As learning analytics use grows across U.S. colleges and universities, so does the need to discuss the plans, purposes, and paths for the data collected via learning analytics. More specifically, students, faculty, and others who are impacted by learning analytics use should have more information about their campus' learning analytics practices…

Learning analytics (LA), like much of machine learning, assumes the training and test datasets come from the same distribution. Therefore, LA models built on past observations are (implicitly) expected to work well for future observations. However, this assumption does not always hold in practice because the dataset may drift. Recently,…

Data are fundamental to Learning Analytics (LA) research and practice. However, the ethical use of data, particularly in terms of respecting learners' privacy rights, is a potential barrier that could hinder the widespread adoption of LA in the education industry. Despite the policies and guidelines of privacy protection being available worldwide,…

A burgeoning literature, policy landscape, and set of practice resources and guidelines have emerged around ethical educational technology development and implementation, particularly in the context of data or artificial intelligence informed tools. However, while these resources provide valuable tools to support navigation of the ethical…

Learning analytics are starting to become standardized in higher education as institutions use the techniques of Big Data analytics to make decisions to help them reach their goals. The widespread use of student information brings forth ethical concerns primarily in relation to privacy. While the overarching ethical issues related to learning…

In recent years, the integration approach of Artificial Intelligence (AI) is called for many disciplines, it also STEM education has paved the way for transformative advancements. This paper provides an example of AI-driven learning analytics within the context of STEM education. It provides a thorough analysis of the AI-driven STEM curriculum and…

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…

Background: Learning Analytics (LA) is an emerging field concerned with measuring, collecting, and analysing data about learners and their contexts to gain insights into learning processes. As the technology of Learning Analytics is evolving, many systems are being implemented. In this context, it is essential to understand stakeholders'…

This article explores potential uses, misuses, beneficiaries, and tensions of learning analytics in higher education. While those promoting and using learning analytics generally agree that ethical practice is imperative, and student privacy and rights are important, navigating the complex maze of ethical dilemmas can be challenging, particularly…

Learning analytics is an emerging trend in American community colleges brought about by technological advancements, institutional accountability, and external pressure on institutions to substantiate and improve learning. Learning analytics can potentially improve student engagement, retention, and success but also possess inherent ethical and…

Drawn from the SQELT Erasmus+ project, this article explores how learning analytics is implemented at a set of six European universities in the context of their performance data management models, including its multiple functions and ethical issues. It further identifies possible good practice and policy recommendations at decision-making level.…

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…

For the developers of next-generation education technology (EdTech), the use of Learning Analytics (LA) is a key competitive advantage as the use of some form of LA in EdTech is fast becoming ubiquitous. At its core LA involves the use of Artificial Intelligence and Analytics on the data generated by technology-mediated learning to gain insights…

Online learning has grown due to the advancement of technology and flexibility. Online examinations measure students' knowledge and skills. Traditional question papers include inconsistent difficulty levels, arbitrary question allocations, and poor grading. The suggested model calibrates question paper difficulty based on student performance to…

Learning analytics promise significant benefit to online education providers through improved, better-targeted student services. Much has been written about the potential of analytics and how they might be technically implemented, and various ethical considerations are published highlighting the significant potential risk of gathering,…