scholarly journals Ethics and Privacy as Enablers of Learning Analytics

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Dragan Gasevic ◽  
Shane Dawson ◽  
Jelena Jovanovic
Keyword(s):  
Learning Analytics ◽  
Special Section ◽  
Editorial Team ◽  
Self Regulated Learning ◽  
New Methods ◽  
Regulated Learning ◽  
The Future

This issue of the Journal of Learning Analytics features a special section on ethics and privacy that is guest edited by a team of researchers involved in the European Learning Analytics Community Exchange (LACE) project. The issue also features a paper that looks at the use of new methods for the measurement of self-regulated learning. This editorial concludes with a summary of the future changes in the editorial team of the journal.

scholarly journals Learning Analytics: Looking to the Future

2017 ◽  
Vol 4 (2) ◽  
Author(s):  
Simon Knight ◽  
Alyssa F. Wise ◽  
Xavier Ochoa ◽  
Arnon Hershkovitz
Keyword(s):  
Learning Analytics ◽  
Special Section ◽  
Research Paper ◽  
Editorial Team ◽  
The Past ◽  
Community Of Learning ◽  
The Future ◽  
Journal Editorial

This second issue of the Journal of Learning Analytics in 2017 is the first edited by the full new journal editorial team. As the baton is passed on, we would like to thank the founding editors for their work initiating the journal and nurturing its development over the past several years. We look forward to continuing that tradition of excellence. This issue includes four research paper contributions, and a special section on the ‘Shape of Educational Data’. This editorial is also an opportunity for us to reflect on the development of the journal so far, and describe some changes we are making to continue the expansion and maturation of a growing community of learning analytics researchers and practitioners.

scholarly journals Understanding, evaluating, and supporting self-regulated learning using learning analytics

2015 ◽  
Vol 2 (1) ◽  
pp. 7-12 ◽  
Author(s):  
Ido Roll ◽  
Philip H. Winne
Keyword(s):  
Learning Environments ◽  
Learning Analytics ◽  
Special Section ◽  
Interactive Learning Environments ◽  
Learning Trajectories ◽  
Self Regulated Learning ◽  
Current State ◽  
Regulated Learning ◽  
State Of Research ◽  
Single Snapshot

Self-regulated learning is an ongoing process rather than a single snapshot in time. Naturally, the field of learning analytics, focusing on interactions and learning trajectories, offers exciting opportunities for analyzing and supporting self-regulated learning. This special section highlights the current state of research in the intersect of self-regulated learning and learning analytics, bridging communities, disciplines, and schools of thoughts. In this editorial, we introduce the papers and identify themes and challenges in understanding and support self-regulated learning in interactive learning environments.

scholarly journals Learning Analytics to Reveal Links Between Learning Design and Self-Regulated Learning

2021 ◽  
Author(s):  
Yizhou Fan ◽  
Wannisa Matcha ◽  
Nora’ayu Ahmad Uzir ◽  
Qiong Wang ◽  
Dragan Gašević
Keyword(s):  
Learning Analytics ◽  
High Performance ◽  
Process Mining ◽  
Learning Experience ◽  
Learning Design ◽  
Metacognitive Control ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
High Level ◽  
Evaluation Of Learning

AbstractThe importance of learning design in education is widely acknowledged in the literature. Should learners make effective use of opportunities provided in a learning design, especially in online environments, previous studies have shown that they need to have strong skills for self-regulated learning (SRL). The literature, which reports the use of learning analytics (LA), shows that SRL skills are best exhibited in choices of learning tactics that are reflective of metacognitive control and monitoring. However, in spite of high significance for evaluation of learning experience, the link between learning design and learning tactics has been under-explored. In order to fill this gap, this paper proposes a novel learning analytic method that combines three data analytic techniques, including a cluster analysis, a process mining technique, and an epistemic network analysis. The proposed method was applied to a dataset collected in a massive open online course (MOOC) on teaching in flipped classrooms which was offered on a Chinese MOOC platform to pre- and in-service teachers. The results showed that the application of the approach detected four learning tactics (Search oriented, Content and assessment oriented, Content oriented and Assessment oriented) which were used by MOOC learners. The analysis of tactics’ usage across learning sessions revealed that learners from different performance groups had different priorities. The study also showed that learning tactics shaped by instructional cues were embedded in different units of study in MOOC. The learners from a high-performance group showed a high level of regulation through strong alignment of the choices of learning tactics with tasks provided in the learning design. The paper also provides a discussion about implications of research and practice.

Using learning analytics to explore self-regulated learning in flipped blended learning music teacher education

2017 ◽  
Vol 50 (1) ◽  
pp. 114-127 ◽  
Author(s):  
Amanda P. Montgomery ◽  
Amin Mousavi ◽  
Michael Carbonaro ◽  
Denyse V. Hayward ◽  
William Dunn
Keyword(s):  
Teacher Education ◽  
Blended Learning ◽  
Learning Analytics ◽  
Music Teacher ◽  
Music Teacher Education ◽  
Self Regulated Learning ◽  
Regulated Learning

Learning Analytics and Technology Through Teaching

2021 ◽  
Author(s):  
Matthew Kaufman ◽  
Kristi Yuthas
Keyword(s):  
Learning Analytics ◽  
Data Analytics ◽  
New Technologies ◽  
Teaching Approach ◽  
Knowledge Gap ◽  
Technical Knowledge ◽  
Self Regulated Learning ◽  
Logical Sequence ◽  
Regulated Learning ◽  
Pedagogical Framework

Data analytics problems, methods and software are changing rapidly. Learning how to learn new technologies might be the most important skill for students to develop in an analytics course. We present a pedagogical framework that promotes self-regulated learning and metacognition and three student-driven assignments that can be used in accounting analytics and other courses that incorporate technology. The assignment can be used by faculty who do not have training in analytics. The assignments adopt a learn-through-teaching approach that helps students: 1) define a conceptual or technical knowledge gap; 2) identify resources available for filling that gap; 3) work independently to acquire the desired knowledge; 4) break knowledge into components and arrange in a logical sequence; and 5) reinforce knowledge by presenting to others in an accessible manner. These assignments equip students with confidence and capabilities that will enable them to keep up with advances in technology.

Associations between learning analytics dashboard exposure and motivation and self-regulated learning

2021 ◽  
Vol 162 ◽  
pp. 104085
Author(s):  
Stephen J. Aguilar ◽  
Stuart A. Karabenick ◽  
Stephanie D. Teasley ◽  
Clare Baek
Keyword(s):  
Learning Analytics ◽  
Self Regulated Learning ◽  
Regulated Learning
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