scholarly journals Task Variation Within Occupations

2017 ◽  
Vol 56 (3) ◽  
pp. 393-410 ◽  
Author(s):  
Hugh Cassidy
Keyword(s):  
2009 ◽  
Vol 33 (2) ◽  
pp. 150-160 ◽  
Author(s):  
Jessi L. Smith ◽  
Jill Wagaman ◽  
Ian M. Handley

1998 ◽  
Vol 19 (2) ◽  
pp. 189-199 ◽  
Author(s):  
Giulio E Lancioni ◽  
Mark F O’Reilly ◽  
Francesca Campodonico ◽  
Margherita Mantini

2017 ◽  
Vol 53 (3) ◽  
pp. 365-408 ◽  
Author(s):  
Ruth van Veelen ◽  
Peter J. C. Sleegers ◽  
Maaike D. Endedijk

Purpose: School leadership is fundamental in efforts to successfully implement school reform and improve student and teacher learning. Although there is an abundant amount of research on school leaders’ formal training, assessment, and practice, little is known about their informal professional learning. In other words, how do school leaders learn at the workplace? To answer this question, we took an interactionist approach and argued that school leadership learning is based on the interplay between the school environment and the person. Specifically, we investigated the effect of school context (learning climate, social support), task (task variation), and the personal factor self-efficacy on both individual (reflection and career awareness) and social (asking for feedback and challenging groupthink) learning activities. Method: A questionnaire was administered among 1,150 school leaders in Dutch secondary education. Structural equation modeling was used to test the proposed relationships between our model variables. Findings: Self-efficacy positively predicted all four learning activities. Task variation positively predicted asking for feedback and reflection. Learning climate positively predicted asking for feedback, reflection, and career awareness. Interestingly, the effect of social support was twofold: It positively predicted social learning, but it negatively predicted individual learning. Finally, self-efficacy was an important mediator in the relationship between school context and professional learning. Conclusions: This study demonstrates how personal, task, and school context factors affect school leaders’ professional learning. These insights help develop tools and conditions for leaders to reflect and discuss on their practice, and to set an example for lifelong learning in schools.


1979 ◽  
Vol 66 (S1) ◽  
pp. S12-S12 ◽  
Author(s):  
K. R. Scherer ◽  
F. Tolkmitt
Keyword(s):  

1987 ◽  
Vol 19 (3) ◽  
pp. 16-19 ◽  
Author(s):  
Lee Kern Dunlap ◽  
Glen Dunlap
Keyword(s):  

2018 ◽  
Author(s):  
Alexander Bowring ◽  
Camille Maumet ◽  
Thomas E. Nichols

AbstractA wealth of analysis tools are available to fMRI researchers in order to extract patterns of task variation and, ultimately, understand cognitive function. However, this ‘methodological plurality’ comes with a drawback. While conceptually similar, two different analysis pipelines applied on the same dataset may not produce the same scientific results. Differences in methods, implementations across software packages, and even operating systems or software versions all contribute to this variability. Consequently, attention in the field has recently been directed to reproducibility and data sharing. Neuroimaging is currently experiencing a surge in initiatives to improve research practices and ensure that all conclusions inferred from an fMRI study are replicable.In this work, our goal is to understand how choice of software package impacts on analysis results. We use publically shared data from three published task fMRI neuroimaging studies, reanalyzing each study using the three main neuroimaging software packages, AFNI, FSL and SPM, using parametric and nonparametric inference. We obtain all information on how to process, analyze, and model each dataset from the publications. We make quantitative and qualitative comparisons between our replications to gauge the scale of variability in our results and assess the fundamental differences between each software package. While qualitatively we find broad similarities between packages, we also discover marked differences, such as Dice similarity coefficients ranging from 0.000 - 0.743 in comparisons of thresholded statistic maps between software. We discuss the challenges involved in trying to reanalyse the published studies, and highlight our own efforts to make this research reproducible.


1987 ◽  
Vol 10 (3) ◽  
pp. 467-477 ◽  
Author(s):  
John R. Anderson

AbstractThe appropriate methodology for psychological research depends on whether one is studying mental algorithms or their implementation. Mental algorithms are abstract specifications of the steps taken by procedures that run in the mind. Implementational issues concern the speed and reliability of these procedures. The algorithmic level can be explored only by studying across-task variation. This contrasts with psychology's dominant methodology of looking for within-task generalities, which is appropriate only for studying implementational issues.The implementation-algorithm distinction is related to a number of other “levels” considered in cognitive science. Its realization in Anderson's ACT theory of cognition is discussed. Research at the algorithmic level is more promising because it is hard to make further fundamental scientific progress at the implementational level with the methodologies available. Protocol data, which are appropriate only for algorithm-level theories, provide a richer source than data at the implementational level. Research at the algorithmic level will also yield more insight into fundamental properties of human knowledge because it is the level at which significant learning transitions are defined.The best way to study the algorithmic level is to look for differential learning outcomes in pedagogical experiments that manipulate instructional experience. This provides control and prediction in realistically complex learning situations. The intelligent tutoring paradigm provides a particularly fruitful way to implement such experiments.The implications of this analysis for the issue of modularity of mind, the status of language, research on human/computer interaction, and connectionist models are also examined.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Orly Shimony ◽  
Noam Einav ◽  
Omer Bonne ◽  
Joshua T. Jordan ◽  
Thomas M. Van Vleet ◽  
...  

AbstractInhibitory control underlies one’s ability to maintain goal-directed behavior by inhibiting prepotent responses or ignoring irrelevant information. Recent models suggest that impaired inhibition of negative information may contribute to depressive symptoms, and that this association is mediated by rumination. However, the exact nature of this association, particularly in non-clinical samples, is unclear. The current study assessed the relationship between inhibitory control over emotional vs. non-emotional information, rumination and depressive symptoms. A non-clinical sample of 119 participants (mean age: 36.44 ± 11.74) with various levels of depressive symptoms completed three variations of a Go/No-Go task online; two of the task variations required either explicit or implicit processing of emotional expressions, and a third variation contained no emotional expressions (i.e., neutral condition). We found reductions in inhibitory control for participants reporting elevated symptoms of depression on all three task variations, relative to less depressed participants. However, for the task variation that required implicit emotion processing, depressive symptoms were associated with inhibitory deficits for sad and neutral, but not for happy expressions. An exploratory analysis showed that the relationship between inhibition and depressive symptoms occurs in part through trait rumination for all three tasks, regardless of emotional content. Collectively, these results indicate that elevated depressive symptoms are associated with both a general inhibitory control deficit, as well as affective interference from negative emotions, with implications for the assessment and treatment of mood disorders.


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