title: Using apps for teaching and learning mathematics: A socio- technological assemblage

authors: Calder, Nigel Stuart

abstract: This paper reports on an aspect of a research project that examined the use of apps in primary school mathematics programmes. It reports teacher and student perspectives on how they used a range of digital tools within the apps to solve problems. We consider the interplay between the affordances of the mobile technologies, including multi- representation and haptic, with other social and pedagogical aspects, and how the assemblage of social and technological entities influences the ways that teachers might integrate apps into their mathematics programmes.

text: Using apps for teaching and learning mathematics: A socio- technological assemblage This paper reports on an aspect of a research project that examined the use of apps in primary school mathematics programmes. It reports teacher and student perspectives on how they used a range of digital tools within the apps to solve problems. We consider the interplay between the affordances of the mobile technologies, including multi- representation and haptic, with other social and pedagogical aspects, and how the assemblage of social and technological entities influences the ways that teachers might integrate apps into their mathematics programmes.

year: 2018

title: Web crippling design of cold-formed stainless steel channels under interior-two-flange loading condition using deep belief network

authors: Fang, Zhiyuan (Arthur)

subjects: stainless steel

abstract: This research presents a deep-learning framework, namely a deep belief network (DBN), for analyzing the interior-two-flange web crippling performance of cold-formed stainless steel channels with centered and offset web holes. An elastoplastic finite element (FE) model, validated using 101 experimental results which were previously reported in the literature, generates a total of 43,200 data points for training the DBN. When compared to a total of 54 experimental results published in the literature, the DBN predictions were shown to be approximately 10% more conservative. Using the same large training data, the developed DBN model outperformed the Backpropagation Neural Network (a typical shallow artificial neural network) and the PaddlePaddle-based linear regression model. A parametric analysis was then performed using the DBN predictions to explore the effect of section size, web holes and bearing length. Design equations for (reduced) web crippling strength are proposed for the cold-formed stainless steel perforated channels, and the feasibility of the proposed equations was assessed by the conducted reliability analysis.

text: Web crippling design of cold-formed stainless steel channels under interior-two-flange loading condition using deep belief network This research presents a deep-learning framework, namely a deep belief network (DBN), for analyzing the interior-two-flange web crippling performance of cold-formed stainless steel channels with centered and offset web holes. An elastoplastic finite element (FE) model, validated using 101 experimental results which were previously reported in the literature, generates a total of 43,200 data points for training the DBN. When compared to a total of 54 experimental results published in the literature, the DBN predictions were shown to be approximately 10% more conservative. Using the same large training data, the developed DBN model outperformed the Backpropagation Neural Network (a typical shallow artificial neural network) and the PaddlePaddle-based linear regression model. A parametric analysis was then performed using the DBN predictions to explore the effect of section size, web holes and bearing length. Design equations for (reduced) web crippling strength are proposed for the cold-formed stainless steel perforated channels, and the feasibility of the proposed equations was assessed by the conducted reliability analysis.

year: 2023-01

title: Haere mai rā

authors: Maxwell, Te Kahautu

abstract: Whakaeke (entrance). Lyrics composed by Te Kahautu Maxwell, Musical score composed by Te Whānau Mitai. Performed by Ōpōtiki Mai Tawhiti (Kapa Haka group) (3mins)

Waiata Tira: Aroha
Whakaeke: Haere mai rā
Mōteatea: Te Pātere a Te Turuki
Poi: Iarararararī
Waiata ā ringa: Taku Mana Māori Motuhake
Haka: Waiwharangi
Whakawātea: Āio

text: Haere mai rā Whakaeke (entrance). Lyrics composed by Te Kahautu Maxwell, Musical score composed by Te Whānau Mitai. Performed by Ōpōtiki Mai Tawhiti (Kapa Haka group) (3mins)

Waiata Tira: Aroha
Whakaeke: Haere mai rā
Mōteatea: Te Pātere a Te Turuki
Poi: Iarararararī
Waiata ā ringa: Taku Mana Māori Motuhake
Haka: Waiwharangi
Whakawātea: Āio

year: 2020