzed to obtain significant misconception data from students. The results of the research obtained, namely, there were misconceptions in stoichiometric material, especially in the subconcepts of Avogadro's Hypothesis, Proust's Law (Law of Fixed Comparison), Dalton's Law (Law of Multiple Comparison), Ar and Mr, the percentage of grade X students at SMA Negeri 1 Jetis on stoichiometric material who experienced misconceptions of 25.35% which was classified as a low category, as well as the causes of misconceptions experienced by grade X students at SMA Negeri 1 Jetis on the material This stoichiometry comes from teachers, learners, and books. Misconceptions originating from teachers are related to learning methods, techniques, and delivery. Misconceptions originating from students are caused by wrong conceptions of learners, dependent algorithmic phenomena, learners' abilities, and students' interests, which need to be improved. At the same time, the misconception from the book is the existence of inappropriate concepts in the student's notebook. Identifying these misconceptions is expected to be the first step to remediate student misconceptions on stoichiometric material and prevent prolonged misconceptions.

The Role of Educational Program Evaluation in Optimizing the Quality of Learning at MA Nurul Fadhilah

Evaluation of educational programs is an effort to find out the goals of education, it can also be interpreted as an effort to provide information that will later be conveyed to decision makers. This study is intended to review how evaluation of educational programs can play a role in optimizing the quality of learning. By using a qualitative ethnographic research approach in which we conducted direct observations and interviews with one of the teaching staff at the target school, this study found: First, evaluation activities were carried out regularly on a weekly basis. Second, the method used in evaluating madrasas is through standards of infrastructure, curriculum and financing. Third, the educational program implemented is a question and answer pattern regarding children's talents and interests. This makes it easier for educational staff to find out which programs are suitable to be implemented. Thus, evaluation of educational programs plays a very important role in improving the quality of learning.

Algebraic Thinking Ability of Junior High School Students in Solving Linear Equations Problems

Algebraic thinking ability are important for students to master and use algebraic concepts in various situations. Students with good algebraic thinking ability will find it easier to solve real-world problems and understand more abstract mathematical concepts. This study aims to describe students' algebraic thinking ability on linear equation material. Thus, the type of research used is phenomenology with a qualitative descriptive approach. The participants in this study consisted of 37 students; six students with heterogeneous abilities (high, medium, low) were then selected. The research subjects were determined by purposive sampling based on the pattern of answers representing the phenomenon that occurred. Data were collected using algebraic thinking ability tests and interviews to fulfill the validity of the data. The results showed that: 1) students with high algebraic thinking ability can use all indicators of algebraic thinking well. 2) students with medium algebraic thinking ability have difficulty in the indicators of analytical thinking, modeling, and dynamic thinking. 3) students with low algebraic thinking ability have difficulty in all indicators of algebraic thinking, namely generalization, modeling, abstraction, analytical thinking, dynamic thinking, and organization. So, teacher efforts are needed to provide students with an understanding of algebraic thinking so that students can solve math problems well.

Is it True That My Students don't Understand the Static Fluid Concepts? Rasch Modeling Perspective

Students' conceptual understanding can be analysed using the Classical Test Theory (CTT) and Item Response Theory (IRT) approaches. CTT can only explain students' conceptual understanding at the group level, while IRT can at the individual level. Therefore, this research evaluates students' conceptual understanding using the Rasch model. This research used a survey research type and was carried out in class XI. Students' conceptual understanding was collected using 10 multiple-choice questions on static fluid material. The level of concept understanding was analyzed in stages using Rasch modelling. Understanding analysis begins by analyzing the difficulty level of the questions and then continues with descriptive analysis. The Wright map is used to see the hierarchy between students' understanding of concepts and the difficulty level of the questions used. Finally, a person diagnostic map will be used to see students' understanding of concepts in detail. The analysis results show that students' conceptual understanding is generally in the high and medium categories. Person diagnostic maps have identified patterns of correct and incorrect answers from students that do not reflect their true abilities. So, it is necessary to analyze the exact location of the weaknesses and strengths of students' conceptual understanding. This has implications for the suitability of the learning plan that the teacher will use.

Early Childhood Self Regulated Learning Based on Javanese Local Wisdom Jawa

Abstract. The study describes early childhood self-regulated learning (SRL) based on the Javanese philosophy in Ki Hadjar Dewantara (KHD) & Ki Ageng Suryamentaram (KAS) concepts. This used a phenomenological qualitative approach on 6 subjects (3-5 years old) dyadic with their mothers in Yogyakarta. The main data were collected through interviews, participant observation, and supported by data of the children’s developmental maturity by filling out the Temperament Assessment Scale, Child's behavior and SDIDTK checklists. Data analysis techniques were carried out by organizing data, grouping based on themes and answer patterns, testing existing assumptions or problems with the data, writing research results. The results showed that the SRL in line with the process of ‘Free Spirit’ of the 6 subjects varied and contained dynamic abilities in their process. The environment (parents/family) had not consistently implemented SRL in their ‘Among’ (Asah Asih Asuh & Ing Ngarsa Sung Tulada) and Kawruh Pamamong (Ngulawantah-lare/regulating children) systems. The ability of SRL subjects to understand (Ngerti Sumerep), to feel (Ngrasa Sih) and to action (Nglakoni Nrimo) still had no clear pattern on subjects. The internalization of SRL was in the process of co-regulatory behavior.

Determination of association rules for mathematical skills

The aim of this study is to classify the subjects and skills of middle school mathematics course in the context of MATH Taxonomy and to determine their relations. For this purpose, the questions and answers related to the mathematics subtest of a national exam were analyzed over the answers of 20154 students. The study continued with the analysis of the data set containing the answer patterns of the students according to the rules of association of data mining methods by using RapidMiner program and FP-Growth algorithm. The results show that students with any level of skill in MATH taxonomy related to mathematical skills could perform skills in subcategories at a high rate. Students who have attainment in the learning areas provided later in the mathematics program had a high rate of knowledge in the previously provided learning areas. However, this result is not valid for Probability of Simple Events.

Factors of school choice in parents’ views: Why do parents choose the Piarist schools in Hungary?

This empirical study focuses on the factors of school parents’ school choice whose children just started to attend one of the Piarist (Catholic) Schools in Hungary. 270 parents completed an online questionnaire where the groups of questions concerned several facets of their beliefs and values. The results show how parental expectations and beliefs are organized, and how they related to the values manifested in the ‘Piarist spirit’. Both the transfer of religious values and secularized goals are very important for parents. Parents’ educational level and the type of settlement they live in have almost negligible role in explaining differences in answer patterns.

The Design and Validation of the Four Tier Test Instrument for Energy Literacy Using the Rasch Model Analysis

The science education curriculum needs to contain content on environmental issues including energy and its use as an effort to equip prospective physics teacher students with knowledge about the importance of reducing the impact of energy use. For this reason, it is important to measure energy literacy knowledge in prospective physics teacher students. This study aims to design and validate a four-tier test instrument to measure the energy literacy knowledge of prospective physics teacher students. The test instrument format used is a four-tier test. This test model has the advantage of being able to capture more accurate information with various answer patterns. The stages of developing this test used the design-based research model which consisted of five stages, namely developing an assessment framework, designing items, developing rubrics, conducting tests, and applying the Racsh Model analysis. The application of the Racsh Model analysis aims to obtain a valid and reliable test instrument with the Item Response Theory (IRT) approach assisted by the Winsteps program. The research method used is a descriptive-exploratory method to describe the results of the development and validation of the Four Tier test to measure Energy Literacy for prospective physics teacher student. The validation of the test was carried out through an assessment by five experts to assess the construct and content of the test instrument. The results of the item validation showed that the questions were acceptable in all aspects. The conclusion is that the test with four-tier format is suitable for identifying the knowledge of prospective physics teacher students about Energy Literacy. The four tier test model in exploring the energy literacy abilities of prospective teacher students can basically also be applied to students at the elementary school, middle school and high school levels. However, the complexity of the content tested needs to be adjusted to the existing curriculum at each level.

Microbes and us: microbiology literacy in Greece.

Microbes are ubiquitous and provide numerous services to humans and our planet. However, a query arises as to whether these microbial services are valued by the general public especially after unprecedented conditions like the COVID-19 pandemic. In this context a survey was conducted to investigate the concept of microbe in Greece. Thematic analysis of 672 anonymous responses (age range 4-75yo) received for the open-ended prompt "What is the first thing that comes to mind when you hear the word microbe?" revealed five thematic categories: Negative emotions, Fuzzy associations, Biology, Entities and Health. Almost 80% of responses fell under "Biology" and "Health" and the general pattern of answers was the same across all age groups. Microbes took a variety of forms in the minds of respondents, however, the concept of "microbe" seems to be more unshaped at younger ages (4-11yo), as revealed in children's language choices. Overall, the often-negative perception of microorganisms seems to be confirmed in this study. Although this research was limited to participants from Greece, it remains relevant to other countries around the world as well. We discuss the reasons behind this negative perception and offer suggestions for reversing it.

ChatGPT’s scorecard after the performance in a series of tests conducted at the multi-country level: A pattern of responses of generative artificial intelligence or large language models

Recently, researchers have shown concern about the ChatGPT-derived answers. Here, we conducted a series of tests using ChatGPT by individual researcher at multi-country level to understand the pattern of its answer accuracy, reproducibility, answer length, plagiarism, and in-depth using two questionnaires (the first set with 15 MCQs and the second 15 KBQ). Among 15 MCQ-generated answers, 13 ± 70 were correct (Median : 82.5; Coefficient variance : 4.85), 3 ± 0.77 were incorrect (Median: 3, Coefficient variance: 25.81), and 1 to 10 were reproducible, and 11 to 15 were not. Among 15 KBQ, the length of each question (in words) is about 294.5 ± 97.60 (mean range varies from 138.7 to 438.09), and the mean similarity index (in words) is about 29.53 ± 11.40 (Coefficient variance: 38.62) for each question. The statistical models were also developed using analyzed parameters of answers. The study shows a pattern of ChatGPT-derive answers with correctness and incorrectness and urges for an error-free, next-generation LLM to avoid users’ misguidance.

The Household Water Insecurity Experiences Scale: answer patterns and cutoffs in Mexico

Objective. To investigate the degree in which answer patterns in the Household Water Insecurity Experiences Scale (HWISE) relate to scores aiming at identifying latent groups with different water insecurity levels in a nationwide representative sample of the Mexican population. Materials and methods. Based on data from the 2021 National Survey on Health and Nutrition (Estudio Nacional de Salud y Nutrición 2021, Ensanut 2021), sequence data representations, and a latent class analysis (LCA), in this article we estimate the likely misclassification errors of different cutoffs proposed for the HWISE scoring system. Results. The main results suggest that a 5-item subset of the HWISE may exhibit a more reliable and cost-effective behavior than the complete 12-item set for a 2-level partition of the sample. Conclusions. Our methodological approach provides new insights regarding the efficiency and likely errors in distinguishing between levels of water insecurity based on the Mexican chapter of the HWISE.

The Analysis of Misconceptions in Simple Airplane Material for 8th Grade Students in SMP Muhammadiyah 3 Gorontalo

This study aims to (1) determine students' conception maps in simple machine material (2) analyze students' misconceptions in simple machine material. This research is a descriptive research. The research subjects were class VIII of SMP Muhamadiyah 3 Gorontalo City with a sample size of 25 students. The/research data were obtained from students' answers through a three-level multiple-choice diagnostic test (Three Tier Multiple Choice) and processed based on the pattern of student answers grouped into categories of understanding the concept, misconception 1, misconception 2, misconception 3 and not understanding the concept. The results showed that category 1 misconception had a percentage of 23%, misconception 2 had a percentage of 17%, and misconception 3 had a percentage of 25%.

This is not US: measuring polarization in multiparty systems. A quasi-replication study

In the last decade, affective polarization (AP) has become an increasingly salient topic in both public discourse and political science. Several different measurement instruments have been developed to empirically capture this phenomenon. With the rising interest that affective polarization is now also enjoying in Europe, it has become of the utmost importance to assess what these different measures capture, and to what extent their application travels to different contexts. In this study we test several AP measures on a student population with various European nationalities. We assess their overlap and effectiveness in mapping AP, to help future research working towards greater empirical clarity, and making informed choices on which kind of measures to include in questionnaires and data collections. The results indicate that, while different items usually produce different point estimates and sometimes different answer patterns, the measurement of affective polarization appears relatively indifferent to the choice of items.

Using Four-Tier Test to Identify Prospective Elementary Teacher Students’ Misconception on Electricity Topic

The topic of electricity is one of the topics in the Advanced Physics course which has abstract dominant characteristics. The abstract nature of electrical material has the potential to be difficult to understand and has the potential for misunderstanding by students. The four-tier test is a type of test that can be used to identify student misconceptions. The research method used in this study is a quantitative descriptive research with a survey research type. The populations of this study were all first-year prospective elementary teacher students who took the advanced physics courses as many as 30 students. The sampling technique of this study used a saturated sample whose number is the same as that of the study population. The research instrument was in the form of a four-tier test related to electricity topics, totaling 20 questions and divided into the sub-topics of electric current, electromotive force and potential difference, resistance, energy and conductivity of electric current, dc electric circuits, and Kirchhoff's Laws. The characteristics of this four-tier test consist of four levels. The first level is a choice of answers by providing five choices. The second level is the level of the respondent's confidence in the answer choices. The third level is the choice of reasons for the answer chosen by the respondent. The fourth level is the level of confidence in the choice of reasons for the selected answer. Respondents' answer patterns were grouped into three categories, namely understanding the concept, not understanding, and misconceptions. The results showed that the percentage of students in the categories of not understanding concepts, understanding concepts, and misconceptions were respectively 49.65%, 29.38% and 20.97%. Overall, it can be concluded that the dominant first grade students do not understand the concept and some experience misconceptions. The findings of this study may have implications for efforts to improve debriefing in Advanced Physics lectures in the future so as to be able to reduce misconceptions and understand student concepts