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A growing body of evidence confirms that good teaching is the most important school-specific factor impacting student achievement and growth. Concerns over teachers’ effectiveness have led to escalating demands for reliable systems that measure teachers’ effectiveness. Such performance systems require a stable and explicit definition of knowledge, skills, actions, and dispositions that comprise the work of teaching. In this chapter, we refer to these as teacher “core competencies” (CCs). Well-defined core competency constructs can anchor investigations of teacher effectiveness for purposes in many different settings, but the field currently lacks a set of common stable descriptors. The descriptors encoded in current standards and assessments are plagued by confusion arising from multiple ideological perspectives, conflicting political views on teacher preparation, and disconnects between stakeholders (e.g., university versus alternative preparation routes).

This chapter presents a study designed to move from descriptive, “input-based” ways to describe teaching to the development and early testing of specific construct descriptors. We begin by distilling many disparate sources of authority regarding what teachers should know and be able to do and assess the validity and usefulness of the resulting descriptors across several measurement applications. We find evidence of stability across multiple populations and different settings and evidence that the constructs can describe preparation program emphases, as well as evidence that some program-level aggregate scores correlate with student assessment scores. We also investigate the stability of competency constructs in different settings, attempting to understand the implications of k-12 school contexts for interpreting core competency measurements of preparation programs.

Additive Manufacturing (AM) conventionally necessitates an intermediary slicing procedure using the standard tessellation language (STL) data, which can be computationally burdensome, especially for intricate microcellular architectures. This study aims to propose a direct slicing method tailored for digital light processing-type AM processes for the efficient generation of slicing data for microcellular structures.

The authors proposed a direct slicing method designed for microcellular structures, encompassing micro-lattice and triply periodic minimal surface (TPMS) structures. The sliced data of these structures were represented mathematically and then convert into 2D monochromatic images, bypassing the time-consuming slicing procedures required by 3D STL data. The efficiency of the proposed method was validated through data preparations for lattice-based nasopharyngeal swabs and TPMS-based ellipsoid components. Furthermore, its adaptability was highlighted by incorporating 2D images of additional features, eliminating the requirement for complex 3D Boolean operations.

The direct slicing method offered significant benefits upon implementation for microcellular structures. For lattice-based nasopharyngeal swabs, it reduced data size by a factor of 1/300 and data preparation time by a factor of 1/8. Similarly, for TPMS-based ellipsoid components, it reduced data size by a factor of 1/60 and preparation time by a factor of 1/16.

The direct slicing method allows for bypasses the computational burdens associated with traditional indirect slicing from 3D STL data, by directly translating complex cellular structures into 2D sliced images. This method not only reduces data volume and processing time significantly but also demonstrates the versatility of sliced data preparation by integrating supplementary features using 2D operations.

Principal preparation program pedagogy and course delivery are critical to principal candidates' preparedness to lead. Research around online program delivery, however, is relatively sparse. This study examined the extent to which university-based educational leadership programs offered fully online (FOL) pathways to the principalship, as well as program geographic locations and institutional characteristics most associated with FOL offerings.

Data were collected through website reviews and coding checks, and then merged with national postsecondary data. Data were analyzed using descriptive statistics, classification tree analysis, and geographic information system (GIS) mapping.

Roughly 43 percent of all reviewed programs offered an FOL pathway to licensure, which suggests substantial growth in FOL offerings over the last 10 years. While a number of factors were deemed important, geographic characteristics were most associated with FOL status. GIS mapping further illustrated findings with a visual landscape of program FOL offerings.

This study considered only programs for which degrees or certificates could be earned without ever visiting campus in-person for classes. Hybrid programs were excluded from the analysis.

Findings make a clear call for more research into online principal preparation program design and course delivery.

This study provides the first overview of fully online university-based principal preparation programs in the United States while also offering a previously unavailable landscape of all programs specifically leading to licensure. It is also the only higher education study to map or investigate factors associated with FOL offerings and raises questions about prior FOL higher education research.

The use of an observational approach in conjunction with isolation techniques for campylobacter and salmonella detection has facilitated a detailed evaluation of the risk of cross contamination during food preparation. Identification of suspected exposure routes has linked naturally contaminated raw foods with important food‐handling malpractices, contaminated contact surfaces and ready‐to‐eat foods. In a model domestic kitchen, 29 per cent of food preparation sessions resulted in positive campylobacter isolations from prepared salads, cleaning materials and food‐contact surfaces. Typing results showed that specific campylobacter strains isolated from prepared chicken salads were the same as the strains isolated from the raw chicken pieces, indicating microbial transfer during food preparation. Data obtained from this study can be used for exposure assessment, risk management and in the development of consumer risk communication strategies.

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.

Citizen Science – public participation in scientific projects – is becoming a global practice engaging volunteer participants, often non-scientists, with scientific research. Citizen Science is facing major challenges, such as quality and consistency, to reap open the full potential of its outputs and outcomes, including data, software and results. In this context, the principles put forth by Data Science and Open Science domains are essential for alleviating these challenges, which have been addressed at length in these domains. The purpose of this study is to explore the extent to which Citizen Science initiatives capitalise on Data Science and Open Science principles.

The authors analysed 48 Citizen Science projects related to pollution and its effects. They compared each project against a set of Data Science and Open Science indicators, exploring how each project defines, collects, analyses and exploits data to present results and contribute to knowledge.

The results indicate several shortcomings with respect to commonly accepted Data Science principles, including lack of a clear definition of research problems and limited description of data management and analysis processes, and Open Science principles, including lack of the necessary contextual information for reusing project outcomes.

In the light of this analysis, the authors provide a set of guidelines and recommendations for better adoption of Data Science and Open Science principles in Citizen Science projects, and introduce a software tool to support this adoption, with a focus on preparation of data management plans in Citizen Science projects.

Integrating sustainability strategies with business processes is the most challenging task for industry professionals due to the lack of a proper understanding of sustainability concepts. At the same time, a lack of proper guidance restricts them from pursuing such activities. As far as the aspects of implementation are concerned, it is very tough to analyse and pick up key points to start with. The purpose of this paper is to utilize a text mining approach to analyse qualitative data and identify the critical issues for implementing sustainability in the manufacturing sector by focussing on turning processes based on the survey responses of researchers and industry professionals.

An integrated method employing principal component analysis (PCA) and the k-means clustering algorithm has been applied to extract useful information from a set of various suggestions provided by both the groups surveyed. The textual data has also been visualized using word clouds and, thus, it has been compared with the results of the text mining approach.

The results of the study indicate the importance of the role of government organizations and the need for a skilled workforce, which are crucial for enhancing aspects of sustainability in the manufacturing sector, as supported by both researchers and industry professionals. Besides this, researchers have highlighted the need to focus more on environmentally related issues, whereas industry professionals have raised performance-related issues.

The findings of the study present the important concerns of both the groups towards sustainability initiatives and, thus, will help to enhance the understanding of the underlying possibilities of negotiating jointly to enhance the performance of machining processes.

The novelty of this paper lies in its identification of important initiatives that are having a direct impact on the sustainable aspects of the machining process, based on the views of researchers and industry professionals.

This paper aims to provide a predictive framework of customer churn through six stages for accurate prediction and preventing customer churn in the field of business.

The six stages are as follows: first, collection of customer behavioral data and preparation of the data; second, the formation of derived variables and selection of influential variables, using a method of discriminant analysis; third, selection of training and testing data and reviewing their proportion; fourth, the development of prediction models using simple, bagging and boosting versions of supervised machine learning; fifth, comparison of churn prediction models based on different versions of machine-learning methods and selected variables; and sixth, providing appropriate strategies based on the proposed model.

According to the results, five variables, the number of items, reception of returned items, the discount, the distribution time and the prize beside the recency, frequency and monetary (RFM) variables (RFMITSDP), were chosen as the best predictor variables. The proposed model with accuracy of 97.92 per cent, in comparison to RFM, had much better performance in churn prediction and among the supervised machine learning methods, artificial neural network (ANN) had the highest accuracy, and decision trees (DT) was the least accurate one. The results show the substantially superiority of boosting versions in prediction compared with simple and bagging models.

The period of the available data was limited to two years. The research data were limited to only one grocery store whereby it may not be applicable to other industries; therefore, generalizing the results to other business centers should be used with caution.

Business owners must try to enforce a clear rule to provide a prize for a certain number of purchased items. Of course, the prize can be something other than the purchased item. Business owners must accept the items returned by the customers for any reasons, and the conditions for accepting returned items and the deadline for accepting the returned items must be clearly communicated to the customers. Store owners must consider a discount for a certain amount of purchase from the store. They have to use an exponential rule to increase the discount when the amount of purchase is increased to encourage customers for more purchase. The managers of large stores must try to quickly deliver the ordered items, and they should use equipped and new transporting vehicles and skilled and friendly workforce for delivering the items. It is recommended that the types of services, the rules for prizes, the discount, the rules for accepting the returned items and the method of distributing the items must be prepared and shown in the store for all the customers to see. The special services and reward rules of the store must be communicated to the customers using new media such as social networks. To predict the customer behaviors based on the data, the future researchers should use the boosting method because it increases efficiency and accuracy of prediction. It is recommended that for predicting the customer behaviors, particularly their churning status, the ANN method be used. To extract and select the important and effective variables influencing customer behaviors, the discriminant analysis method can be used which is a very accurate and powerful method for predicting the classes of the customers.

The current study tries to fill this gap by considering five basic and important variables besides RFM in stores, i.e. prize, discount, accepting returns, delay in distribution and the number of items, so that the business owners can understand the role services such as prizes, discount, distribution and accepting returns play in retraining the customers and preventing them from churning. Another innovation of the current study is the comparison of machine-learning methods with their boosting and bagging versions, especially considering the fact that previous studies do not consider the bagging method. The other reason for the study is the conflicting results regarding the superiority of machine-learning methods in a more accurate prediction of customer behaviors, including churning. For example, some studies introduce ANN (Huang et al., 2010; Hung and Wang, 2004; Keramati et al., 2014; Runge et al., 2014), some introduce support vector machine ( Guo-en and Wei-dong, 2008; Vafeiadis et al., 2015; Yu et al., 2011) and some introduce DT (Freund and Schapire, 1996; Qureshi et al., 2013; Umayaparvathi and Iyakutti, 2012) as the best predictor, confusing the users of the results of these studies regarding the best prediction method. The current study identifies the best prediction method specifically in the field of store businesses for researchers and the owners. Moreover, another innovation of the current study is using discriminant analysis for selecting and filtering variables which are important and effective in predicting churners and non-churners, which is not used in previous studies. Therefore, the current study is unique considering the used variables, the method of comparing their accuracy and the method of selecting effective variables.

This paper aims to understand the current development situation of scientific data management policy in China, analyze the content structure of the policy and provide a theoretical basis for the improvement and optimization of the policy system.

China's scientific data management policies were obtained through various channels such as searching government websites and policy and legal database, and 209 policies were finally identified as the sample for analysis after being screened and integrated. A three-dimensional framework was constructed based on the perspective of policy tools, combining stakeholder and lifecycle theories. And the content of policy texts was coded and quantitatively analyzed according to this framework.

China's scientific data management policies can be divided into four stages according to the time sequence: infancy, preliminary exploration, comprehensive promotion and key implementation. The policies use a combination of three types of policy tools: supply-side, environmental-side and demand-side, involving multiple stakeholders and covering all stages of the lifecycle. But policy tools and their application to stakeholders and lifecycle stages are imbalanced. The development of future scientific data management policy should strengthen the balance of policy tools, promote the participation of multiple subjects and focus on the supervision of the whole lifecycle.

This paper constructs a three-dimensional analytical framework and uses content analysis to quantitatively analyze scientific data management policy texts, extending the research perspective and research content in the field of scientific data management. The study identifies policy focuses and proposes several strategies that will help optimize the scientific data management policy.

Many organizations are seeking unicorn data scientists, that rarest of breeds that can do it all. They are said to be experts in many traditionally distinct disciplines, including mathematics, statistics, computer science, artificial intelligence, and more. The purpose of this paper is to describe authors’ pursuit of these elusive mythical creatures.

Qualitative data were collected through semi-structured interviews with managers/directors from nine Australian state and federal government agencies with relatively mature data science functions.

Although the authors failed to find evidence of unicorn data scientists, they are pleased to report on six key roles that are considered to be required for an effective data science team. Primary and secondary skills for each of the roles are identified and the resulting framework is then used to illustratively evaluate three data science Master-level degrees offered by Australian universities.

Given that the findings presented in this paper have been based on a study with large government agencies with relatively mature data science functions, they may not be directly transferable to less mature, smaller, and less well-resourced agencies and firms.

The skills framework provides a theoretical contribution that may be applied in practice to evaluate and improve the composition of data science teams and related training programs.