Accuracy Of Judgments Research Articles

Development and validation of a text-based instrument measuring teachers’ professional vision of student thinking

Abstract Accurate teacher judgments of student thinking are key to adaptive teaching. Yet, making such judgments represents a particularly challenging task for teachers. Developing instruments that can elicit and capture teachers’ cognitive processes of noticing and interpreting is essential for assessing their skills. These instruments should be sensitive to the teachers’ cognitive processes and specific qualities of student thinking in the respective domain. In this article, we present an approach to develop and validate a text-based vignette test which meets these requirements. We used the example of teachers’ noticing and interpreting of cognitive aspects in learners’ attitudes on creation and evolution. We identified relevant components of student thinking in this domain and included these components systematically varied in written classroom vignettes. Experts confirmed a clear and salient representation of the components of student thinking that we intended to include.

Accuracy in imagining

Recent treatments of imagination have increasingly treated imagining as a skill. Insofar as imaginative accuracy is one of the factors that underwrites this skill, it is important to understand what it means to say that an imagining is accurate. This paper takes up that task. The discussion proceeds in four parts. First, I address two worries that may naturally arise about the coherence of the notion of imaginative accuracy. Second, with those worries addressed, I turn to an exploration of what is meant by imaginative accuracy. My discussion relies on two key points: first, that accuracy is best understood in terms of aim; and second, that imaginings aim at the representation of fictional states of affairs. I call this line of thought the fictionality approach. Third, I look more closely at six different types of imaginings in an effort to develop and clarify the fictionality approach. Finally, I turn to what I call the calibration objection. Given the nature of imagination, there seems to be no way to calibrate one’s judgments of imaginative accuracy. After showing how much of the force of the calibration objection can be defused, I offer some brief concluding remarks.

Improvement of gram staining effect by ethanol pretreatment and quantization of staining image by unsupervised machine learning.

In this study, we propose an Ethanol Pretreatment Gram staining method that significantly enhances the color contrast of the stain, thereby improving the accuracy of judgement, and demonstrated the effectiveness of the modification by eliminating unaided-eye observational errors with unsupervised machine learning image analysis. By comparing the traditional Gram staining method with the improved method on various bacterial samples, results showed that the improved method offers distinct color contrast. Using multimodal assessment strategies, including unaided-eye observation, manual image segmentation, and advanced unsupervised machine learning automatic image segmentation, the practicality of ethanol pretreatment on Gram staining was comprehensively validated. In our quantitative analysis, the application of the CIEDE2000, and CMC color difference standards confirmed the significant effect of the method in enhancing the discrimination of Gram staining.This study not only improved the efficacy of Gram staining, but also provided a more accurate and standardized strategy for analyzing Gram staining results, which might provide an useful analytical tool in microbiological diagnostics.

Uncovering the connection: how do preservice teachers’ inquiry skills relate to practices of judgment and feedback regarding lower secondary school students’ laboratory reports?

ABSTRACT Recent studies have shown that preservice teachers often struggle to effectively implement formative assessment practices to support students’ inquiry skills development. Therefore, it is crucial to investigate factors related to the successful implementation of these practices by preservice teachers. In this study, the relation between the inquiry skill levels of preservice science teachers and the accuracy of their judgments and the quality and quantity of feedback given on lower secondary school students’ laboratory reports of different levels of achievement were examined. Therefore, 61 preservice science teachers evaluated laboratory reports and provided feedback using a rubric, after their inquiry skills were assessed with a paper-and-pencil test. The results show that preservice teachers’ inquiry skills significantly explained the variance in judgment accuracy for all reports. Additionally, the inquiry skill levels of preservice teachers were positively related to the amount of correct feedback they gave in all reports and the amount of prescriptive feedback they gave in the low-achieving report. Finally, there was a negative correlation between the inquiry skill levels of the preservice teachers and the amount of false feedback they gave in high-achieving reports. The results are discussed in terms of effective formative assessment practices in the context of inquiry skill.

Are visual artists better visual perspective takers? An exploratory study with an unexpected outcome.

Visual perspective taking (VPT) has been argued to elicit image-like representations of other people's visual experiences. Separately, it has been demonstrated that there are inter-individual differences in the ability to successfully take other people's visual perspectives. In the present study, adults were asked to judge how long two lines appeared visually from the point of view of an agent. The lines were of identical length, but the agent was always closer to one of the lines than the other, meaning that the closer line should be judged as appearing visually longer. It was hypothesised that adults with experience in the visual arts would perform better at this task for one or both of two reasons: (1) they should be more familiar with the knowledge that the closer an object is the larger it appears visually (i.e., the retinal image is larger), and (2) they might be able to "draw" an image-like representation that more accurately reflects the effect of distance on perceived size. Consistent with previous experiments with this paradigm, adults generally failed to judge the closest line as appearing longer; indeed, as many judged this line would appear visually shorter. Crucially, increasing experience in the visual arts failed to improve the accuracy of VPT judgements; even a group of professional illustrators failed to recognise that the line closest to the agent would appear longer than the line furthest from the agent. These results are discussed in the context of the processes and representation types potentially involved in VPT.

Testing the validity of online psychophysical measurement of body image perception.

This body image study tests the viability of transferring a complex psychophysical paradigm from a controlled in-person laboratory task to an online environment. 172 female participants made online judgements about their own body size when viewing images of computer-generated female bodies presented in either in front-view or at 45-degrees in a method of adjustment (MOA) paradigm. The results of these judgements were then compared to the results of two laboratory-based studies (with 96 and 40 female participants respectively) to establish three key findings. Firstly, the results show that the accuracy of online and in-lab estimates of body size are comparable, secondly that the same patterns of visual biases in judgements are shown both in-lab and online, and thirdly online data shows the same view-orientation advantage in accuracy in body size judgements as the laboratory studies. Thus, this study suggests that that online sampling potentially represents a rapid and accurate way of collecting reliable complex behavioural and perceptual data from a more diverse range of participants than is normally sampled in laboratory-based studies. It also offers the potential for designing stratified sampling strategies to construct a truly representative sample of a target population.

Research on the construction method of virtual mapping digital twin system for substation

Abstract At present, there are problems in the process of fault location and cause analysis in the actual work of substations, such as long time consumption, high investment in manpower and material resources, and low returns. A Digital Twin (DT) system construction method that can be used for virtual mapping in substations is proposed to address this problem. Firstly, the design principles and overall structure of the substation DT system were formulated by comprehensively considering the needs of transient modeling, debugging, and verification calculations in the substation. Then, the construction method and process of the three-dimensional twin model of the primary system (PS) in the substation were analyzed by using SolidWorks and Unity3D, and the graphical modeling method and process of the general protection and secondary circuit logic modules were analyzed by using the Cybersim integrated simulation support system. Finally, a three-dimensional twin model of the PS and secondary system (SS) equipment inside the substation was constructed. The reliability of the proposed method was verified through simulation experiments, and the results showed that the fault identification accuracy, fault location accuracy, and fault cause judgment accuracy were the highest during bus faults, with 96.10%, 94.17%, and 92.93%, respectively. The capacitor had the lowest accuracy, with 94.86%, 92.96%, and 91.73%, respectively. The DT system can accurately achieve functions such as status viewing and alarm of the substation, precise positioning and navigation of equipment, and positioning of faulty and abnormal equipment.

Nursing Care and Quality of Life Among Children with Hepatolenticular Degeneration

Hepatolenticular degeneration also known as Wilson’s disease (WD), is an autosomal recessive inherited copper metabolic disorder. Objective: The study aimed to determine the relationship between nursing care and the quality of life among children with hepatolenticular degeneration in Anhui Province, China. Methods: This research used the descriptive-correlational design. The researcher investigated 45 regular staff nurses and 96 children with hepatolenticular degeneration ages 12 and 18 years old. Two instruments were used. First, the Nursing Care Score Tool was developed by the researcher based on review of related the literature which is used to measure nursing care. Second, the revised PedsQLTM4.0 Generic Core Scales (Pediatric Quality of Life Inventory™ Version 4.0) is used to measure the quality of life among children with hepatolenticular degeneration. The data were analyzed using the following: Frequency, Percentage distribution, Mean, Median scores, IQR (Inter Quartile Range), One-Way Analysis of Variance (ANOVA), t-Test and Pearson Coefficient. Results: In terms of quality of life, except for age, significant differences were found in terms of children’s sex and educational status. Except for knowledge, there were significant differences in terms of physiological care, medication, psychological care, therapeutic communication and functional training as rated by the children and nurses. There was a significant positive correlation (r = .583, p = .000) between nursing care as rated by the children and quality of life of children with hepatolenticular degeneration. Conclusion: Nursing care is positively correlated with quality of life. A closer look at the results revealed that the quality of life of female children is generally lower than that of male, hence, more attention is recommended to be given to female children in improving their quality of life. Children with hepatolenticular degeneration should be encouraged to continue to receive education for a better quality of life. It is necessary for nurses to continuously explain to the child the disease process and the signs and symptoms of the disease. Careful observation of the physiological status of the child, the provision of timely care, regular functional training, making accurate judgements in the response to medications guidance in the intake of oral injectable medications, regular provision of psychological care and therapeutic communication to the children with this condition important tasks of the nurses. All these can lead to having these children live a better quality of life.

Analysis of the Spectrum of Common Pathogens and the Resistance of Mycoplasma in Sialidase-Positive Bacterial Vaginosis.

The objective of this study is to understand the characteristics of the common spectrum of pathogen and the resistance of Mycoplasma in Sialidase-positive bacterial vaginosis. The vaginal secretion specimens collected from August 2018 to October 2018 for the analysis of bacterial vaginosis (BV) were subjected to various techniques. These included routine leukorrhea examination, bacterial vaginosis sialidase testing, routine culture for common pathogens, mass spectrometry identification, and Mycoplasma resistance testing. A total of 238 patients with BV were identified. The cleanliness grading was mostly clean (+) and clean (2+), accounting for 38.24% and 30.67%, respectively. The bacterial vaginosis test for vaginal secretions showed leukocyte esterase positivity in 220 cases, resulting in a positivity rate of 92.44%. The spectrum of routine culture was analyzed and divided into four groups: A, B, C, and D. Group A consisted of Candidal vaginitis (13.45%); group B consisted of Gardnerella vaginalis vaginitis (32.77%); group C consisted of gram-negative bacillus vaginitis (46.22%); and group D consisted of Streptococcus agalactiae vaginitis (7.56%). The identification and antimicrobial susceptibility testing results for Mycoplasma showed a high detection rate of BV, with a positivity rate of 86.13%. There was a high sensitivity to tetracyclines for Ureaplasma urealyticum and Mycoplasma hominis, but a high resistance to macrolides and quinolones. Bacterial vaginosis existed in various complex forms, including Candida, Gardnerella vaginalis, Gram-negative bacillus, and Streptococcus agalactiae types. Moreover, there was an increasing trend of multi-drug resistance in Mycoplasma hominis. Therefore, it is crucial to pay attention to this condition and make accurate judgments based on the etiological characteristics and common antimicrobial susceptibility tests. This will enable the implementation of effective therapeutic interventions.

Surgeon assessment of significant rectal polyps using white light endoscopy alone and in comparison to fluorescence-augmented AI lesion classification

PurposePerioperative decision making for large (> 2 cm) rectal polyps with ambiguous features is complex. The most common intraprocedural assessment is clinician judgement alone while radiological and endoscopic biopsy can provide periprocedural detail. Fluorescence-augmented machine learning (FA-ML) methods may optimise local treatment strategy.MethodsSurgeons of varying grades, all performing colonoscopies independently, were asked to visually judge endoscopic videos of large benign and early-stage malignant (potentially suitable for local excision) rectal lesions on an interactive video platform (Mindstamp) with results compared with and between final pathology, radiology and a novel FA-ML classifier. Statistical analyses of data used Fleiss Multi-rater Kappa scoring, Spearman Coefficient and Frequency tables.ResultsThirty-two surgeons judged 14 ambiguous polyp videos (7 benign, 7 malignant). In all cancers, initial endoscopic biopsy had yielded false-negative results. Five of each lesion type had had a pre-excision MRI with a 60% false-positive malignancy prediction in benign lesions and a 60% over-staging and 40% equivocal rate in cancers. Average clinical visual cancer judgement accuracy was 49% (with only ‘fair’ inter-rater agreement), many reporting uncertainty and higher reported decision confidence did not correspond to higher accuracy. This compared to 86% ML accuracy. Size was misjudged visually by a mean of 20% with polyp size underestimated in 4/6 and overestimated in 2/6. Subjective narratives regarding decision-making requested for 7/14 lesions revealed wide rationale variation between participants.ConclusionCurrent available clinical means of ambiguous rectal lesion assessment is suboptimal with wide inter-observer variation. Fluorescence based AI augmentation may advance this field via objective, explainable ML methods.

Virtual lab coats: The effects of verified source information on social media post credibility.

Social media platform's lack of control over its content made way to the fundamental problem of misinformation. As users struggle with determining the truth, social media platforms should strive to empower users to make more accurate credibility judgements. A good starting point is a more accurate perception of the credibility of the message's source. Two pre-registered online experiments (N = 525;N = 590) were conducted to investigate how verified source information affects perceptions of Tweets (study 1) and generic social media posts (study 2). In both studies, participants reviewed posts by an unknown author and rated source and message credibility, as well as likelihood of sharing. Posts varied by the information provided about the account holder: (1) none, (2) the popular method of verified source identity, or (3) verified credential of the account holder (e.g., employer, role), a novel approach. The credential was either relevant to the content of the post or not. Study 1 presented the credential as a badge, whereas study 2 included the credential as both a badge and a signature. During an initial intuitive response, the effects of these cues were generally unpredictable. Yet, after explanation how to interpret the different source cues, two prevalent reasoning errors surfaced. First, participants conflated source authenticity and message credibility. Second, messages from sources with a verified credential were perceived as more credible, regardless of whether this credential was context relevant (i.e., virtual lab coat effect). These reasoning errors are particularly concerning in the context of misinformation. In sum, credential verification as tested in this paper seems ineffective in empowering users to make more accurate credibility judgements. Yet, future research could investigate alternative implementations of this promising technology.

Merging multi-level evidential observations for dynamic reliability assessment of hierarchical multi-state systems: A dynamic Bayesian network approach

Dynamic reliability assessment has offered a new paradigm for engineered systems to integrate multi-level observations to update the reliability measures of a specific running system. The existent work on dynamic reliability assessment can only leverage precise observations and cannot be straightforwardly implemented in scenarios where observations are imprecise. However, the observations are inevitably imprecise owing to the limited accuracy of inspection techniques and vague judgments of the system state. In engineering scenarios, multi-state systems (MSSs) with a hierarchical structure are commonly existent, and the imprecise observations can be collected across multiple physical levels of the system. In this article, the uncertainty associated with imprecise observations is characterized by the evidence theory, and it can be therefore regarded as an evidential form. A dynamic Bayesian network (DBN) model is utilized to evaluate the reliability of hierarchical MSSs with multi-level evidential observations. Subsequently, the evidence theory is implemented to quantify epistemic uncertainties associated with imprecise observations. These observations, sourced from multiple physical levels, are merged by the DBN model using the Dempster rule of combination (DRC) to update the reliability of a specific running system in a dynamic fashion. The feasibility and correctness of the proposed method have been demonstrated through a numerical case and a real engineering case of a kerosene filling control system (KFCS), and the result indicates that the proposed DBN-based algorithm methods have high applicability and generalizability.

Prefrontal cortex activity and functional organisation in dual-task ocular pursuit is affected by concurrent upper limb movement.

Tracking a moving object with the eyes seems like a simple task but involves areas of prefrontal cortex (PFC) associated with attention, working memory and prediction. Increasing the demand on these processes with secondary tasks can affect eye movements and/or perceptual judgments. This is particularly evident in chronic or acute neurological conditions such as Alzheimer's disease or mild traumatic brain injury. Here, we combined near infrared spectroscopy and video-oculography to examine the effects of concurrent upper limb movement, which provides additional afference and efference that facilitates tracking of a moving object, in a novel dual-task pursuit protocol. We confirmed the expected effects on judgement accuracy in the primary and secondary tasks, as well as a reduction in eye velocity when the moving object was occluded. Although there was limited evidence of oculo-manual facilitation on behavioural measures, performing concurrent upper limb movement did result in lower activity in left medial PFC, as well as a change in PFC network organisation, which was shown by Graph analysis to be locally and globally more efficient. These findings extend upon previous work by showing how PFC is functionally organised to support eye-hand coordination when task demands more closely replicate daily activities.

Heart Disease Prediction Using ML

Machine Learning and artificial intelligence have found valuable on variety of disciplines during their growth, particularly in the light of massive increase in data in recent years. It has the potential to be more dependable in terms of producing quicker and more accurate illness prediction judgments. Therefore, the use of machine learning algorithms to forecast different diseases is growing. Building a model can also aid in the visualization and analysis of diseases to increase the accuracy and consistency of reporting. This article has looked into using several machine learning algorithms to identify cardiac disease. This article's study has demonstrated a step procedure. In a dataset on heart disease initially prepared in the format needed to run machine learning algorithms. The UCI is the source of patient medical records and other data. The presence are absence of heart disease in patients is then ascertained using the heart disease dataset. Second, this paper presents a number of noteworthy findings. The confusion matrix is used to validate the accuracy rate of machine learning methods, including Gradient Boosting Classifier, Support Vector Machine, and Logistic Regression. According to recent research, the Logistic Regression method outperforms other algorithms in terms of accuracy, yielding a high 95% rate. It also outperforms the other four algorithms in terms of recall, precision, and f1-score correctness. The difficult and future research component of this project will be raising the accuracy rates of the machine learning algorithms to between 97% and 100%.

Exploration on the Practice of Translation Teaching for English Majors Based on the Internet of Things and Multimedia Assistance

In this paper, an automatic judgment algorithm of English text is proposed, which is split and filtered first, then extracted and optimized, and finally interactively fused, and a BP neural machine judgment system is designed. After machine judgment and teachers' self-judgment of the same English sentence sample, the test results show that the ETSS system has excellent performance, improves the reliability and accuracy of judgment, and reduces the degree of human intervention and misjudgment rate in English translation judgment work. The results of the model translation of the Generalized Maximum Probability Ratio Detection (GLR) algorithm and its recognition include nested data points so that accuracy cannot be effectively guaranteed. In the GLR based comprehensive evaluation of content recognition acquisition improvement, the ID accuracy rate exceeded 95%, and the total result was 92.3%.

Effects of availability of diagnostic and non-diagnostic cues on the accuracy of teachers’ judgments of students’ text comprehension

Accurately judging students’ comprehension is a key professional competence for teachers. It is crucial for adapting instruction to students’ needs and thereby promoting student learning. According to the cue-utilization framework, the accuracy of teachers’ judgments depends on how predictive (or diagnostic) the information (or cues) that teachers use to make judgments is of student performance. It is, however, unclear from prior studies if merely providing access to diagnostic cues aids accuracy, or whether this only helps if non-diagnostic cues are unavailable or ignored. Therefore, we investigated, using a within-subjects experimental design, the accuracy of secondary school teachers’ (N = 33) judgments of anonymous students’ text comprehension under four cue availability conditions: 1) non-diagnostic cues only; 2) diagnostic cues only; 3) a mix of diagnostic and non-diagnostic cues; and, 4) after an intervention informing them of the diagnosticity of cues, again a mix of diagnostic and non-diagnostic cues. Access to diagnostic cues enhanced teachers’ judgment accuracy, while access to non-diagnostic cues hindered it. While teachers’ judgment accuracy was not enhanced by the intervention (presumably because it was already relatively high), their diagnostic cue utilization increased, and non-diagnostic cue utilization decreased. In addition, teachers’ calibration increased after the intervention: They knew better when their judgments were (in)accurate. Furthermore, teachers were quite aware that diagnostic cues are diagnostic, but their awareness that non-diagnostic cues (especially students’ interest) are not, could be improved. These results could be useful in designing effective interventions to further foster teachers’ judgment accuracy.

Primary school students’ awareness of their monitoring and regulation judgment accuracy

BackgroundImproving students' monitoring and regulation judgment accuracy is necessary for improving the effectiveness of self-regulated learning, but might not be sufficient: Students presumably also need to feel confident about the accuracy of their judgments to act upon them. However, little is known about students’ awareness of their monitoring judgment accuracy, and awareness of their regulation judgment accuracy has not yet been investigated. AimsWe investigated (1) primary school students’ awareness of their monitoring and regulation judgment accuracy in mathematics and (2) whether self-scoring, which is known to improve monitoring/regulation accuracy, would also improve awareness of their regulation judgment accuracy. Sample(s)Primary school students (9-10 year-olds) from 34 classes (N = 564). MethodsStudents completed problem-solving tasks twice (parallel versions) on two different days and made monitoring/regulation judgments, rated their confidence in the accuracy of those judgments, self-scored their work, and again made (confidence) judgments, on both occasions. If an increase/decrease in judgment accuracy from day 1 to day 2 would be accompanied by an increase/decrease in their confidence in the accuracy of their judgements, students show accuracy awareness. ResultsStudents' judgment accuracy did not predict their confidence in the accuracy of their judgments, indicating that students were not aware of their monitoring/regulation accuracy. Self-scoring improved students’ awareness of their regulation judgment accuracy for students whose regulation judgment accuracy increased or stayed maximally accurate after self-scoring, but not for students whose regulation judgment accuracy decreased or stayed equally inaccurate after self-scoring. ConclusionsPrimary school students were not aware of their monitoring/regulation judgment accuracy. Self-scoring improved the awareness of their regulation judgment accuracy for some students.

Does teacher judgment accuracy matter? How judgment accuracy, teaching quality, and student achievement development are related

Teacher judgment accuracy is assumed to be positively related to student achievement; however, the empirical evidence for this assumption is inconclusive. Using two accuracy indicators measured at different levels (class/teacher- and student-level), we examined the theoretically hypothesized effects of judgment accuracy on German language achievement over a three-year period, and tested whether teaching quality mediates these effects. We applied multilevel mediation models and small sample methods to data from 35 language teachers and 646 students from 42 classes. While no mediating effects were found, the student-level indicator positively predicted achievement, suggesting that student-level accuracy measures should receive more attention.

Action capture and VR interactive system for online experimental teaching

The development of technology has intensified people’s interest in virtual reality technology. Virtual reality technology is also widely used in fields such as teaching and healthcare. To improve the real-time and operability of virtual reality interaction systems, a motion capture and virtual reality interaction system design for online experimental teaching is proposed. The active apparent model is used for facial feature point localization, and the joint capture method is improved by combining threshold segmentation and forward kinematics algorithms. Experimental data confirms that compared to Kinect method, the improved hand joint detection method has higher joint positioning accuracy, with a loss rate of less than 40 % for hand joints. The average judgment accuracy of the six facial expression tests is 82 %, 81 %, 79 %, 78 %, 80 %, and 81 %, respectively. The comprehensive recognition accuracy of facial expression recognition is 80.17 %. The read, write, and update times for each frame of the virtual reality interaction system are 102.6 ms and 427 ms, respectively. The system memory usage is 920.5 M, and the CPU and GPU usage rates are 25.2 % and 4.4 %, respectively. The system can run smoothly. The virtual reality system can run continuously for 24 h, with strong operability and low difficulty in expanding system functions, achieving design goals.

Stereotypes and Stereotyping: Measuring the Accuracy of Lifestyle-Based Judgments on Political Affiliation

Abstract People often draw inferences about others’ underlying characteristics from single and static samples of their appearance, such as facial features, or attractiveness. Evidence also suggests that these judgments occur spontaneously and rapidly. Are humans also able to detect political preferences based on appearance? This article examines to what extent observable lifestyle characteristics influence people’s judgments about one’s political affiliation and, more importantly, to what degree these judgments are accurate. A conjoint analysis allows for the identification of the specific lifestyle cues that people use to infer one’s political affiliation. These results are contrasted with a large and unique dataset (n = 64,745), enabling the assessment of how accurate these cues are. Results suggest that certain lifestyle characteristics, such as type of car or leisure activities, are clearly associated with different political parties, at least in people’s minds. Results also suggest that, despite the potential detrimental effects of appearance-based judgments, people are generally pretty good at guessing others’ political preferences. This study contributes to a growing body of research on the relationship between lifestyle and political preferences. More generally, it sheds light on the diagnostic value of appearances in everyday social judgments.