Discriminant Coefficients Research Articles

Classroom Climate, Teacher Support and Self-Agency: Perceptions of Students with and without Disabilities

ABSTRACT This study investigated perceptions of classroom climate, the physical environment, and self-agency of students with and without disabilities. Participants were 868 high school students from four mixed-sex day schools and 1 same-sex boarding school in Western Kenya. Of the 868 participants, 431 had disabilities. Participants completed a questionnaire on classroom climate and self-agency. Overall, results showed that even though students perceived their classroom climate to be high in competition and difficulty, they were satisfied with their learning environments and regarded their teachers as being supportive. Friction was low and self-agency was moderate. ANOVA results revealed that there were no statistically significant differences between those with and without disabilities ( F 1 , 862 = 0.69 , p = 0.41 ). MANOVA results for disability status was statistically significant with respect to the classroom climate subscales ( F 6 , 857 = 2.22 , p = 0.04 ). Discriminant analysis structure coefficients identified friction, competition, and cohesion as the primary drivers of this significant result. Group means reveal students identified with a disability had a higher mean for friction, and lower means for cohesion and competition. Self-agency was positively correlated with satisfaction, cohesiveness, competition, and teacher support, and negatively correlated with friction and difficulty. These findings suggest that teacher support plays a significant role in student satisfaction, cohesion, and self-agency, regardless of disability.

The effectiveness of a proposed strategy according to the Fraunhofer model of knowledge management in the achievement of second intermediate female students in mathematics

The goal of the research is to identify the effectiveness of using a proposed strategy according to the Fraunhofer model of knowledge management in mathematics achievement for second-grade female students in middle and high schools affiliated with the General Directorate of Education in Baghdad / Al-Karkh II. The objective was to prove the following null hypothesis: "The average scores of the experimental group who will study with the proposed strategy according to the Fraunhofer model and the scores of the control group students who will study in the usual way in the mathematics achievement test are not statistically significant different at the significance level (0.05)." The General Directorate of Education of Baghdad / Al-Karkh II identified the research population, which represents middle and secondary schools for girls. Sayyida Zainab (peace be upon her) middle school was specifically selected as the research sample, which was divided into two groups and included sixty-one female students in the second middle grade. For the experimental group, represented by Section (A), there were thirty-three female students; for the control group, represented by Section (C), there were twenty-eight female students. Achievement test was developed to gather data for the study. Its final form included 36 objective items of the multiple choice type with four alternatives. Parity was carried out statistically between the female students of the experimental and control groups in the variables (previous achievement in mathematics, previous mathematical knowledge, intelligence). Appropriate statistical techniques were applied when administering the test to the female students in the experimental and control groups (difficulty and ease coefficient, discrimination coefficient, efficacy of alternatives). After the two researchers applied suitable statistical techniques to analyze the data and administer a test like the Leaven's test, the results showed that teaching with the suggested strategy according to the Fraunhofer model of knowledge management has a significant impact in improving the achievement of the female students of the experimental group compared to the control group. Its psychometric properties were acknowledged.

The effect of Disney's creative strategy on the achievement of fourth-grader students in literature and texts

The current research aims to identify the impact of the Disney Creative Strategy on the achievement of fourth-grade literary students in the subject of literature and texts by testing the following null hypothesis: There is no statistically significant difference at the level of (0.05) between the mean scores of students in the experimental group who study literature and texts according to the Disney Creative Strategy and those in the control group who study using the traditional method. The researchers followed the procedures of the quasi-experimental method consisting of an experimental group and a control group. After completing the experiment and using the following statistical tools (t-test, Chi-square, difficulty coefficient, discrimination coefficient, effectiveness of incorrect alternatives, retest method, and effect size), it was found that the students in the experimental group who studied according to the Disney Creative Strategy outperformed those in the control group who studied using the traditional method in their achievement. The researchers suggested several procedures, including conducting a similar study to the current one, focusing on other variables such as gender and grade level, and concept acquisition.

An evaluation of a final year multiple choice questions examination at Faculty of medicine-university of Benghazi

Background: Multiple choice questions (MCQs) are widely used in medical education, they have the feature of sampling broad domains of knowledge efficiently and reliably. They represent one of the most important examination tools used in undergraduate and postgraduate medical examinations. Exam analysis is an assessment tool that provides important information about validity and reliability of test. Item analysis is used to analyze the difficulty and the discrimination ability of individual items and the total test scores. The purpose of this study is to examine the quality of one of the final exam papers, that is composed of single best of five MCQs, and to examine the correlation between the difficulty and discrimination index of each item. Subject and method: The authors collected the exam results of 425 students who set for the final exam at the end of the a academic year 2021- 2022. The exam consisted of 70 MCQs and was aiming to assess the students` knowledge, understanding, application and analysis of the subject matter. The test was analyzed regarding content validity and reliability of the test as a whole as well as the difficulty coefficient and discrimination coefficient for each item. Results: Out of the 425 students who set for the exam about 86% has passed (scored ≥ 60% of the total marks), the test reliability was excellent (0.91) and the mean difficulty index (P) was 75.22 ± 19.4 (easy exam). Out of the 70 items six (8.57%) had a poor discrimination (D < 0.1). When comparing the exam questions with the blueprint 90% of the topics of the subject materials were either over or under represented. About 77% of the exam questions were testing either students` knowledge or understanding. The correlation between the difficulty index and discrimination index was moderately positive which was statistically significant (0.633, P = 0.000). Conclusion: although the current study showed a good exam reliability, however the exam content validity was suffering from many weakness regarding the total number of the items, and distribution of the items according to the topics and the cognitive levels, therefore the number of exam questions should be increased and a blueprint should be developed for assessment. The overall exam difficulty was low as there were too many easy questions and this could impair both exam validity and reliability. The number of average and difficult questions should be considerably increased.

Method of thromboembolism prediction in advanced atherosclerosis

Aim. To develop a method for thromboembolism prediction in patients with ad­vanced atherosclerosis.Material and methods. The study involved 170 patients with advanced atheroscle­rosis and 110 patients with local atherosclerotic lesions. At the first stage, speciali­zed experts generated signs characterizing the severity of prothrombotic conditions. To determine whether patients belonged to the first or second group, the multivariate statistics method was used. Decision rules (DRs) was developed based on soft computing (SC), as well as the methodology for the synthesis of hybrid fuzzy DRs.Results. According to the discriminant function coefficient (DFC), the division into classes was made with the formation of an intersection area, which affected the diagnostic sensitivity of this method (=86%). Due to difficulties of separating more heterogeneous groups with a small sample, RP was synthesized according to the atherosclerosis severity classification using SC technology. Atherosclerosis severity classification was adopted according to fuzzy DRs: initiation stage (up to 20%); reverse stage (21-40%); progressive stage (41-65%); critical stage (more than 65%). As­ses­sment of thromboembolism risk in advanced atherosclerosis in the examined pati­ents showed the following trends: progressive stage — 55% of patients; critical stage — 21% of patients; reverse stage — 16% of patients; initiation stage — 7% of patients.Conclusion. DFC for DRs makes it possible to identify groups with high and low thromboembolism risk. The resulting final fuzzy DRs make it possible to differentiate the atherothrombotic condition in advanced atherosclerosis by severity, which can help to timely determine preventive and therapeutic measures.

Assessing the Impact of COVID-19 on Subjective Well-Being and Quality of Life in Mexico: Insights from Structural Equation Modeling

Amid the backdrop of the COVID-19 pandemic, the living conditions of the population were dramatically altered, with social distancing measures and the looming threat to public health leaving a profound impact on people’s lives. This study aims to assess the influence of COVID-19 on subjective well-being and overall quality of life in Mexico. A structural model with latent variables was used. Data were extracted from the National Self-Reported Well-being Survey (SWLS) from October 2020 and January 2021, featuring a robust sample size of 3615 residents from urban areas in Mexico, all aged 18 and above. Findings revealed that around 38% of the variance in overall life satisfaction in October 2020 and January 2021 could be attributed to Personal well-being (0.231), Personal satisfaction (0.320), Satisfaction with the environment (0.076), and Negative emotional states (−0.116). In comparison, October 2019 to January 2020 saw a lower 20% explained variance, primarily associated with Personal well-being (0.184), Personal satisfaction (0.270), and Satisfaction with the environment (0.052). Reliability assessments, including Cronbach’s Alpha coefficients, Rho_a, and Composite Reliability, all surpassed 0.70 for each subscale. In addition, our study confirmed convergent validity, as the Average Variance Extracted (AVE) consistently exceeded 0.50 across all subscales, while the discriminant coefficient exceeded 0.70.

Spatio‐temporal attention modules in orientation‐magnitude‐response guided multi‐stream CNNs for human action recognition

AbstractThis paper introduces a new descriptor called orientation‐magnitude response maps as a single 2D image to effectively explore motion patterns. Moreover, boosted multi‐stream CNN‐based model with various attention modules is designed for human action recognition. The model incorporates a convolutional self‐attention autoencoder to represent compressed and high‐level motion features. Sequential convolutional self‐attention modules are used to exploit the implicit relationships within motion patterns. Furthermore, 2D discrete wavelet transform is employed to decompose RGB frames into discriminative coefficients, providing supplementary spatial information related to the actors actions. A spatial attention block, implemented through the weighted inception module in a CNN‐based structure, is designed to weigh the multi‐scale neighbours of various image patches. Moreover, local and global body pose features are combined by extracting informative joints based on geometry features and joint trajectories in 3D space. To provide the importance of specific channels in pose descriptors, a multi‐scale channel attention module is proposed. For each data modality, a boosted CNN‐based model is designed, and the action predictions from different streams are seamlessly integrated. The effectiveness of the proposed model is evaluated across multiple datasets, including HMDB51, UTD‐MHAD, and MSR‐daily activity, showcasing its potential in the field of action recognition.

Emotional Regulation Among Postgraduate Students

The current research aims to identify the emotional regulation among postgraduate students and the significance of differences in emotional regulation according to the variables of gender (male - female) and specialization (scientific - humanities). The researchers prepared an emotional regulation scale consisting of (24) items distributed over five domains (situation selection, situation modification, cognitive change, attention deployment, response modulation). The research was conducted on a sample of (277) male and female students at the University of Dhi Qar. The items of the scale were statistically analyzed to calculate their discriminative ability and validity coefficients. The researchers verified the face validity and construct validity indicators of the scale and calculated the reliability coefficient (alpha - test-retest). To ensure the research procedures and results, the researchers used appropriate statistical methods. The results are as follows: Postgraduate students possess emotional regulation. There are no statistically significant differences according to the variables of gender (male - female) and specialization (scientific - humanities). The results were interpreted based on theory, the theoretical framework, and the results of previous studies. In light of the findings of the current research, the researchers made several recommendations and suggestions. Recommendations: The Ministry of Education should hold training workshops and specialized seminars to increase awareness of emotional regulation at all educational stages, which can enhance and stimulate strategic thinking. Suggestions: Study the correlational relationship between emotional regulation and other variables such as aggressive behavior. :

THE EFFECT OF THE KNOWLEDGE KEYS STRATEGY IN THE ACHIEVEMENT OF BIOLOGY FOR AMONG SECOND-GRADE INTERMEDIATE STUDENTS

The research aims to identify the effect of the Knowledge Keys strategy in the achievement of science subject among second-grade intermediate students. The researcher adopted the experimental approach with an experimental design for two equal groups. The biology subject will be taught according to the Knowledge Keys strategy, and Division (B) to represent the control group that will study the same subject in the usual way. The total number of students in the two groups has reached (60) students; (30) students in the experimental group, and (30) students in the control group; It was statistically rewarded between the members of the two groups in the following variables: (chronological age calculated in months, previous information test, intelligence test), and the researcher specified the study material with subjects from the science book for the second-grade intermediate students, then the researcher formulated behavioral goals as they numbered (146). ) a behavioral goal. As for the research tool, the researcher built the achievement test, which consisted of (30) objective test items of the type of multiple-choice four-alternatives according to the (table of specifications), and the validity, discrimination coefficient, difficulty, effectiveness of alternatives, and stability were verified; Its stability was verified by the split-half method. The researcher used the appropriate statistical methods to extract the data, and the results showed the superiority of the students of the experimental group over the students of the control group.

The Short Executive Function Scale.

This study involved a psychometric evaluation of the Short Executive Function Scale (SEFS), a new 15-item self-report questionnaire measuring five constructs: Planning, Inhibition, Working Memory, Shifting, and Emotional Control. Participants included 717 U.S. undergraduate students (M = 18.9 years old, SD = 1.9; 78.8% cisgender female, 81.7% White) who completed the SEFS. A subset of 156 participants (M = 18.8 years old, SD = 0.9; 79.5% cisgender female, 83.3% White) completed the SEFS again at 2- to 3-month retest along with the Behavior Rating Inventory of Executive Function-Adult (BRIEF-A) and the Patient Health Questionnaire (PHQ-8). The five-factor model fit well (CFI = 0.941, RMSEA = 0.079) and each scale had acceptable internal consistency (ω range: .68-.81) and test-retest reliability (ICC range: .75-.89). Apart from Shifting, all SEFS scales had significantly larger convergent validity coefficients with their respective BRIEF-A scales (r range: -.25 to -.70) than discriminant validity coefficients with the PHQ-8 (r range: -.06 to -.28). These findings provide preliminary psychometric support for the SEFS.

Adaptation of the Social Determinants of Health Scale for University Students to Turkish: A Validity and Reliability Study

Aim: Focusing, researching, identifying and producing solutions for the social health needs of university students is an important step in terms of individual and public health. The main purpose of this study is to make the Turkish adaptation of the social determinants of health scale and psychometric analyses to identify university students who are at risk for their social health needs and to solve their problems. Method: The social determinants of health scale for university students was administered to 322 students. For the validity of the scale, content validity index, confirmatory factor analysis, similarity, and discriminant validity coefficients were used, while for reliability, item-total correlations, internal consistency coefficient, and test-retest analysis were utilized. Results: The content validity index of the Turkish form of the social determinants of health scale for university students was found to be 0.89, and it was decided that there was a consensus among experts. The test-retest reliability correlation was 0.81 and the Cronbach alpha reliability coefficient was 0.71, which was found to be a reliable scale. The scale, which consists of three sub-dimensions measuring general social health needs, social health needs of university students, and promotive social health factors, was compatible with its original form. Conclusion: The social determinants of the health scale for university students were found to be valid and reliable for Turkish culture. It is thought that it will be useful in determining the social health needs of university students.

Gray correlation analysis of the big ideology and politics course and academic style construction in colleges and universities oriented to the three-whole-parenting approach

Abstract Academic style, as the core of college culture, has a decisive impact on the development of schools. This paper adopts the gray correlation algorithm and introduces the sequence of dynamic discrimination coefficients to improve the flexibility and accuracy of the analysis. Through the improved gray correlation method, 11 factors affecting the construction of academic style in colleges and universities are studied. The results showed that teachers’ professionalism and teaching methods (15.23), individual students (11.34), social environment (9.51) and teachers’ teaching and management level (7.21) were the main influencing factors. This study provides data support for educational administrators and schools to formulate relevant strategies to promote the construction of excellent school spirit.

A Robust Noise Estimation Algorithm Based on Redundant Prediction and Local Statistics.

Blind noise level estimation is a key issue in image processing applications that helps improve the visualization and perceptual quality of images. In this paper, we propose an improved block-based noise level estimation algorithm. The proposed algorithm first extracts homogenous patches from a single noisy image using local features, obtaining the covariance matrix eigenvalues of the patches, and constructs dynamic thresholds for outlier discrimination. By analyzing the correlations between scene complexity, noise strength, and other parameters, a nonlinear discriminant coefficient regression model is fitted to accurately predict the number of redundant dimensions and calculate the actual noise level according to the statistical properties of the elements in the redundancy dimension. The experimental results show that the accuracy and robustness of the proposed algorithm are better than those of the existing noise estimation algorithms in various scenes under different noise levels. It performs well overall in terms of performance and execution speed.

Somatotypes of East-Zone Indian Inter-University Kho-Kho Players

Background. Somatotyping in modern sports can be an important factor to be considered. Different studies have already taken the initial step to identify the sport-specific somatotypes for different games and sports. In contrast with the Kho-Kho game, some attempts have been made to identify somatotypes specific for these games. But there are only few studies that have been conducted, and various areas are still waiting to be explored. Study purpose. The present study aimed to identify the specific somatotypes of the kho-kho players. Additionally, this study also made an attempt to explore the important somatic traits that influence the somatotypes of the Kho-Kho players. Materials and methods. A total number of 74 players from six Kho-Kho teams that participated in East Zone Inter-University Tournament were purposively selected for the study. The anthropometric data collection was done according to the standard procedures given by ISAK (International Society for Advancement of Kinanthropometry). The statistical analysis was done by using MS Excel (2021) and SPSS’23. Results. The results of the study showed that most of the Kho-Kho players (90.6%) were possessing ectomorphic and mesomorphic dominant traits. There was also a significant difference found in ectomorphic and mesomorphic traits among the players possessing mesomorphic-ectomorph (p<0.05), ectomorphic-mesomorph (p<0.05) and ectomorph-mesomorph (p<0.05) somatotypes. Furthermore, the discriminant analysis also highlighted that attributes of ectomorphic traits (Discriminant coefficient = 1.168) and mesomorphic traits (Discriminant coefficient = 0.812) were important somatic traits for the Kho-Kho players. Overall, 88.6% of the original cases have been correctly represented by the discriminant analysis. Conclusions. The present study showed that Kho-Kho players for the most part possess the ectomorphic and mesomorphic dominant somatotypes. Furthermore, based on the study results, it can be also concluded that somatic attributes in the similar body types also play important role to determine players’ performance level.

Learning with Euler Collaborative Representation for Robust Pattern Analysis

The Collaborative Representation (CR) framework has provided various effective and efficient solutions to pattern analysis. By leveraging between discriminative coefficient coding (l 2 regularization) and the best reconstruction quality (collaboration), the CR framework can exploit discriminative patterns efficiently in high-dimensional space. Due to the limitations of its linear representation mechanism, the CR must sacrifice its superior efficiency for capturing the non-linear information with the kernel trick. Besides this, even if the coding is indispensable, there is no mechanism designed to keep the CR free from inevitable noise brought by real-world information systems. In addition, the CR only emphasizes exploiting discriminative patterns on coefficients rather than on the reconstruction. To tackle the problems of primitive CR with a unified framework, in this article we propose the Euler Collaborative Representation (E-CR) framework. Inferred from the Euler formula, in the proposed method, we map the samples to a complex space to capture discriminative and non-linear information without the high-dimensional hidden kernel space. Based on the proposed E-CR framework, we form two specific classifiers: the Euler Collaborative Representation based Classifier (E-CRC) and the Euler Probabilistic Collaborative Representation based Classifier (E-PROCRC). Furthermore, we specifically designed a robust algorithm for E-CR (termed as R-E-CR ) to deal with the inevitable noises in real-world systems. Robust iterative algorithms have been specially designed for solving E-CRC and E-PROCRC. We correspondingly present a series of theoretical proofs to ensure the completeness of the theory for the proposed robust algorithms. We evaluated E-CR and R-E-CR with various experiments to show its competitive performance and efficiency.

Construction of a characteristic price model for urban real estate based on characteristic price models

Abstract The study of real estate, especially the study of real estate prices, has always been the focus of scholars. Therefore, this paper constructs a real estate characteristic price model based on the rural land system. Firstly, the impact of the rural land system on rural social production relations is studied, and the field of application of the characteristic price model is proposed. Then, it constructs the urban real estate characteristic price model through the characteristic price method under the real estate batch estimation method and the spatial characteristic method. Finally, the correlation variables are selected to test the variance chi-square and autocorrelation of the real estate characteristic price model. The study shows that the majority of the observations of the characteristic price model are randomly distributed in the interval of [-2,2], presenting a more concentrated effect. The discriminant coefficient R 2 and the adjusted discriminant coefficient R 2 of the model are 0.5754 and 0.5175, respectively, and the model fits well.

Neighborhood contrastive representation learning for attributed graph clustering

Attributed graph clustering is a fundamental task in graph learning field. Because of the high-dimensional node features and the complex non-Euclidean graph structure, it is challenging for attributed graph clustering methods to exploit graph information. Recent studies on graph contrastive learning (GCL) have achieved promising results. However, existing GCL-based methods neither consider a clustering-friendly node representation nor a clustering-oriented loss function, resulting in inferior performance. To this end, we propose NCAGC, a neighborhood contrastive representation learning method for attributed graph clustering task. Specifically, NCAGC constrains the representation learning of similar nodes by a neighborhood contrast module to ensure the compactness in the latent space, thus facilitating the clustering task. Meanwhile, a contrastive self-expression module is present for learning a discriminative self-expression coefficient matrix, which is crucial for the subsequent subspace clustering. Moreover, the two designed modules are trained and optimized jointly, which benefits the node representation learning and clustering to achieve mutual refinement. Extensive experimental results on four attributed graph datasets demonstrate the superiority of NCAGC compared with 16 state-of-the-art methods, which surpasses the sub-optimal method on Cora dataset by 2.1%, 4.3%, and 3.7% in terms of ACC, NMI, and ARI, respectively. Our code and dataset is available at https://github.com/wangtong627/NCAGC-NeuroCom.

On Examining Residential Segregation in Rural Gadag, Karnataka, India: The Case of the Banjara Tandas.

Residential segregation of settlements on caste lines is common across Indian villages. Banjara settlements or tandas are an extreme form of residential segregation, rooted in colonial history and India's complex caste system, and an outcome of structural discrimination. This analysis examines the structural discrimination of tandas in the distribution of various infrastructure and compares it with the villages in proximity to it. A cross-sectional comparative study of infrastructure was conducted in Banjara tandas and villages in Gadag district, Karnataka, India. Composite scores were computed for various infrastructure and discriminant analysis done to classify tandas and villages. The villages have better physical infrastructure such as paved roads, multiple water supply sources, and better drains and amenities such as the community water filter, ration shop, veterinary center, milk society, banks, post office, and health facilities compared to tandas. Discriminant analysis indicated that social infrastructural indicators of health and education (Discriminant function coefficient -0.8689), followed by transportation (Discriminant function coefficient -0.3576) and water supply (Discriminant function coefficient -0.2939), are the most significant discriminating factors between tanda and villages. This disadvantage indicates structural discrimination that is a continuation of the stigma and labeling perpetrated upon them historically and the complex caste system in Indian societies.

Discriminative Ability of Electrophysiological Tests Such as Nerve Conduction Velocities for The Classification of Malnourished Children from Normal Children

Background: Nerve Conduction Velocities (NCVs) measures electrical changes and speed in the nerve. Objectives of this study were: to compare mean velocities, to find rank of importance of different velocities and to frame equation to classify severely acute malnourished (SAM) children with normal children. Material & Methods: Present case- control study was conducted on 50 SAM children and 50 normal children aged 6 months to 59 months. Independent t test and Discriminant analysis was performed. Standardized discriminant coefficient, canonical correlation and Wilks’ Lambda was calculated and p value was judges at 5% level of significance. Results: NCVs were observed significantly lower among the cases as compared with the controls. Sural Sensory Nerve Velocity holds first position followed by Sensory Nerve Velocity. So, in final discriminant model 3 variables i.e., Sural Sensory Nerve Velocity; Median Sensory Nerve Velocity; Tibial Motor Nerve Velocity were used and 42.1 % of the total variance in the discriminant scores not explained by differences among the groups by the three-variable model. Model is able to classify 82.5% cases correctly. Conclusion: Sural Sensory Nerve Velocity; Median Sensory Nerve Velocity; Tibial Motor Nerve Velocity were found as most important nerve conduction velocities with a good classification ability.

Three-Dimensional Angularity Characterization of Coarse Aggregates by the Optimal Ellipsoid Approximation Algorithm

The three-dimensional (3D) angularity of coarse aggregates was investigated in this study by an optimal ellipsoid approximation algorithm. The minimum volume enclosing ellipsoid (MVEE) of the 3D coarse aggregate particle represented by the surface point cloud was computed by a Python program. An algorithm was proposed to compute the optimal ellipsoid that is inscribed in the aggregate particle as far as possible. In the algorithm, a fitting index was proposed and a cycle program was coded to search the optimal ellipsoid. Based on the optimal ellipsoid and the point cloud of the coarse aggregate particle, a 3D angularity index was proposed. The critical discriminant coefficient was introduced into the angularity index to filter out the tiny convex parts on the particle. Six 3D geometric models were generated and five 3D aggregate particles were randomly selected to verify the effectiveness of the angularity index. The results demonstrate that the angularity index proposed in this study can effectively distinguish the angularity of different geometric models. The effect of mesh precision on the angularity index was analyzed and the critical discriminant coefficient was studied by the parameter analysis method and Delphi expert investigation method.