What is the state of the field in Learning Analytics?

October 22, 2018

PhD Candidate Kamila Misiejuk and SLATE Director, Professor Barbara Wasson have completed a comprehensive State of the Field Report on Learning Analytics.

 

 

A "must-read"

The report is well written, accessible and illustrated with informative tables, graphs, diagrams, and word clouds of relevant terminology. A “must-read” for anyone interested in learning analytics. Read the report.

 

“Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.”

(most cited definition of Learning Analytics: Buckinham Shumm & Ferguson, 2012, p. 4.)

 

 

 

Misiejuk and Wasson write in their Executive Summary

 

“Learning analytics is a young, rapidly growing field of research and practice. In this State of the Field study our goal was to conduct an objective and comprehensive review of learning analytics in order to summarise the field by answering the following questions:

  • What are the main research themes within the field of learning analytics?

  • What data and methods are being used?

  • What are the characteristics of the learning analytics studies?”

 

The review was based on a thematic analysis of a corpus of articles identified in a systematic search. The primary research themes that emerged included:

“algorithms and models, data, predictive analysis, learning analytics for educators, learning analytics for institutions, network analysis, tool development, visualisations, overviews, text analysis, and ethics, philosophy & policy.”

 

The Executive Summary concluded with the following observations:

 

1. “An analysis of the corpus showed that

  • learning analytics is a wide field with articles published in education, computer science, and psychology journals

  • the research is data rich, but theory poor

  • the majority of the research has been carried out in higher education

  • predictive analysis is a very popular research area addressing HE institutional problems such as dropouts, retention, and curriculum issues

  • predictive models/algorithms are situation dependent and there is little evidence that they are transferable between different contexts

 

2. There are also a number of gaps in the research on learning analytics:

 

  • the application of learning analytics in K-12 education (at macro, meso, micro levels)

  • research on everyday analytics in classrooms (i.e., how do we collect data in classrooms)

  • research on assessment/feedback

  • research on learning-centric analytics, as opposed to learner-centric analytics

  • implementation and impact of learning analytics

  • data literacy, although there are a few studies addressing whether or not stakeholders can understand the visualisations they are presented.”

Read the report.

 

REFERENCE:

Misiejuk, K. & Wasson, B. (2017). State of the Field report on Learning Analytics. SLATE Report 2017-2. Bergen.

 

Word clouds from the text analyses with (left) and without (right) "learning analytics"

 

 

 

 

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