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Teacher quality assessment commonly includes reviews of qualifications, tests of teacher knowledge, observations of practice, and measurements of student learning gains. [1] [2] Assessments of teacher quality are currently used for policymaking, employment and tenure decisions, teacher evaluations, merit pay awards, and as data to inform the professional growth of teachers.
The American Board for Certification of Teacher Excellence, often referred to as the American Board, was launched with a $5 million federal grant from the U.S. Department of Education in 2001. [1] The non-partisan, non-profit organization 's mission is to certify subject experts, experienced professionals, career changers, and military veterans ...
The teacher version of the Vanderbilt ADHD Diagnostic Rating Scale contains 5 subscales. [8] Behaviors are included in the total for each subscale if they are scored as a 2 or a 3. A score of 1 or 2 on at least one question in the performance section indicates impairment.
This is repeated on all ways to cut the original sample on a validation set of p observations and a training set. [12] LpO cross-validation require training and validating the model times, where n is the number of observations in the original sample, and where is the binomial coefficient.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
In statistics, the jackknife (jackknife cross-validation) is a cross-validation technique and, therefore, a form of resampling. It is especially useful for bias and variance estimation. The jackknife pre-dates other common resampling methods such as the bootstrap .
Fleiss' kappa is a generalisation of Scott's pi statistic, [2] a statistical measure of inter-rater reliability. [3] It is also related to Cohen's kappa statistic and Youden's J statistic which may be more appropriate in certain instances. [4]
The best example of the plug-in principle, the bootstrapping method. Bootstrapping is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample, most often with the purpose of deriving robust estimates of standard errors and confidence intervals of a population parameter like a mean, median, proportion, odds ratio ...