This dissertation examined classification outcome differences among four popular individual supervised machine learning (ISML) models (logistic regression, decision tree, support vector machine, and multilayer perceptron) when predicting minor class membership within imbalanced datasets. The study context and the theoretical population sampled…

Diagnostic classification models (DCMs) are psychometric models that yield probabilistic classifications of respondents according to a set of discrete latent variables. The current study examines the recently introduced one-parameter log-linear cognitive diagnosis model (1-PLCDM), which has increased interpretability compared with general DCMs due…

Learning outcomes and assessment frameworks guide educators in curricular decisionmaking, impact assessment, gap identification, and equity evaluation, aligning with anticipated learning objectives. Common frameworks include Bloom's taxonomy, Kirkpatrick's model, Fink's taxonomy, and Moore's Outcomes model. The authors identified a lack of focus…

Cloze items are a foundational approach to assessing readability. However, they require human data collection, thus making them impractical in automated metrics. The present study revisits the idea of assessing readability with cloze items and compares human cloze scores and readability judgments with predictions made by T5, a popular deep…

This article explores the models and frameworks developed on "research impact'. We aim to provide a comprehensive overview of related literature through scoping study method. The present research investigates the nature, objectives, approaches, and other main attributes of the research impact models. It examines to analyze and classify models…

Purpose: The purpose of this paper is to explore the phenomenon of organizational unlearning with a focus on challenging path dependence and its implications on the organizational change field. By generating a taxonomy of unlearning definitions and examining the dimensions, actors and processes involved, the authors aim to offer a holistic…

This tool is intended to support state and local education agencies in developing a statistical model for estimating student postsecondary success at the school or district level. The tool guides education agency researchers, analysts, and decisionmakers through options to consider when developing their own model. The resulting model generates an…

Student learning outcomes are a critical indicator of the quality of instruction and the competence of faculty members and students in higher education settings. This research explored the students' perceptions, the relationship of student individual characteristics and how educational environment at university influenced on the assessment of…

A number of machine learning methods can be employed in the prediction of suicide attempts. However, many models do not predict new cases well in cases with unbalanced data. The present study improved prediction of suicide attempts via the use of a generative adversarial network.

Many early childhood programs are not prepared to meet the needs of children who have significant social, emotional, and behavioral challenges. "Program-Wide Supports for Pyramid Model Implementation" (PWS-PMI) provides a systematic approach to supporting early childhood programs using Pyramid Model practices and enhancing children's…

State agencies and Extension professionals often employ IMPLAN software and associated data to conduct economic contribution analyses of the forest sector. Economic contribution reports often vary with regard to modeling, results presentation, and interpretation of estimates. We present practical guidelines for report users on how to better…

Analyses that reveal how treatment effects vary allow researchers, practitioners, and policymakers to better understand the efficacy of educational interventions. In practice, however, standard statistical methods for addressing Heterogeneous Treatment Effects (HTE) fail to address the HTE that may exist within outcome measures. In this study, we…

Research impact features heavily in debates about 'the measured university' and is now formally assessed by governments in the UK and Australia. Yet clear guidance on how impact can be measured in non-monetary ways is often lacking because of confused thinking and the context-specific nature of outcomes. To help resolve this, we first propose a…

Independent schools have long been searching for better ways to prove their value, and in a data-centric world, there is enormous pressure to measure, measure, measure, and abide by partially obsolete college entrance requirements. Yet there is a strong consensus that the majority of currently used large-scale assessments are not comprehensive…

In this paper, we set out the first step towards the measurement of learning gain in higher education by putting forward a conceptual framework for understanding learning gain that is relevant across disciplines. We then introduce the operationalisation of this conceptual framework into a new set of measurement tools. With the use of data from a…