title: Using Output Codes for Two-class Classification Problems

authors: Zeng, Fanhua

subjects: Machine Learning

abstract: Error-correcting output codes (ECOCs) have been widely used in many applications for multi-class classification problems. The problem is that ECOCs cannot be ap- plied directly on two-class datasets. The goal of this thesis is to design and evaluate an approach to solve this problem, and then investigate whether the approach can yield better classification models. To be able to use ECOCs, we turn two-class datasets into multi-class datasets first, by using clustering. With the resulting multi-class datasets in hand, we evalu- ate three different encoding methods for ECOCs: exhaustive coding, random coding and a “pre-defined” code that is found using random search. The exhaustive coding method has the highest error-correcting abilities. However, this method is limited due to the exponential growth of bit columns in the codeword matrix precluding it from being used for problems with large numbers of classes. Random coding can be used to cover situations with large numbers of classes in the data. To improve on completely random matrices, “pre-defined” codeword matrices can be generated by using random search that optimizes row separation yielding better error correction than a purely random matrix. To speed up the process of finding good matrices, GPU parallel programming is investigated in this thesis. From the empirical results, we can say that the new algorithm, which applies multi-class ECOCs on two-class data using clustering, does improve the performance for some base learners, when compared to applying them directly to the original two- class datasets.

text: Using Output Codes for Two-class Classification Problems Error-correcting output codes (ECOCs) have been widely used in many applications for multi-class classification problems. The problem is that ECOCs cannot be ap- plied directly on two-class datasets. The goal of this thesis is to design and evaluate an approach to solve this problem, and then investigate whether the approach can yield better classification models. To be able to use ECOCs, we turn two-class datasets into multi-class datasets first, by using clustering. With the resulting multi-class datasets in hand, we evalu- ate three different encoding methods for ECOCs: exhaustive coding, random coding and a “pre-defined” code that is found using random search. The exhaustive coding method has the highest error-correcting abilities. However, this method is limited due to the exponential growth of bit columns in the codeword matrix precluding it from being used for problems with large numbers of classes. Random coding can be used to cover situations with large numbers of classes in the data. To improve on completely random matrices, “pre-defined” codeword matrices can be generated by using random search that optimizes row separation yielding better error correction than a purely random matrix. To speed up the process of finding good matrices, GPU parallel programming is investigated in this thesis. From the empirical results, we can say that the new algorithm, which applies multi-class ECOCs on two-class data using clustering, does improve the performance for some base learners, when compared to applying them directly to the original two- class datasets.

year: 2011

title: Like a box of chocolates: Introduction to articles in this issue

authors: Earl Rinehart, (Suzanne) Kerry

abstract: I am delighted to present this Waikato Journal of Education (WJE) General Issue 2022. WJEis an open access,  fully-online  free  journal,  supported  by  the  Wilf  Malcolm  Institute  of  Educational  Research (WMIER). WJEpublishes  peer-reviewed  articles  and  is  indexed  in  Scopus  and  China  National Knowledge  Institute  website  (CKNI). Regular readers of WJEwill have  recognised  the  expansion of contributions  over  the  years  from  international  researchers.

text: Like a box of chocolates: Introduction to articles in this issue I am delighted to present this Waikato Journal of Education (WJE) General Issue 2022. WJEis an open access,  fully-online  free  journal,  supported  by  the  Wilf  Malcolm  Institute  of  Educational  Research (WMIER). WJEpublishes  peer-reviewed  articles  and  is  indexed  in  Scopus  and  China  National Knowledge  Institute  website  (CKNI). Regular readers of WJEwill have  recognised  the  expansion of contributions  over  the  years  from  international  researchers.

year: 2022-12-09

title: Exploring the experiences of Māori Wāhine in a STEM project-based secondary classroom: A relational and intersectional perspective

authors: Cheryl, Mitchell

abstract: Using phenomenology, an intersectional lens, and the principles of Kaupapa Māori research, this study investigates how young Māori (Indigenous) wāhine (women) ākonga (students) experience interdisciplinary project-based STEM education. Historical educational policies in Aotearoa New Zealand required Māori to assimilate into a Pākehā (European) school system, contributing to persistent academic underperformance, particularly in science. Māori have been more likely to leave school early, and those who remain often opt out of science subjects, especially Māori wāhine. International literature suggests that transdisciplinary STEM education grounded in social justice and equity can improve engagement and outcomes for underrepresented learners. The study was guided by two questions: (1) how Māori wāhine experienced the STEM programme, and (2) what their challenges were to succeed in STEM.

In 2016, while teaching at a low-decile (socioeconomic disadvantaged), urban, co-educational English-medium high school, I trialled an integrated, interdisciplinary project-based STEM class that incorporated meaningful phenomena and culturally responsive pedagogy. The class’s success led to STEM becoming a core component of a broader school initiative. In 2018, the seven Māori wāhine ākonga enrolled in the Year 11 (age 15–16) STEM class were invited to become “Story Sharers” and reflect on their experiences. As a teacher-researcher (insider), I used reflexive practices and clear role delineation to leverage contextual knowledge while minimising bias. Narrative data were collected over two years (2018–2019) through six semi-structured discussion groups and one individual interview. Quantitative data included tracking attendance records, retention, and academic achievement.

Findings indicate that the Story Sharers valued co-constructed topics and tasks, a supportive whānau (family/community) class format, and authentic project work undertaken over extended learning periods. They consistently identified strengthened relationships, particularly among peers as central to engagement, wellbeing, motivation to learn, and as the most salient indicator of success. Academic performance increased relative to prior Year-10 e-asTTle (Electronic Assessment Tool for Teaching and Learning in mathematics, reading, and writing) benchmarks, alongside improved STEM class attendance, retention in school, and intentions to continue in STEM.

Interpretive thematic analysis using an intersectional lens revealed that racism, stereotyping, and impacts on wellbeing were significant barriers to learning. These discriminatory experiences were pervasive across school, community, and online spaces and were compounded by gendered and intergenerational dynamics. The Story Sharers described complex, fluid, and intersecting identities shaped by lived experiences, including witnessing discrimination against Māori wāhine in their whānau and the legacy of colonial schooling practices such as the suppression of te reo Māori.

In this study, STEM project-based learning was shaped by two interwoven design elements: relational pedagogy and relational thinking. Relational pedagogy prioritised relationships and co-construction within a whānau-formatted classroom. Relational thinking described the interdisciplinary logic of the programme itself, connecting disciplinary knowledge to place, context, and community so that learning is organised around meaningful phenomena rather than siloed subjects. Together, these elements functioned as a counterspace that fostered inclusive engagement, affirmed identities, and supported belonging for Māori wāhine. Grounded in culturally responsive and whānau-based approaches, this model enabled learners to challenge deficit narratives and engage meaningfully in STEM. The outcomes offer assurance that integrated, project-based STEM education, underpinned by these relational frameworks, can improve outcomes for learners whose identities and experiences have been marginalised in traditional schooling. However, policymakers must remain attentive to historical policy barriers and intersectional factors that continue to perpetuate inequities within the education system.

text: Exploring the experiences of Māori Wāhine in a STEM project-based secondary classroom: A relational and intersectional perspective Using phenomenology, an intersectional lens, and the principles of Kaupapa Māori research, this study investigates how young Māori (Indigenous) wāhine (women) ākonga (students) experience interdisciplinary project-based STEM education. Historical educational policies in Aotearoa New Zealand required Māori to assimilate into a Pākehā (European) school system, contributing to persistent academic underperformance, particularly in science. Māori have been more likely to leave school early, and those who remain often opt out of science subjects, especially Māori wāhine. International literature suggests that transdisciplinary STEM education grounded in social justice and equity can improve engagement and outcomes for underrepresented learners. The study was guided by two questions: (1) how Māori wāhine experienced the STEM programme, and (2) what their challenges were to succeed in STEM.

In 2016, while teaching at a low-decile (socioeconomic disadvantaged), urban, co-educational English-medium high school, I trialled an integrated, interdisciplinary project-based STEM class that incorporated meaningful phenomena and culturally responsive pedagogy. The class’s success led to STEM becoming a core component of a broader school initiative. In 2018, the seven Māori wāhine ākonga enrolled in the Year 11 (age 15–16) STEM class were invited to become “Story Sharers” and reflect on their experiences. As a teacher-researcher (insider), I used reflexive practices and clear role delineation to leverage contextual knowledge while minimising bias. Narrative data were collected over two years (2018–2019) through six semi-structured discussion groups and one individual interview. Quantitative data included tracking attendance records, retention, and academic achievement.

Findings indicate that the Story Sharers valued co-constructed topics and tasks, a supportive whānau (family/community) class format, and authentic project work undertaken over extended learning periods. They consistently identified strengthened relationships, particularly among peers as central to engagement, wellbeing, motivation to learn, and as the most salient indicator of success. Academic performance increased relative to prior Year-10 e-asTTle (Electronic Assessment Tool for Teaching and Learning in mathematics, reading, and writing) benchmarks, alongside improved STEM class attendance, retention in school, and intentions to continue in STEM.

Interpretive thematic analysis using an intersectional lens revealed that racism, stereotyping, and impacts on wellbeing were significant barriers to learning. These discriminatory experiences were pervasive across school, community, and online spaces and were compounded by gendered and intergenerational dynamics. The Story Sharers described complex, fluid, and intersecting identities shaped by lived experiences, including witnessing discrimination against Māori wāhine in their whānau and the legacy of colonial schooling practices such as the suppression of te reo Māori.

In this study, STEM project-based learning was shaped by two interwoven design elements: relational pedagogy and relational thinking. Relational pedagogy prioritised relationships and co-construction within a whānau-formatted classroom. Relational thinking described the interdisciplinary logic of the programme itself, connecting disciplinary knowledge to place, context, and community so that learning is organised around meaningful phenomena rather than siloed subjects. Together, these elements functioned as a counterspace that fostered inclusive engagement, affirmed identities, and supported belonging for Māori wāhine. Grounded in culturally responsive and whānau-based approaches, this model enabled learners to challenge deficit narratives and engage meaningfully in STEM. The outcomes offer assurance that integrated, project-based STEM education, underpinned by these relational frameworks, can improve outcomes for learners whose identities and experiences have been marginalised in traditional schooling. However, policymakers must remain attentive to historical policy barriers and intersectional factors that continue to perpetuate inequities within the education system.

year: 2025-10-17