Big data integration for transition from e-learning to smart learning framework . Dr. Prakash Kumar Udupi Mr. Puttaswamy Malali Mr. Herald Noronha Department of Computing Department of Computing Department of Computing Middle East College Middle East College Middle East College .

1. Content
Technology
Analysis
Transmit
Manage
Evaluation
Store
Access
E Learning
Framework
Big data integration for transition from
e-learning to smart learning framework
Dr. Prakash Kumar Udupi Mr. Puttaswamy Malali Mr. Herald Noronha
Department of Computing Department of Computing Department of Computing
Middle East College Middle East College Middle East College
Sultanate of Oman Sultanate of Oman Sultanate of Oman
Abstract— Technology innovations triggered enhancement of
e-learning paradigm towards smart learning environment. Large
data availability, information processing tools and techniques
further created new approach and explorations in teaching and
learning. Expansion of education technology framework,
diversified content characteristics and huge volume of
information availability further made the e-learning to spearhead
the learning platform irrespective of technology constraints.
Current trends in big data analysis and abreast technology
interventions spearhead new dimensions of e-leaning towards
smart learning. This paper identifies and evaluates the key points
of e-learning paradigm and proposes the possibility creating new
framework for big data integration. This paper further explores
the possible research on an integrated system and proposes a new
models for integration of smart technology and big data
framework within the e-learning paradigm, that leads towards
smart learning.
Keywords—Bigdata, e-learning, smart learning, smart systems
I. INTRODUCTION
E-learning and its expansion accommodated a number of
new learning models with in the basic framework of teaching
and learning. Internet and its influences ensured e-learning to
spread widely across geographical barriers and reach to the
masses. Technology innovations also triggered the evolution of
various new teaching concepts and models for better
learning[1]. Large volume of data and information availability,
complex characteristics of these data, innovations in
extractions, filtration and analysis of data using tools and
techniques helped to create knowledge centric resources and
repositories to strengthen the e-learning process. Research in
network and transmission technology, birth of smart devices,
accelerated data framework, availability of new technology for
transmission and transformation of information ensured e-
learning to facilitate varieties of education including social
science, music, art, skill based education along with
multidimensional characteristics and on demand availability
features [2].
II. EVALUATION OF EXISTING E-LEARNING PARADIGM
State of the art e-learning framework are normally
integrated with various components of new e-learning systems
with the basic e-learning contents. Education technology like
learning management Systems (Moodle, Blackboard etc.),
various hardware and software tools provide the extended
facility for information transmissions and collaborations[3].
Integration of various analysis and evaluation tools with in the
learning management, with the help of powerful interfaces
enables performance evaluation of the learners thereby helps to
analyses, how much learning has happed?[4]. Technology
advancement in storage techniques, availability of new and
compact storage devices ensured storage of complex data and
information irrespective of size, volume and characteristics.
Complex e-learning data also consists of multimedia
information like audio, video, schematics, text, animations, 3d
models, which normally occupies huge spaces. Accessing and
authentication method incorporation in e-learning framework
facilitated selective and desired information exchange between
learners. Integration of data management and database tools
helps to combine and manage the e-learning information using
content management systems[5].
Figure 1. E-learning paradigm
New research in transmission techniques, advancement in
wired and wireless technology accelerated the e-learning
information transmission process towards a newer dimensions
and ensured learning to happen during mobility along with
tracking. Hence integration of various new features derived
new education technology tools like LMS (learning
Management System), CMS (Content Management System),
ILMS(Integrated Learning Management System), LRMS
(Learning Resource Management System) etc.
Enhancement of e-learning paradigm with administrative
and management capabilities, tracking, performance evaluation
techniques and other new features derived new learning
978-1-4673-9584-7/16/$31.00 ©2016 IEEE
2016 3rd MEC International Conference on Big Data and Smart City

2. systems like Learning management System. In addition,
sharable content object reference model(SCROM), embedding
user authoring capabilities, incorporation of customization,
content authoring, user interface authoring, collaborative
learning, customizer user interface features, further strengthen
the e-learning system[6].
E-learning
System
Components
LMS CMS E-
portal
Virtual
Classroom
ILMS
Multimedia
Performance
Evaluation
x x x
Collaborative
Features
x
Content
Authoring
x
Interface
Authoring
x x x x
Sharable
Contents
x x
Tracking
Features
x x
Table 1. Evaluation of features in e-learning system
An evaluation of advanced features with the present e-
learning systems are shown in Table1.
III. STUDY OF PERVASIVE SYSTEMS AND SMART SYSTEMS
Traditional E-learning environment expanded and spread
out beyond boundaries due to introduction of mobile
computing. Introduction of ubiquitous devices also made
learning during mobility as well. Transitions from wired
communications to wireless communication, innovations in
wireless technology broadened and increased utilization of
mobile learning. Seamless information transmission, blending
of hybrid information for learning without interventions,
continuous information updates and tracking learners
information are made possible by these pervasive systems[7].
Personalization of learning environment, instant
connectivity, wide availability of devices and infrastructures,
easy user interfaces, simple interfaces and interactivity, broad
reach, real time changes, monitoring of performances and
evaluations are some of the advanced features, which further
empowered as well as helps to distinguish the E-learning
environments from learners. Introduction of wearable devices
helped for constant monitoring , tracking and support for E-
learning environment. At present, these wearable devices are
playing important roles in automating the E-learning
environment.
Transformations from E-learning to augmented learning
was the new area of research, where adaption of environment
occurs for the learners[8]. Replacement of smart systems for
pervasive systems, ubiquitous computing or wearable devices
helps to strengthen the augmented learning. Moving from
information simulation, graphic simulation or learning
simulator towards augmented reality helps the learner to bypass
and learn the specific needs, which is very much required
during skill based information learning. Smart devices further
helps to make decision by capturing the information and
incorporating analysis, predictions or actions.
Figure 2. Smart System framework
Smart data transmission process and transmits the huge
volume of raw data into desired, refined and required data
blocks. Smart system frameworks are resulted due to smart
integration of various different technologies into one systems
as shown in figure 2. Hence embedding smart system
framework into e-learning takes e-learning to a higher level of
augmented learning. Big data integration in e-learning
paradigm incorporating smart system framework widen the
scope of smart learning.
IV. EVALUATION OF BIG DATA FRAMEWORK WITH
REFERENCE TO E-LEARNING
Big data with reference to e-learning are the data created by
the learners, while using e-learning framework. Big data
analysis helps to identify and evaluate quantum of data
acquired by the learner, pace of learning, understanding,
pattern of learning and learning behavior etc. Apart from e-
learning portal, learning management system, content
management system, e-learning also happens through social
media, multimedia, education portal etc.
Big data framework for smart learning begins with
identification of data source and can be defined as data
framework. Here data are made available from all e-learning
sources including LMS, CMS, File systems, Web, Social
Media. Extraction framework extract the data from data
framework using taxonomy, collaboration, faceting, tagging
Intelligence extraction. Once extraction is over, analytic
framework begins for semantic processing, ontology study,
clustering, relevancy study, thesauri, entity formation etc.[9]
Data process framework takes care for searching, indexing,
crawling, converting of data after the analytics. Once data
processing is finished, big data application framework
2016 3rd MEC International Conference on Big Data and Smart City

3. Data Framework
(E portal, LMS, CMS…)
Extraction Framework
(facet, Tag, Collaborate..)
Analytic Framework
(Cluster, Semantic Process..)
Data Process Framework
(Search, Index, Convert..)
Big Data Application Framework
(Context Creation, Query…)
Big Data Exploration Framework
(Knowledge Discovery, Navigate..)
facilitate for managing user interactions. Query generation,
context creation, query routing are done in this framework.
Figure 3. Big Data Framework for Smart Learning
The output of application framework fed to big data
exploration framework, where knowledge discovery,
navigation of information for learning happens, as shown if
figure 3.
V. PROPOSED SMART LEARNING SYSTEM
Smart learning framework facilitate integration of e-
learning paradigm with the benefit of big data analysis and
smart system utilization. Proposed framework integrates three
layers of different technology frameworks.
E-learning framework, which is the bottom most layer of
smart learning system, which also consists of teaching and
learning framework and education technology framework.
Information from these framework synthesizes the data in the
form of contents, user information or learners information and
data of user performance evaluation. These data are very
important to collect the learners information, learning pattern
identifications and clustering of learning models with learners
information
Information from these framework are passed through big
data framework. Basic three layers of big data framework are
fast data for connecting , data framework and extraction layer
for data extractions like tagging and faceting. From these
extraction layer, data passed through analytic layer, which is
the combination of data analytic operation, data processing
operation, big data application operation and exploration
operations.
Figure 4. Big Data Framework for Smart Learning
Smart technology framework, which forms the top layers of
smart learning system consists of smart data transmission layer,
smart device layer and smart application layer as shown in
figure 4. Together these layer facilitate information
transmission between user and the system. Features of smart
devices also supports complex and hybrid data capture,
predictive analysis , corrective actions, which are derived from
the big data framework.
Smart learning framework derived from e-learning system
enables learning to facilitate wider dimensions, by capturing
learners data along with situational parameters. Integration of
Smart Technology Framework
E Learning Framework
Education Technology Framework
(LMS, CMS, LRMS, Virtual Class Room…)
Big Data Framework
Teaching and Learning Framework
(Methods, Pedagogy, Curriculum, Syllabus, Activity
Based Learning, Project Based Learning, Online
Learning, E Learning…)
Contents
User
Information
database
Data
Evaluation
Fast Data
Extraction Techniques
Data Analytics
Smart Data Transmission
Smart Devices
Smart Applications
User
2016 3rd MEC International Conference on Big Data and Smart City

4. e-learning framework, big data framework and smart
technology framework will help complex, hybrid, and
collaborative data to be utilized for analytical and evaluation
purposes[10]. Further smart technology enables support of
technology need for capturing, predicting, analyzing, decision
making and initiate necessary actions as a control parameters.
Features e-Learning Smart
Learning
Smart Device
Integration
Information
Mining
x
Knowledge
Discovery
x
Content
Repository
Knowledge
Repository
x
Analytics x
Patter
Recognition
x
Intelligent
Mining
x
Machine
Learning
x
Table 2. Evaluation e-learning features versus smart learning
Information mining with reference to e-learning along with
learners information itself is a larger area of data handling.
Hence smart learning framework ensure advanced
requirements of large data handling including associated
information as shown in table 2.
Smart learning system is superior, because it facilitates
knowledge discovery, information analytics, learning patter
recognition.
VI. CONCLUSION
Smart learning framework, when developed have the
potential to cater the need of learning environment not only
with the learning resource , but also with the learners data as
well. New learning techniques like adaptive learning, skill
based learning, project based learning, assignment based
learning, flipped learning are explored till date, to provide
better learning methods. But none of these are having the in-
depth pattern recognitions of learners information or clustering
of learning patterns, which is one of the most required factors
for next generation augmented learning. In this direction,
smart learning system integrates various dimensions of
learning framework including big data integration, which
required for next generation learning.
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2016 3rd MEC International Conference on Big Data and Smart City