Forging new pathways of research and innovation in open and distance learning

Learning Analytics in Distance Education: A Systematic Literature Review
Selcan Kilis, Yasemin Gülbahar
Learning analytics offer a premise for predicting and improving learner success and retention,
allowing for data-driven decisions (Olmos & Corrin, 2012; Smith, Lange, & Huston, 2012).
With the help of learning analytics, curriculum can also be enhanced to include more
comprehensive programmes (Gülbahar & Ilgaz, 2014; Long & Siemens, 2011). Learning
analytics also facilitate personalised learning, enabling learners to have a more effective
learning experience. It also guides instructors in grading learners’ performances, keeping track
of their logs, their engagement and pace (Smith et al., 2012). Actually, the purpose of learning
analytics is stated as being to encourage and motivate instructors and educational institutions
to adapt educational opportunities in accordance with learners’ level of needs and ability
(Johnson, Adams, & Cummins, 2012).
The technological and educational aspects of learning analytics, as mentioned in Greller and
Drachsler’s study (2012), include educational data, objectives, stakeholders, internal
limitations, external constraints, and instruments. Educational data feeds learning analytics as
well as some challenges such as availability. The aspect of objectives refers to the fundamental
objectives of learning analytics and includes two main dimensions: reflection and prediction.
Reflection serves as the critical self-evaluation of a data client like self-observation, or working
similar to human-computer interaction. Prediction facilitates establishing acts of automatic
decision making for learning paths with the aim of predicting and modelling learning
activities. Stakeholders include data clients (e.g., teachers) and data subjects (e.g., learners).
Internal limitations refer to the human factors which enable or pose barriers, such as
competencies and acceptance. External constraints refer to the outside barriers which can
limit the beneficial applications of learning analytics. Finally, instruments are means of
dealing with datasets including technology, algorithms, and theories. The authors concluded
their proposed framework for LA application with their statement that optimal exploitation of
LA can occur only if all six critical dimensions in the design process are taken into
consideration.
Research on LA, after some attention to the aforementioned dimensions, has started to focus
on ways and methods to interpret big data for learning purposes. The point worth noting here
is the software used in the analysis of tracked data. Tools utilised for learning analytics include
Purdue's Course Signals Program, Blackboard Analytics, SNAPP (Social Networks Adapting
Pedagogical Practice), Gephi and Northern Arizona University GPS, Google Analytics, and
Moodle Analytics. These tools display learners’ interactions that are stored in the background
as either some form of visualisation e.g., social network analysis techniques or in the format of
sociogram which consists of nodes (students) and links (communication, message exchanges,
replies of forum postings) showing central and isolated learners (Park & Jo, 2015). Other
software used to visualise this stored data for learning include Wolfram, NodeXL (Network
Overview, Discovery and Exploration for Excel), and radar charts.
Initial attempts with LA started with pioneer universities including Purdue University,
University of Alabama, and Arizona State University. After the field started to gain more
attention, a steadily growing body of literature has materialised; however, there are still gaps
Reaching from the roots – 9 th EDEN Research Workshop Proceedings, 2016, Oldenburg 311
ISBN 978-615-5511-12-7