Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque…

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/Context: Predictive analytics has emerged as an indispensable tool in the education sector, offering insights that can improve student outcomes and inform more equitable policies (Friedler et al., 2019; Kleinberg et al., 2018). However, the widespread adoption of predictive models is hindered by several challenges, including the lack of…

Quality Assurance (QA) aims to ensure and enhance educational quality, promote accountability, and foster sustainable improvement and is considered a crucial element for higher education systems in a world of constant change, increased competitiveness, technological innovation, and rising costs. In the last several years, quality assurance in…

The anticipation, inclusion, responsiveness, and reflexivity (AIRR) framework (Stilgoe et al., 2013) is a novel framework that has helped those in science and technology fields shift their focus from products to the processes used to create those products. However, the framework has not been known to be applied to the development and…

This article advances an abolitionist reframing of learning analytics (LA) that explores the benefits of productive disorientation, considers potential harms and care made possible by LA, and suggests the abolitionist imagination as an important educational practice. By applying abolitionist concepts to LA, we propose it may be feasible to open…

The rapid recent use of learning analytics (LA) in higher education, specifically during the COVID-19 pandemic, allows the monitoring of users' behavior while learning. Using LA may promote students' learning outcomes but also intrude into their privacy. This study aimed to explore students' behaviour and perceptions towards privacy and data…

This study explores the role of perceived utility, social influence, and ethical concerns in the adoption of AI-based data analysis tools among academic researchers in China, focusing on differences between public and private universities. The research aims to identify key drivers and barriers influencing the integration of AI technology in…

Evidence shows that appropriate use of technology in education has the potential to increase the effectiveness of, eg, teaching, learning and student support. There is also evidence that technology can introduce new problems and ethical issues, e.g., student privacy. This article maps some limitations of technological approaches that ensure…

Multimodal learning analytics (MMLA) integrates novel sensing technologies and artificial intelligence algorithms, providing opportunities to enhance student reflection during complex, collaborative learning experiences. Although recent advancements in MMLA have shown its capability to generate insights into diverse learning behaviours across…

Transparency and trustworthiness are among the key requirements for the ethical use of learning analytics (LA) and artificial intelligence (AI) in the context of social inclusion and equity. However, research on these issues pertaining to users is lacking, leaving it unclear as to how transparent and trustworthy current LA tools are for their…

Technology integration and learning analytics offer insights to improve educational experiences and outcomes. In advancing these efforts, laws and policies govern these environments placing protections, standards, and developmental opportunities for higher education, students, faculty, and even the nation-state. Nonetheless, evidence of…

Although an increasing number of ethical data science and AI courses is available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on algorithmic development or data analysis. In this paper we recount a recent experience in developing and…

This paper introduces a novel approach to identify at-risk students with a focus on output interpretability through analyzing learning activities at a finer granularity on a weekly basis. Specifically, this approach converts the predicted output from the former weeks into meaningful probabilities to infer the predictions in the current week for…

The digitalisation and datafication of education has raised profound questions about the changing role of teachers' educational expertise and agency, as automated processes, data-driven analytics and accountability regimes produce new forms of knowledge and governance. Increasingly, research is paying greater attention to the significant role of…