Dolueg: A measurement network’s face

Dolueg is a two-component framework to dynamically display time series. It serves as outreach to other researchers and the local public, educational resource and quality control tool. The first component is a set of Python functions. These create different types of visualisation with meta information about the data in the zoomable, modern SVG format. The second component is a simple but highly customizable website, that groups these figures according to the displayed data. We provide the code in two separate repositories on GitHub for interested parties including more detailed instructions for the installation.

Empirical Study of Toyota Motor Corporation Relative to the Good to Great Framework

Best-selling business bookGood to Great was published in 2001 as the result of an effort to understand what characteristics, if any, companies who experience an extended run of greatness have in common compared to companies who do not. The resulting seven-component framework of Good to Great has brought the book both wild acclaim in management circles and heavy scrutiny in the research arena. While the book originally studied only American companies, this research will examine Good to Great’s research methodology and definition of “great” in order to compare the framework to Toyota Motor Corporation. A consistent tenant in Fortune’s Global 500 top 10, Toyota is arguably one of the most successful companies in the world, showing a growth that has been remarkably steady for almost 80 years. This paper examines empirical data and evidence from Toyota research and analyses the degree of fit relative to the Good to Great framework. The outcome of the paper offers evidence to support Good to Great framework by putting it on trial against a large international organization.

An exploratory study of teacher buoyancy

PurposeThe purpose of this paper is threefold. Firstly, a new concept, teacher buoyancy, is introduced. Based on the significance to study how teachers bounce back from minor and frequent setbacks (vs. major adversities emphasized in resilience) in their daily work and the research on buoyancy by Martin and Marsh, a dual-component framework to conceptualize this new concept is introduced. Secondly, the development of a new instrument, the Teacher Buoyancy Scale (TBS), to measure it is presented. Thirdly, results of a study using the TBS are reported, which provide insights into how teacher buoyancy can be fostered.Design/methodology/approachThe study employed a quantitative design. A total of 258 teachers taking a part-time initial teacher education (ITE) program completed the TBS. Their responses were analyzed by exploratory factor analysis (EFA). In addition to descriptive statistics and reliability coefficients, Pearson correlation coefficients were calculated to examine the relationship among the factors.FindingsThe data analysis indicated five factors, namely, Coping with difficulties, Bouncing back cognitively and emotionally, Working hard and appraising difficulties positively, Caring for one's well-being and Striving for professional growth. These factors can be readily interpreted by the dual-component framework. Correlations among the factors further revealed that enabling factors can be subdivided into more proximal personal strengths relating to direct coping, and more distal personal assets pertaining to personal well-being. It is the latter that correlates most highly with perceived teacher buoyancy.Originality/valueThe most original contribution of this paper is the proposal of the new concept of teacher buoyancy which is teachers' capacity to deal with the everyday challenges that most teachers face in their teaching. The delineation between buoyancy and resilience sharpens the focus of the problem domain that is most relevant to teachers. The development of the TBS provides a useful and reliable instrument to examine teacher buoyancy in future studies.

Personalized nudging

A criticism of behavioural nudges is that they lack precision, sometimes nudging people who – had their personal circumstances been known – would have benefitted from being nudged differently. This problem may be solved through a programme of personalized nudging. This paper proposes a two-component framework for personalization that suggests choice architects can personalize both the choices being nudged towards (choice personalization) and the method of nudging itself (delivery personalization). To do so, choice architects will require access to heterogeneous data. This paper argues that such data need not take the form of big data, but agrees with previous authors that the opportunities to personalize nudges increase as data become more accessible. Finally, this paper considers two challenges that a personalized nudging programme must consider, namely the risk personalization poses to the universality of laws, regulation and social experiences, and the data access challenges policy-makers may encounter.

Exploring Gamification Component Framework to Enhance Motivation in Higher Education: Literature Review

One of approaches to increase learning motivation is by using gamification in education. The framework of gamification implementation is MDA (Mechanic, Dynamic, and Aesthetic). Now days, gamification is used more frequently in higher education. Therefore, it is necessary to investigate any MDA components that able to increase learning motivation and learning outcomes. By knowing the trends in the implementation of gamification components that can increase learning motivation and outcomes, then gamification designer can have a basic foundation in the application of gamification in higher education. Furthermore, this study also investigates the application of counterproductive MDA components related to learning motivation and learning outcomes. This study involved gami- fication research at higher education conducted from 2015 to 2018.

Validation of Four-dimensional Components for Measuring Quality of the Public Health Data Collection Process: Expert Elicitation (Preprint)

BACKGROUND Identification of the essential components of quality of data collection is the starting point for the design of effective data quality management strategies in public health information systems. An inductive analysis of global public health informatics literature on the data collection process derived a four-dimensional (4D) component framework that focuses on four dimensions of the process: management, personnel, data collection system, and environment. It is necessary to empirically validate the framework for further research and practice. OBJECTIVE This study aimed to obtain empirical evidence to confirm the components of the 4D framework, and if needed, to further develop this preliminary framework. METHODS Expert elicitation was used to evaluate the preliminary framework in the context of Chinese national AIDS information management system. The research processes included the development of an interview guide and data collection form, data collection, and data analysis. Twenty-eight experts, including three public health administrators, fifteen public health work-ers, and ten healthcare practitioners participated in the elicitation session. A framework quali-tative data analysis approach was followed to elicit themes from interview transcripts and to compare with the elements of the 4D framework. RESULTS A total of 302 codes were extracted from the interview transcripts, which verified 116 (78%) original indicators and generated 47 new indicators. The final 4D component framework consists of 116 indicators including 82 facilitators and 34 barriers. The first component, data collection management, includes data collection protocol and quality assurance, which is measured by 41 (35% of the 116) indicators. It was followed by data collection environment measured by 37 (32%) indicators, which comprises leadership, training, and funding, as well as three newly added subcomponents, i.e., organisational policy, high-level management support, collaboration among parallel organisations. The third component, data collection personnel, is described by a perception of data collection, skill/competence, communication, and staffing pattern, which is measured by 22 (19%) indicators. The fourth, data collection system, contain-ing functions, integration of different data collection systems, technical support, and device for data collection, is measured by 16 (14%) indicators. CONCLUSIONS This expert elicitation study situated in national AIDS information management systems validated and made improvements to the 4D component framework measuring the quality of the data collection process for public health information systems. The validated 4D component framework can be applied by researchers and practitioners in designing and managing the public health data collection process.

A two-component framework captures cross-cultural similarities and differences in essentialist thinking about social categories

Social essentialism is the intuitive assumption that members of social categories share underlying properties that determine category membership and cause observable regularities. We investigate cultural differences in social essentialism in the USA, Northern Ireland, and China. In Study 1, 106 undergraduates from the US and Northern Ireland rated 44 social categories on 9 scales representing distinct aspects of social essentialism. In Study 2, 157 undergraduates from the US and China rated 31 social categories on 6 scales. Results showed that a single two-component framework—describing variability in social categories with respect to perceived naturalness (objectivity, immutability) and cohesiveness (homogeneity, informativeness)—explained representations of social categories in all three cultures. Differences emerged as well; on average, American participants rated social categories as more natural and less cohesive than Northern Irish or Chinese participants. Moreover, specific social dimensions were seen as more natural in cultures where those dimensions had particular cultural salience (religion in Northern Ireland, home region in China). Together, these findings demonstrate cross-cultural similarities (a common two- component framework for representing social kinds, a common way to essentialize historically salient social dimensions) and differences (in the general extent to which social categories were perceived to be natural and cohesive) across disparate cultural groups.