title: Te matakite - Te kupu whakaari

authors: Temara, Pou

subjects: Matakite

abstract: Ko te whāinga o tēnei tuhinga poto he matapaki i te matakite me te kupu whakaari, arā, he whakatakoto i tētahi tūāpapa kōrero e mārama ai te kaipānui ki te tikanga o ēnei kaupapa e rua e ai ki tā te Māori i matapaki ai. Otirā, he kōrero tā ngā iwi katoa o te ao mō te matakite me te kupu whakaari, kai tēnā iwi tāna kōrero, kai tēnā iwi tāna. Kāore tēnei tuhinga e rere ki te whakamārama i ā ētahi kōrero, engari mau tonu ki te ao Māori. Mēnā ka whakahua i ngā whakamārama a ētahi atu iwi, e whai ana kia mārama ai te tauira ki tā te Māori i kōrero ai.

text: Te matakite - Te kupu whakaari Ko te whāinga o tēnei tuhinga poto he matapaki i te matakite me te kupu whakaari, arā, he whakatakoto i tētahi tūāpapa kōrero e mārama ai te kaipānui ki te tikanga o ēnei kaupapa e rua e ai ki tā te Māori i matapaki ai. Otirā, he kōrero tā ngā iwi katoa o te ao mō te matakite me te kupu whakaari, kai tēnā iwi tāna kōrero, kai tēnā iwi tāna. Kāore tēnei tuhinga e rere ki te whakamārama i ā ētahi kōrero, engari mau tonu ki te ao Māori. Mēnā ka whakahua i ngā whakamārama a ētahi atu iwi, e whai ana kia mārama ai te tauira ki tā te Māori i kōrero ai.

year: 2016

title: Weaving the mat of Māori and Pacific learner success

authors: Tiakiwai, Sarah-Jane

subjects: Supporting learners, Pacific learner success, Māori learner success, higher education

abstract: The mat we are trying to weave at the University of Waikato to address educational outcomes, disparities and inequities in education for Māori and Pacific learners will weave rich data, stories and experience. Our goal is to use data to be better informed about what works for Māori and Pacific learners and what does not. Data is useful in building a more complete picture of what is happening to students, but student voice and previous research that has already captured the voices of Māori and Pacific experts, educators, students, families and communities can tell us more about why we are getting these outcomes. As we harness data to improve our knowledge, we must not forget the stories already in the mat and the stories being woven in the mat, for the stories are the gold.

This report details our work on this data-informed rather than data-driven project. It discusses how improved data and data analytic capability enhanced our understanding of the literature on Māori and Pacific learner success and the experience of Māori and Pacific learners at UoW. Our research and the report, however, also unapologetically privilege case studies through literature and Māori and Pacific student voices. We lay out the various materials – data, literature and voice – that will form our mat with this in mind, weaving them together in order to achieve better outcomes for Māori and Pacific learners at our university. These learners are the why of this mahi. Their success will create a beautiful pattern in the
weave.

text: Weaving the mat of Māori and Pacific learner success The mat we are trying to weave at the University of Waikato to address educational outcomes, disparities and inequities in education for Māori and Pacific learners will weave rich data, stories and experience. Our goal is to use data to be better informed about what works for Māori and Pacific learners and what does not. Data is useful in building a more complete picture of what is happening to students, but student voice and previous research that has already captured the voices of Māori and Pacific experts, educators, students, families and communities can tell us more about why we are getting these outcomes. As we harness data to improve our knowledge, we must not forget the stories already in the mat and the stories being woven in the mat, for the stories are the gold.

This report details our work on this data-informed rather than data-driven project. It discusses how improved data and data analytic capability enhanced our understanding of the literature on Māori and Pacific learner success and the experience of Māori and Pacific learners at UoW. Our research and the report, however, also unapologetically privilege case studies through literature and Māori and Pacific student voices. We lay out the various materials – data, literature and voice – that will form our mat with this in mind, weaving them together in order to achieve better outcomes for Māori and Pacific learners at our university. These learners are the why of this mahi. Their success will create a beautiful pattern in the
weave.

year: 2022-06

title: Barlow Twins for semi-supervised learning in NIR spectroscopy

authors: Wohlers, Mark W.

subjects: Barlow Twins

abstract: Near-infrared (NIR) spectroscopy is a widely used technology in the horticulture industry for non-destructive fruit grading. Partial Least Squares (PLS) regression is the dominant method for producing fruit quality predictions from measured spectra. Alternative deep learning methods have shown promise, but often require large amounts of labelled data to train. This study proposes a semi-supervised method based on Barlow Twins to include unlabelled data in the training process. We adopt the Barlow Twins method by using repeated measurements on the same fruit from different devices as different “views” to encode into the same latent space and combine the encoder network with a regression head for prediction. Our approach demonstrates improved performance over PLS with up to 17% lower RMSE, especially when the labelled data is limited. The Barlow loss function also improves calibration transfer results.

text: Barlow Twins for semi-supervised learning in NIR spectroscopy Near-infrared (NIR) spectroscopy is a widely used technology in the horticulture industry for non-destructive fruit grading. Partial Least Squares (PLS) regression is the dominant method for producing fruit quality predictions from measured spectra. Alternative deep learning methods have shown promise, but often require large amounts of labelled data to train. This study proposes a semi-supervised method based on Barlow Twins to include unlabelled data in the training process. We adopt the Barlow Twins method by using repeated measurements on the same fruit from different devices as different “views” to encode into the same latent space and combine the encoder network with a regression head for prediction. Our approach demonstrates improved performance over PLS with up to 17% lower RMSE, especially when the labelled data is limited. The Barlow loss function also improves calibration transfer results.

year: 2026-02-15