Background and Context: We replicated and expanded on previous work about how well students learn dynamic programming, a difficult topic for students in algorithms class. Their study interviewed a number of students at one university in a single term. We recruited a larger sample size of students, over several terms, in both large public and…

Algorithmic decision-making systems such as Learning Analytics (LA) are widely used in an educational setting ranging from kindergarten to university. Most research focuses on how LA is used and adopted by teachers. However, the perspective of students and parents who experience the (in)direct consequences of these systems is underexplored. This…

Aim/Purpose: The purpose of this study is to review and categorize current trends in student engagement and performance prediction using machine learning techniques during online learning in higher education. The goal is to gain a better understanding of student engagement prediction research that is important for current educational planning and…

Design education includes many pursuits that deal with creativity, thinking and visual communication techniques in the learning process. This study aims to create a participatory learning algorithm based on a location, while measuring the spatial impact through an emergent situation. The exemplified issue is determined as fire that emerged due to…

Before they even talk, infants become sensitive to the speech sounds of their native language and recognize the auditory form of an increasing number of words. Traditionally, these early perceptual changes are attributed to an emerging knowledge of linguistic categories such as phonemes or words. However, there is growing skepticism surrounding…

Computational thinking (CT) is becoming increasingly important as a learning content. Subject-integrated approaches aim to develop CT within other subjects like mathematics. The question is how exactly CT can be integrated and learned in mathematics classrooms. In a case study involving 12 sixth-grade learners, CT activities were explored that…

Despite the growing use of large language models (LLMs) in educational contexts, there is no evidence on how these can be operationalized by students to generate custom datasets suitable for teaching and learning. Moreover, in the context of network science, little is known about whether LLMs can replicate real-life network properties. This study…

Much has been written about how to improve the fairness of AI tools for decision-making but less has been said about how to approach this new field from the perspective of philosophy of education. My goal in this paper is to bring together criteria from the general algorithmic fairness literature with prominent values of justice defended by…

The proliferation of digital learning platforms has revolutionized the generation, accessibility, and dissemination of educational resources, fostered collaborative learning environments and producing vast amounts of interaction data. Machine learning (ML) algorithms have emerged as powerful tools for analyzing these complex datasets, uncovering…

The increasing integration of Artificial Intelligence (AI) in educational settings is transforming the role of school leaders, reshaping how decisions are made, and introducing both opportunities and challenges. This paper presents the findings of a scoping review that synthesises the current literature on AI's impact on educational leadership.…

While high-accuracy machine learning (ML) models for predicting student learning performance have been widely explored, their deployment in real educational settings can lead to unintended harm if the predictions are biased. This study systematically examines the trade-offs between prediction accuracy and fairness in ML models trained on the…

Background: Metacognitive processes have been linked to the development of conceptual knowledge in STEM courses, but previous work has centered on the regulatory aspects of metacognition. Purpose: We interrogated the relationship between epistemic metacognition and conceptual knowledge in engineering statics courses across six universities by…

Self-regulated learning is an essential skill that can help students plan, monitor, and reflect on their learning in order to achieve their learning goals. However, in situations where there is a lack of effective feedback and recommendations, it becomes challenging for students to self-regulate their learning. In this paper, we propose an…

As algorithmic decision making is increasingly deployed in every walk of life, many researchers have raised concerns about fairness-related bias from such algorithms. But there is little research on harnessing psychometric methods to uncover potential discriminatory bias inside decision-making algorithms. The main goal of this article is to…

Computer science development, especially machine learning, is a thriving innovation essential for education. It makes the process of teaching and learning more accessible and manageable and also promotes equality. The positive influence of machine learning can also be felt in Islamic studies, particularly in Hadith studies. This literature review…