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This study aims to describe the pedagogical content knowledge (PCK) of a kindergarten educator who implements a lesson plan about informal inferential reasoning designed in a lesson study group.

To this end, we analyzed teaching interventions in two kindergarten lessons focused on the playful task of tossing two coins, associated with inferential statistical reasoning. The study highlights the importance of arguing and promoting this reasoning to develop statistical thinking. It is crucial to recognize how early students can be subject to learning experiences that promote a language of uncertainty, assess the evidence provided by the data, and make generalizations.

The results reveal that while the educator demonstrated knowledge and skills relevant to the curriculum and conceptual teaching strategies, the understanding of the content by the students and the integration of the PCK components still present a challenge.

The lesson study collaborative teaching practices that promote PCK have proven effective for informing the design and implementation of instructional practices supporting the development of early statistical thinking in young children.

The study enriches the knowledge regarding the potential of the lesson study (LS) in the professional learning of kindergarten educators. It also contributes to a comprehensive approach based on authentic playful experiences in grade K that supports the development of early statistical thinking in young children.

Vietnam's construction technology (CT) adoption is low when compared to other countries with similar gross domestic product (GDP) per capita resulting in lesser productivity. The research objectives are: (1) To undertake an extensive literature review on CT adoption challenges; (2) To investigate CT adoption challenges unique to Vietnam's construction sector; and (3) To propose data-driven solutions for a greater rate of CT adoption.

A two-stage descriptive survey method was adopted in alignment with the research aim and objectives. Based on the literature review of 215 articles, a questionnaire was designed and administered to experienced construction managers (CM) to identify whether CT has been adopted, barriers to adoption, drivers, and the most popular CT tools. Descriptive statistics were used to summarize the characteristics of interest in the empirical dataset and SPSS-based inferential statistics to estimate the means, frequency counts, variance and test hypotheses that informed the drawing of conclusions concerning the research objectives.

The popular CT tools identified were Autodesk, Microsoft Office and Primavera. The most influential CT adoption barriers: (1) Unknow`n impact on productivity, (2) Late implementation of software in construction projects, (3) Lack of understanding of importance and needs in the construction industry (4) Lack of funds during budget planning for technological advances and implementation (5) Lack of experts required for technological change, and insufficient skills in the industry.

It is expected that the findings could inform data-driven regulatory and practice reforms targeted at increasing greater uptake of CT in Vietnam with potential for replication in countries facing similar adoption challenges.

The findings are intended to support data-driven regulatory and practice improvements aimed at improving CT adoption in Vietnam, with the possibility for replication in other countries facing comparable problems.

Construction projects in Malaysia are often delayed and over budget due to heavy reliance on labor. Linear regression (LR) models have been used in most labor cost (LC) studies, which are less accurate than machine learning (ML) tools. Construction management applications have increasingly used ML tools in recent years and have greatly impacted forecasting. The research aims to identify the most influential LC factors using statistical approaches, collect data and forecast LC models for improved forecasts of LC.

A thorough literature review was completed to identify LC factors. Experienced project managers were administered to rank the factors based on importance and relevance. Then, data were collected for the six highest ranked factors, and five ML models were created. Finally, five categorical indices were used to analyze and measure the effectiveness of models in determining the performance category.

Worker age, construction skills, worker origin, worker training/education, type of work and worker experience were identified as the most influencing factors on LC. SVM provided the best in comparison to other models.

The findings support data-driven regulatory and practice improvements aimed at improving labor issues in Malaysia, with the possibility for replication in other countries facing comparable problems.

The purpose of this study was to examine the digital competence displayed by a primary school teacher who implemented an interdisciplinary cross-border lesson that was designed with the lesson study methodology and involved two countries: Brazil and Chile.

A qualitative methodology was adopted via the case study method through which the case of a sixth-grade Chilean teacher participating in the study was documented. The data were collected through a lesson plan, a videotape of the implemented lesson and a questionnaire. A professional digital competence framework for teachers provided an analytical perspective via data analysis.

The results show that the teacher displayed digital competence in a synchronous collaborative learning environment in which she had time to reflect on the educational value of technology and appropriate teaching methods involving information and communication technologies. Certain elements of the studied case contributed to this performance, namely the cross-border context, the classroom setting and the collaborative Lesson Study methodology.

The lesson study methodology facilitated the teacher's performance in the “Pedagogy and didactics” digital competence by enabling her to participate in planning and implementing a lesson that allowed all those who collaborated, including teachers and researchers, to reflect on teaching in a digital learning environment.

The cross-border context, which involved co-designing and implementing a lesson in two countries, allowed the teacher to display her “School in society” and “Ethics” competencies. This was achieved through connecting two classrooms with different languages and cultures digitally and synchronously, thereby providing students the opportunity to debate and participate in a global and local problem such as a country's responsibility for energy consumption.

Modern society requires the transformation of school practices, and new teaching approaches should include the provision of collaborative spaces that incorporate digital technologies. In this sense, this paper shows that cross-border lessons involving a synchronous learning environment offer a potential alternative, as digital teaching competence enables teachers to bring together different social and cultural groups virtually, thereby contributing to the reduction of social gaps and to the promotion of positive identity among less advantaged students.

The study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.

Categorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.

Meta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R², were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.

The study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.

The study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.

Homelessness has been a growing problem in many countries since the 1980s. The purpose of this paper is to examine homeless peoples’ perspectives on public library services, and to compare these with the attitudes of library staff.

Eight interviews have been conducted with rough sleepers and formerly homeless library patrons to examine their experiences, needs and expectations or the library. In addition, four library staff have been interviewed about their attitudes to services for members of the homeless community.

Most rough sleepers and formerly homeless patrons who participated in this research viewed the public library as a safe place because it offered a welcoming atmosphere, access to a variety of resources and services targeted at the homeless. Nonetheless, sleeping has always been a major reason for their use of the library. Most staff members agreed that providing services targeted toward rough sleepers changed the dynamic between staff and the homeless patrons, but there was some disagreement between staff members about whether targeted services were the most appropriate approach.

Understanding homeless and rough sleepers’ expectations for public library services may be useful in revising policy and developing services for this community. In particular, developing partnerships with organisations supporting the homeless is an effective way of developing relevant services for this often-marginalised group.

Little research has considered public library services from the perspective of homeless people. This research is intended to fill that gap.

Particles are of the controlled release delivery systems. Also, topically applied olive oil has a protective effect against ultraviolet B (UVB) exposure. Due to its sensitivity to oxidation, various studies have investigated the production of olive oil particles. The purpose of this study was to use chitosan and sodium alginate as the vehicle polymers for olive oil.

The gelation method used to prepare the sodium alginate miliparticles containing olive oil and particles were coated with chitosan. Morphology and size, zeta potential, infrared spectrum of olive oil miliparticles, encapsulation efficiency and oil release profile were investigated. Among 12 primary fabricated formulations, formulations F₅ (olive oil loaded alginate miliparticles) and F₁₁ (olive oil loaded alginate miliparticles + chitosan coat) were selected for further evaluations.

The size of the miliparticles was in the range of 1,100–1,600 µm. Particles had a spherical appearance, and chitosan coat made a smoother surface according to the scanning electron microscopy. The zeta potential of miliparticles were −30 mV for F₅ and +2.7 mV for F₁₁. Fourier transform infrared analysis showed that there was no interaction between olive oil and other excipients. Encapsulation efficiency showed the highest value of 85% in 1:4 (olive oil:alginate solution) miliparticles in F₁₁. Release study indicated a maximum release of 68.22% for F₅ and 60.68% for F₁₁ in 24 h (p-value < 0.016). Therefore, coating with chitosan had a marked effect on slowing the release of olive oil. These results indicated that olive oil in various amounts can be successfully encapsulated into the sodium-alginate capsules cross-linked with glutaraldehyde.

To the best of the authors’ knowledge, no study has used chitosan and sodium alginate as the vehicle polymers for microencapsulation of olive oil.

Soybean has great nutritional potential. Its high protein content makes it an alternative protein source to milk in situations where milk cannot be used due to allergic reactions or intolerance. However, the potential benefits of soybean might be limited by the presence of antinutritional factors, including trypsin inhibitor activity (TIA). The purpose of the study is to evaluate the effect of dehulling and germination on the nutritive value of the soy flour and on the factors that could negatively affect the nutritional potential of the bean.

Soybean seeds were soaked for 24 h and allowed to germinate for one to three days. Soaked soybeans were manually dehulled and the flours obtained were evaluated for nutritional and antinutritional factors.

Dehulling and germination produce significant increase in crude protein and crude fiber and ash content (p = 0.05). Crude fat and starch content decreased, but the reduction was insignificant. Trypsin inhibitor levels were significantly lower after germination and dehulling of the seeds (p = 0.05).

Dehulling and germination are cost-effective processing techniques to improve the nutritional quality of the legume.

Few studies have analysed the determinants of exports in emerging economies, which are critical in the exporting reality of firms. Two variables decisively affect the export performance of firms – their size and their export experience. This paper analyses the relationship between size and export experience in the export intensity of Brazilian firms. In addition, it considers two variables (location and sector) that identify firms in Brazil and could affect their export intensity. This research answers the question of which characteristics of Brazilian companies determine their export intensity.

A statistically significant sample of 318 firms is collected from Brazilian exporting companies. Regression analysis is performed, and data describing the relationship between the export determinants and the export intensity of Brazilian firms are presented. Three evaluation models are proposed. In the first, the location and sector variables are considered. In the second, the firm size, firm location and sector are presented. In the third, the firm size, export experience, location and sector are proposed.

The results of the third model confirm that only export experience is statistically significant. Therefore, there is no relationship between firms' size, location and sector and export intensity for companies in Brazil.

This work shows the organisational characteristics that affect export performance in Brazil from the firm’s perspective; these are aspects that have been analysed less in emerging economies.