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The progress monitoring (PM) of construction projects is an essential aspect of project control that enables the stakeholders to make timely decisions to ensure successful project delivery, but ongoing practices are largely manual and document-centric. However, the integration of technologically advanced tools into construction practices has shown the potential to automate construction PM (CPM) using real-time data collection, analysis, and visualization for effective and timely decision making. In this study, we assess the level of automation achieved through various methods that enable automated computer vision (CV)-based CPM. A detailed literature review is presented, discussing the complete process of CV-based CPM based on the research conducted between 2011 and 2021. The CV-based CPM process comprises four sub-processes: data acquisition, information retrieval, progress estimation, and output visualization. Most techniques encompassing these sub-processes require human intervention to perform the desired tasks, and the inter-connectivity among them is absent. We conclude that CV-based CPM research is centric on resolving technical feasibility studies using image-based processing of site data, which are still experimental and lack connectivity to its applications for construction management. This review highlighted the most efficient techniques involved in the CV-based CPM and accentuated the need for the inter-connectivity between sub-processes for an effective alternative to traditional practices.

BackgroundProgress monitoring is a vital strategy for evaluating skill development of young children receiving disability related services. Few effective progress monitoring tools exist for infants and toddlers, and research is needed to examine feasibility as they become available.ObjectiveThe current study examined the implementation of an assessment protocol for monitoring the developmental progress of children receiving Early Intervention (EI) services. Specifically, we examined the implementation of Infant/Toddler Individual Growth and Development Indicators (I/T IGDIs). Our questions were related to fidelity of implementation by examining learning and using I/T IGDIs in practice; and coaching support utilized by practitioners.MethodFifty practitioners serving 92 participating families received training and coaching support to reliably administer and score up to 4 I/T IGDI assessments and use them in practice. Quantitative and qualitative implementation data revealed interesting results.ResultsPractitioners needed relatively few sessions in order to become certified to administer and score I/T IGDIs. On average, practitioners used 1–2 I/T IGDIs with each child and children received a range of assessment frequency with the communication indicator being the most frequently used and the social indicator least frequent. Both supports and challenges related to time, organizational supports and research/technology were identified.ConclusionsEI practitioners are skilled at learning new assessment strategies and implementing them into practice given adequate time, organizational support and coaching for implementation. Practitioners expressed concerns with I/T IGDI use given time limitations during their visits. In addition, the inclusion of research activities into the implementation process may produce unintended challenges.

Household labor is commonly defined as a set of physical tasks such as cooking, cleaning, and shopping. Sociologists sometimes reference non-physical activities related to “household management,” but these are typically mentioned in passing, imprecisely defined, or treated as equivalent to physical tasks. Using 70 in-depth interviews with members of 35 couples, this study argues that such tasks are better understood as examples of a unique dimension of housework: cognitive labor. The data demonstrate that cognitive labor entails anticipating needs, identifying options for filling them, making decisions, and monitoring progress. Because such work is taxing but often invisible to both cognitive laborers and their partners, it is a frequent source of conflict for couples. Cognitive labor is also a gendered phenomenon: women in this study do more cognitive labor overall and more of the anticipation and monitoring work in particular. However, male and female participation in decision-making, arguably the cognitive labor component most closely linked to power and influence, is roughly equal. These findings identify and define an overlooked—yet potentially consequential—source of gender inequality at the household level and suggest a new direction for research on the division of household labor.

Purpose>The purpose of this research is to develop a generic framework of a digital twin (DT)-based automated construction progress monitoring through reality capture to extended reality (RC-to-XR).Design/methodology/approach>IDEF0 data modeling method has been designed to establish an integration of reality capturing technologies by using BIM, DTs and XR for automated construction progress monitoring. Structural equation modeling (SEM) method has been used to test the proposed hypotheses and develop the skill model to examine the reliability, validity and contribution of the framework to understand the DRX model's effectiveness if implemented in real practice.Findings>The research findings validate the positive impact and importance of utilizing technology integration in a logical framework such as DRX, which provides trustable, real-time, transparent and digital construction progress monitoring.Practical implications>DRX system captures accurate, real-time and comprehensive data at construction stage, analyses data and information precisely and quickly, visualizes information and reports in a real scale environment, facilitates information flows and communication, learns from itself, historical data and accessible online data to predict future actions, provides semantic and digitalize construction information with analytical capabilities and optimizes decision-making process.Originality/value>The research presents a framework of an automated construction progress monitoring system that integrates BIM, various reality capturing technologies, DT and XR technologies (VR, AR and MR), arraying the steps on how these technologies work collaboratively to create, capture, generate, analyze, manage and visualize construction progress data, information and reports.

Socioeconomic status is a major predictor of educational achievement. This systematic review and meta-analysis seeks to identify effective academic interventions for elementary and middle school students with low socioeconomic status. Included studies have used a treatment-control group design, were performed in OECD and EU countries, and measured achievement by standardized tests in mathematics or reading. The analysis included 101 studies performed during 2000 to 2014, 76% of which were randomized controlled trials. The effect sizes (ES) of many interventions indicate that it is possible to substantially improve educational achievement for the target group. Intervention components such as tutoring (ES = 0.36), feedback and progress monitoring (ES = 0.32), and cooperative learning (ES = 0.22) have average ES that are educationally important, statistically significant, and robust. There is also substantial variation in effect sizes, within and between components, which cannot be fully explained by observable study characteristics.

The number of university students with autism is increasing, and it is crucial that these students can access adequate support. An online questionnaire was completed by 26 autistic students and 158 non-autistic students enrolled at UK universities to investigate social and academic experiences. Autistic students self-reported significant challenges and more mental health difficulties than non-autistic students. Significant challenges focused on the social components of university life, including social skills, social support opportunities, and levels of ASD awareness from others. Many strengths were also reported regarding academic skills of autistic university students. Importantly, there were more thoughts of withdrawal by the students with autism highlighting the need for support. These data can inform university student support services.

Self-monitoring is an integral part of self-regulated learning. Tools that foster learners’ monitoring, such as learning journals, portfolios, or rubrics, are supposed to promote self-regulation and to improve performance. The aim of this meta-analysis was to examine the effectiveness of tools designed to foster monitoring on learning-related variables (academic achievement, self-regulated learning, and motivation). As these tools vary greatly in their design and the addressed components, this meta-analysis aims to uncover how such tools should be implemented to foster monitoring most effectively. The results of this meta-analysis, integrating 109 effect sizes with 3492 participants from 32 intervention studies, supported a reactivity effect by revealing a moderate effect size on academic achievement (d = 0.42), and low effects on self-regulated learning (d = 0.19) and motivation (d = 0.17). These effects were moderated by characteristics of the tool and their implementation. Effect sizes were highest for tools that (1) focused on the monitoring of both learning content as well as learning behavior, (2) stimulated metacognitive monitoring, and (3) were implemented in shorter studies. On a descriptive level, higher effects were found in favor of monitoring interventions that included teacher feedback on self-monitoring entries and allowed learners to directly revise their work based on this feedback. The findings show that there is substantial variation across tools, which yield theoretical and methodological implications on how to foster monitoring as important parts of the self-regulation cycle.

Point cloud processing is an essential task in many applications in the AEC domain, such as automated progress assessment, quality control and 3D reconstruction. As much of the procedure used to process the point clouds is shared among these applications, we identify common processing steps and analyse relevant algorithms found in the literature published in the last 5 years. We start by describing current efforts on both progress and quality monitoring and their particular requirements. Then, in the context of those applications, we dive into the specific procedures related to processing point clouds acquired using laser scanners. An emphasis is given to the scan planning process, as it can greatly influence the data collection process and the quality of the data. The data collection phase is discussed, focusing on point cloud data acquired by laser scanning. Its operating mode is explained and the factors that influence its performance are detailed. Data preprocessing methodologies are presented, aiming to introduce techniques used in the literature to, among other aspects, increase the registration performance by identifying and removing redundant data. Geometry extraction techniques are described, concerning both interior and outdoor reconstruction, as well as currently used relationship representation structures. In the end, we identify certain gaps in the literature that may constitute interesting topics for future research. Based on this review, it is evident that a key limitation associated with both Scan-to-BIM and Scan-vs-BIM algorithms is handling missing data due to occlusion, which can be reduced by multi-platform sensor fusion and efficient scan planning. Another limitation is the lack of consideration for laser scanner performance characteristics when planning the scanning operation and the apparent disconnection between the planning and data collection stages. Furthermore, the lack of representative benchmark datasets is hindering proper comparison of Scan-to-BIM and Scan-vs-BIM techniques, as well as the integration of state-of-the-art deep-learning methods that can give a positive contribution in scene interpretation and modelling.

Monitoring progress towards the Sustainable Development Goals by 2030 requires the global community to disaggregate targets along socio-economic lines, but little has been published critically analyzing the appropriateness of wealth indices to measure socioeconomic status in low- and middle-income countries. This critical interpretive synthesis analyzes the appropriateness of wealth indices for measuring social health inequalities and provides an overview of alternative methods to calculate wealth indices using data captured in standardized household surveys. Our aggregation of all published associations of wealth indices indicates a mean Spearman’s rho of 0.42 and 0.55 with income and consumption, respectively. Context-specific factors such as country development level may affect the concordance of health and educational outcomes with wealth indices and urban–rural disparities can be more pronounced using wealth indices compared to income or consumption. Synthesis of potential future uses of wealth indices suggests that it is possible to quantify wealth inequality using household assets, that the index can be used to study SES across national boundaries, and that technological innovations may soon change how asset wealth is measured. Finally, a review of alternative approaches to constructing household asset indices suggests lack of evidence of superiority for count measures, item response theory, and Mokken scale analysis, but points to evidence-based advantages for multiple correspondence analysis, polychoric PCA and predicted income. In sum, wealth indices are an equally valid, but distinct measure of household SES from income and consumption measures, and more research is needed into their potential applications for international health inequality measurement.

Drawing from Marks, Mathieu, and Zaccaro (2001), we proposed that narrowly focused teamwork processes load onto 3 higher‐order teamwork process dimensions, which in turn load onto a general teamwork process factor. Results of model testing using meta‐analyses of relationships among narrow teamwork processes provided support for the structure of this multidimensional theory of teamwork process. Meta‐analytic results also indicated that teamwork processes have positive relationships with team performance and member satisfaction, and that the relationships are similar across the teamwork dimensions and levels of process specificity. Supplemental analyses revealed that the 3 intermediate‐level teamwork processes are positively and strongly related to cohesion and potency. Results of moderator analyses suggested that relationships among teamwork processes and team performance are somewhat dependent on task interdependence and team size.