Examining Filipino older teachers’ technology learning during the COVID-19 pandemic: a hierarchical Cluster analysis

The problem in this study is the difficulty the teachers at SD Negeri 08 Bilah Hilir Labuhan Batu T.A 2022/2023 have in implementing technology to the learning process. The purpose of this study is to determine how challenging it is for teachers to implement technology-based learning and what factors instructors face when implementing technology-based learning.This study is a descriptive quantitative study. This study's sample consisted of 32 respondents from SD Negeri 08 Bilah Hilir. The questionnaire method is used to collect data, with the Guttman scale serving as the research instrument. Descriptive analysis with a quantitative approach is employed for data analysis.The difficulty for teachers in implementing technology-based learning is due to the lowest indicator of the age factor; there are older teachers; it is difficult to apply technology in learning with a percentage of 37.5% in the sufficient category, supported by respondents from students with school facilities indicators as high as 40.17 % in the sufficient category, so the required facilities are not met. Thus, it was determined that teachers at SD Negeri 08 Bilah Hilir Labuhan Batu T.A 2022/2023 had sufficient difficulties applying technology in the learning process, but could still apply technology-based learning, which was supported by the highest percentage of indicators.

Medical resources and usage data collected regularly and could be considered a major benchmark for countries. We aimed to classify the Organisation for Economic Co-operation and Development(OECD) member countries using the World Bank data in terms of medical resources and usage. This is a cross - sectional study. The medical resources and usage data between 2010-2015 were evaluated. Among 36 OECD member countries, 13 of them that lacked data couldn’t be included in the study. Hierarchical clustering analysis was used for grouping countries with similar medical resources and usage. Although there are various methods of hierarchical clustering analysis, Ward method, has been used. Square Euclidean distance was used to obtain clusters by Ward method. Pearson correlation analysis was used to evaluate the relationship between variables. Hierarchical clustering analysis resulted in 4 clusters for selected 23 OECD countries. It was found that Turkey was in the first cluster together with Israel, Spain, Greece and Mexico. The heterogeneous clustering among the OECD countries may have been produced by the financial disparities between the countries. The results are expected to provide helpful insight on the comparison of health status.

There is limited research into older teachers (veterans), especially those who remain enthusiastic and committed to teaching. Still less is known about the role of social support from colleagues inside school in helping positive veteran teachers maintain their commitment to teaching. Drawing broadly from a typology which identifies four types of social support (emotional, instrumental, informational and appraisal), this article reports on a series of interviews with positive veteran teachers. It explores the role of support using the lens of this typology, and discusses emergent themes in relation to it. The findings highlight how positive veteran teachers deliberately seek out positive colleagues and the professional nature of these social supports, and how reciprocal support from colleagues in turn helps these teachers maintain their enthusiasm and engagement for the profession. Given a federally mandated increase in the retirement age and looming teacher shortage with so many new teachers entering the profession, the findings are timely and have implications for teacher well being and school leadership.

QuestionsHow many different silicicolous lichen syntaxa occur on the Ukrainian Crystalline Shield and what ecological factors are influencing their distribution?LocationUkrainian Crystalline Shield, Steppe and Forest–Steppe zones, Ukraine.MethodsThe field research was performed during 2013–2020. Analysis was done with the JUICE software using agglomerative hierarchical cluster analysis in the integrated PC‐Ord with data transformation, pseudospecies cut levels 0–5–25, distance measure – Jaccard, group linkage – Ward's method. For the ordination of the plots we used detrended correspondence analysis (DCA) and non‐metric multidimensional scaling (NMDS) with the “envfit” function, implemented in the R package vegan. As environmental variables we used mean indicator values obtained for each species from the ITALIC database. A generalized synoptic table, which combined both our own and literature data, was clustered using Euclidean distances and the Ward error sum of squares hierarchical clustering method with Ward's criterion.ResultsSeven clusters were obtained after JUICE analysis based on 197 relevés of lichen communities that included 119 lichen species. The main axis in both DCA and NMDS ordination is similar to the vector of substrate pH value. Communities on overhanging surfaces are acidophilous. Communities of water tracks are moderately basophilous to basophilous. Other communities included relevés on different granite characterized as mainly neutrophilous. Lichen communities on siliceous rocks of the Ukrainian Crystalline Shield are assigned to the classes Rhizocarpetea geographici, Leprarietea chlorinae, and Collematetea cristati. Two new alliances (Aspicilion intermutantis and Staurothelion frustulentae), four new associations and three subassociation are described.ConclusionSilicicolous lichen communities of the Ukrainian Crystalline Shield differ from the known units of Central Europe by species composition, where lichens with arid ecology are present. Their local distribution depends mainly on a combination of substrate pH, degree of eutrophication, protection from rain, and substrate moisture.

BackgroundGranulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA), two different ANCA-associated vasculitides (AAV), are clinically heterogeneous. Little is known about potential differences in clinical presentation of disease between females and...

A hierarchical cluster analysis for the cardboard manufacturing industry was developed with the aim of identifying similarities between workstations in terms of musculoskeletal stress factors (MSFs) and their impact on workers' bodies. The hierarchical clustering analysis was conducted using data from 44 workstations, which included 6 body part divisions and 51 stress factors as variables with 36 answer options. Firstly, the content of the dataset was defined. Secondly, the data were agglomerated and classified into 3 clusters using Ward's method based on the presence or absence of stress factors. Finally, the multivariable matrix was reduced to 2 factors: the x-axis representing the risk level of the workstation, and the y-axis representing the impact on the workers' health. The study found that workers' knees were the most affected body parts, with 47 cases. The resulting work-related musculoskeletal disorders included tendinitis, arthralgia, chondromalacia, and gonarthrosis. The MSF with the most significant impact on the body's health was fatiguing work that involves repeated jumps, prolonged squatting, or kneeling, present in cluster 1 and cluster 3. Moreover, two of the most common MSFs associated with repeated work positions were identified: a mild forward flexion of the back (63 cases) in combination with one leg being used more often to support the body (56 cases). It was possible to identify the main work-related musculoskeletal disorders and the associated stress factors for the cardboard manufacturing industry by using hierarchical cluster classification and analysis, enabling the classification of risk levels and the impact on body health for each workstation.

Online learning is growing in popularity due to its flexibility and personalization. Therefore, this study explored the connections between online learning interactions, technology proficiencies and self-regulated learning. Technology proficiencies were also used as a mediator variable. A conceptual model was developed based on established learning theories. A survey was carried out to gather information about students’ online learning interactions, technology proficiencies, and self-regulated learning. The survey consisted of 23 questions and was administered to 447 students enrolled in cohort courses at universities in Guangdong province, China. Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4 was used to analyze relationships. Results suggested that online learning interactions have a positive and significant direct impact on self-regulated learning. Technology proficiencies mediated the relationship between online learning interactions and self-regulated learning. Therefore, it was concluded that online learning interactions play a crucial role in promoting self-regulated learning, and technology proficiency amplifies the effectiveness of these interactions. Consequently, learners with higher technology proficiencies are likely to benefit more from online learning interactions in enhancing their self-regulated learning. The implications of these results for university management, students, and teachers are presented, and suggestions made for potential avenues of future research.

This study explores the impact of device ownership, frequency of use, and technology proficiency on integrating mobile technologies (IMT) in learning by students in higher education institutions (HEIs) in Ghana. The objective is to understand how these factors are influenced by demographic variables such as age, gender, and program of study. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from 457 students selected with a disproportionate stratified sampling technique across multiple HEIs offering STEM programs to analyse these relationships. The results demonstrated that technology proficiency significantly enhances the integration of mobile technologies into learning. It was noted that device ownership alone was insufficient to improve technology proficiency; frequent and effective usage was more essential. Additionally, gender and program of study moderated the effects of device ownership and frequency of use on technology proficiency. These findings highlight the importance of promoting effective usage patterns and advancing technology proficiency to integrate mobile technologies in educational settings better. It is recommended that HEIs focus on providing training programs to staff to integrate more learning activities that promote frequent use of mobile technologies in academic activities to enhance students' technology proficiency.

According to NCTM’s Principles and Standards for School mathematics, “Technology is essential in teaching and learning of mathematics; it influences the mathematics that is taught and it enhances students’ learning.” How does research inform this clarion call for technology in mathematics teaching and learning? In response to the need to craft appropriate roles for technology in school mathematics new technological approaches have been applied to the teaching and learning of mathematics, and these approaches have been examined by researchers world-wide. The first volume provides insight into what research suggests about the nature of mathematics learning in technological environments. Included in this volume are syntheses of research on technology in the learning of rational number, algebra, elementary and secondary geometry, mathematical modeling, and calculus. Additional chapters synthesize research on technology in the practice of teaching and on equity issues in the use of technology in mathematics instruction. Instead of simply reporting achievement scores of students who use technology in their learning, authors provide thoughtful analyses of bodies of research with the goal of understanding the ways in which technology affects what and how students learn. Each of the chapters in this volume is written by a team of experts whose own research has provided important guidance to the field.

The objective of this chapter was to review four studies from our lab on genotypic difference in molecular spectral features of cellulosic compounds in hull, endosperm and whole seeds of barley grown in Western Canada using advanced molecular spectroscopy techniques – synchrotron-based SR-IMS and DRIFT techniques and nutrient utilization availability profiles in barley. The emphasis of this review is to focus on molecular spectral analysis methodology, results summary and implications. In study 1, the synchrotron experiments were carried out in Canadian Light Source (CLS, Saskatoon) and National Synchrotron Light Sources (NSLS) and Brookhaven National Laboratory (New York, US Department of Energy) to study genotypic effect on spectral feature in endosperm tissue within cellular and subcellular dimension. In study 2, the DRIFT experiments were carried out in Saskatchewan Structure Science Center (SSSC, University of Saskatchewan) to study genotypic effect on spectral features in hull and whole seed tissues. The mole-cular spectral features included ca. 1,293–1,212 cm−1which were attributed mainly to cellulosic compounds in the hull, ca. 1,273–1,217 cm−1which were attributed mainly to cellulosic compounds in the endosperm and ca. 1,269–1,217 cm−1which were attributed mainly to cellulosic compound in the whole seeds. Spectral analyses included univariate and multivariate molecular analyses including an agglomerative hierarchical cluster and principal component spectral analyses. In study 3, in situ and in vitro experiments were carried out to study genotypic effect on degradation kinetics, degradation rate and effective degradability. In study 4, nutrient modelling was applied to study genotypic effect on predicted nutrient supply to ruminants. Our results showed that (1) the molecular spectral techniques (synchrotron SR-IMS and DFIRT) plus agglomerative hierarchical cluster and principal component spectral analyses were able to reveal cellulosic compounds spectral features and were able to identify molecular structure spectral differences at a spatial resolution among the barley genotypes; (2) genotype did have significant effect on nutrient utilization and nutrient supply to ruminants.

Pain-Associated Clusters Among Nursing Home Residents and Older Adults Receiving Home Care in Germany

The claim that Ward’s linkage algorithm in hierarchical clustering is limited to use with Euclidean distances is investigated. In this paper, Ward’s clustering algorithm is generalised to use with l1 norm or Manhattan distances. We argue that the generalisation of Ward’s linkage method to incorporate Manhattan distances is theoretically sound and provide an example of where this method outperforms the method using Euclidean distances. As an application, we perform statistical analyses on languages using methods normally applied to biology and genetic classification. We aim to quantify differences in character traits between languages and use a statistical language signature based on relative bi-gram (sequence of two letters) frequencies to calculate a distance matrix between 32 Indo-European languages. We then use Ward’s method of hierarchical clustering to classify the languages, using the Euclidean distance and the Manhattan distance. Results obtained from using the different distance metrics are compared to show that the Ward’s algorithm characteristic of minimising intra-cluster variation and maximising inter-cluster variation is not violated when using the Manhattan metric.

Hierarchical clustering analysis is one basic multivariate statistical instrument which is used in classifying research in multifarious fields widely. The most three common clustering methods are Single Linkage Method, Group Average Method and Ward Method in hierarchical clustering analysis. As it is about to confirm the sources, approaches and conditions of water leakage in BaYi tunnel, this article analyses the advantages, the disadvantages and the applicable conditions of these three methods taking BaYi tunnel as an example to hierarchical clustering analyse in hydrochemical classification. The results indicate that the Single Linkage Method is not capable dealing with complicated actual situations and the Ward Method is applicable in samples of narrow amount while the Group Average Method can be used competently in general conditions.

The article aims to evaluate the varying labour market development within European Union (EU) Member States – including Poland – after the Eastern Enlargement of the EU in 2004-2005, and 20 years following said enlargement (2021-2022). The presented study consists of two phases. In the first phase, an analysis of the labour market situation in the EU Member States, including Poland, was carried out for the years 2004-2005 and 2021-2022. Using the non-linear ordering method – Ward's method – clusters of countries with similar labour market situations were identified. In the second stage, the TMAI measure was used to rank countries using the linear ordering method and to compare labour market differentiation between countries over the period considered. The situation on the labour market in Poland in the analysed period improved, as verified by the taxonomic analysis, which included an agglomeration method – cluster analysis utilizing Ward's minimum variance method with Euclidean distance, as well as the hierarchical method – the benchmark method (TMAI measure). The results obtained provide a comprehensive outlook on both the Polish and other European labour markets situation. After 20 years of EU membership, Poland's position relative to other EU27 countries in terms of labour market status has notably strengthened. Spain was most analogous to Poland in terms of labour market status during the year of accession, whereas the Czech Republic shares the closest resemblance two decades later.

Schools are increasingly using technology to personalize instruction. Programs such as Khan Academy and Zearn produce a vast array of data on students’ behaviors and outcomes when engaged in technology-based instruction. However, these datasets rarely include equally detailed information from when students engage in non-technological learning activities. This study explores the implementation of an innovative model for adolescent mathematics instruction that includes detailed data from both technological and non-technological learning modalities. Much of the research on the implementation of technology-based personalization has focused on the effects of technology programs as isolated interventions rather than within blended models that combine instructional technology with significant changes to teacher-led instruction. Furthermore, existing studies of blended, personalized programs very rarely utilize daily programmatic data to push inside the “black box” of day-to-day interactions among students, teachers, and content. We attempt to address these gaps by using hierarchical cluster analysis, cluster trees, and clustergram heatmaps to explore and visualize data from 170,075 daily lesson assignments and assessments generated by 1,238 unique fifth through eighth grade students across five schools implementing an innovative model for blended and personalized middle school math instruction. We explore three research questions: (1) To what degree did the daily implementation of this program reflect its stated goal of personalizing instruction? (2) Did student outcomes vary based on exposure to each of the learning modalities utilized by this program? (3) Did student outcomes vary based on the academic proficiency of students entering the program? These analyses support three main findings: (a) The instructional reform succeeds in creating a highly personalized student experience, but was likely hampered in implementation by policy and logistical constraints; (b) Participation in a learning modality focused on long-term projects was associated with a lower degree of personalization but higher student outcomes than the other six learning modalities utilized by the program, particularly for some latent clusters of students; and (c) Initially higher-performing students earned higher scores on daily assessments than initially lower-performing students, despite the program’s intended goal of fostering equity in student outcomes through personalization of content to meet each student’s supposed level of readiness.