Class Size Research Articles

Ore/waste identification in underground mining through geochemical calibration of drilling data using machine learning techniques

Chemical X-ray fluorescence (XRF) analyses of drill cuttings and measurement-while-drilling (MWD) records were jointly collected in two production levels with different mineralogical characteristics of a narrow vein underground mine. More than 840 chemical samples and 23,000 MWD records with geological and geotechnical accompanying data constitute the database used in this study. A classification methodology and a site-specific model were developed following two approaches: a k-means clustering heuristic algorithm to define rock classes based on their physicochemical features, and a machine learning (ML) ensemble model trained with those classes to distinguish ore and waste as function of drilling parameters. Oversampling and under sampling techniques are required due to the unbalanced size of the rock classes. The prediction success rate (F1 score) is about 70% for the ore when associated with a high content of silica, and about 86% for the waste. When continuous drilling chips sampling is carried out, the operator bias is reduced and the F1 scores exceed 80% for all classes using different ML techniques. The study presents a complete analysis of the correlations between rock mineralogical characteristics and their mechanical response during drilling, highlighting the benefits that MWD-based information can bring to the analysis-while-drilling (AWD) for ore grading.

EXAMINING COMMUNITY FINANCING OF SECONDARY EDUCATION LUNCH PROGRAMMES AND THEIR EFFECT ON PROVISION OF QUALITY EDUCATION IN KISUMU COUNTY, KENYA

The effectiveness of public financial support for secondary education is an issue of concern given the delay in submitting government grants, challenges of increased enrolment and inadequate resources to support quality teaching and learning. This study was based in Kenya; however, Kisumu County was chosen for its below-average KCSE mean score of 4.08 (D+) in 2017 compared to the average mean of 6 (C); low teacher-student ratio at 1:59 compared to the required 1:45 and absolute poverty index of 41% compared to the national poverty index of 35.6. The purpose of this study was to analyze community financing of public secondary schools and its effect on the quality of education in Kisumu County, Kenya. The specific objective of the study was to examine community financing of secondary school lunch programs and their effect on the provision of quality education in Kisumu County. A descriptive and correlation research design was used in the study. The target population for this study comprised 214 secondary schools in Kisumu County, 214 school principals, 214 BOM chairpersons, the CDE, 48 CBOs and 51,243 students. Questionnaires, document analysis and interview schedules for school principals, BOM chairpersons, the CDE, CBOs and students were used in this study. A stratified simple random sampling procedure was used to sample the 64 public secondary schools in Kisumu County from which 64 school principals, 64 BOM chairpersons, 16 CBOs and 387 purposively selected students were used for the study. The content and face validity of the instruments were determined by employing experts in the Department of Education Management and Foundations. The reliability of the instruments was calculated by using the test re-test and was calculated at Pearson r coefficient of 0.879. Qualitative data was transcribed and analyzed in emergent themes and sub-themes. The results showed that there was a positive correlation of 0.952 between Community financing, lunch programs and academic performance. The study concluded that community financing of adequate lunch programs significantly influences the academic performance of learners to a great extent. The study recommended that community financing of lunch programmes should be increased in order to achieve fully the objectives of the secondary education policy. The findings may be significant to policymakers, education planners and implementers on the requirement for the registration of institutions of higher learning.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0707/a.php" alt="Hit counter" /></p>

Teachers’ motivation and engagement to harness generative AI for teaching and learning: The role of contextual, occupational, and background factors

Since their release in late 2022, generative AI (genAI) tools have led to widespread use, including among teachers. The aim of our study is to examine several factors that may be implicated in teachers' motivation and engagement to harness genAI in teaching and learning. We examined contextual (i.e., autonomy-supportive leadership, autonomy-thwarting leadership), occupational experience (i.e., professional growth striving, change-related stress), and background factors (i.e., gender, age, teaching experience, contract length, class size, school level) as predictors of motivation (i.e., genAI valuing) and, in turn, engagement (i.e., integration in teaching-related work and student learning activities). Among 339 Australian teachers, our findings revealed that perceived autonomy-supportive leadership, professional growth striving, and change-related stress were linked with greater genAI valuing. In turn, genAI valuing was associated with greater genAI integration in both teaching-related work and student learning activities. Perceived autonomy-thwarting leadership was directly linked with greater genAI integration in student learning activities, and professional growth striving was directly associated with greater genAI integration in teaching-related work. Teachers’ gender and school level were also linked with the motivation and engagement factors, and there were several indirect associations as well. Our results pinpoint areas of focus for future research, policy, and practice to support genAI and its application in schools.

Community structure, population dynamics and biomass estimation of Bombacaceae in the 50 ha plot at Pasoh Forest Reserve, Negeri Sembilan, Malaysia

A study was carried out to determine the population dynamics and biomass estimation of Bombacaceae in the 50 ha plot at Pasoh Forest Reserve, Negeri Sembilan. Census data for the year 1985, 1990, 1990, 2000 and 2005 were obtained from the Forest Research Institute Malaysia, Kepong. The study was mainly focused on data of the year 2005. Nevertheless, comparisons between each census year were carried out to observe variations between each census. Based on the 2005 data, a total of 1,152 stems of Bombacaceae was recorded, representing three genera and five species. Durio recorded the highest number of stems with 950 individuals. The species with the highest total number of individuals was Durio griffithii with 781 individuals. Tree class size of 1.0–9.9 cm, consisting of 840 stems, accounted for 72.92% of the total abundance. The growth rate was 0.06 cm/year and the total basal area measured 0.19 m2/ha. Through distribution pattern analysis using Morisita’s Index of Dispersion (Id), Bombacaceae shows a clustered pattern distribution, with value Id = 11.46. The stimation of biomass for the year 2005 was at 2.24 t/ha, with above ground biomass at 1.98 t/ha and below ground biomass at 0.27 t/ha. Durio has the highest biomass with an estimation 2.09 t/ha, while Durio griffithii contributed to an estimated 1.01 t/ha of the biomass. A total of 237 stems were found dead (12 stems/year) and 118 new recruitments (6 stems/year) had been recorded.

Quality of education in migration hotspot areas in Ethiopia: Input‐process‐outcome‐context approach

AbstractWhile the lack of educational opportunities limits the future of most people in Ethiopia, they pose dire consequences to young people in migration hotspot areas. Using input‐process‐outcome‐context‐education quality framework, this study investigated the quality of education in eight migration hotspot areas of Ethiopia and its association with migration. A random sample of 1,187 participants (793 students, 262 teachers and 132 parents) completed a survey. Data were analysed using descriptive and inferential statistics. Results indicated that participants’ overall rating of education quality was low. Over a third of participants perceived that input, process and outcome components of education in migration hotspot areas have a quality problem. Over 80% of the participants believed that poor quality of education could be a cause for migration. More specifically, a significant portion of the participants (37%) reported that there was no pedagogical centre in their respective schools; continuous assessment was practiced rarely (36%); and creativity and discovery among students was almost non‐existent (40%). The participants' ratings indicated that the top five factors that contribute to the poor quality of education are low level of teacher competence, poor student motivation, poverty, value attached to education and large class size.

Addressing student diversity to improve mathematics achievement through differentiated instruction

Managing student diversity is one of the issues that occur in mathematics education. Students typically achieve poorly in school if it is not appropriately addressed. Therefore, differentiated instruction (DI) strategies were used in mathematics classes to address the issue. This study aimed to evaluate the efficiency of DI strategies in addressing student diversity and raising mathematics proficiency levels. DI was carefully implemented in one school using the plan-do-study-act methodology. Data were gathered via test scores, questionnaires, and interviews, and professionals in the field of mathematics education verified the content validity of the said instruments. The study discovered that more incredible mathematics achievement resulted from addressing student diversity. Mathematics classes were better delivered and adapted to students’ learning styles using DI. However, mathematics teachers faced difficulties like lengthy preparation, poor classroom management, large class sizes, and a lack of resources.

Analysis of the Influencing Factors and Predictions of Poston house Prices based on a Multiple Linear Regression Model

In this study, the classic Boston house price data set is selected for the analysis of house price correlation. According to the variables in the Boston housing price data set, the linear regression model of Boston housing prices is established by using Python software. The regression equation and regression coefficient were tested for significance, excluding the variable of p >=0.5, multiple linear regression was carried out, and the regression equation with good fitting was obtained. It is found that there are too many variables after multiple linear regression, which is difficult to analyze and predict, so the correlation analysis of variables is carried out. This paper gets the percentage of lower status population, pupil-teacher ratio and average number of rooms per dwelling with medv (The median quoted price for an owner-occupied home, $1,000 per unit) has a significant relationship. Finally, a linear regression equation is established for the independent variable whose correlation coefficient is greater than 0.5, and the housing price is predicted.

A criterion for a normal subgroup to be hypercentral based on class sizes

AbstractLet G be a finite group and N a normal subgroup of G. We prove that the knowledge of the sizes of the conjugacy classes of G that are contained in N and of their multiplicities provides information of N in relation to the structure of G. Among other results, we obtain a criterion to determine whether a Sylow p-subgroup of N lies in the hypercentre of G for a fixed prime p, and therefore, whether the whole subgroup N is hypercentral in G.

Explainability in AI-based behavioral malware detection systems

Nowadays, our security and privacy are strongly threatened by malware programs which aim to steal our confidential data and make our systems out of service, among other things. While traditional signature-based malware detection methods or statistical analysis have proven to be ineffective and time-consuming, recently data-driven Artificial Intelligence (AI) techniques, i.e. Machine Learning (ML) and Deep Learning (DL) approaches, have been successfully applied leveraging the behavior of malware in terms of API calls, and achieving promising performances. However, their black-box behavior leads to a lack of explainability thus preventing their application in real world scenarios. In light of this, eXplainable Artificial Intelligence (XAI) methodologies and tools can be effectively embedded within an AI-based malware detection process in order to make more understandable the produced results. In this paper, we propose a XAI framework for behavioral malware detection problems and evaluate the usefulness of four XAI methods (SHAP, LIME, LRP and Attention mechanism) on three datasets with different size, sequence length and number of classes, by which we could evaluate the strengths and weaknesses – from effectiveness and efficiency point of views – of recurrent deep architectures (i.e. Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) models), and their applicability in the modern Cyber Security (CS) scenarios.

Exploring Physical Educators' Self-Efficacy to Teach Students With Disabilities in General Physical Education.

The purpose of this study was to explore the ways in which in-service physical education teachers construct their self-efficacy beliefs toward teaching students with disabilities in general physical education classes. Using a qualitative descriptive approach situated within self-efficacy theory, data were collected via semistructured audio-recorded interviews with 16 in-service physical educators. Three interrelated themes were constructed: (a)The more I do it, the better I feel: the importance of professional experiences; (b)I've learned from others: the influence of colleagues; and (c)Being in the general educational setting is a challenge: the impact of contextual factors. Findings supported the influence of the four sources of self-efficacy (i.e.,mastery experience, vicarious experience, social persuasion, and affective and physiological state), in addition to potential contextual factors (i.e.,class sizes and availability of hands-on support), impacting participants' self-efficacy to teach students with disabilities in general physical education classes.

Current Practices and Pitfalls of ELT Syllabi for Developing Engineering Students' Communicative English in Bangladesh*

The current practices and shortcomings of engineering students' English language teaching (ELT) syllabi were examined from the perspectives of learners and teachers in English as a foreign language (EFL) context. The syllabi included content that had little impact on students' communicative competence in English (CC-E). Students were generally concerned about their professional communication abilities. In this study, the researchers collected data from ten engineering-focused universities in Bangladesh. These universities' existing ELT syllabi (ELT-S) were examined, seeking the current practices and pitfalls. The study sampled 152 participants from the selected universities. The study followed a mixed-method approach. In the qualitative technique, content analysis, focus group discussion (FGD), and interviews were employed for data collection, while survey questions were used in the quantitative approach. The study's findings revealed that the existing English syllabi of those selected universities required updating and modification to meet the identified professional requirements regarding the type, credit allotment, content, classroom practices, class size, policies, etc. The improvements included redesigning English syllabi, material, and teaching methods to improve engineering students' communicative abilities. A uniform curriculum with at least one English language sessional course per semester in all engineering majors was strongly recommended.

The Vaccine-Education Paradox in a Cross-Country Analysis: Education Predicts Higher and Lower Vaccination Rates

Research has shown a mixed relationship between education and vaccination rates. In the current analysis, we aimed to determine the relationship between educational level and coronavirus disease 2019 (COVID-19) vaccination rates. We performed a cross-country analysis on data from 133 countries. Correlation analyses showed that higher and better education was associated with higher COVID-19 vaccination rates. When we performed the regression analysis including the education, health system, and economic development variables, education-COVID-19 vaccination relationships were mostly reversed. In particular, in wealthy countries, as the mean years of schooling decreased and the pupil-teacher ratio increased, COVID-19 vaccination rates increased. In less affluent countries, with greater education expenditures, COVID-19 vaccination rates decreased. We explain these contradictions by describing links between vaccination rates, life expectancy, and education-related variables. Our findings may aid in promoting more effective uptake of COVID-19 vaccination.

Tracing Rwandan TVET Graduates’ Competences and Employability with their Potential Employers’ Satisfaction

The government of Rwanda set its development agenda with a focus on transforming the country from a predominantly agrarian to a knowledge-based and service-driven economy by the year 2020. The role of WDA in strengthening linkages between education and employment is imperative for sustainable economic development. However, the relationship between education and employment cannot be ascertained without conducting a tracer study. It’s against this background that a tracer study about the employment status of TVET graduates across the trades/ disciplines was needed to assess their employability and the employers’ levels of satisfaction with the graduates’ competencies in the various agencies where the latter are employed, an in-depth assessment of how graduates assess themselves concerning the relevance and quality of their training and the labor market demands and research-based recommendations on what curriculum changes TVET institutions could be adapted to enhance the competence and employability level of the TVET graduates. We found that the employment rate of IBT graduates was quite impressive, as 70.1% of the graduates were employed, and the average earning per month was 63,592 Rwf. In addition, findings reveal that most respondents (64.9%) reported having spent less or equal to 3 months before they got employment, 14.4% indicated 4-6 months, and 15.2% reported 7-12 months. Regarding employer satisfaction of IBT graduates, competencies, 90.6% noted that they were delighted with the graduates’ work. This directly implies their level of performance on the job. However, some of the following challenges were highlighted. These included high student-teacher ratios, insufficient time for training, lack of meaningful network, and lack of job-related information and opportunities for further training.

Implementing Learner-Centered Teaching Strategies in Family Science

Learner-Centered Teaching (LCT), as opposed to teacher-centered practices, actively engages students in the learning process. In this paper, we discuss the importance of adopting LCT strategies in family science courses to improve student learning outcomes. Family science is particularly apt for LCT because of the interpersonal focus of the discipline. To help fill an important gap in the literature, seven LCT strategies are articulated within the specific context of family science. We highlight the following seven approaches: small group activities, questioning, think-pair-share discussions, gaming, in-class writing, low-stakes quizzes, and flipped classroom design. In addition to describing the approaches, we discuss class size, adapting the techniques for online courses, and working with students who require accommodations. Finally, we share evaluation data from students that speaks to four of the seven LCT strategies. Our hope is that this paper will offer ready-made ideas that family science instructors may adopt. To conclude, we discuss implications, challenges in implementing LCT, and areas of future research.

Program Factors Associated With Improved American Board of Surgery Examination Pass Rates.

The American Board of Surgery awards board certification after successful completion of both the Qualifying Exam and Certifying Exam. Although multiple studies have evaluated board performance at the resident level, fewer studies have evaluated board performance at the program level. Program pass rates, available through the American Board of Surgery, for 2019-2021 were compared to program information through the American Medical Association Fellowship and Residency Electronic Interactive Database Access (FREIDA). A significant positive correlation of Certifying Exam performance to residency length, resident class size, and number of total physician faculty within the program was seen. Greater average hours of didactics per week had a significant positive correlation to improved Qualifying Exam performance but not Certifying Exam. Programs with higher percentages of residents graduating from a United States MD program, compared to international or DO schools, were associated with improved performances. It also appears that more established programs performed better than younger programs <20years old. Programs in the West and Midwest performed significantly better on the Qualifying Exam than programs in the South and Northeast. Board certification serves as the capstone for surgeons after completing general surgery residency. Multiple program factors demonstrate a significant correlation to board performance.

시도교육청의 중장기 교원수요 추계모형 제안과 적용

Local education offices cannot set teacher supply and demand policies independently of the Ministry of Education. These offices conduct student placement based on the teacher quota set by the ministry for each local education office. They adjust the number of students per class, organize classes by grade and school, and assign teachers, taking into account facilities, teacher supply, and demand in a comprehensive way. In the medium to long term, it is also necessary to manage the size of new hires in order to maintain stable teacher retention. In this study, we have attempted to project the demand for teachers at the local education office level through 2042. We believe that the demand for teachers should be viewed from a medium- to long-term perspective, even taking into account the decline in the school-age population at the city and provincial levels. To make an accurate and reliable projection of teacher demand, we considered policy variables that affect the supply and demand of teachers, such as the number of students and the number of students per class, curriculum revision, and the introduction of the high school credit system. In addition, we proposed a method of assigning classes and staffing them by equalizing the number of students in each class. We projected the number of students per grade by school, and based on that, projected the number of classes and teachers. To do this, we developed an equation for calculating the number of teachers needed and modeled the relationship between the two variables based on the number of classes and teachers per school in 2022 from the National Education Information System. The paper concludes that the difference between the average number of students per class and the same number of students in each class is significant, and that the demand for teachers may vary significantly depending on the appropriate class size and the degree of change in educational conditions. The results also suggest that it is necessary to consider the appropriate class size and improve the method of projecting the number of students and teachers to estimate the number of teachers.

SOCIO-ECONOMIC ANALYSIS OF CONDITIONS OF DEVELOPMENT OF HIGHER EDUCATION INSTITUTIONS

This article delves into the socio-economic analysis of higher education institutions, investigating the intricate relationship between economic conditions and educational development. Through a mixed-methods approach, quantitative data analysis and qualitative insights illuminate key factors influencing institutional growth, including funding sources, enrollment demographics, faculty-student ratios, and financial sustainability measures. By integrating findings from theoretical frameworks and localized case studies, the discussion underscores the broader societal implications of educational dynamics. The synthesis of insights underscores the imperative for policymakers, administrators, and stakeholders to prioritize equity, innovation, and resilience in shaping the future of higher education.

Improving left-behind children’s academic performance yields positive spillover effects for peers in China

ABSTRACT This study explores the impact of left-behind children on their classmates’ academic performance in China. Results suggest that improving the grades of left-behind students can positively affect the academic achievement of their peers, particularly when considering factors such as gender, school rank, and class size. Additionally, enhancing test scores among peers can improve the overall quality of school life for all students. Prioritizing the academic performance of left-behind children may prove to be an effective means of promoting academic success within a classroom in China.

Enhancing Teachers’ Capacity to Foster Autonomous Learning in EFL Acquisition – A Case Study of Georgia

With learning contexts globally promoting putting learners in the centre, it is no surprise that autonomous learning will gain ground in contemporary classrooms. As there is no learner autonomy without autonomous teachers, investigating instructors’ preferences about autonomous learning is necessary for more successful language education. The research assessed Georgian teachers’ viewpoints on fostering autonomous learning in EFL acquisition. The study employed a close-ended questionnaire completed by over 100 teachers. The analysis concludes that Georgian teachers associate obstacles with a lack of resources, technical problems, and class size, while challenges linked to teachers include lack of experience, limited development opportunities, and time management.

Comparing Library Science Education in India and Australia: Issues, Challenges and Growth Strategies for India

Library education in India and Australia has developed independently with distinct features. This paper provides brief profile of the history of India and Australia followed by discussion of the evolution of LIS education in both the countries. The paper is a comparative study. It discusses the different issues and challenges encountered in LIS education in India and Australia. This comparison is an attempt to critique improvements that must be made to LIS education in India to ensure on going sustainability and relevance. In India, the growth of library schools; dated LIS curricula; increasing student faculty ratios; accreditation; and the employability of graduates are some key issues. In Australia challenges include the broad employment landscape; mismatch between pay and qualifications; decreasing numbers of educators; existence of a multi-tiered system; LIS students and institutional affiliations. A number of strategies have been used in both India and Australia to enhance LIS education. Using LIS education in Australia as a benchmark the authors show how LIS education in India can be enhanced and improved.