title: Secondary school student' experiences of restorative practices

authors: Yule, Abbey

subjects: Restorative

abstract: Restorative practices in education have gained prominence as an alternative to traditional punitive disciplinary measures. This alternative aims to foster accountability, healing and relationships within the school community. Despite the increasing implementation, there is little attention or research given to voices and experiences of those most affected – the students themselves.

This study investigates the lived experiences of students and how students in Aotearoa New Zealand perceive and experience restorative practices. Using narrative inquiry and qualitative research methods, the study generates accounts of nine student’s experiences of restorative practices. These accounts include the
meanings they made of interactions with teachers, their peers and other aspects of the restorative processes that informed their experiences.

Drawing on individual semi-structured interviews with students, this research employed thematic analysis to identify five key themes. Particular discourses were identified that inform power relations in restorative practices, personal agency, communication and supportive relationships. The findings highlight participants’ preferences for trust-based connections with staff and peers, and contexts that foster open and honest communication that address fear and exclude punitive approaches and consequences. The complexity of discursive power relations and how they are exercised in relationships between students and teaching staff show that understanding discursive power and how to address its effects is important for schools to consider when engaging in restorative practices.

By focusing on the perspectives of students, this study contributes to an evolving social justice education discourse. Social justice in education fosters student agency, relational approaches to school disciplinary policies, and promotes educational, justice-based school communities. A social justice approach offers valuable insights for educators and policymakers seeking to enhance the effectiveness of restorative approaches within school communities. Working towards a more meaningful, equitable and impactful process for conflict resolution.

text: Secondary school student' experiences of restorative practices Restorative practices in education have gained prominence as an alternative to traditional punitive disciplinary measures. This alternative aims to foster accountability, healing and relationships within the school community. Despite the increasing implementation, there is little attention or research given to voices and experiences of those most affected – the students themselves.

This study investigates the lived experiences of students and how students in Aotearoa New Zealand perceive and experience restorative practices. Using narrative inquiry and qualitative research methods, the study generates accounts of nine student’s experiences of restorative practices. These accounts include the
meanings they made of interactions with teachers, their peers and other aspects of the restorative processes that informed their experiences.

Drawing on individual semi-structured interviews with students, this research employed thematic analysis to identify five key themes. Particular discourses were identified that inform power relations in restorative practices, personal agency, communication and supportive relationships. The findings highlight participants’ preferences for trust-based connections with staff and peers, and contexts that foster open and honest communication that address fear and exclude punitive approaches and consequences. The complexity of discursive power relations and how they are exercised in relationships between students and teaching staff show that understanding discursive power and how to address its effects is important for schools to consider when engaging in restorative practices.

By focusing on the perspectives of students, this study contributes to an evolving social justice education discourse. Social justice in education fosters student agency, relational approaches to school disciplinary policies, and promotes educational, justice-based school communities. A social justice approach offers valuable insights for educators and policymakers seeking to enhance the effectiveness of restorative approaches within school communities. Working towards a more meaningful, equitable and impactful process for conflict resolution.

year: 2025

title: Efficiently correcting machine learning: considering the role of example ordering in human-in-the-loop training of image classification models

authors: Holmes, Geoffrey

subjects: computer science

abstract: Arguably the most popular application task in artificial intelligence is image classification using transfer learning. Transfer learning enables models pre-trained on general classes of images, available in large numbers, to be refined for a specific application. This enables domain experts with their own—generally, substantially smaller—collections of images to build deep learning models. The good performance of such models poses the question of whether it is possible to further reduce the effort required to label training data by adopting a human-in-the-loop interface that presents the expert with the current predictions of the model on a new batch of data and only requires correction of these predictions—rather than de novo labelling by the expert—before retraining the model on the extended data. This paper looks at how to order the data in this iterative training scheme to achieve the highest model performance while minimising the effort needed to correct misclassified examples. Experiments are conducted involving five methods of ordering, using four image classification datasets, and three popular pre-trained models. Two of the methods we consider order the examples a priori whereas the other three employ an active learning approach where the ordering is updated iteratively after each new batch of data and retraining of the model. The main finding is that it is important to consider accuracy of the model in relation to the number of corrections that are required: using accuracy in relation to the number of labelled training examples—as is common practice in the literature—can be misleading. More specifically, active methods require more cumulative corrections than a priori methods for a given level of accuracy. Within their groups, active and a priori methods perform similarly. Preliminary evidence is provided that suggests that for “simple” problems, i.e., those involving fewer examples and classes, no method improves upon random selection of examples. For more complex problems, an a priori strategy based on a greedy sample selection method known as “kernel herding” performs best.

text: Efficiently correcting machine learning: considering the role of example ordering in human-in-the-loop training of image classification models Arguably the most popular application task in artificial intelligence is image classification using transfer learning. Transfer learning enables models pre-trained on general classes of images, available in large numbers, to be refined for a specific application. This enables domain experts with their own—generally, substantially smaller—collections of images to build deep learning models. The good performance of such models poses the question of whether it is possible to further reduce the effort required to label training data by adopting a human-in-the-loop interface that presents the expert with the current predictions of the model on a new batch of data and only requires correction of these predictions—rather than de novo labelling by the expert—before retraining the model on the extended data. This paper looks at how to order the data in this iterative training scheme to achieve the highest model performance while minimising the effort needed to correct misclassified examples. Experiments are conducted involving five methods of ordering, using four image classification datasets, and three popular pre-trained models. Two of the methods we consider order the examples a priori whereas the other three employ an active learning approach where the ordering is updated iteratively after each new batch of data and retraining of the model. The main finding is that it is important to consider accuracy of the model in relation to the number of corrections that are required: using accuracy in relation to the number of labelled training examples—as is common practice in the literature—can be misleading. More specifically, active methods require more cumulative corrections than a priori methods for a given level of accuracy. Within their groups, active and a priori methods perform similarly. Preliminary evidence is provided that suggests that for “simple” problems, i.e., those involving fewer examples and classes, no method improves upon random selection of examples. For more complex problems, an a priori strategy based on a greedy sample selection method known as “kernel herding” performs best.

year: 2022

title: Sociocultural realities: exploring new horizons by Angus Macfarlane, Sonja Macfarlane and Melinda Webber [Book Review]

authors: McNae, Rachel Elizabeth

abstract: This article reviews the book "Sociocultural realities: exploring new horizons", by Angus Macfarlane, Sonja Macfarlane and Melinda Webber

text: Sociocultural realities: exploring new horizons by Angus Macfarlane, Sonja Macfarlane and Melinda Webber [Book Review] This article reviews the book "Sociocultural realities: exploring new horizons", by Angus Macfarlane, Sonja Macfarlane and Melinda Webber

year: 2017