This study explores how youth engage with literacy practices in the age of AI through the use of counter-cartographies within the Nayah-Irú curriculum. By critically examining digital platforms and the underlying algorithms, students embarked on a journey to understand and challenge the pervasive influence of artificial intelligence in their…

In confirmatory factor analysis (CFA), model parameters are usually estimated by iteratively minimizing the Maximum Likelihood (ML) fit function. In optimal circumstances, the ML estimator yields the desirable statistical properties of asymptotic unbiasedness, efficiency, normality, and consistency. In practice, however, real-life data tend to be…

Grouped and right-censored (GRC) counts have been used in a wide range of attitudinal and behavioural surveys yet they cannot be readily analyzed or assessed by conventional statistical models. This study develops a unified regression framework for the design and optimality of GRC counts in surveys. To process infinitely many grouping schemes for…

This paper presents an improved Distributed Genetic Algorithm (DGA) that surpasses the classic genetic algorithm (GA) in terms of both efficiency and effectiveness when it comes to optimizing faculty scheduling systems. The proposed Differential Evolution Genetic Algorithm (DGA) combines DE with chaotic mapping and asynchronous communication to…

Predictive models play a pivotal role in education by aiding learning, teaching, and assessment processes. However, they have the potential to perpetuate educational inequalities through algorithmic biases. This paper investigates how behavioral differences across demographic groups of different sizes propagate through the student success modeling…

Collaborative problem solving (CPS) enables student groups to complete learning tasks, construct knowledge, and solve problems. Previous research has argued the importance of examining the complexity of CPS, including its multimodality, dynamics, and synergy from the complex adaptive systems perspective. However, there is limited empirical…

Recent approaches to the detection of cheaters in tests employ detectors from the field of machine learning. Detectors based on supervised learning algorithms achieve high accuracy but require labeled data sets with identified cheaters for training. Labeled data sets are usually not available at an early stage of the assessment period. In this…

Online peer feedback has proven to be practically useful for instructors and to be useful for learning, especially for the feedback provider. Because students can vary widely in skill level, some research has explored matching reviewer and author by performance level. However, past research on the impacts of reviewer matching has found little…

This study proposed Gibbs sampling algorithms for variable selection in a latent regression model under a unidimensional two-parameter logistic item response theory model. Three types of shrinkage priors were employed to obtain shrinkage estimates: double-exponential (i.e., Laplace), horseshoe, and horseshoe+ priors. These shrinkage priors were…

This paper summarises exploratory analysis undertaken to evaluate the effectiveness of using machine learning approaches to calculate projected completion rates for vocational education and training (VET) programs, and compares this with the current approach used at the National Centre for Vocational Education Research (NCVER) -- Markov chains…

Purpose: This study aims to explore the perception of algorithm accuracy among data professionals in higher education. Design/methodology/approach: Social justice theory guided the qualitative descriptive study and emphasized four principles: access, participation, equity and human rights. Data collection included eight online open-ended…

Background: The study focuses on the need to optimise assessment scales for support needs in individuals with intellectual and developmental disabilities. Current scales are often lengthy and redundant, leading to exhaustion and response burden. The goal is to use machine learning techniques, specifically item-reduction methods and selection…

Our work investigated how six primary school students used a non-traditional method for adding and subtracting: the ABN method, a Spanish acronym for Open (method) Based on Numbers. Commognitive theory [Sfard, A. 2008. "Thinking as Communicating: Human Development, the Growth of Discourses, and Mathematizing." New York: Cambridge…

Explainable Artificial Intelligence (XAI) refers to systems that make AI models more transparent, helping users understand how outputs are generated. XAI algorithms are considered valuable in educational research, supporting outcomes like student success, trust, and motivation. Their potential to enhance transparency and reliability in online…

To be an effective teacher, one must possess strong learning abilities. Developing lesson planning, pursuing learning objectives, and assessing post-lesson accomplishments all these depend on reflection and ongoing learning. As education is context-specific, the iterative process of preparing, reflecting, and improving is what makes teaching…