This document discusses learning analytics, which involves measuring, retrieving, collecting, and analyzing student data from various learning environments. Learning analytics can help educators track student progress and behavior to improve instruction and support. However, there are also challenges around data storage, privacy, and ensuring analytics are aligned with educational goals. Opportunities exist to capture more detailed behavioral data through tools, but institutions must have the capacity to maintain analytics systems and apply insights pedagogically.

1. CURRENT
LEARNING
ANALYTICS
People and Culture
ABSTRACT
Collaboration specifications and
architectures for learning analytics is
evolving, but the education sector is poorly
prepared to understand the limitations of
these developments. The current situation of
specifications and tools is studied and
understood
Prakash Hegde
Report for Kulari Lokuge

2. Summary on Current Learning Analytics
By
Prakash Hegde
e-Learning Business Analyst Intern
People & Culture
Monash College
Monash University,
Level 6, 271 Collins Street
Melbourne VIC 3000
Australia
Executive Summary
There are a lot of students who come to college unprepared, sometimes students do not
complete their course within the required time. This tends to put these students in a pit of
risk. To control and observe the way students study and to support them, technological
advancements are made which help students to improve knowledge and career readiness.
(Roy Pea, D.Phil., Oxon, David Jacks, 2014) stated that Teachers, students, school
administrators and parents need to track the learning activity and progress to accomplish the
goal of college and career success for every student. As teachers and administrators are
responsible for tracking the progress of many students, there is always a need to visualize
learning at various levels or aggression and to use this information as a guide to decision
making. These visualizations help in timely instructional interventions of learning progress
and can guide in provision of progressive learning resources and experiences. This report
introduces us and helps us understand what learning analytics is, how learning analytics
introduces to methods of measuring learner data and the retrieval and collection of data by
using various learning environments. This achievement would allow for further instructional
interventions and provision of progressive learning resources and experiences. Personalized
learning as a vision indicates that we need sensing systems for learning. We can now come to
an understanding and have a broader definition of what learning is and where data on
learning can come from.

3. Introduction
Learning analytics is a measurement, retrieval, collection and also analysing of data about
how learners use various learning environments. By analysing such data, a large group of
people such as teachers, learning analytics team, developers and also the organization on the
whole can report and retrieve data or a behavioural pattern of the students and the teachers.
This in pure context refers to the educational data mining and recognizing patterns of both
student behaviour and performance. This document overviews on the use and
implementation of learning analytics, research on a few known tools and the areas which can
be worked upon to improve learning analytic tools.
The goal/aim of the learning analytics tool and its functionality is to gather utmost
information about learner behaviour. Also the aim here is to understand and learn what
improvements can be done in order to improve the performance and use of learning analytics
tools. Research has been done on certain areas and also on tools which have been used and
alsothe tools which are in pilot stage.A carefulresearch has been done and an understanding
of learning analytics has been developed. Various findings on learning analytics tools such as
Dream Box learning, Data#3, Blackboard and Knewton have been performed.
Media Management Systems (Kaltura), Learning Management System (Moodle), portfolio
Systems (Mahara), are some of the tools which are commonly found in wide range of
universities around Australia. These tools can be used to track and observe the performances
of students and also help in student retention in some cases. However there are limitations
on a few areas where LA can fall under a broad, often into overlapping categories such as:-
1. Location of the data (Database and the Warehouse)
2. Privacy and informed consent of the data
3. Classification and categorization of the data
4. Ethical background and framework

4. Challenges
There are a few challenges which can be faced with learning analytics. Simon Paul Atkinson
(as cited in Adaptive Learning and Learning Analytics: a new learning design paradigm,
January 2015, p.3) stated that, There are challenges of collection significant value of learner
data or student data is very large. The storage of such data becomes a more viable
proposition being aware of the importance of such data on the contrary. There are issues
around the storage of virtual data, its security and privacy, the relationship and merging
with the retrieval tools and the collaboration with LA tools such as Blackboard, Moodle,
Mahara, Dream Box learning to name a few.
Some universities are working very hard towards the implementation and development of
their own tools so as to gain the utmost experience in collection of the learner’s pattern, in
information retrieval and data extraction. The technology innovation could experience in
change in significant disruption to privacy rules and regulations in near future. There are
many tools and applications which are available as well as online data about students, their
achievements, learning experiences and the types of teaching resources available.
A particularly rich area for future research is open learning analytics. Open learning analytics
has the potential to deal with the challenges in increasingly complex and fast-changing
learning environments. Its main goal is to produce an understanding of how open and
networked learning environments and how educators, institutions, and researchers can best
support this process for learners. The article on learning analytics and its challenges in
Education Sector a Survey (J Meenakumari Jayashree M. Kudari, ICCTAC, 2015, p.8) stated
that an institution’s capacity to maintain a learning analytics systemdesign and
interventions is critical. The critical element of this is data literacy, or the ability to make
data used effectively. The analysis of educational data must be aligned with a pedagogically-
based plan for effective and relevant action resulting from that analysis

5. Opportunities
Opportunities evolve around the areas of having the data being captured is to be seen
through the tool that actually retrieves it and captures it. Simon Paul Atkinson (as cited in
Adaptive Learning and Learning Analytics:a new learning designparadigm, January 2015, p.3)
stated that A browser activity background and the platform which can record or observe the
students behaviour and study pattern with every click of the student’s engagement with the
browser. This in turn would help track the progress of the student and the data can be
recorded on how quickly the student completed the task, how long were they engaged to the
browser, if they completed all the modules and also their progress. LA systems would also
help record how long the student takes to answer a question, if they made a selection and
then decided to change their mind. There is scope of capturing even more data such as if the
student has the positive attitude, if the student was in the verge of just completing the
module or a test and weather the student has followed the required instructions which have
to be followed before or after an exam or after reading or submitting an online document.
Learning analytics is concerned with the holistic experience but necessarily engages with this
wider picture.
Conclusion
The institutional aim is to measure the student success at course level so as to help students
and the institute itself to come up with solutions which can help serve student education
better. The article on Learning Analytics in Higher Education (Niall Sclater Alice Peasgood
Joel Mullan, 2016, p.27) stipulated that in certain case studies found performed by a few
universities, showed that predictive algorithm is based on student performance (points
earned so far). Effort put in (interaction with virtual learning environment), prior academic
history and student characteristics (e.g. age or credits attempted). Such methods are
implemented in order to measure student performances and help universities come up with
results and help students complete their education without delay or risk.

6. References
 http://w3.unisa.edu.au/tiu/TEACHING/Learning_analytics/Adaptive%20Learning%20
and%20Learning%20Analytics%20-%20a%20new%20learning%20design%20paradig
m.pdf
 https://www.jcu.edu.au/learning-and-teaching/designing-for-learning/blended-
learning/learning-analytics/screencast-guides/reading-data
 http://www.ascilite.org/conferences/sydney13/program/papers/Atif.pdf
 Roy Pea, D.Phil., Oxon. David Jacks Professor of Education and Learning Sciences
Professor, Computer Science (Courtesy) Stanford University, Graduate School of
Education, and Director: H-STAR Institute (Human Sciences and Technologies
Advanced Research) Viewed 10 November 2016
https://ed.stanford.edu/sites/default/files/law_report_executivesummary_24-
pager_09-02-2014.pdf
 Learning Analytics in Higher Education
https://www.jisc.ac.uk/sites/default/files/learning-analytics-in-he-v2_0.pdf
 https://eleed.campussource.de/archive/10/4035
 Challenges of Learning Analytics, Viewed on 10 November 2016
http://research.ijcaonline.org/icctac2015/number2/icctac2012.pdf