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Heiser, Rebecca E.; Stritto, Mary Ellen Dello; Brown, Allen S.; Croft, Benjamin – Journal of Learning Analytics, 2023
When higher education institutions (HEIs) have the potential to collect large amounts of learner data, it is important to consider the spectrum of stakeholders involved with and impacted by the use of learning analytics. This qualitative research study aims to understand the degree of concern with issues of bias and equity in the uses of learner…
Descriptors: Student Attitudes, Administrator Attitudes, Equal Education, Bias
Patterson, Chris R.; York, Emily; Maxham, Danielle; Molina, Rudy; Mabrey, Paul, III – Journal of Learning Analytics, 2023
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…
Descriptors: Learning Analytics, Innovation, School Holding Power, At Risk Students
Lee, Hakeoung Hannah; Gargroetzi, Emma C. – Journal of Learning Analytics, 2023
Data-driven learning analytics (LA) exploits artificial intelligence, data-mining, and emerging technologies, rapidly expanding the collection and uses of learner data. Considerations of potential harm and ethical implications have not kept pace, raising concerns about ethical and privacy issues (Holstein & Doroudi, 2019; Prinsloo & Slade,…
Descriptors: Learning Analytics, Mentors, Ethics, Responsibility
Swauger, Shea; Kalir, Remi – Journal of Learning Analytics, 2023
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…
Descriptors: Learning Analytics, Justice, Imagination, Futures (of Society)
Grimm, Adrian; Steegh, Anneke; Kubsch, Marcus; Neumann, Knut – Journal of Learning Analytics, 2023
Learning Analytics are an academic field with promising usage scenarios for many educational domains. At the same time, learning analytics come with threats such as the amplification of historically grown inequalities. A range of general guidelines for more equity-focused learning analytics have been proposed but fail to provide sufficiently clear…
Descriptors: Physics, Science Instruction, Learning Analytics, Equal Education
Pargman, Teresa Cerratto; McGrath, Cormac; Viberg, Olga; Knight, Simon – Journal of Learning Analytics, 2023
The focus of ethics in learning analytics (LA) frameworks and guidelines is predominantly on procedural elements of data management and accountability. Another, less represented focus is on the duty to act and LA as a moral practice. Data feminism as a critical theoretical approach to data science practices may offer LA research and practitioners…
Descriptors: Learning Analytics, Responsibility, Feminism, Ethics
Lu, Wenyi; Griffin, Joe; Sadler, Troy D.; Laffey, James; Goggins, Sean P. – Journal of Learning Analytics, 2023
The construction of prediction models reflecting players' learning performance in serious games currently faces various challenges for learning analytics. In this study, we design, implement, and field test a learning analytics system for a serious game, advancing the field by explicitly showing which in-game features correspond to differences in…
Descriptors: Educational Games, Learning Analytics, Design, Game Based Learning
Conijn, Rianne; Kahr, Patricia; Snijders, Chris – Journal of Learning Analytics, 2023
Ethical considerations, including transparency, play an important role when using artificial intelligence (AI) in education. Explainable AI has been coined as a solution to provide more insight into the inner workings of AI algorithms. However, carefully designed user studies on how to design explanations for AI in education are still limited. The…
Descriptors: Ethics, Writing Evaluation, Artificial Intelligence, Essays
Rets, Irina; Herodotou, Christothea; Gillespie, Anna – Journal of Learning Analytics, 2023
The progressive move of higher education institutions (HEIs) towards blended and online environments, accelerated by COVID-19, and their access to a greater variety of student data has heightened the need for ethical learning analytics (LA). This need is particularly salient in light of a lack of comprehensive, evidence-based guidelines on ethics…
Descriptors: Ethics, Learning Analytics, Evidence Based Practice, Guidelines
Meaney, Michael J.; Fikes, Tom – Journal of Learning Analytics, 2023
This paper leverages cluster analysis to provide insight into how traditionally underrepresented learners engage with entry-level massive open online courses (MOOCs) intended to lower the barrier to university enrolment, produced by a major research university in the United States. From an initial sample of 260,239 learners, we cluster analyze a…
Descriptors: MOOCs, Ethics, Equal Education, Socioeconomic Status
Gedrimiene, Egle; Celik, Ismail; Mäkitalo, Kati; Muukkonen, Hanni – Journal of Learning Analytics, 2023
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…
Descriptors: Learning Analytics, Accountability, Trust (Psychology), Artificial Intelligence
Dirk Tempelaar; Bart Rienties; Bas Giesbers; Quan Nguyen – Journal of Learning Analytics, 2023
Learning analytics needs to pay more attention to the temporal aspect of learning processes, especially in self-regulated learning (SRL) research. In doing so, learning analytics models should incorporate both the duration and frequency of learning activities, the passage of time, and the temporal order of learning activities. However, where this…
Descriptors: Time Factors (Learning), Learning Analytics, Models, Statistical Analysis
Sina Nazeri; Marek Hatala; Carman Neustaedter – Journal of Learning Analytics, 2023
Learning has a temporal characteristic in nature, which means that it occurs over the passage of time. The research on the temporal aspects of learning faces several challenges, one of which is utilizing appropriate analytical techniques to exploit the temporal data. There is no coherent guide to selecting certain temporal techniques to lead to…
Descriptors: Educational Research, Time Factors (Learning), Learning Analytics, Research Methodology
Kaveri, Anceli; Silvola, Anni; Muukkonen, Hanni – Journal of Learning Analytics, 2023
Learning analytics dashboard (LAD) development has been criticized for being too data-driven and for developers lacking an understanding of the nontechnical aspects of learning analytics (LA). The ability of developers to address their understanding of learners as well as systematic efforts to involve students in the development process are…
Descriptors: Personal Autonomy, Student Empowerment, Learning Analytics, Educational Technology
Damien S. Fleur; Max Marshall; Miguel Pieters; Natasa Brouwer; Gerrit Oomens; Angelos Konstantinidis; Koos Winnips; Sylvia Moes; Wouter van den Bos; Bert Bredeweg; Erwin A. van Vliet – Journal of Learning Analytics, 2023
Personalized feedback is important for the learning process, but it is time consuming and particularly problematic in large-scale courses. While automatic feedback may help for self-regulated learning, not all forms of feedback are effective. Social comparison offers powerful feedback but is often loosely designed. We propose that intertwining…
Descriptors: Feedback (Response), Peer Influence, Learning Analytics, Undergraduate Students