This study explores the potential to enhance the performance of convolutional neural networks (CNNs) for automated scoring of kinematic graph answers through data augmentation using Deep Convolutional Generative Adversarial Networks (DCGANs). By developing and fine-tuning a DCGAN model to generate high-quality graph images, we explored its…

Background: Training the memorization of declarative knowledge requires the repetitive presentation of various forms of factual questions to learners. Educational games designed for this purpose should offer activities that are both tailored to individual learners and varied to prevent boredom. Whilst the Technology-Enhanced Learning (TEL)…

In educational systems, predictive models face significant challenges during initial deployment and when new students begin to use them or when new exercises are added to the system due to a lack of data for making initial inferences, often called the cold start problem. This paper tests logitdec and logitdecevol, "evolutionary" features…

"Internet plus Music Education" has the characteristics of full-scene learning ecology, data-driven model innovation, and immersion experience upgrade, and the exploration of educational metauniverse and the breakthrough of educational big model have become an important development trend, and the industrial ecology is also undergoing…

Across Europe, university governance models are diverse and changing, but how they approach inclusiveness, collaboration and competences increasingly informs institutional transformation and societal impact. This report explores the evolving landscape of university governance in Europe, drawing on data from the latest edition of EUA's Autonomy…

Research suggests that generative AI (GAI) responds to divergent thinking (DT) prompts with multiple ideas, some of which seem to be original. The present investigation administered 55 DT tasks to three GAI services (Bard, GPT 3.5, and GPT 4.0). Instead of examining individual responses, an Idea Density algorithm was used to assess the output.…

Cognitive diagnosis models (CDMs) are widely used to assess individuals' latent characteristics, offering detailed diagnostic insights for tailored instructional development. Maximum likelihood estimation using the expectation-maximization algorithm (MLE-EM) or its variants, such as the EM algorithm with monotonic constraints and Bayes modal…

This report shows that districts can use data they already routinely collect to predict which students will become chronically absent. Existing work to predict absenteeism in advance either is academic and too challenging for districts to use themselves or uses proprietary systems that are not publicly accessible. Accordingly, in this report, the…

Post-instrument covariates are often included as controls in instrumental variable (IV) analyses to address a violation of the exclusion restriction. However, we show that such analyses are subject to biases unless strong assumptions hold. Using linear constant-effects models, we present asymptotic bias formulas for three estimators (with and…

Researchers tasked with understanding the effects of educational technology innovations face the challenge of providing evidence of causality. Given the complexities of studying learning in authentic contexts interwoven with technological affordances, conducting tightly-controlled randomized experiments is not always feasible nor desirable. Today,…

In parallel process latent growth curve mediation models, the mediation pathways from treatment to the intercept or slope of outcome through the intercept or slope of mediator are often of interest. In this study, we developed causal mediation analysis methods for these mediation pathways. Particularly, we provided causal definitions and…

Intensive longitudinal data has been widely used to examine reciprocal or causal relations between variables. However, these variables may not be temporally aligned. This study examined the consequences and solutions of the problem of temporal misalignment in intensive longitudinal data based on dynamic structural equation models. First the impact…

Generalized structured component analysis (GSCA) is a multivariate method for specifying and examining interrelationships between observed variables and components. Despite its data-analytic flexibility honed over the decade, GSCA always defines every component as a linear function of observed variables, which can be less optimal when observed…

Environmental, social, and governance (ESG) considerations are garnering increasing interest from various stakeholders. Nevertheless, the impact of ESG evaluation policies--whether voluntary or mandatory--are complex, particularly in competitive markets. Consequently, this study develops a game-theoretic model to investigate the propensity of…

Discrete time models, one linear and one non-linear, are investigated, both with a herbivore species that consumes a basal food source species. Results are presented for coexistence of the species and to illustrate chaotic behaviour as parameters are varied in the non-linear model. The results indicate the benefit of fertilization in terms of the…