1. Kalvi: An Adaptive Tamil mLearning System
Keshava Rangarajan, Chief Architect, Landmark (Halliburton) Corporation
Jayaradha Natarajan, Software consultant at TIBCO
Arivoli Tirouvingadame, Principal Member of Technical Staff, Oracle America, Inc.
Abstract: Learning Management Systems (LMS) in Tamil are at an early stage today.
They typically model users as a homogenous group with content representing a static
structural organization of the course material determined by the educator. In most cases,
LMS users who are the primary consumers, represent heterogeneous groups with diverse
learning characteristics, needs and goals. This lack of sophistication in LMS is
particularly noticeable when they are used to deliver course content that are typically
taught in languages other than Tamil. The adaptation of e/m-education systems to an
individual or to a group based on their characteristics, expectations, knowledge,
background and preferences of the students is understood as critical but is under served
today. Emphasis is moving slowly towards learner-oriented platforms and putting the
learner’s expectations, motivations, habits, learning styles, needs, etc. as the focus of
interest. This paper delves into an LMS for Tamil language education. It also explores
the mobility aspect of the LMS systems, which makes the e-learning systems available
via mobile devices like iPad, iPhone, Android based devices, etc., thus making the LMS
much more accessible and in line with current computing trends.
Keywords: Tamil, learning management systems, LMS, Data mining, Machine learning,
Analytics, e-learning, m-learning, adaptive learning, mobile devices
Introduction
This paper proposes Kalvi, an adaptive Tamil mLearning System, which is based on the
Sakai project. Sakai is a community of academic institutions, commercial organizations
and individuals who work together to develop a common Collaboration and Learning
Environment (CLE). The Sakai CLE is used for teaching, research and collaboration. It
is a free, community source, educational software platform distributed under the
Educational Community License. Sakai is a Java-based, service-oriented application suite
that is designed to be scalable, reliable, interoperable and extensible.
Anatomy of an academic course
Before we begin, let’s first take a closer look at the structure of a typical educational
course. A course typically consists of an ordered sequence of learning modules. Each of
these learning modules consists of a sequence of topics/lessons that introduce and
illustrate concepts. The lessons could include quizzes at various points that test the
learner’s grasp of concepts and reinforces previously learned concepts. Such a course
can be viewed as a directed, typically acyclic graph where modules and concepts (and
potentially quizzes) form the nodes. These nodes are connected by links that represent the
learner’s transitions from topic to topic. Educators and students progress via links across
the vertices (modules and concepts) from start to end during the life cycle of a course.

2. Non-adaptive and adaptive courses
In a non-adaptive course, the connecting links/arcs are static, pre-determined globally
and follow a pre-determined path. In an adaptive course, firstly, the links are initially
configured based on the information (descriptive attributes) available about the learner.
Additionally, there are many possible link flow paths. These paths are conditional i.e.
based on an ongoing evaluation/scoring of the learner’s progress through the topics over
a given period. Additional nodes/topics may be brought in dynamically based on a
dynamic evaluation of the learner’s level of knowledge as she/he progresses through the
course. The topics introduced are driven by analytical insight gained from community
use.
Existing problems
To summarize, existing problems in Tamil LMSs are as follows:
1. There are very few modern Learning Management Systems for education via
Tamil language especially ones that deliver content typically taught in other
languages (like English)
2. Even if they do exist, these LMS systems deliver content in a static fashion; they
do not take into account the user’s preferences, level of skill, learning goals and
other factors explicitly into account and use this as the basis for learning content
delivery and learn from user activity
Data mining, Machine learning and Analytics in LMS
Data mining, Machine learning and Analytics forms the core of LMS systems.
• Data mining and Machine learning: Learning management systems and
Learning Content management systems deal with volumes of data. Users
consuming the course material leave a trail of data while performing their
activities. These data can and needs to be mined to extract insight into learning
patterns, learner groupings, Topic classifications (eg: easy, difficult, etc.).
Machine learning techniques like Dynamic Regression, Support Vector Machines
(SVM), Neural Net engines, etc. can be employed to mine the data to extract
insight
• Analytics: Analytics plays a big role in LMSs. The broad promise of analytics is
that new insights can be gained from in-depth analysis of the data trails left by
individuals in their interactions with others, with information, with technology,
and with organizations. At a high level, the following are the types of analytics of
interest here:
• Learning Analytics: Wikipedia defines Learning Analytics as 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. Learning analytics are largely
concerned with improving learner success.

3. • Academic Analytics: Wikipedia describes Academic analytics as the term
for Business Intelligence used in an academic setting. Academic analytics
is the improvement of organizational processes, workflows, resource
allocation, and institutional measurement through the use of learner,
academic, and institutional data. Academic analytics, akin to business
analytics, are concerned with improving organizational effectiveness.
Adaptive e-learning systems
Now let’s take a look at adaptive e-learning systems. An e-learning system should be
designed to match students’ needs and desires as closely as possible, and adapt during
course progression. It is considered to be adaptive if it is capable of:
1. Modeling users, monitoring the activities of its users;
2. Interpreting these on the basis of domain-specific models;
3. Inferring user requirements and preferences out of the interpreted activities,
appropriately representing these in associated models; and
4. Acting upon the available knowledge on its users and the subject matter at hand,
to dynamically facilitate the learning process.
Thus, adaptive e-learning system can be described as a personalized system, which is
able to:
1. Perform content discovery and assembly,
2. Provide an adaptive course delivery, an adaptive interaction, and adaptive
collaboration support
Architecture of KALVI system
The Kalvi system proposed in the paper is built on Sakai LMS platform.
At a high level, the Kalvi system has two parts to it:
1. Kalvi server
2. Kalvi client
Kalvi server
The Kalvi server is the backend module. It supports all the full-fledged features of a
typical LMS. There is a central repository of the offered Course list. Educators can build
and publish new courses via the publishing site. The students can search the course list
and select their courses of interest and take them via the community site. The server has
the Adaptive Learning system as well, which is responsible for making the LMS
adaptive. All data is persisted in a central backend database.

4. Here is the architecture diagram of Kalvi:
Kalvi client
Kalvi supports both web based and mobile clients. Students can take a course via mobile
devices like iPad, iPhone, Android based devices, etc. The mobile client downloads the
course from the server and saves it locally. Along with the course, the client piece of the
Adaptive learning system pertinent to the course is also downloaded to the mobile device.
The student then takes the course in the mobile device. While taking a course from the
mobile device, it is not required to stay connected to the server. That is, courses can be
taken from the mobile devices both in online and offline modes. All the data obtained by
monitoring and recording student activities during the course life cycle are persisted in a
local database in the mobile device. When they are connected, the Kalvi server and client
can sync up periodically.

5. Here is proposed sample screen shot of a typical Tamil course taken from iPad:

6. Here is proposed sample screen shot of a typical Tamil course taken from iPhone:
Concluding thoughts and future work
The key barrier here is not the veracity of the concept or the implementation of the LMS
but it is their incorporation into the current educational processes and culture which is a
rather static. This is more so in Tamil LMSs and Tamil educational systems today. This
requires evangelization as well as a high level of engagement from all participants in the
education process to effect a change. But this is the clear trend forward. Irrespective of
the subjects and courses offered, the demography served, and the medium of languages
delivered to, the learning methodologies and techniques are the same as they broadly rely
on data mining, machine learning and analytics to deliver adaptive learner-centric content
in mobile form factors for the current and next generation of learners. The promising
aspect of this paper is that the proposed adaptive LMS system could be applied
ubiquitously.

7. Acknowledgement
The proposed Kalvi LMS is based on the Sakai project. A free trial hosted instance of the
Sakai CLE from Longsight (https://trysakai.longsight.com/portal) was used during this
research. The authors would like to acknowledge the contributions of all the people
involved in Sakai project and Longsight, and their numerous colleagues.
References
• U.S. Department of Education - "Enhancing Teaching and Learning Through
Educational Data Mining and Learning Analytics", an Issue brief.
http://www.ed.gov/edblogs/technology/files/2012/03/edm-la-brief.pdf
• George Siemens & Dragan Gasevic, Caroline Haythornthwaite & Shane Dawson,
Simon Buckingham Shum & Rebecca Ferguson, Erik Duval & Katrien Verbert,
Ryan S. J. d. Baker - Society for Learning and Analytics Research - "Open
Learning Analytics: an integrated & modularized platform", July 28, 2011
http://solaresearch.org/OpenLearningAnalytics.pdf
• http://www.sakaiproject.org/
• http://en.wikipedia.org/wiki/Sakai_Project
• http://en.wikipedia.org/wiki/Machine_learning
• http://www.longsight.com/
• https://trysakai.longsight.com/portal
• https://moodle.org/
• www.apple.com/ipad
• www.apple.com/iphone