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Showing 1 to 15 of 16 results Save | Export
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Yang, Christopher C. Y.; Ogata, Hiroaki – Education and Information Technologies, 2023
The application of student interaction data is a promising field for blended learning (BL), which combines conventional face-to-face and online learning activities. However, the application of online learning technologies in BL settings is particularly challenging for students with lower self-regulatory abilities. In this study, a personalized…
Descriptors: Individualized Instruction, Learning Analytics, Intervention, Academic Achievement
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Brown, Alice; Lawrence, Jill; Basson, Marita; Axelsen, Megan; Redmond, Petrea; Turner, Joanna; Maloney, Suzanne; Galligan, Linda – Active Learning in Higher Education, 2023
Combining nudge theory with learning analytics, 'nudge analytics', is a relatively recent phenomenon in the educational context. Used, for example, to address such issues as concerns with student (dis)engagement, nudging students to take certain action or to change a behaviour towards active learning, can make a difference. However, knowing who to…
Descriptors: Online Courses, Learner Engagement, Learning Analytics, Intervention
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Kelly Linden; Neil van der Ploeg; Noelia Roman – Journal of Higher Education Policy and Management, 2023
There is a small window of opportunity at the beginning of semester for a university to provide commencing students with timely and targeted support. However, there is limited information available on interventions that identify and support disengaged students from equity groups without using equity group status as the basis for the contact. The…
Descriptors: Learner Engagement, Identification, Intervention, Learning Analytics
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Kew, Si Na; Tasir, Zaidatun – Education and Information Technologies, 2022
The emergence of Learning Analytics has brought benefits to the educational field, as it can be used to analyse authentic data from students to identify the problems encountered in e-learning and to provide intervention to assist students. However, much is still unknown about the development of Learning Analytics intervention in terms of providing…
Descriptors: Learning Analytics, Intervention, Electronic Learning, Educational Technology
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Utamachant, Piriya; Anutariya, Chutiporn; Pongnumkul, Suporn – Smart Learning Environments, 2023
Apart from good instructional design and delivery, effective intervention is another key to strengthen student academic performance. However, intervention has been recognized as a great challenge. Most instructors struggle to identify at-risk students, determine a proper intervention approach, trace and evaluate whether the intervention works.…
Descriptors: Intervention, Learning Analytics, Learning Management Systems, Programming
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Lynnette Brice; Alison Harrison; Alan Cadwallader – Journal of Open, Flexible and Distance Learning, 2023
The purpose of this paper is to share insights gained from the discovery, design, and delivery phases of creating a three-tiered model of non-academic learning support in open, distance, and flexible learning (ODFL): "Learner Engagement and Success Services (LESS)", at Open Polytechnic | Te Pukenga, New Zealand. Presented as a case…
Descriptors: Ethics, Learning Analytics, Intervention, Foreign Countries
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Tepgeç, Mustafa; Ifenthaler, Dirk – International Association for Development of the Information Society, 2022
Learning analytics includes interventions that will support learning and improve learning environments. Despite the fact that learning analytics is a promising field of study, the lack of empirical evidence on the effects of learning analytics-based interventions has been widely addressed in recent years. In this context, insights validated by…
Descriptors: Learning Analytics, Intervention, Meta Analysis, Learning Management Systems
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Karaoglan Yilmaz, Fatma Gizem; Yilmaz, Ramazan – Technology, Knowledge and Learning, 2022
One of the main problems encountered in the online learning process is the low or absence of students' engagement. They may face problems with behavioral engagement, cognitive engagement, emotional engagement in online learning environments. It is thought that the problems related to students' engagements can be overcome with personalized…
Descriptors: Learning Analytics, Intervention, Learner Engagement, Electronic Learning
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Orji, Fidelia A.; Vassileva, Julita; Greer, Jim – International Journal of Artificial Intelligence in Education, 2021
Persuasive Technologies (PT) are computational methods, strategies, and design techniques, grounded in social psychology to change user attitudes/behaviours. PTs have been applied in diverse areas, such as eCommerce, health, workplace, vehicles, urban and ambient environments. A kind of PT that has become popular in eLearning is known under the…
Descriptors: Intervention, Program Effectiveness, Learner Engagement, Class Size
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Lawrence, Jill; Brown, Alice; Redmond, Petrea; Maloney, Suzanne; Basson, Marita; Galligan, Linda; Turner, Joanna – Student Success, 2021
Low levels of online student engagement impact negatively on student success and adversely affect attrition. Course learning analytics data (CLAD), combined with nudging initiatives, have emerged as strategies for engaging online students. This article presents a mixed method case study involving a staged intervention strategy focussing on the…
Descriptors: Learner Engagement, Intervention, Online Courses, Learning Analytics
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Kay, Ellie; Bostock, Paul – Student Success, 2023
Providing timely nudges to students has been shown to improve engagement and persistence in tertiary education. However, many studies focus on small-scale pilots rather than institution-wide initiatives. This article assesses the impact of a pan-institution Early Alert System at the University of Canterbury that utilises nudging when students are…
Descriptors: At Risk Students, Learner Engagement, Undergraduate Students, Handheld Devices
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Shabbir, Shahzad; Ayub, Muhammad Adnan; Khan, Farman Ali; Davis, Jeffrey – Interactive Technology and Smart Education, 2021
Purpose: Short-term motivation encompasses specific, challenging and attainable goals that develop in the limited timespan. On the other hand, long-term motivation indicates a sort of continuing commitment that is required to complete assigned task. As short-term motivational problems span for a limited period of time, such as a session,…
Descriptors: Learning Motivation, Electronic Learning, Time Factors (Learning), Learning Processes
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Jill Lawrence; Alice Brown; Petrea Redmond; Marita Basson – Student Success, 2019
Universities increasingly implement online delivery to strengthen students' access and flexibility. However, they often do so with limited understanding of the impact of online pedagogy on student engagement. To explore these issues, a research project was conducted investigating the use of course-specific learning analytics to 'nudge' students…
Descriptors: Learner Engagement, Learning Analytics, Data Use, Electronic Learning
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Iraj, Hamideh; Fudge, Anthea; Khan, Huda; Faulkner, Margaret; Pardo, Abelardo; Kovanovic, Vitomir – Journal of Learning Analytics, 2021
One of the major factors affecting student learning is feedback. Although the importance of feedback has been recognized in educational institutions, dramatic changes--such as bigger class sizes and a more diverse student population--challenged the provision of effective feedback. In light of these changes, educators have increasingly been using…
Descriptors: Learner Engagement, Learning Analytics, Feedback (Response), Class Size
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Herodotou, Christothea; Naydenova, Galina; Boroowa, Avi; Gilmour, Alison; Rienties, Bart – Journal of Learning Analytics, 2020
Despite the potential of Predictive Learning Analytics (PLAs) to identify students at risk of failing their studies, research demonstrating effective application of PLAs to higher education is relatively limited. The aims of this study are: (1) to identify whether and how PLAs can inform the design of motivational interventions; and (2) to capture…
Descriptors: Learning Analytics, Predictive Measurement, Student Motivation, Intervention
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