Beyond education by ranking: Let's not return to 'normal'

FORUM ◽  
2021 ◽  
Vol 63 (2) ◽  
pp. 9-19
Author(s):  
Phil Taylor
Keyword(s):  
Bell Curve ◽  
Exam System ◽  
Normally Distributed

The cancellation of public examinations in England during the coronavirus pandemic drew attention to a long-standing educational concern. Grading and ranking students, in various ways, has taken place for many years, but in summer 2020 this process was shared between teachers and, initially, an 'algorithm'. Maintaining standards and consistent grade distributions is a feature of the exam system in 'normal' times. This article considers why exam grades are (roughly) normally distributed, tracing origins of bell-curve thinking, to suggest that we should not be returning to this kind of 'normal'.

Race, IQ, and the APA report on The Bell Curve.

1997 ◽  
Vol 52 (1) ◽  
pp. 69-70 ◽  
Author(s):  
J. Philippe Rushton
Keyword(s):  
Bell Curve

The Bell Curve is factually incorrect: Reply to Jacobs and Doering

1996 ◽  
Vol 41 (4) ◽  
pp. 398-399
Author(s):  
Donald D. Dorfman
Keyword(s):  
Bell Curve

Shared Perspectives: Social factors carve the bell curve

1995 ◽  
Author(s):  
Howard E. Gruber ◽  
Curtis Branch ◽  
Jeanne Brooks-Gunn ◽  
John M. Broughton ◽  
Morton Deutsch ◽  
...  
Keyword(s):  
Social Factors ◽  
Bell Curve

Intuitive inference about normally distributed populations.

1968 ◽  
Vol 78 (2, Pt.1) ◽  
pp. 269-275 ◽  
Author(s):  
Wesley M. DuCharme ◽  
Cameron R. Peterson
Keyword(s):  
Normally Distributed

The Estimation of Blood Platelet Survival

1972 ◽  
Vol 28 (03) ◽  
pp. 447-456 ◽  
Author(s):  
E. A Murphy ◽  
M. E Francis ◽  
J. F Mustard
Keyword(s):  
Standard Deviation ◽  
Least Squares ◽  
Experimental Error ◽  
Blood Platelet ◽  
Least Squares Estimation ◽  
Systematic Relationship ◽  
Platelet Survival ◽  
The Mean ◽  
Normally Distributed

SummaryThe characteristics of experimental error in measurement of platelet radioactivity have been explored by blind replicate determinations on specimens taken on several days on each of three Walker hounds.Analysis suggests that it is not unreasonable to suppose that error for each sample is normally distributed ; and while there is evidence that the variance is heterogeneous, no systematic relationship has been discovered between the mean and the standard deviation of the determinations on individual samples. Thus, since it would be impracticable for investigators to do replicate determinations as a routine, no improvement over simple unweighted least squares estimation on untransformed data suggests itself.

scholarly journals Learning Subject-Generalized Topographical EEG Embeddings Using Deep Variational Autoencoders and Domain-Adversarial Regularization

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1792
Author(s):  
Juan Hagad ◽  
Tsukasa Kimura ◽  
Ken-ichi Fukui ◽  
Masayuki Numao
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Emotion Classification ◽  
Limited Data ◽  
Neural Network Architecture ◽  
Building Models ◽  
The Subject ◽  
Data Constraints ◽  
Input Level ◽  
Normally Distributed

Two of the biggest challenges in building models for detecting emotions from electroencephalography (EEG) devices are the relatively small amount of labeled samples and the strong variability of signal feature distributions between different subjects. In this study, we propose a context-generalized model that tackles the data constraints and subject variability simultaneously using a deep neural network architecture optimized for normally distributed subject-independent feature embeddings. Variational autoencoders (VAEs) at the input level allow the lower feature layers of the model to be trained on both labeled and unlabeled samples, maximizing the use of the limited data resources. Meanwhile, variational regularization encourages the model to learn Gaussian-distributed feature embeddings, resulting in robustness to small dataset imbalances. Subject-adversarial regularization applied to the bi-lateral features further enforces subject-independence on the final feature embedding used for emotion classification. The results from subject-independent performance experiments on the SEED and DEAP EEG-emotion datasets show that our model generalizes better across subjects than other state-of-the-art feature embeddings when paired with deep learning classifiers. Furthermore, qualitative analysis of the embedding space reveals that our proposed subject-invariant bi-lateral variational domain adversarial neural network (BiVDANN) architecture may improve the subject-independent performance by discovering normally distributed features.

Thermoelasticity of a moving disk heated by a moving normally distributed heat flux

1987 ◽  
Vol 23 (1) ◽  
pp. 70-75
Author(s):  
Yu. M. Kolyano ◽  
I. I. Bernar
Keyword(s):  
Heat Flux ◽  
Normally Distributed
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