title: Student perceptions of the impact of student-generated feedback in formative essay writing in the English classroom

authors: Hill, Lee Roberta

subjects: feedback

abstract: Student-generated feedback is an Assessment for Learning practice which, when used well, can lead to strong student engagement and self-regulation. In the New Zealand context high value is placed on the principles of Assessment for Learning, yet teachers struggle to implement strategies successfully. In order to evaluate the impact of the use of student-generated feedback practices in essay writing in the English classroom student perceptions have been sought. This practitioner research has used a mixed methods approach to gather students’ perceptions by means of a Likert scale questionnaire, one-on-one semi-structured interviews, a teacher diary and feedback artefacts. The results show that students perceived student-generated feedback practices to be beneficial for themselves and their learning. There is evidence that these practices led to significant student engagement, as well as the development of student self-regulation. When seen from a social constructivist perspective, student-generated feedback practices created new identities for students and the teacher in the classroom, impacting on power dynamics and classroom culture. While the teacher continued to be a valuable guide and contributor to learning, students saw themselves as agents of their own learning, in an apprentice-like relationship with their teacher. The findings of this study are relevant to teachers who want to improve their practice by using student-generated feedback as part of a broader picture of partnership with students. This is becoming increasingly important for all teachers as our education system moves away from a traditional top-down, teacher-led style of teaching and learning towards a student–teacher collaborative approach.

text: Student perceptions of the impact of student-generated feedback in formative essay writing in the English classroom Student-generated feedback is an Assessment for Learning practice which, when used well, can lead to strong student engagement and self-regulation. In the New Zealand context high value is placed on the principles of Assessment for Learning, yet teachers struggle to implement strategies successfully. In order to evaluate the impact of the use of student-generated feedback practices in essay writing in the English classroom student perceptions have been sought. This practitioner research has used a mixed methods approach to gather students’ perceptions by means of a Likert scale questionnaire, one-on-one semi-structured interviews, a teacher diary and feedback artefacts. The results show that students perceived student-generated feedback practices to be beneficial for themselves and their learning. There is evidence that these practices led to significant student engagement, as well as the development of student self-regulation. When seen from a social constructivist perspective, student-generated feedback practices created new identities for students and the teacher in the classroom, impacting on power dynamics and classroom culture. While the teacher continued to be a valuable guide and contributor to learning, students saw themselves as agents of their own learning, in an apprentice-like relationship with their teacher. The findings of this study are relevant to teachers who want to improve their practice by using student-generated feedback as part of a broader picture of partnership with students. This is becoming increasingly important for all teachers as our education system moves away from a traditional top-down, teacher-led style of teaching and learning towards a student–teacher collaborative approach.

year: 2016

title: High-throughput machine learning algorithms

authors: Mitchell, Rory

subjects: thesis with publication

abstract: The field of machine learning has become strongly compute driven, such that emerging research and applications require larger amounts of specialised hardware or smarter algorithms to advance beyond the state-of-the-art. This thesis develops specialised techniques and algorithms for a subset of computationally difficult machine learning problems. The applications under investigation are quantile approximation in the limited-memory data streaming setting, interpretability of decision tree ensembles, efficient sampling methods in the space of permutations, and the generation of large numbers of pseudorandom permutations. These specific applications are investigated as they represent significant bottlenecks in real-world machine learning pipelines, where improvements to throughput have significant impact on the outcomes of machine learning projects in both industry and research. To address these bottlenecks, we discuss both theoretical improvements, such as improved convergence rates, and hardware/software related improvements, such as optimised algorithm design for high throughput hardware accelerators. 

Some contributions include: the evaluation of bin-packing methods for efficiently scheduling small batches of dependent computations to GPU hardware execution units, numerically stable reduction operators for higher-order statistical moments, and memory bandwidth optimisation for GPU shuffling. Additionally, we apply theory of the symmetric group of permutations in reproducing kernel Hilbert spaces, resulting in improved analysis of Monte Carlo methods for Shapley value estimation and new, computationally more efficient algorithms based on kernel herding and Bayesian quadrature. We also utilise reproducing kernels over permutations to develop a novel statistical test for the hypothesis that a sample of permutations is drawn from a uniform distribution.

The techniques discussed lie at the intersection of machine learning, high-performance computing, and applied mathematics. Much of the above work resulted in open source software used in real applications, including the GPUTreeShap library [38], shuffling primitives for the Thrust parallel computing library [2], extensions to the Shap package [31], and extensions to the XGBoost library [6].

text: High-throughput machine learning algorithms The field of machine learning has become strongly compute driven, such that emerging research and applications require larger amounts of specialised hardware or smarter algorithms to advance beyond the state-of-the-art. This thesis develops specialised techniques and algorithms for a subset of computationally difficult machine learning problems. The applications under investigation are quantile approximation in the limited-memory data streaming setting, interpretability of decision tree ensembles, efficient sampling methods in the space of permutations, and the generation of large numbers of pseudorandom permutations. These specific applications are investigated as they represent significant bottlenecks in real-world machine learning pipelines, where improvements to throughput have significant impact on the outcomes of machine learning projects in both industry and research. To address these bottlenecks, we discuss both theoretical improvements, such as improved convergence rates, and hardware/software related improvements, such as optimised algorithm design for high throughput hardware accelerators. 

Some contributions include: the evaluation of bin-packing methods for efficiently scheduling small batches of dependent computations to GPU hardware execution units, numerically stable reduction operators for higher-order statistical moments, and memory bandwidth optimisation for GPU shuffling. Additionally, we apply theory of the symmetric group of permutations in reproducing kernel Hilbert spaces, resulting in improved analysis of Monte Carlo methods for Shapley value estimation and new, computationally more efficient algorithms based on kernel herding and Bayesian quadrature. We also utilise reproducing kernels over permutations to develop a novel statistical test for the hypothesis that a sample of permutations is drawn from a uniform distribution.

The techniques discussed lie at the intersection of machine learning, high-performance computing, and applied mathematics. Much of the above work resulted in open source software used in real applications, including the GPUTreeShap library [38], shuffling primitives for the Thrust parallel computing library [2], extensions to the Shap package [31], and extensions to the XGBoost library [6].

year: 2021

title: Delivering the Maori-language newspapers on the Internet

authors: Apperley, Mark

subjects: computer science

abstract: Although any collection of historical newspapers provides a particularly rich and valuable record of events and social and political commentary, the content tends to be difficult to access and extremely time-consuming to browse or search. The advent of digital libraries has meant that for electronically stored text, full-text searching is now a tool readily available for researchers, or indeed anyone wishing to have asscess to specific information in text. Text in this form can be readily distributed via CD-ROM or the Internet, with a significant impact on accessibility over traditional microfiche or hard-copy distribution. For the majority of text being generated de nouveau, availability in electronic form is standard, and hence the increasing use of full-text search facilities. However, for legacy text available only in printed form, the provision of these electronic search tools is dependent on the prior electronic capture of digital facsimile images of the printed text, followed by the conversion of these images to electronic text through the process of optical character recognition (OCR). This article describes a project undertaken at the University of Waikato over the period 1999 to 2001 to produce a full-text searchable version of the Niupepa or Maori- language newspaper collection for delivery over the Internet.

text: Delivering the Maori-language newspapers on the Internet Although any collection of historical newspapers provides a particularly rich and valuable record of events and social and political commentary, the content tends to be difficult to access and extremely time-consuming to browse or search. The advent of digital libraries has meant that for electronically stored text, full-text searching is now a tool readily available for researchers, or indeed anyone wishing to have asscess to specific information in text. Text in this form can be readily distributed via CD-ROM or the Internet, with a significant impact on accessibility over traditional microfiche or hard-copy distribution. For the majority of text being generated de nouveau, availability in electronic form is standard, and hence the increasing use of full-text search facilities. However, for legacy text available only in printed form, the provision of these electronic search tools is dependent on the prior electronic capture of digital facsimile images of the printed text, followed by the conversion of these images to electronic text through the process of optical character recognition (OCR). This article describes a project undertaken at the University of Waikato over the period 1999 to 2001 to produce a full-text searchable version of the Niupepa or Maori- language newspaper collection for delivery over the Internet.

year: 2002