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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
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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
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Mladen Rakovic; Nora'ayu Ahmad Uzir; Wannisa Matcha; Brendan Eagan; Jelena Jovanovic; David Williamson Shaffer; Abelardo Pardo; Dragan Gaševic – Journal of Learning Analytics, 2023
Preparatory learning tasks are considered critical for student success in flipped classroom courses. However, less is known regarding which learning strategies students use and when they use those strategies in a flipped classroom course. In this study, we aimed to address this research gap. In particular, we investigated mutual connections…
Descriptors: Time Management, Academic Achievement, Flipped Classroom, Learning Strategies
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Gomathy Ramaswami; Teo Susnjak; Anuradha Mathrani – Journal of Learning Analytics, 2023
Learning Analytics Dashboards (LADs) are gaining popularity as a platform for providing students with insights into their learning behaviour patterns in online environments. Existing LAD studies are mainly centred on displaying students' online behaviours with simplistic descriptive insights. Only a few studies have integrated predictive…
Descriptors: Learner Engagement, Learning Analytics, Electronic Learning, Student Behavior
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Tatel, Corey E.; Lyndgaard, Sibley F.; Kanfer, Ruth; Melkers, Julia E. – Journal of Learning Analytics, 2022
As the demand for lifelong learning increases, many working adults have turned to online graduate education in order to update their skillsets and pursue advanced credentials. Simultaneously, the volume of data available to educators and scholars interested in online learning continues to rise. This study seeks to extend learning analytics…
Descriptors: Course Selection (Students), Enrollment Trends, Academic Achievement, Learning Analytics
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Saqr, Mohammed; López-Pernas, Sonsoles – Journal of Learning Analytics, 2022
There has been extensive research using centrality measures in educational settings. One of the most common lines of such research has tested network centrality measures as indicators of success. The increasing interest in centrality measures has been kindled by the proliferation of learning analytics. Previous works have been dominated by…
Descriptors: Measurement Techniques, Learning Analytics, Data Analysis, Academic Achievement
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Harrison, Scott; Villano, Renato; Lynch, Grace; Chen, George – Journal of Learning Analytics, 2021
Early alert systems (EAS) are an important technological tool to help manage and improve student retention. Data spanning 16,091 students over 156 weeks was collected from a regionally based university in Australia to explore various microeconometric approaches that establish links between EAS and student retention outcomes. Controlling for…
Descriptors: Learning Analytics, School Holding Power, Integrated Learning Systems, Microeconomics
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Sense, Florian; van der Velde, Maarten; van Rijn, Hedderik – Journal of Learning Analytics, 2021
Modern educational technology has the potential to support students to use their study time more effectively. Learning analytics can indicate relevant individual differences between learners, which adaptive learning systems can use to tailor the learning experience to individual learners. For fact learning, cognitive models of human memory are…
Descriptors: Predictor Variables, Undergraduate Students, Learning Analytics, Cognitive Psychology
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Matcha, Wannisa; Gasevic, Dragan; Uzir, Nora'ayu Ahmad; Jovanovic, Jelena; Pardo, Abelardo; Lim, Lisa; Maldonado-Mahauad, Jorge; Gentili, Sheridan; Perez-Sanagustin, Mar; Tsai, Yi-Shan – Journal of Learning Analytics, 2020
Generalizability of the value of methods based on learning analytics remains one of the big challenges in the field of learning analytics. One approach to testing generalizability of a method is to apply it consistently in different learning contexts. This study extends a previously published work by examining the generalizability of a learning…
Descriptors: Learning Analytics, Learning Strategies, Instructional Design, Delivery Systems
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Munguia, Pablo; Brennan, Amelia – Journal of Learning Analytics, 2020
No course exists in isolation, so examining student progression through courses within a broader program context is an important step in integrating course-level and program-level analytics. Integration in this manner allows us to see the impact of course-level changes to the program, as well as identify points in the program structure where…
Descriptors: Learning Analytics, Courses, College Programs, Foreign Countries
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Hadavand, Aboozar; Muschelli, John; Leek, Jeffrey – Journal of Learning Analytics, 2019
Due to the fundamental differences between traditional education and massive open online courses (MOOCs), and because of the ever-increasing popularity of the latter, more research is needed to understand current and future trends in MOOCs. Although research in the field has grown rapidly in recent years, one of the main challenges facing…
Descriptors: Learning Analytics, Student Behavior, Online Courses, Large Group Instruction
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Rehrey, George; Shepard, Linda; Hostetter, Carol; Reynolds, Amberly; Groth, Dennis – Journal of Learning Analytics, 2019
To successfully implement Learning Analytics (LA) systems within higher education, we need to engage administrators, faculty, and staff alike. This paper is by and primarily for practitioners. We suggest implementation strategies that consider the human factor in adopting new technologies by analyzing the viability of our Learning Analytics…
Descriptors: Learning Analytics, Change Agents, School Culture, Technology Integration
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Shum, Simon J. Buckingham – Journal of Learning Analytics, 2019
This editorial introduces a special section of the "Journal of Learning Analytics," for which Neil Selwyn's keynote address to LAK '18 has been written up as an article, "What's the problem with learning analytics?" His claims and arguments are engaged in commentaries from Alfred Essa, Rebecca Ferguson, Paul Prinsloo, and…
Descriptors: Data Analysis, Speeches, Conferences (Gatherings), Problems
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Essa, Alfred – Journal of Learning Analytics, 2019
In "Funes the Memorius," Jorge Luis Borges tells the tale of an Argentinian man who falls off a horse, becomes paralyzed, and acquires the strange gift of infinite memory (Borges, 1993). Funes remembers everything, which is to say he forgets nothing. The author will use Borges's story as the backdrop for his response to Professor…
Descriptors: Literature, Memory, College Faculty, Criticism
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Ferguson, Rebecca – Journal of Learning Analytics, 2019
This response to Neil Selwyn's paper, 'What's the problem with learning analytics?', relates his work to the ethical challenges associated with learning analytics and proposes six ethical challenges for the field.
Descriptors: Ethics, Data Analysis, Barriers, Justice
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