Learning Analytics in the Learning Sciences

2018 ◽  
pp. 511-519 ◽  
Author(s):  
Carolyn P. Rosé
Keyword(s):  
Learning Analytics ◽  
Learning Sciences

Learning analytics: where information science and the learning sciences meet

2019 ◽  
Vol 120 (1/2) ◽  
pp. 59-73 ◽  
Author(s):  
Stephanie Danell Teasley
Keyword(s):  
Learning Analytics ◽  
Design Methodology ◽  
Significant Contribution ◽  
Information Science ◽  
Research Area ◽  
Learning Sciences ◽  
Digital Tools ◽  
Content Type ◽  
Working Together ◽  
Everyday Learning

Purpose The explosive growth in the number of digital tools utilized in everyday learning activities generates data at an unprecedented scale, providing exciting challenges that cross scholarly communities. This paper aims to provide an overview of learning analytics (LA) with the aim of helping members of the information and learning sciences communities understand how educational Big Data is relevant to their research agendas and how they can contribute to this growing new field. Design/methodology/approach Highlighting shared values and issues illustrates why LA is the perfect meeting ground for information and the learning sciences, and suggests how by working together effective LA tools can be designed to innovate education. Findings Analytics-driven performance dashboards are offered as a specific example of one research area where information and learning scientists can make a significant contribution to LA research. Recent reviews of existing dashboard studies point to a dearth of evaluation with regard to either theory or outcomes. Here, the relevant expertise from researchers in both the learning sciences and information science is offered as an important opportunity to improve the design and evaluation of student-facing dashboards. Originality/value This paper outlines important ties between three scholarly communities to illustrate how their combined research expertise is crucial to advancing how we understand learning and for developing LA-based interventions that meet the values that we all share.

scholarly journals Designing a Social Learning Analytics Tool for Open Annotation and Collaborative Learning

2020 ◽  
Author(s):  
Jeremiah (Remi) Kalir
Keyword(s):  
Distance Learning ◽  
Social Learning ◽  
Collaborative Learning ◽  
Learning Analytics ◽  
Learning Sciences ◽  
Trade Offs ◽  
Collaborative Annotation ◽  
Social Learning Analytics

This book chapter recounts one approach to ethically co-designing a public dashboard that reports social learning analytics and encourages learners’ collaborative annotation across open texts and contexts. As a design narrative in the learning sciences, this chapter is a reflective, first-hand account organized around three related objectives: 1) Naming the theoretical stances toward open and social learning that informed design and research; 2) Describing key decisions and trade-offs pertinent to four iterations of a social learning analytics dashboard; and 3) Considering epistemological, technological, and infrastructural implications for the development and use of social learning analytics in open, flexible, and distance learning.

scholarly journals Towards a Convergent Development of Learning Analytics

2017 ◽  
Vol 4 (3) ◽  
Author(s):  
Xavier Ochoa ◽  
Arnon Hershkovitz ◽  
Alyssa Wise ◽  
Simon Knight
Keyword(s):  
Human Computer Interaction ◽  
Computer Science ◽  
Small Group ◽  
Learning Analytics ◽  
Data Science ◽  
Learning Processes ◽  
Learning Sciences ◽  
Educational Technologies ◽  
New Instrument ◽  
Behavioural Sciences

In the last 7 years, since the first LAK conference, Learning Analytics has grown rapidly as a field from a small group of interested scholars and practitioners to one of the most scientifically successful and institutionally accepted areas of Learning and Educational Technologies. Learning Analytics is often referred as a "Middle-Space" where experts from diverse fields (from the Learning Sciences, Computer Science, Human-Computer Interaction, Psychology and Behavioural Sciences, just to name a few) share their perspectives on how to better understand and optimize learning processes and environments using this new instrument called Data Science.

Learning Analytics Considered Harmful

2012 ◽  
Vol 16 (3) ◽  
Author(s):  
Laurie P Dringus
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Learning Analytics ◽  
Online Courses ◽  
Research Community ◽  
Current Status ◽  
Online Course ◽  
Full Potential

This essay is written to present a prospective stance on how learning analytics, as a core evaluative approach, must help instructors uncover the important trends and evidence of quality learner data in the online course. A critique is presented of strategic and tactical issues of learning analytics. The approach to the critique is taken through the lens of questioning the current status of applying learning analytics to online courses. The goal of the discussion is twofold: (1) to inform online learning practitioners (e.g., instructors and administrators) of the potential of learning analytics in online courses and (2) to broaden discussion in the research community about the advancement of learning analytics in online learning. In recognizing the full potential of formalizing big data in online coures, the community must address this issue also in the context of the potentially "harmful" application of learning analytics.

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