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2021 ◽  
Author(s):  
Jyoti Raina

<p><i>This research is an exploratory study of pre-service teacher education students need for learning support in writing. The participants were 81 student-teachers enrolled for a Bachelor’s of Elementary Education (B.El.Ed.) degree programme at Department of Elementary Education at University of Delhi. The study was data-driven as the need was explored by administering a questionnaire to student-teacher participants. The focus was on gathering empirical data on what their perceived writing needs were and how these could be addressed. The participant responses overwhelmingly articulated a gap between their writing skills and the writing demands of their curriculum. The need for creating a writing centre (henceforth WC) aimed at learning support for writing was reported while also explicating the nature of support that students seek. The findings demonstrate necessity, benefits and wide-ranging value of establishment of a WC at undergraduate institutions of teacher education. This is a pressing student need that begs the attention of educational administrators, policy-makers and higher education faculty in the global south.</i></p>


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Takahiro Ezaki ◽  
Naoto Imura ◽  
Katsuhiro Nishinari

AbstractDemand forecasting based on empirical data is a viable approach for optimizing a supply chain. However, in this approach, a model constructed from past data occasionally becomes outdated due to long-term changes in the environment, in which case the model should be updated (i.e., retrained) using the latest data. In this study, we examine the effects of updating models in a supply chain using a minimal setting. We demonstrate that when each party in the supply chain has its own forecasting model, uncoordinated model retraining causes the bullwhip effect even if a very simple replenishment policy is applied. Our results also indicate that sharing the forecasting model among the parties involved significantly reduces the bullwhip effect.


2021 ◽  
Author(s):  
Thomas W. Keelin ◽  
Ronald A. Howard

Users of probability distributions frequently need to convert data (empirical, simulated, or elicited) into a continuous probability distribution and to update that distribution when new data becomes available. Often, it is unclear which traditional probability distribution(s) to use, fitting to data is laborious and unsatisfactory, little insight emerges, and updating with Bayes rule is impractical. Here we offer an alternative -- a family of continuous probability distributions, fitting methods, and tools that: provide sufficient shape and boundedness flexibility to closely match virtually any probability distribution and most data sets; involve a single set of simple closed-form equations; stimulate potentially valuable insights when applied to empirical data; are simply fit to data with ordinary least squares; are easy to combine (as when weighting the opinion of multiple experts), and, under certain conditions, are easily updated in closed form according to Bayes rule when new data becomes available. The Bayesian updating method is presented in a way that is readily understandable as a fisherman updates his catch probabilities when changing the river on which he fishes. While metalog applications have been shown to improve decision-making, the methods and results herein are broadly applicable to virtually any use of continuous probability in any field of human endeavor. Diverse data sets may be explored and modeled in these new ways with freely available spreadsheets and tools.


2021 ◽  
Vol 4 (4) ◽  
Author(s):  
Giovanni Battista Martino

Mozambique’s donor-inspired ongoing programme of ‘traditional authorities’ ‘(re-)integration’ carries considerable emancipatory potential for local communities in their relations with central political institutions and the globalized economy. By analysing ‘traditional authorities’’ specifically elaborated discourse and highlighting their agency within the dynamics emerging from state institutions’ attempts at ‘incorporating’ them in the sense indicated by Zenker and Hoehne, that is, to deny them all political auton-omy, this article aims to clarify ‘traditional’ leaders’ role in defending their own com-munities’ interests and rights vis-à-vis the state, private enterprises, and development actors/donors. Close examination of empirical data collected during field research in Inhambane province provides convincing evidence of traditional authorities’ general inability to develop effective discursive strategies for the representation and defence of their communities’ interests and rights. By choosing to retreat within the domain of spirituality and to cede much of their statutory prerogatives to more dynamic and bet-ter resourced actors, ‘traditional authorities’ end up accepting their ‘incorporation’ into the institutional structure of the state as merely symbolic objects and sources of inter-nal as well as international legitimacy, thus obliterating their role as natural repre-sentatives of their communities.


2021 ◽  
Author(s):  
Mehdi Senoussi ◽  
Pieter Verbeke ◽  
Tom Verguts

Why can't we keep as many items as we want in working memory? It has long been debated whether this resource limitation is a bug (a downside of our fallible biological system) or instead a feature (an optimal response to a computational problem). We propose that the resource limitation is a consequence of a useful feature. Specifically, we propose that flexible cognition requires time-based binding, and time-based binding necessarily limits the number of (bound) memoranda that can be stored simultaneously. Time-based binding is most naturally instantiated via neural oscillations, for which there exists ample experimental evidence. We report simulations that illustrate this theory and that relate it to empirical data. We also compare the theory to several other (feature and bug) resource theories.


2021 ◽  
Author(s):  
Jyoti Raina

<p><i>This research is an exploratory study of pre-service teacher education students need for learning support in writing. The participants were 81 student-teachers enrolled for a Bachelor’s of Elementary Education (B.El.Ed.) degree programme at Department of Elementary Education at University of Delhi. The study was data-driven as the need was explored by administering a questionnaire to student-teacher participants. The focus was on gathering empirical data on what their perceived writing needs were and how these could be addressed. The participant responses overwhelmingly articulated a gap between their writing skills and the writing demands of their curriculum. The need for creating a writing centre (henceforth WC) aimed at learning support for writing was reported while also explicating the nature of support that students seek. The findings demonstrate necessity, benefits and wide-ranging value of establishment of a WC at undergraduate institutions of teacher education. This is a pressing student need that begs the attention of educational administrators, policy-makers and higher education faculty in the global south.</i></p>


2021 ◽  
pp. 1-13
Author(s):  
Marzena Stor ◽  
Łukasz Haromszeki

BACKGROUND: Competency management (CM) is one of the basic subfunctions of HRM, which can significantly affect the results achieved by organizations. This is because the competencies of employees have become the key capital of enterprises and a factor of their success. Research on the relationships between CM and organizational performance results in Polish MNCs creates a research gap due to the object and subject of research. OBJECTIVE: The main goal of the article is to identify the potential regularities that may exist between the advancement level of CM and its composing activities and the company financial performance results. Of particular interest are the relationships that may appear between the studied variables in the context of their relationships with other subfunctions of HRM and the significance of human capital as a competitive factor. METHODS: The research sample includes 200 non-financial business entities with Polish capital, whose headquarters are in Poland, and local subsidiaries are located outside the country. The research was conducted using CATI and CAWI methods. Descriptive and correlation statistics were used to analyze the collected empirical data. RESULTS: Two fundamental regularities are observable. It turns out that the higher the advancement level of activities within CM, and in particular its links with other HRM subfunctions, the better the financial results of enterprises. CONCLUSIONS: The empirical research results lead to the conclusion that human capital is treated as a company’s competitive factor, and CM is of significant meaning to the financial results of the organization.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maria Spante ◽  
Anita Varga ◽  
Linnéa Carlsson

Purpose This study aims to depict how a change laboratory (CL) promotes sustainable professional practice at the workplace to tackle unequal access to educational success. Design/methodology/approach The empirical findings are from a CL focusing on school professionals’ agency and a follow-up study one year after the CL. Findings The study shows how the staff gained insight that professional agency is a collective and relational practice. Furthermore, the staff explored how to make a difference with viable means to create new workplace models for students’ success despite experiencing a conundrum. Research limitations/implications This study examined participants’ perspectives in workplace change and provided support for further research examining how professionally and collectively designed models gain sustainability in schools. Practical implications This study provides empirical data of how professional agency for change driven by collective visions can be accelerated with the interventionist method CL among school professionals. Social implications This study emphasizes the value of professional collective learning at the workplace, driven by several professional groups in school, and the need to follow up to detect sustainable change. Originality/value This study emphasizes the value of professional collective learning at the workplace, driven by several professional groups in school, and the need to follow up to detect sustainable change.


2021 ◽  
Author(s):  
Matthias Guggenmos

The human ability to introspect on thoughts, perceptions or actions – metacognitive ability – has become a focal topic of both cognitive basic and clinical research. At the same time it has become increasingly clear that currently available quantitative tools are limited in their ability to make unconfounded inferences about metacognition. As a step forward, the present work introduces a comprehensive framework and model of metacognition that allows for inferences about metacognitive noise and metacognitive biases during the readout of type 1 decision values or at the confidence reporting stage. The model assumes that confidence results from a continuous but noisy and potentially biased transformation of decision values, described by a confidence link function. A canonical set of metacognitive noise distributions is introduced which differ, amongst others, in their predictions about metacognitive sign flips of type 1 decision values. Successful recovery of model parameters is demonstrated and the model is validated on an empirical data set. In particular, it is shown that metacognitive noise and bias parameters correlate with conventional behavioral measures. Crucially, in contrast to these conventional measures, metacognitive noise parameters inferred from the model are shown to be independent of type 1 performance. This work is accompanied by a toolbox (ReMeta) that allows researchers to estimate key parameters of metacognition in confidence datasets.


Author(s):  
Caglar Cakan ◽  
Nikola Jajcay ◽  
Klaus Obermayer

Abstractneurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e., the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is made possible using a parameter exploration module, which allows one to characterize a model’s behavior as a function of changing parameters. An optimization module is provided for fitting models to multimodal empirical data using evolutionary algorithms. neurolib is designed to be extendable and allows for easy implementation of custom neural mass models, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure–function relationship of brain networks, and for performing in-silico optimization of whole-brain models.


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