title: Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning.

authors: Mayo, Michael

abstract: Techniques using machine learning for short term blood glucose level prediction in patients with Type 1 Diabetes are investigated. This problem is significant for the development of effective artificial pancreas technology so accurate alerts (e.g. hypoglycemia alarms) and other forecasts can be generated. It is shown that two factors must be considered when selecting the best machine learning technique for blood glucose level regression: (i) the regression model performance metrics being used to select the model, and (ii) the preprocessing techniques required to account for the imbalanced time spent by patients in different portions of the glycemic range. Using standard benchmark data, it is demonstrated that different regression model/preprocessing technique combinations exhibit different accuracies depending on the glycemic subrange under consideration. Therefore technique selection depends on the type of alert required. Specific findings are that a linear Support Vector Regression-based model, trained with normal as well as polynomial features, is best for blood glucose level forecasting in the normal and hyperglycemic ranges while a Multilayer Perceptron trained on oversampled data is ideal for predictions in the hypoglycemic range.

text: Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning. Techniques using machine learning for short term blood glucose level prediction in patients with Type 1 Diabetes are investigated. This problem is significant for the development of effective artificial pancreas technology so accurate alerts (e.g. hypoglycemia alarms) and other forecasts can be generated. It is shown that two factors must be considered when selecting the best machine learning technique for blood glucose level regression: (i) the regression model performance metrics being used to select the model, and (ii) the preprocessing techniques required to account for the imbalanced time spent by patients in different portions of the glycemic range. Using standard benchmark data, it is demonstrated that different regression model/preprocessing technique combinations exhibit different accuracies depending on the glycemic subrange under consideration. Therefore technique selection depends on the type of alert required. Specific findings are that a linear Support Vector Regression-based model, trained with normal as well as polynomial features, is best for blood glucose level forecasting in the normal and hyperglycemic ranges while a Multilayer Perceptron trained on oversampled data is ideal for predictions in the hypoglycemic range.

year: 2019

title: Developing an individual and collective self-efficacy scale for the teaching of writing in high schools

authors: Locke, Terry

abstract: The study reported on here focuses on self-efficacy in relation to high-school teachers’ teaching of writing. 140 New Zealand teachers from four schools completed a teacher-of- writing self-efficacy scale (TWSES) based on a rhetorical model of the writing process and incorporating five hypothesized dimensions. An initial principal components analysis was undertaken on 25 individual self-efficacy items to investigate the dimensionality of the data and the extent to which it reflected the dimensions hypothesized. A two-component solution emerged, termed “pre-writing instructional strategies” (accounting for 52% of total variance) and “compositional strategy demonstration” (7% of variance). Further principal components analyses conducted on groups of items deemed to be thematically coherent, that loaded on each component, confirmed that the data set for each group, treated sepa- rately to any other items, was approximately uni-dimensional. Measurement scales were calibrated to each group of items, and served as the dependent variables for comparisons of teachers’ self-efficacy in different subjects. Statistically significant variations occurred in the resultant scale locations for teachers of English, the humanities, science and mathe- matics. The study findings have implications for the teaching of writing as conceptualized in the secondary school, and indicate a value in viewing disciplinary literacies in rhetorical terms.

text: Developing an individual and collective self-efficacy scale for the teaching of writing in high schools The study reported on here focuses on self-efficacy in relation to high-school teachers’ teaching of writing. 140 New Zealand teachers from four schools completed a teacher-of- writing self-efficacy scale (TWSES) based on a rhetorical model of the writing process and incorporating five hypothesized dimensions. An initial principal components analysis was undertaken on 25 individual self-efficacy items to investigate the dimensionality of the data and the extent to which it reflected the dimensions hypothesized. A two-component solution emerged, termed “pre-writing instructional strategies” (accounting for 52% of total variance) and “compositional strategy demonstration” (7% of variance). Further principal components analyses conducted on groups of items deemed to be thematically coherent, that loaded on each component, confirmed that the data set for each group, treated sepa- rately to any other items, was approximately uni-dimensional. Measurement scales were calibrated to each group of items, and served as the dependent variables for comparisons of teachers’ self-efficacy in different subjects. Statistically significant variations occurred in the resultant scale locations for teachers of English, the humanities, science and mathe- matics. The study findings have implications for the teaching of writing as conceptualized in the secondary school, and indicate a value in viewing disciplinary literacies in rhetorical terms.

year: 2016

title: Te Kōtiritiri, Te Kōtaratara

authors: Maxwell, Te Kahautu

abstract: Haka. Lyrics composed by Te Kahautu Maxwell. Performed by Ōpōtiki Mai Tawhiti (Kapa Haka group) (3mins)

Waiata ā Tira: Aroha
Whakaeke: Pinepine Te Kura, Hau Te Kura
Mōteatea: Te Ao o Te Pārerā
Waiata ā Ringa: E pā to reo, Te Matatini, Herewini Parata
Poi: E ia e ia
Haka: Te Kōtiritiri, Te Kōtaratara
Whakawātea: Ōranga Tōiti, tōnui kōrero

text: Te Kōtiritiri, Te Kōtaratara Haka. Lyrics composed by Te Kahautu Maxwell. Performed by Ōpōtiki Mai Tawhiti (Kapa Haka group) (3mins)

Waiata ā Tira: Aroha
Whakaeke: Pinepine Te Kura, Hau Te Kura
Mōteatea: Te Ao o Te Pārerā
Waiata ā Ringa: E pā to reo, Te Matatini, Herewini Parata
Poi: E ia e ia
Haka: Te Kōtiritiri, Te Kōtaratara
Whakawātea: Ōranga Tōiti, tōnui kōrero

year: 2023-02-22