title: A teacher’s voice: Embracing change to make a difference

authors: Blakeney-Williams, Marilyn

subjects: education

abstract: The challenge to understand the impact and demands of new forms of literacy for teachers with considerable expertise is intensified when making sense of multiple forms of texts. This paper examines how an experienced teacher made changes to her literacy pedagogy when faced with a class problem for which she felt unprepared. The story is told predominantly through the voice of the teacher using her reflective journal. An example of Web 2.0 technology, specifically the construction of a wiki, is planned and implemented to reflect the teacher’s changing views of literacy. The key focus is how this teacher will transition her practice to meet the new demands of literacy teaching, what critical moves are required of her in order to do this and how does she shift from the traditional role of facilitator to designer of online learning. Through such analysis it is proposed that it is possible for an experienced teacher to theorise, implement and adopt a stance that encompasses a broader view of literacy and literacy instruction.

text: A teacher’s voice: Embracing change to make a difference The challenge to understand the impact and demands of new forms of literacy for teachers with considerable expertise is intensified when making sense of multiple forms of texts. This paper examines how an experienced teacher made changes to her literacy pedagogy when faced with a class problem for which she felt unprepared. The story is told predominantly through the voice of the teacher using her reflective journal. An example of Web 2.0 technology, specifically the construction of a wiki, is planned and implemented to reflect the teacher’s changing views of literacy. The key focus is how this teacher will transition her practice to meet the new demands of literacy teaching, what critical moves are required of her in order to do this and how does she shift from the traditional role of facilitator to designer of online learning. Through such analysis it is proposed that it is possible for an experienced teacher to theorise, implement and adopt a stance that encompasses a broader view of literacy and literacy instruction.

year: 2011

title: An entropy gain measure of numeric prediction performance

authors: Trigg, Leonard E.

subjects: computer science

abstract: Categorical classifier performance is typically evaluated with respect to error rate, expressed as a percentage of test instances that were not correctly classified. When a classifier produces multiple classifications for a test instance, the prediction is counted as incorrect (even if the correct class was one of the predictions). Although commonly used in the literature, error rate is a coarse measure of classifier performance, as it is based only on a single prediction offered for a test instance. Since many classifiers can produce a class distribution as a prediction, we should use this to provide a better measure of how much information the classifier is extracting from the domain. 
Numeric classifiers are a relatively new development in machine learning, and as such there is no single performance measure that has become standard. Typically these machine learning schemes predict a single real number for each test instance, and the error between the predicted and actual value is used to calculate a myriad of performance measures such as correlation coefficient, root mean squared error, mean absolute error, relative absolute error, and root relative squared error. With so many performance measures it is difficult to establish an overall performance evaluation.
The next section describes a performance measure for machine learning schemes that attempts to overcome the problems with current measures. In addition, the same evaluation measure is used for categorical and numeric classifier.

text: An entropy gain measure of numeric prediction performance Categorical classifier performance is typically evaluated with respect to error rate, expressed as a percentage of test instances that were not correctly classified. When a classifier produces multiple classifications for a test instance, the prediction is counted as incorrect (even if the correct class was one of the predictions). Although commonly used in the literature, error rate is a coarse measure of classifier performance, as it is based only on a single prediction offered for a test instance. Since many classifiers can produce a class distribution as a prediction, we should use this to provide a better measure of how much information the classifier is extracting from the domain. 
Numeric classifiers are a relatively new development in machine learning, and as such there is no single performance measure that has become standard. Typically these machine learning schemes predict a single real number for each test instance, and the error between the predicted and actual value is used to calculate a myriad of performance measures such as correlation coefficient, root mean squared error, mean absolute error, relative absolute error, and root relative squared error. With so many performance measures it is difficult to establish an overall performance evaluation.
The next section describes a performance measure for machine learning schemes that attempts to overcome the problems with current measures. In addition, the same evaluation measure is used for categorical and numeric classifier.

year: 1998-05

title: Integrating kaupapa Māori in a pākeha philosophy programme

authors: Weijers, Dan M.

abstract: Presented at the University of Waikato, Hamilton, New Zealand on 15 September 2022

text: Integrating kaupapa Māori in a pākeha philosophy programme Presented at the University of Waikato, Hamilton, New Zealand on 15 September 2022

year: 2022-09-15