There is less attention on examining algorithmic fairness in secondary education dropout predictions. Also, the inclusion of protected attributes in machine learning models remains a subject of debate. This study delves into the use of machine learning models for predicting high school dropouts, focusing on the role of protected attributes like…

This study presents a monocular approach for capturing students' prototyping activities and interactions in digital-fabrication-based makerspaces. The proposed method uses images from a single camera and applies object reidentification, tracking, and depth estimation algorithms to track and uniquely label participants in the space, extracting both…

Predictive analytics are increasingly pervasive in higher education. However, algorithmic bias has the potential to reinforce racial inequities in postsecondary success. We provide a comprehensive and translational investigation of algorithmic bias in two separate prediction models--one predicting course completion, the second predicting degree…

Background/purpose: The integration of artificial intelligence (AI) in education has the potential to address inequalities and enhance teaching and learning outcomes. However, challenges such as AI biases, limited teacher literacy, and resource constraints hinder equitable implementation, especially in contexts like South Africa. This study…

Focusing efficiently on potential weaknesses in the validity argument of writing assessments--such as writing subjectivity, content coverage, criteria vagueness, and raters' incompetence--has been shown to positively enhance teachers' overall writing assessment competence (AC). In this study, we propose a computational bootstrapping model of…

Artificial intelligence (AI) presents a unique technological quandary for music educators. Never before has a new tool been lauded and feared to the degree that AI is presently. As AI is an emerging influence in music teaching and learning, in this paper, I examine the past to inform critical action moving forward. Using prior literature in music…

It is very important to identify talented students as soon as they are admitted to college so that appropriate resources are provided and allocated to them to optimize and excel in their education. Currently, this process is labor-intensive and time-consuming, as it involves manual reviews of each student's academic record. This raises the…

This study provides a thorough bibliometric analysis of the research landscape concerning the application of soft computing in higher education. This study collects 5,140 pieces including books, book chapters, journal articles published in respected journals, and conference papers presented at notable international conferences that were published…

Previous studies on writing and understanding programs presented evidence that programmers beyond a novice stage utilize plans or plan-like structures. Other studies on code composition showed that learners have difficulties with writing, reading, and debugging code where interacting plans are merged into a short piece of code. In this article, we…

Machine learning algorithms that automatically score scientific explanations can be used to measure students' conceptual understanding, identify gaps in their reasoning, and provide them with timely and individualized feedback. This paper presents the results of a study that uses Hebrew NLP to automatically score student explanations in Biology…

Cognitive diagnosis models (CDMs) are the statistical framework for cognitive diagnostic assessment in education and psychology. They generally assume that subjects' latent attributes are dichotomous--mastery or nonmastery, which seems quite deterministic. As an alternative to dichotomous attribute mastery, attention is drawn to the use of a…

In low-stakes assessment settings, students' performance is not only influenced by students' ability level but also their test-taking engagement. In computerized adaptive tests (CATs), disengaged responses (e.g., rapid guesses) that fail to reflect students' true ability levels may lead to the selection of less informative items and thereby…

In this study, we used multivariate decoding methods to study processing differences between canonical (montring and count) and noncanonical finger numeral configurations (FNCs). While previous research investigated these processing differences using behavioral and event-related potentials (ERP) methods, conventional univariate ERP analyses focus…

The aim of this paper is to present a teaching proposal for the theoretical part relating to the first- and second-order linear difference equations with constant coefficients suitable for the first-year students at various types of universities. In contradistinction to the methods often applied (memorization of algorithms without a proper…

In this paper, a key node mining algorithm of entropy-CRITIC combined weighted GRA-TOPSIS method is proposed, which is based on the network structure features. First, the method obtained multi-dimensional data of students' identities, seating relationships, social relationships, and so on to build a database. Then, the seating similarity among…