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Thompson, Terrie Lynn; Prinsloo, Paul – Learning, Media and Technology, 2023
Learning analytics offer centralization of a particular understanding of learning, teaching, and student support alongside data-informed insight and foresight. As such, student-related data in higher education can be imagined and enacted as a 'data frontier' in which the data gaze is expanding, intensifying, and performing new meanings and…
Descriptors: Learning Analytics, Data, Activism, Higher Education
MOOC Student Dropout Prediction Model Based on Learning Behavior Features and Parameter Optimization
Jin, Cong – Interactive Learning Environments, 2023
Since the advent of massive open online courses (MOOC), it has been the focus of educators and learners around the world, however the high dropout rate of MOOC has had a serious negative impact on its popularity and promotion. How to effectively predict students' dropout status in MOOC for early intervention has become a hot topic in MOOC…
Descriptors: MOOCs, Potential Dropouts, Prediction, Models
Wong, Billy Tak-ming; Li, Kam Cheong; Cheung, Simon K. S. – Journal of Computing in Higher Education, 2023
This paper presents an analysis of learning analytics practices which aimed to achieve personalised learning. It addresses the need for a systematic analysis of the increasing amount of practices of learning analytics which are targeted at personalised learning. The paper summarises and highlights the characteristics and trends in relevant…
Descriptors: Learning Analytics, Individualized Instruction, Context Effect, Stakeholders
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
Biedermann, Daniel; Ciordas-Hertel, George-Petru; Winter, Marc; Mordel, Julia; Drachsler, Hendrik – Journal of Learning Analytics, 2023
Learners use digital media during learning for a variety of reasons. Sometimes media use can be considered "on-task," e.g., to perform research or to collaborate with peers. In other cases, media use is "off-task," meaning that learners use content unrelated to their current learning task. Given the well-known problems with…
Descriptors: Learning Processes, Learning Analytics, Information Technology, Behavior Patterns
Adam Sales; Ethan Prihar; Johann Gagnon-Bartsch; Neil Heffernan – Society for Research on Educational Effectiveness, 2023
Background: Randomized controlled trials (RCTs) give unbiased estimates of average effects. However, positive effects for the majority of students may mask harmful effects for smaller subgroups, and RCTs often have too small a sample to estimate these subgroup effects. In many RCTs, covariate and outcome data are drawn from a larger database. For…
Descriptors: Learning Analytics, Randomized Controlled Trials, Data Use, Accuracy
Amida, Ademola; Herbert, Michael J.; Omojiba, Makinde; Stupnisky, Robert – Journal of Computing in Higher Education, 2022
The purpose of this mixed-methods study was to explore factors affecting faculty members' motivation to use learning analytics (LA) to improve their teaching. In the quantitative phase, 107 faculty members completed an online survey about their motivation to use LA. The results showed that cost, utility, attainment value, and competence all…
Descriptors: Teacher Motivation, Teacher Effectiveness, College Faculty, Learning Analytics
Ye, Dan – TechTrends: Linking Research and Practice to Improve Learning, 2022
This article introduces the evolution of themes and ideas related to the history, theory, and practice of learning analytics within the learning, design, and technology field through four eras. This review provides researchers with a fundamental understanding of the origin of learning analytics from a historical perspective and distinguishes…
Descriptors: Learning Analytics, Educational History, Theory Practice Relationship, Ethics
Krieter, Philipp – IEEE Transactions on Learning Technologies, 2022
The time students spend in a learning management system (LMS) is an important measurement in learning analytics (LA). One of the most common data sources is log files from LMS, which do not directly reveal the online time, the duration of which needs to be estimated. As this measurement has a great impact on the results of statistical models in…
Descriptors: Integrated Learning Systems, Learning Analytics, Electronic Learning, Students
Karaoglan Yilmaz, Fatma Gizem – Asia-Pacific Education Researcher, 2022
The use of the flipped classroom (FC) model in higher education is becoming increasingly common. Although the FC model has many benefits, there are some limitations using this model for learners who do not have self-directed learning skills and do not have a developed learner autonomy. One of these limitations is that students with low academic…
Descriptors: Learning Analytics, Self Efficacy, Problem Solving, Flipped Classroom
López-Zambrano, Javier; Lara, Juan A.; Romero, Cristóbal – Journal of Computing in Higher Education, 2022
One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models' excessive dependence on the low-level…
Descriptors: Learning Analytics, Prediction, Models, Semantics
Hu, Yung-Hsiang – Education and Information Technologies, 2022
The research presents precision education that aims to regulate students' behaviors through the learning analytics dashboard (LAD) in the AI-supported smart learning environment (SLE). The LAD basically tracks and visualizes traces of learning actions to make students aware of their learning behaviors and reflect these against the agreed goals.…
Descriptors: Precision Teaching, Artificial Intelligence, Educational Environment, Student Behavior
Goal-Oriented Student Motivation in Learning Analytics: How Can a Requirements-Driven Approach Help?
Talbi, Omar; Ouared, Abdelkader – Education and Information Technologies, 2022
Determining student motivation within the context of Learning Analytics is fundamental for academic students to realize their educational goals. We aim to perceive the student's motivation state at a high level of abstraction and act accordingly to deal with motivation issues. We investigate how Model-Driven Engineering paradigms capture the…
Descriptors: Goal Orientation, Student Motivation, Learning Analytics, Teaching Methods
Švábenský, Valdemar; Vykopal, Jan; Celeda, Pavel; Kraus, Lydia – Education and Information Technologies, 2022
Cybersecurity professionals need hands-on training to prepare for managing the current advanced cyber threats. To practice cybersecurity skills, training participants use numerous software tools in computer-supported interactive learning environments to perform offensive or defensive actions. The interaction involves typing commands, communicating…
Descriptors: Data Use, Learning Analytics, Information Security, Training
Benz, Gregor; Buhlinger, Carsten; Ludwig, Tobias – Physics Education, 2022
With the availability of educational digital data acquisition systems, it has also become possible in physics education to generate 'big' data sets by (a) measuring multiple variables simultaneously, (b) increasing the sample rate, (c) extending the measurement duration, or (d) choosing a combination among these three options. In the context of…
Descriptors: Physics, Science Instruction, Learning Analytics, Data Analysis

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