scholarly journals Quantitative and Qualitative Analysis of the Learning Analytics and Knowledge Conference 2018

2018 ◽  
Vol 5 (3) ◽  
Author(s):  
Xavier Ochoa ◽  
Agathe Merceron
Keyword(s):  
Qualitative Analysis ◽  
Learning Analytics ◽  
Special Section

Editorial of the Special Section of LAK-18

scholarly journals Critical Data Studies, Abstraction & Learning Analytics

2019 ◽  
Vol 6 (3) ◽  
Author(s):  
Simon Buckingham Shum
Keyword(s):  
Social Science ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Special Section ◽  
Keynote Address ◽  
Critical Data

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 Carolyn Rosé, who provide diverse perspectives on Selwyn’s proposals and arguments, from applause to refutation. Reflecting on the debate, I note some of the tensions to be resolved for learning analytics and social science critiques to engage productively, observing that central to the debate is how we understand the role of abstraction in the analysis of data about teaching and learning, and hence the opportunities and risks this entails.  

scholarly journals Let’s Grow Together: Tutorials on Learning Analytics Methods

2016 ◽  
Vol 3 (3) ◽  
pp. 5-8 ◽  
Author(s):  
Dragan Gasevic ◽  
Mykola Pechenizkiy
Keyword(s):  
Network Analysis ◽  
Learning Analytics ◽  
Guest Editorial ◽  
Pattern Mining ◽  
Special Section ◽  
Visual Learning ◽  
Coherence Analysis ◽  
Text Coherence ◽  
Social Media Text ◽  
Visual Learning Analytics

This paper is a guest editorial into a special section that offers a collection of tutorials on methods that can be used in learning analytics. The special section is prepared as a response to the growing need of learning analytics practitioners and researchers to learn and use novel methods. In spite of this need, papers that systematically introduce some of the methods have been underrepresented in the literature. Specifically, the special section features papers that introduce epistemic network analysis, automated content and network analysis of social media, text coherence analysis with Coh-Metrix, microgenetic analysis with sequence pattern mining, and design of visual learning analytics guided by educational theory informed goals.

Guest Editorial: Special Section on Learning Analytics

2017 ◽  
Vol 10 (1) ◽  
pp. 3-5 ◽  
Author(s):  
Dragan Gasevic ◽  
George Siemens ◽  
Carolyn Penstein Rose
Keyword(s):  
Learning Analytics ◽  
Guest Editorial ◽  
Special Section

scholarly journals Research with Simulated Data

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Arnon Hershkovitz ◽  
Simon Knight ◽  
Jelena Jovanovic ◽  
Shane Dawson ◽  
Dragan Gasevic
Keyword(s):  
Learning Analytics ◽  
Special Section ◽  
Simulated Data ◽  
Theoretical Assumptions

This first issue of the Journal of Learning Analytics in 2017 features a special section of invited papers from the recent Learning Analytics and Knowledge conference (LAK'16). The theme of the conference, and this special section, relates to the need for Learning Analytics research to challenge our methodological and theoretical assumptions and build new interdisciplinary connections to further our thinking.

scholarly journals Why Theory Matters More than Ever in the Age of Big Data

2015 ◽  
Vol 2 (2) ◽  
pp. 5-13 ◽  
Author(s):  
Alyssa Friend Wise ◽  
David Williamson Shaffer
Keyword(s):  
Learning Analytics ◽  
Large Scale ◽  
Special Section ◽  
Critical Role ◽  
Large Scale Data ◽  
Critical Problems ◽  
Important Time ◽  
Conducting Research ◽  
The Many ◽  
Scale Data

It is an exhilarating and important time for conducting research on learning, with unprecedented quantities of data available. There is danger, however, in thinking that with enough data, the numbers speak for themselves. In fact, with larger amounts of data, theory plays an ever-more critical role in analysis. In this introduction to the special section on learning analytics and learning theory we describe some critical problems in the analysis of large-scale data that occur when theory is not involved. These range from the question of to which of the many possible variables a researcher should attend to how to interpret a multitude of micro-results and make them actionable. We conclude our comments with a discussion of how the collection of empirical papers included in the special section and the commentaries that were invited on them speak to these challenges, and in doing so represent important steps towards theory-informed and theory-contributing learning analytics work. Our ultimate goal is to provoke a critical dialogue in the field about the ways in which learning analytics work draws on and contributes to theory.

scholarly journals Learning Analytics: Richer Perspectives Across Stakeholders

2016 ◽  
Vol 3 (3) ◽  
pp. 1-4 ◽  
Author(s):  
Simon Knight ◽  
Shane Dawson ◽  
Dragan Gašević ◽  
Jelena Jovanović ◽  
Arnon Hershkovitz
Keyword(s):  
Hot Spots ◽  
Learning Analytics ◽  
Special Section ◽  
Practitioner Research ◽  
Editorial Team ◽  
Learning Material ◽  
Research Papers ◽  
Work Habits ◽  
Complex Interactions ◽  
Material Interaction

This issue of the Journal of Learning Analytics features seven research papers, complemented by a practitioner research paper (Dvorak & Jia). Papers by McCoy and Shih, and Knight, Brozina, and Novoselich discuss the important topic of educators working with educational data, alongside (in the latter paper) student perspectives on learning analytics. Douglas, Bermel, Alam, and Madhavan; and Waddington, Nam, Lonn, and Teasley offer empirical insight on developing a richer perspective on learning material interaction and engagement in online learning contexts (MOOCs, and LMS’ respectively). Dvorak and Jia bring a practitioner perspective to the issue in their discussion of approaches to analyzing online work habits via timeliness, regularity, and intensity. Sutherland and White, and Vieira, Goldstein, Purzer, and Magana offer focus on specific subject-based learning activities (algebra learning, and student experimentation strategies in engineering design, respectively). Finally, Howley and Rosé discuss the complex interactions of theory and method in computational modeling of group learning processes. The issue also features a special section on learning analytics tutorials, edited by Gašević and Pechenizkiy. The editorial concludes with a report of the recent ‘hot spots section’ consultation from the editorial team of the journal.

scholarly journals Challenging Assumptions in Learning Analytics

2016 ◽  
Vol 2 (3) ◽  
pp. 1-3 ◽  
Author(s):  
Shane Dawson ◽  
Dragan Gasevic ◽  
Negin Mirriahi
Keyword(s):  
Big Data ◽  
Learning Analytics ◽  
Special Section ◽  
Scaling Up ◽  
Future Directions ◽  
Emerging Trends ◽  
Conference Theme

This final issue for 2015 includes a special section of invited papers from the recent Learning Analytics and Knowledge conference (LAK15). The collected papers connect with the conference theme of “Scaling up: Big data to Big Impact” and reflect the emerging trends and future directions of learning analytics research.

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 Introduction to Section Two: MOOCs, Psychological Constructs, Communication Behaviors

2016 ◽  
Vol 20 (2) ◽  
Author(s):  
Peter Shea
Keyword(s):  
Online Learning ◽  
Qualitative Analysis ◽  
Learning Analytics ◽  
Online Students ◽  
Communication Behaviors ◽  
Psychological Constructs ◽  
Information Behaviors ◽  
Imposter Phenomenon ◽  
Anxiety Levels

This issue of Online Learning also contains four articles outside the theme of learning analytics. This section contains papers investigating MOOCs, a comparison of anxiety levels and the “imposter phenomenon” between online and classroom students, and a qualitative analysis of information behaviors among online students.

scholarly journals Editorial: Datasets for Learning Analytics

2016 ◽  
Vol 3 (2) ◽  
pp. 307-311 ◽  
Author(s):  
Stefan Dietze ◽  
George Siemens ◽  
Davide Taibi ◽  
Hendrik Drachsler
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Special Section ◽  
Educational Data Mining ◽  
Data Sets ◽  
Mining Communities ◽  
New Research ◽  
Machine Readable ◽  
Access Data ◽  
Research Analysis

The European LinkedUp and LACE (Learning Analytics Community Exchange) project have been responsible for setting up a series of data challenges at the LAK conferences 2013 and 2014 around the LAK dataset. The LAK datasets consists of a rich collection of full text publications in the domain of Learning Analytics and Educational Data Mining. The LAK dataset offers publicly available, machine-readable versions of research articles from the Learning Analytics and Educational Data Mining communities in various formats, where the main goal is to facilitate research, analysis, and smart explorative applications. Based on the insights gained from these data challenges, the idea was born to make more Learning Analytics data sets publicly available for researchers to get to a more open access data-driven research community within Learning Analytics. With this special section, we publish four data sets that answered the call for data sets by the journal. It is our vision to collect more data sets like these in this initial collection and reward their creators through citing the datasets and connecting new research outcomes to them.

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