University Of Waikato Research Articles

Reshaping Educational Leadership Learning: Adding Politics to Practice

AbstractDuring a distinguished academic career, Professor Martin Thrupp alerted diverse audiences to the politics shaping educational practice in Aotearoa New Zealand and beyond. The focus of this commemorative article is on Martin’s teaching legacy, in particular, the re-design and enduring impact of a compulsory postgraduate leadership paper at Te Kura Toi Tangata, University of Waikato. Key themes include the three neoliberal policy technologies of markets, managerialism and performativity, and the contested notion of context. Qualitative data from this year’s online student cohort illustrate in representative ways the profound influence that adding politics to practice has had on their educational leadership thinking and practice. This attests to the ongoing importance of sociopolitical insight in informed, responsive and successful leadership praxis.

Bat box monitoring: comparing multiple contemporary methods

ABSTRACT Bat boxes can be employed to provide mitigation for the loss of trees with bat roost potential. Eighty bat boxes were deployed by the Hamilton City Council as part of the bat mitigation strategy for the Southern Links Project in southern Hamilton. Several contemporary methods were employed to determine bat box occupancy to ensure effective monitoring. This included visual scoring assessments (e.g. staining, wetness), endoscope surveys, thermal imaging, and eDNA. Emergence survey data were provided by the University of Waikato which provided further insight into the accuracy of the methods. Jointly the methods confirmed the use of 34 Kent style boxes (n = 70; 49%). Indicating a high success rate of box utilisation over a relatively short period (two years). Of the methods, eDNA confirmed the largest percentage of bat box use. Comparatively, thermal imaging provided an alternative and less intrusive means of surveying on-the-day bat presence. However, there were instances where survey conditions (e.g. terrain, vegetation clutter, solar irradiance, rainfall) influenced the reliability of thermal imaging. While visual assessment and thermal imaging cannot be solely relied on, they provide a supportive tool for other monitoring techniques. They can help guide the application of more definitive methods such as eDNA.

A Critical Tiriti Analysis of the Treaty Statement From a University in Aotearoa New Zealand

Universities in Aotearoa New Zealand have been placed under the spotlight for claims of systemic racism. Following the claims made in relation to the University of Waikato in 2020 and University of Otago in 2022, universities have the responsibility to create an inclusive environment where systemic racism is dismantled, to meaningfully partner with Māori to construct a model for decolonisation, and to enhance the academic experience of Māori staff and students by weaving in mātauranga Māori into existing teaching and research practices. The University of Waikato progresses ahead of other universities in its attempt to fulfil Te Tiriti o Waitangi commitment through the introduction of the Treaty Statement. In this paper, the Treaty Statement is used as a case study to identify how universities articulate bicultural commitments. To do so, our team (consisting of Māori and tauiwi scholars) performed a critical Tiriti analysis comprising five phases to investigate the degree of alignment of the statement with Te Tiriti. The desktop analysis showed poor engagement with most Te Tiriti elements. The analysis evaluation of “good” (more than satisfactory) was measured for the consideration of “Māori exercising equitable citizenship”. This article concludes with recommendations of how universities can strengthen their practices to live up to Te Tiriti aspirations.

Driving and forgetting: Why what we remember isn’t what occured

It is a commonly held belief that our memories are good, that we can recall all the things that we saw and that happened to us. On our drive to or from work we can describe with confidence the route we took and what the traffic was like. But driving is very proceduralised, and our memory for what happened most recently is often based on what we usually see, a pastiche of events that may have happened, or not. This experiment set out to extend our understanding of memory reliability by testing people’s recollections about a recent drive. Over two drives participants traversed familiar roads in the University of Waikato driving simulator. Participants took three memory tests about what they saw during the drives. The first one was after a control drive, the second one after a drive in which the participant called out interesting, unusual, and hazardous objects (a detection task), and a third memory test was 48 h after completing the second drive. The results of the memory test showed a typicality effect, with drivers’ memories being almost twice as accurate for very atypical events they encountered. There was also a delayed recognition effect, with recall 48 h after experiencing lower rates of recognition than either immediate condition. The rates of false alarms were over 50% for the delayed recognition of very typical and medium items. Immediate memories of very typical items by the control group were also high (nearly 40%). The act of looking for something while driving (the detection task) had the effect of reducing the false alarm rate for all items. The rate of false alarms was very low for very atypical items under all tasks. The results are interpreted as being of importance for those interested in memory and attention, as a means of determining what drivers attend to, and what they do not.

An interview with Professor Roger Moltzen

Roger Moltzen is an Emeritus Professor at the University of Waikato, New Zealand and an Adjunct Professor of Psychology at Universitas Airlangga, Indonesia. Prior to becoming an academic, he was a school teacher and principal. His previous roles at the University of Waikato include foundation chair of the Department of Human Development and Counselling, and Faculty Dean. He is a former Chair of the New Zealand Council of Deans of Education. His research and publications are in the areas of inclusive education, talent development, and tertiary teaching and learning. In 2005 he received the New Zealand Prime Minister's Supreme Tertiary Teaching Excellence Award. In 2018 he was made a Member of the New Zealand Order of Merit (MNZM) for services to education.

Editorial

Welcome to the first issue of the Australasian Journal of Gifted Education for 2023. Demonstrating the global interest in, and the international impact of, the journal, we have in this issue contributions from scholars based in Jordan, Isreal, and New Zealand, representing various institutions in these countries (i.e., Al al-Bayt University, Tel Aviv University, University of Canterbury, Waikato University, and the New Zealand Ministry of Education). Each of the articles that appear in the issue touch upon different aspects of gifted education, that will no doubt stimulate thought and discussion, and inform practices to more optimally support gifted students in the future.

Reviewer Acknowledgements for International Journal of Social Science Studies, Vol. 11, No. 2

International Journal of Social Science Studies (IJSSS) would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether IJSSS publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue.Reviewers for Volume 11, Number 2Ayşegül Sili Kalem, Necmettin Erbakan Universitesi, TurkeyE.Ozan Aksoz, Anadolu University, TurkeyFroilan Mobo, Philippine Merchant Marine Academy, PhilippinesGülsüm Depeli, Hacettepe University, TurkeyLingkai Kong, Izmir University of Economics, TurkeyMei-Ling Lin, National Open University, TaiwanSana Ali, Allama Iqbal Open University, Islamabad, PakistanShyue Chuan CHONG, Tunku Abdul Rahman University College, MalaysiaYaghoob Foroutan, The University of Waikato, New ZealandYusramizza Md Isa, Universiti Utara Malaysia, Malaysia James YoungEditorial AssistantOn behalf of,The Editorial Board of International Journal of Social Science StudiesRedfame Publishing9450 SW Gemini Dr. #99416Beaverton, OR 97008, USAURL: http://ijsss.redfame.com

Measuring the performance of New Zealand universities

Education plays a crucial role in every country and contributes to economic productivity growth. The performance of public NZ universities is of broader interest, and the teaching and research labour productivity vary across NZ universities. This study is built on the work of Gemmell et al. (2017), who established quality-adjusted productivity indices for the tertiary education sector. In this presentation, I will introduce the productivity estimates across NZ universities over 2008-18 by using a range of teaching and research quality indexes to adjust both output and input amounts, as well as present a summary for alternative weights to adjust these measures. I used partial labour productivity (methodology) for this study, a ratio of a university's outputs to labour input. Valid productivity measures account for changes in the quality-adjusted teaching and research outputs, such as the use of qualification completions weighted by credit or/and income weights and citation weights from bibliometric databases. The preliminary data analysis aims to determine whether adding a quality adjustment impacts the university's labour productivity and how the impacts vary between different quality adjustments. Providing such an empirical base is crucial as a platform for further analysis that would lead to meaningful policy advice to increase university productivity. My results suggest that quality adjustments might significantly affect conclusions about labour productivity comparison. For teaching labour productivity, the University of Waikato, the University of Otago and Lincoln University all show positive annual average growth rates when qualification completions are adjusted for credit or/and income weights. Instead, Victoria University of Wellington and the University of Canterbury experienced negative annual growth in labour productivity. For research, research outputs weighted for citation rates increase labour productivity growth for all NZ universities while decelerating labour productivity growth by introducing PBRF-ranked adjusted research staff, except for Massey University.

Editors' Introduction

Review of Income and WealthVolume 68, Issue S2 p. S261-S263 Editor’s Introduction Editors' Introduction Dean Jolliffe, Corresponding Author Dean Jolliffe [email protected] World Bank, Washington, DC, USACorrespondence to: Dean Jolliffe, World Bank, Development Data Group, 1818 H Street NW, Washington, DC 20433, USA ([email protected]).Search for more papers by this authorGrazia Pittau, Grazia Pittau Sapienza University of Rome, Roma, ItalySearch for more papers by this authorJohn Gibson, John Gibson University of Waikato, Hamilton, New ZealandSearch for more papers by this author Dean Jolliffe, Corresponding Author Dean Jolliffe [email protected] World Bank, Washington, DC, USACorrespondence to: Dean Jolliffe, World Bank, Development Data Group, 1818 H Street NW, Washington, DC 20433, USA ([email protected]).Search for more papers by this authorGrazia Pittau, Grazia Pittau Sapienza University of Rome, Roma, ItalySearch for more papers by this authorJohn Gibson, John Gibson University of Waikato, Hamilton, New ZealandSearch for more papers by this author First published: 07 November 2022 https://doi.org/10.1111/roiw.12623 Note: The coeditors of this special issue would like to acknowledge the contributions of the cochair of the conference organizing committee, Andrew Sharpe, as well as the work of the program committee, comprising John Gibson, Dean Jolliffe, Grazia Pittau, Stephen Jenkins, and Tim Smeeding. The conference was made possible through financial support from the UK government through the Data and Evidence for Tackling Extreme Poverty (DEEP) research program. The coeditors thank the regular editors of the Review, Conchita D'Ambrosio and Prasada Rao, for their support; the referees for their work that enabled this issue; and Sandra Stevens for excellent conference support. Finally, a special thanks to Muhammad Abdullah Ali Malik for his numerous contributions to the conference and for coordinating the review of the papers in this issue. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume68, IssueS2Special Issue: “New Approaches to Defining and Measuring Poverty in a Growing World,” held on November 7–8, 2019 in Washington, D.C.December 2022Pages S261-S263 RelatedInformation

A Journey of Healing, Discovery, and Transformation: Hohou Te Rongo

This is a review of Hohou Te Rongo: A Strategy towards Health & Wellbeing, an exhibition curated by Margaret Aull and Cerys Davidson that was on view at the Gallagher Academy of Performing Arts Gallery, Waikato University, Hamilton, New Zealand, July 2–September 3, 2021. It will be on view at the Waikato Museum from July 2022 through January 2023 and will be called Toi is Rongoā.

"THERE'S STILL SO MUCH MORE TO LEARN": LEARNING TO TEACH ONLINE DURING A GLOBAL PANDEMIC

The COVID-19 pandemic forced rapid closures of educational institutions worldwide in 2020. Online delivery has become a common means of providing continuity of learning, particularly for tertiary institutions. It remains unclear what impact this experience of online teaching under emergency conditions will have on future online teaching. This paper explores this question through a case study of 25 tertiary teaching staff at the University of Waikato in Aotearoa, New Zealand. Applying Bourdieu's categories of doxic and heterodox habitus, the paper argues that, for many staff, the experience of learning to teach online during a pandemic destabilized a prior doxic professional habitus. For some staff, this destabilization led to the construction of a more fluid, creative heterodox habitus open to innovative online teaching in the future. For others, the prepandemic doxic habitus instead spiralled into ongoing self-criticism and an associated collapse in professional confidence. Professional development initiatives seeking to build on the pandemic teaching experience need to be mindful of these contrasting experiences to increase the chances of improving online teaching practice in the longer term.

A Māori Model of Leadership Practice

He Waka Hiringa (HWH) is a Masters of Applied Indigenous Knowledge offered as a programme of two years’ study by Te Wānanga o Aotearoa. The main pre-requisite for enrolment in to this graduate degree is for the student to be a master of their own practice, whatever that practice may be. In other words, they are already leaders in their own field of practice. My task is to help them clarify how they indigenise their practice; introduce them to academic processes to achieve the rangahau (research) around this and encourage them to create their own Models of Practice (MsOP) to guide them as they work with students or clients.
 In six years three cohorts of students have succesfully graduated through my encouragement in the development and approval of about 100 different new MsOP, each unique in its own way. These add to the use by graduates of HWH to models such as Whare Tapatoru ( Wi Te Tau Huata Snr. 1967, personal communication), Whare Tapawhā (Durie, M. 1984), Te Wheke (Pere, R. 1997) and Poutama Pōwhiri (Huata, P. 2011) to name a few well known MsOP.
 In terms of a Leadership MOP I have not seen a better model than that created by Te Wairere Te Pūāwaitanga o te Whakaaro Ngaia (my youngest child and daughter) to fulfil the requirements of her Masters in Management Communications and Te Reo Māori (Māori Language) graduate degree at The University of Waikato. I am going to use her MOP for leadership in competitive Kapa Haka[1] (Māori performing arts) as my model in this delivery with her permission. The title comes from a waiata-ā-ringa (action song) composed by one of her tuākana (older sisters), Te Ingo Karangaroa Ngaia, entitled ‘He Rākau Taumatua!’[2], for their whānau (family) kapa haka, Te Haona Kaha.
 
 [1] I use capital letters when talking about the art form and small letters when talking about a group that does the art form.
 [2] “He rākau taumatua” was first performed as a whakawātea by Te Haona Kaha kapa haka at the Tainui Waka Cultural Trust Regional Kapa Haka competitions in 2016.

PSIII-5 Predicting live weight using linear body measurements in growing beef calves

Abstract The objective of this study is to investigate how linear body measurements relate to and can be used to predict calf body weight using linear and machine learning models. To meet these objectives, a total of 103 Angus cross calves were enrolled in the study from wk 2 - 8. Calves were weighed and linear measurements were collected weekly, such as: poll to nose, width across the eyes (WE), width across the right ear, neck length, wither height, heart girth (HG), midpiece height (MH), midpiece circumference, midpiece width (MW), midpiece depth (MD), hook height, hook width, pin height, top of pin bones width (PW), width across the ends of pin bones, nose to tail body length, the length between the withers and pins, forearm to hoof, cannon bone to hoof. These measurements were taken using a commercial soft tape measure and calipers. To assess relationships between traits and to fit a model to predict BW, data were analyzed using the Weka (The University of Waikato, New Zealand) software using both linear regression (LR) and random forest (RF) machine learning models. The models were trained using a 10-fold cross-validation approach. The automatically derived LR model used 11 traits to fit the data to weekly BW (r2 = 0.97), where the traits with the highest coefficients were HG, PW and WE. The RF model improved further the BW predictions (r2= 0.98). Additionally, sex differences were examined. Although the BW model continued to fit well (r2 0.97), some of the top linear traits differed. The results of this study suggest that linear models built on linear measurements can accurately estimate body weight in beef calves, and that machine learning can further improve the model fit.

PSX-A-21 Late-Breaking: Predicting live weight using linear body measurements in growing dairy calves

Abstract The objective of this study was to explore how linear body measurements are related to body weight and can be used to predict calf body weight using linear and machine learning models. To meet these objectives, a total of 69 Holstein calves from a commercial dairy farm were enrolled in the study from wk 2 – 8 of age. Calves were weighed and linear measurements were collected weekly. Nineteen linear measurements were obtained each week, including: poll to nose, width across the eyes, width across the right ear, neck length (NL), wither height (WH), heart girth (HG), midpiece height (MH), midpiece circumference (MC), midpiece width (MW), midpiece depth (MD), midpiece width across the 13th rib (MW13), hook height, hook width, pin height, top of pin bones width (PW), nose to tail body length, the length between the withers and pins (WPL), forearm to hoof, cannon bone to hoof. These measurements were taken using a commercial soft tape measure and calipers. Using a machine learning approach, models were generated to predict BW from calf linear measurements using Weka software 3.8.5 (University of Waikato, New Zealand) using a 10-fold cross-validation method. Both linear regression (LR) and random forest (RF) models were evaluated. Across all weeks the LR model derived 12 of the 19 traits to fit the BW model (r2 = 0.93). These included: PN, NL, WH, HG, MC, MW, MD, HW, PW, MW13, WPL. The RF model slightly reduced BW predictions (r2= 0.92). The results of this study suggest that linear models built on linear measurements can accurately estimate body weight in dairy calves. These data and models generated are important to further the development of visualized weighing systems for young dairy calves and may be used to accurately predict BW without a scale.

Imua: Reflections on imua, talanoa-vā and leadership in the ongoing strategic journey of a New Zealand university

The appointment of the University of Waikato’s first Assistant Vice-Chancellor Pacific in February 2019 was an important milestone, not only recognising years of work and dedication by numerous Pacific and Māori staff, but triggering a new strategic direction for ‘Pacific at Waikato’. This paper explains that journey, one that is underpinned by Pacific cultural legacies, strengths, values and identity; built on talanoa-vā; informed by research, data analytics, student and community voice; combined with strategic thought and planning; and outworked in the pan-Pacific epithet, ‘imua’. This is a story of resilience, determination, negotiating a pandemic, problem-solving and innovation in an environment that seeks a ‘culture of belonging’ and where Pacific learners are encouraged to be themselves in the pursuit of educational achievement. This paper will be of interest to education providers, stakeholders and policy makers.

Patient-Based Prediction Algorithm of Relapse after Allo-HSCT for Acute Leukemia Using Machine Learning Approach

Background:Although allogeneic hematopoietic stem-cell transplantation (allo-HSCT) is a curative therapy for patients with high risk acute leukemia (AL), a method of reduction in relapse after allo-HSCT remains to be elucidated. Prognostic factors for allo-HSCT for AL, such as the Disease Risk Index (DRI), donor and conditioning type, hematopoietic cell transplantation -specific comorbidity index (HCT-CI), have been determined with conventional statistic methods like uni- and multivariate analysis. However, patient-based prediction of relapse, which would be useful for bedside decision making or for a protocol of diversified allo-HSCT, is difficult because all of these factors are related with each other in the patient.Alternating decision tree (ADTree) is a successful classification method that combines decision trees with the predictive accuracy of boosting into a set of interpretable classification rules. It is one part of the machine learning (ML) approach based on artificial intelligence and can handle multiple factors simultaneously. Recently, some ML approaches like ADTree have been highlighted as promising approaches for the development of new prediction models.Purpose:To construct a patient-based prediction algorithm of AL relapse within 1-year after allo-HSCT to help in deciding on therapy options to prevent relapse.Patients and Method:Our analysis was a retrospective, supervised data mining learning study that included 223 AL patients (acute myelogenous leukemia; AML n=132, acute lymphoblastic leukemia; ALL n=91). They received allo-HSCT at Niigata University Hospital (n=144) and Nagaoka Red Cross Hospital (n=79) from 1990 to 2016. The median age at HSCT was 38 years old (range 18-71). The median follow-up for living patients was 61 months (range 12-223). Myeloablative conditioning was used in 174 (78%) and reduced-intensity in 49 (22%) patients. Donors were related for 97 (43.5%) and unrelated for 126 (56.5%) patients. Grafts were peripheral blood stem cells in 49 (22.0%), bone marrow in 128 (57.4%) and cord blood in 46 (20.6%) patients. According to the DRI for low, intermediate, high and very high-risk, there were 12 (5.4%), 124 (55.6%), 56 (25.1%) and 31 (13.9%) patients, respectively.ADTree was performed using the WEKA software (Ver.3.9.1, Machine Learning Group at the University of Waikato, New Zealand). The algorithm model was trained and tested using 10-fold cross validation on the training data set (Niigata group) and validated again on the validation data set (Nagaoka group). The model was evaluated for prediction accuracy and the area under curve (AUC) of the receiver operating characteristics (ROC) analysis, which discriminates the true prediction rate from the false prediction rate, was determined.Result:The 1-year relapse rate and 1-year overall survival of all patients were 35.5% and 69.7%, respectively. The ADTree model selected 10 clinical variables based on the statistical values during analysis. As the accuracy rate and the AUC of prediction of this algorithm were 76.2% and 0.690 in the training data set and 77.2% and 0.714 in the validation data set, respectively (Table 1), this model has high accuracy and generalization ability for predicting AL relapse.Figure 1 shows the graphical output of the ADTree prediction model. Each score indicates the prediction node weight (NW); NW<0 lower risk and NW>0 higher risk to relapse. In the same disease stage categories (DRI; very high), AML patients had lower risk (NW, -0.133) than ALL patients (NW, 0.568). The final judgment of AL relapse prediction within 1-year was as follows; total NW sum> 0 was predicted relapse and <0 was no predicted relapse, respectively.For example, in the case of a patient (DRI; high, under 40 years old and HCT-CI ≦2) who received CBT (cord blood transplantation) with total body irradiation (TBI), the NW sum was -1.165<0, which indicates no predicted relapse. On the other hand, if the same patient received allo-HSCT not with CBT and without TBI, his NW sum would be 0.116>0, which indicates predicted relapse. (Table 2)Conclusion:This algorithm has high prediction accuracy and generalization ability. Our analysis suggested that the AL relapse within 1-year may be changed by therapy options. The ML approach like ADTree is promising to construct a new patient-based prognostic prediction algorithm and it may be useful in bed-side decision-making for diversified allo-HSCT protocols. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Iris Claus Comments on COVID-19 in Korea: Success Based on Past Failure

Iris Claus Comments on COVID-19 in Korea: Success Based on Past Failure

Flying freshwater jellyfish

Flying freshwater jellyfish

Betel-Nut Chewing Increases Difficulty in Tracheal Intubation.

Betel-nut chewing (BC) causes oral submucous fibrosis (OSF), and this leads to difficult tracheal intubation (DI). Unanticipated DI was reported in chewers with apparently normal preoperative airway evaluations (PAEs). This analysis aims to investigate whether BC is an independent risk on DI besides the common DI risk prediction factors. After the approval of Institutional Review Board and the written informed consent were obtained, 2,682 patients were enrolled in a cohort. PAEs, intubation difficulty scale (IDS), intubation time, and perceived DI were recorded prospectively. All 805 male patients received classical intubation, 307 with BC and 498 without BC were analyzed. Data were analyzed by Student's t-test and chi-square test. Stepwise logistic regression was performed to identify BC effects on IDS adjusting for related factors with WEKA (Waikato Environment for Knowledge Analysis; Machine Learning Group at the University of Waikato, Hamilton, New Zealand). Fewer BC patients were Cormack-Lehane (CL) grade I (38.9% vs. 47.6%) or IDS degree 〞Easy〞 (24.8% vs. 33.5%). Compared with IDS degree 〞Easy〞, patients in the BC group had a significantly higher odds ratio (OR) for 〞Slight + Moderate-Major〞 degree than in the non-BC group (adjusted OR, 1.75; 95% CI, 1.15-2.68). Compared with CL grade I, patients with BC was an independent risk for II (adjusted OR, 1.53; 95% CI, 1.02-2.32) and IV (adjusted OR, 3.25; 95% CI 1.01-10.49). Otherwise, patient's age ≥ 46 and the presence of teeth were also significant risk factors for IDS degree 〞Slight + Moderate-Major〞. BC increased not only the tracheal intubation difficulty in patients with apparent OSF but also in patients with PAEs. BC is an independent risk factor besides the commonly used DI prediction factors. We suggest physicians operating on BC patients to be better prepared for DI.

The Waikato Open Source Frameworks (WEKA and MOA) for Machine Learning Techniques

WEKA and MOA are a free open-source software project specific for data mining and data stream mining, respectively. They are written in Java and developed at the University of Waikato, New Zealand. This research paper presents a comprehensive study of both consists of algorithms, evaluation, visualization, correlation between WEKA and MOA, workflow of implementation, and the classification accuracy.