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Start date: 2022Subject: search.filters.subject.390408 Learning analytics

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    Latent profiles of children’s capabilities : measurement invariance and differential item functioning across gender and ethnicity within a New Zealand preschool-aged cohort : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Education at Massey University, Manawatū, Aotearoa New Zealand
    (Massey University, 2025) Zeng, Hui
    A robust understanding of young children’s learning and development is key to unlocking their immense potential. Within education, young children’s learning and development are increasingly being addressed from strengths-based, holistic, and culturally connected perspectives. Nevertheless, previous research on children’s learning and development has primarily adopted a single-lens approach, highlighting developmental delays or risks associated with domain-specific deficits. The current thesis sought to better understand and account for children’s learning and development in strengths-based, holistic, and culturally connected ways. Two phases of the research were designed to: 1) support a holistic approach to understanding young children’s capabilities of social, emotional, language, and executive function; 2) investigate the comparability of the shared patterns of children’s capabilities across gender and ethnicity, with implications for fairness in assessment and inclusion in early childhood research. Secondary data analyses were conducted in two sequential phases, with data drawn from the Growing Up in New Zealand study 54-month dataset. The first phase of the research used latent profile analysis to identify shared patterns of children’s capabilities. Data were available for six specific areas of children’s capabilities: prosocial behaviours, emotional symptoms, emotional knowledge, expressive language, receptive language, and executive function. Information about children’s capabilities in these areas was gathered using the following measures: prosocial behaviour and emotional symptom subscales in the Strengths and Difficulties Questionnaire, the modified Affect Knowledge Test, the Parent Rating of Oral Language, the shortened version of Peabody Picture Vocabulary Test III, and the Luria Hand Clap task. Latent profile analysis is often used to identify unique patterns of capabilities across a range of domains. The present research identified a 3-group model of young children’s capabilities, labelled as Emerging, Progressing, and Competent profiles. The associations within this 3-group model were characterised by overall differences in capabilities rather than distinct patterns. The Emerging profile made up 9.1% of the sample, representing children with capabilities lower than their peers in other profiles.The Progressing profile made up 45.1% of the sample, representing children with close to average capabilities, between the Emerging and Competent profiles. The Competent profile made up 45.8% of the sample, representing children with the highest capabilities relative to peers. The second phase of the research tested the measurement invariance of the selected measures through multi-group confirmatory factor analysis and examined differential item functioning for gender and ethnicity as covariates to the latent profile factors, via Multiple Indicators Multiple Causes modelling. Findings from multi-group confirmatory factor analyses established partial invariance for the prosocial behaviours and emotional symptoms subscales in the Strengths and Difficulties Questionnaire, full invariance for the modified Affect Knowledge Test and the Parent Rating of Oral Language across gender groups, and partial invariance for these four measures across ethnic groups. Due to the nature of the data available, it was not possible to examine measurement invariance for the shortened version of the Peabody Picture Vocabulary Test III and the Luria Hand Clap task. Nonetheless, differential item functioning via Multiple Indicators Multiple Causes modelling suggested gender was a potential source of uniform differential item functioning for prosocial behaviours, emotional symptoms, emotional knowledge, and expressive language, and nonuniform differential item functioning for receptive language and executive function; however, these effects were determined to be small or negligible. Ethnicity was also identified as a potential source of uniform differential item functioning for emotional symptoms, and nonuniform differential item functioning for emotional knowledge, expressive language, receptive language, and executive function. These effects were determined to be more notable, suggesting the assessments used may not measure the constructs equivalently across ethnic groups, potentially indicating bias in measurement. While the negligible or small uniform and nonuniform differential item functioning effects across gender groups supported the comparability of the 3-group model, the more substantial effects did not support the comparability of the 3-group model across ethnic groups. Findings from the present research offer important insights into understanding children’s learning and development in strengths-based, holistic, and culturally connected ways. First, the investigation sought to use a strengths-based lens by focusing on what children could do relative to peers (e.g., capabilities) rather than framing scores from a deficit or risk-based perspective. Second, the 3-group model centralised the ‘whole learner’ perspective by incorporating six different areas of children’s capabilities into one investigation, highlighting the interconnected nature of these capabilities. Rather than forming distinct patterns, the model reflected overall differences in capabilities across domains, reinforcing the holistic nature of development. Third, the focus on culturally connected assessment sought to understand how appropriate the measures available were for different aspects of children’s identity (i.e., gender and ethnicity). Testing measurement invariance was key to highlighting the importance of fairness in educational assessment and practices. Failure to account for differential item functioning effects may result in inappropriate conclusions about the comparability of latent profiles across observed groups, such as gender or ethnicity. Findings offer significant implications for early childhood educational research and practices, which can inform decisions about service design and policy development. The holistic approach reflected by the 3-group model is a shift away from singular, domain-specific perspectives and research. Given the importance of strengths based, holistic, and culturally connected teaching and learning support reflected in the early childhood curriculum in New Zealand, current research and programmes targeting isolated capabilities from risk-based perspectives may have limited or misaligned applications in early childhood education. Additionally, factors such as gender and ethnicity may introduce biases in the estimation of effects for the latent profiles identified in the present research, thereby potentially limiting their comparability across diverse subpopulations. This underscores the importance of fairness in assessment and highlights the cautions and limitations of existing research. Taken together, the thesis highlights the importance of holistic and unbiased assessments to better understand and report on children’s capabilities, which can in turn influence teaching and learning policy and practices in inclusive and equitable ways.
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    Learning analytics : on effectiveness of dashboarding for enhancing student learning : a thesis with publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Technology, School of Mathematical & Computational Sciences, Massey University, Auckland, New Zealand
    (Massey University, 2022) Ramaswami, Gomathy
    Ongoing advancements in learning analytics have provided institutions with immense opportunities to identify and discern student learning patterns across different course offerings. These patterns can help identify those students who may be at some risk of course failure (or of course completion) as soon as possible, which further allows institutions in timely offering them guidance and support for overcoming their learning difficulties. Learning Analytics Dashboards (LAD) are currently used to deliver graphical representations of data-driven insights timeously to support management teams, instructors, and students. LADs provide a comprehensive overview on current learning environments with much use of visualizations for displaying learning patterns that can capture various aspects of the student learning experience. Hence, LADs are increasingly being used as a pedagogical approach for motivating students and supporting them in meeting their learning goals. This research study has developed a student-facing LAD that shows a snapshot of students' online learning behaviors by implementing descriptive analytics components and also incorporates machine learning in a way that enables both predictive and prescriptive analytics. The study is divided into two parts. First, a generic predictive model has been developed to identify the at-risk students across a wide variety of courses. After generating the predictive model, model explainability using anchors has demonstrated the reasoning behind predictive models to enable transparency of the predictive models and increase students’ trust as they interact with the LAD. Machine learning models used in this study have implemented prescriptive components by prioritizing which changes in learning behaviors and which learning strategies adopted by a student will most likely translate into favorable results. Second, a LAD is developed. The dashboard provides visualizations that incorporates graphical and statistical information of online behavioral student patterns as they engage with the coursework. An online student survey that gauged LAD effectiveness for its usefulness and the motivational impact of its prescriptive output to better engage students with the coursework has shown promising results. The LAD design, as far as we know, is the first in the learning analytics domain that has combined all three analytics, namely descriptive, predictive, and prescriptive. This thesis has investigated an active area of research and has paved the way for more meaningful LAD design and implementation, thereby contributing to both theory and practice.