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International Journal of Current Trends in Engineering & Research (IJCTER)
e-ISSN 2455–1392 Volume 3 Issue 6, June 2017 pp. 38 – 49
Scientific Journal Impact Factor : 3.468
http://www.ijcter.com
@IJCTER-2017, All rights Reserved 38
Comparative Study of Learning Analytics Tools
Likhita Ashok Kadam
MCA , Bharati Vidyapeeths Institute of Management and Information Technology, India
Abstract—Acquisition of knowledge Analytics is an advance to decompound the learners which
improves the educational execution. Acquisition of knowledge Analytics also helps the higher
educational institutions to make better the educational practices and techniques. The pass in review
of Acquisition of knowledge Analytics is based on some of existing Acquisition of knowledge
Analytics applications, design of the Acquisition of knowledge Analytics in the development
combination of parts to form a whole and the stamp of students. Higher development institutions
recently have been attracting on methods from acquisition of knowledge analytics to make decisions
about learners’ lettered advancement, predictions about futurity execution and to recognise in posse
issues. As the use of acquisition of knowledge analytics in higher development is a relatively new
circuit of custom and inquiry, the closely fixed of this document is to make ready an overview of
acquisition of knowledge analytics including a comparative investigation of some pattern tools.
Keywords—Learning Analytics, tools, techniques
I. INTRODUCTION
Course selection is severe for all students to deem the career. Demand rate for the courses after that
adjust from one academic year to the adding going on. Therefore, it is important for the student to
select the best courses based about some of the factors later marks, personal interests, peer pressure,
parental pressure, job opportunity etc. Success of the student mainly depends regarding the
assimilation of the student towards the course. In India, the percentage of the students after the pre
academic circles or Higher Secondary School education continuing the degree level education is less.
For all academic year, maintaining the university records and mapping the student after the HSC or
PU is utterly fused. It is observed that most of the problems of learners are to a specific geographical
region, specific course, online courses and inattentive courses.
Higher education institutions should consent to the learners requirements in such a way that a
manufacturing company understands the customer needs. Traditionally, questionnaire and survey
methods were adopted to identify the strategy of teaching learning environments. Every student who
right of entry the digital learning resource, leave a reference. This relish is so large and it could be
collected and used as Big Data. Recently, due to the advancements in software industry, it is feasible
to have oscillate admin areas upon theoretical data behind Educational data mining, Academic
Analytics, Learning Analytics, Networked learning, Technology enhanced learning, Computer-
supported collaborative learning and Mobile learning.
The main set sights on of Learning Analytics is to optional late growth taking place the take steps of
learners. Also, the vibes of learning in which the learner undergoes is enhanced which will ultimately
result in a character education. Learning Analytics helps educator/ literary to disclose the students.
Learning capabilities can be enlarged for the learners.
II. LEARNING ANALYTICS
Software analytics has taken a interchange route in all fields. Traditionally, education data was kept
in files and chronicles. Data generated by the conservatory system is ear-splitting. Then, the storage
systems were introduced to build up the accomplice professor data. The first stage of chaos happened
International Journal of Current Trends in Engineering & Research (IJCTER)
Volume 03, Issue 06; June – 2017 [Online ISSN 2455–1392]
@IJCTER-2017, All rights Reserved 39
in education is following learning dealing out system (LMS) got introduced. The second stage of
lawlessness in education is due to education enterprise system. The third stage is learning analytics.
Learning Analytics is nearly the growth, analysis of data very approximately the learners. It is an
emerging arena in research which uses data analysis almost all tier of educational system. The
analysis could be
About the influence an court prosecution of learners through assessment
About the participation of learners in received class rooms
About the participation of learners in online learning rooms
Participation in discussions
About the learners capacity which helps the scholastic
Personalized suggestions
Decision making (Enrolments for universities)
Success factors of students
Feedback system
Pedagogy
Academic analytics is for the institution level. Institution can use academic analysis to know the
produce a upshot of the students. It can in addition to be used to reach the attention of public. Report
of the analysis can be used for the marketing of the institution.
Types of analytics that is possible on an educational data:
Course level analytics:
Is based on the analysis almost the course. Both the learners and educators are benefitted by
this type of analysis.
Aggregate analytics:
Is based upon aggregate analysis which involves predictive modelling and the pattern of
execution or failure of the course.
This paper is organized as comparative laboratory analysis of various Learning Analytics tools. The
take goal of the Learning Analytics is reviewed. Data heritage methods used to concern the Learning
analytics is discussed.
III. LITERATURE SURVEY
The major research work carried in the field of course advisory systems is multi – discipline in
nature which includes education, psychology and computer science. Also, the research activities
performed in education using Learning All types of organizations from, businesses to higher
discipline institutions, distribute some of the same employment reasons for adopting analytics:
increasing pecuniary/operational efficacy; expanding topical and global impression; establishing new
funding models during a changing relating to housekeeping or the household usual weather and
responding to the demands for greater responsibility.
Luan illustrated that many of the accurate questions in employment are paralleled in higher
discipline. For example, while employment may ask “who are my true customers” or “who is likely
to enlarge his/her buy,” academia may ask “who are the persisters at my school” or “which alumni
are likely to make immense donations.”
International Journal of Current Trends in Engineering & Research (IJCTER)
Volume 03, Issue 06; June – 2017 [Online ISSN 2455–1392]
@IJCTER-2017, All rights Reserved 40
Like employment, higher discipline is adopting practices to render certain organizational issue at all
levels by addressing questions about retaining, admissions, capital raising, and operational
efficacy.\newline \newline Natsu reported that analytics could help discipline leaders cut costs and
better education and acquisition of knowledge. She fixed that the use of betokening analytics could
rank from “improving efficiencies to sparing cash to enhancing pupil achievement” and included
examples such as planning menses, recruiting and retaining society students, optimizing the
scheduling of classrooms, and maximizing alumni donations.Yet despite the limpid the reason why
for adopting/using analytics, the diversity of conditions used to trace analytics is unclear, especially
in the of the university sway. Watson fixed “analytics is not abundantly understood. There are many
untrue, imprecise, and defective understandings.”
Analytics are customized to need of the people in the society. This shows that needs are different for
different people like Rural/urban. The existing advisory systems in this field are designed
To improve the performance of students
To be used after joining a particular course.
IV. BRIEF OVERVIEW OF LEARNINIG ANALYTICS TOOLS
1. THE SPSS TOOL:
SPSS (Statistical Package for the Social Scientists) is a data turn and statistical analysis tool which
has a terribly versatile data dispensation completion
In the context of little-scale learning and teaching review you are single-handedly likely to use a
definitely limited range of the full comport yourself of SPSS. But it can be useful for:
Electronically storing questionnaire data. Data is stored in a spread sheet-in the setting of
table same to that of Microsoft Excel.
Generating routine descriptive statistical data for ask responses, such as frequency counts of
closed questions, distribution of join up-choice ask responses etc.
Creating graphical presentations of questionnaire data for reporting, presentations or
publications.
Exploring relationships surrounded by responses to strange questions.
Technical Specification:
First released in 1968.
Latest version available is SPSS 22.0.0.0
Has Trialware license.
Platform independent software.
Supported by Java
General specification:
The software can be obtained from Corporate Information and Computing Services (CICS).
Training courses are let not guilty and can be booked through the Learning Management
System, accessed through your Muse account.
If you are in the works to date subsequent to using spread sheets and are confident finding
your own pretentiousness scratchily supplementary technology, there is a associated tutorial
built into the software which is going on to okay for getting you started in the way of creature
of the basics.
Data is entered into the SPSS Data Editor. This consists of two viewers:
International Journal of Current Trends in Engineering & Research (IJCTER)
Volume 03, Issue 06; June – 2017 [Online ISSN 2455–1392]
@IJCTER-2017, All rights Reserved 41
1. The Variable viewer which enables you to add details to and view the every second
fields or variables for the data you have collected.
2. The Data viewer for entering and viewing the data for each of the adaptable. Each
argument in the data viewer represents a warfare, i.e. all the data from a single
questionnaire.
Data is analyzed using the Analyze other from the menu bar above the Data Editor. A number
of swing analysis options can be agreed from a slip down menu. You are likely to most
commonly use the Descriptive Statistics jarring.
Data can be presented in graphical form using the Graphs another, by now more from the
menu bar above the Data Editor. Graphs can be edited using the Chart Editor.
The results of the theatre arts analyses and producing graphs make Outputs which can be
saved.
Strengths:
Given conditions can easily be imported from Surpass or .csv files.
Limitations:
Imported .csv files may not contain variables in the appropriate numerical format for SPSS,
particularly if these have been exported from another application such as MOLE.
You may have to spend a little time correcting the variable definitions in the variable viewer.
Text fields cannot accommodate more than 255 characters and many open question responses
may exceed this limit. Data imported from large comments fields in MOLE surveys or Excel
spread sheets in which fields can accommodate more than 255 characters may therefore be
lost in the transfer process and may need to be added manually, or collated using an
alternative application such as Word.
Fig 1.: The SPSS Tool.
2. THE Rapidminer TOOL:
International Journal of Current Trends in Engineering & Research (IJCTER)
Volume 03, Issue 06; June – 2017 [Online ISSN 2455–1392]
@IJCTER-2017, All rights Reserved 42
is a software platform developed by the crew of the same name that provides an integrated
environment for engine acquisition of knowledge, premises burrowing, true copy burrowing,
prophetic analytics and avocation analytics. It is used for avocation and of the products of labor
applications as well as for examination, discipline, discipline, swift prototyping, and applying
unfolding and supports all steps of the premises burrowing train. Swift Miner uses a
dependant/server archetype with the server offered as Software as a Menial duties or on haze
infrastructures.
Technical Specification:
Released on 2006
Latest lection available is Swift miner 6.
Licensed by AGPL Preprinter
Gibbet platform i.e. can be installed on any operating a whole
Tongue Unrestricted
General specification:
is an environment for instrument of force acquisition of knowledge and facts burrowing
processes.
It represents a new draw near to concoct even very complicated problems by using a modular
executor general or abstract notion which allows concoct of manifold nested executor bonds
for immense call more than of acquisition of knowledge problems.
Uses XML to mark out the executor trees modelling judgment disclosure course.
It has supple operators for facts input and output toothed or threaded tool formats.
It contains more than 100 acquisitions of knowledge schemes for return regress grouping and
clustering dissection.
Supports about twenty two toothed or threaded tool formats.
Has a lot of functionality, is burnished and has advantageous connectivity.
Includes many acquisitions of knowledge algorithms from WEKA.
Hard and perfect parcel.
It easily reads and writes Outvie files and different databases.
You program by sickly components together in a well-drawn ETL be in action flows.
If you set up an unlicensed be in action flows it remind of Ready Fixes to make it according
to law.
Strengths:
Has the filled easiness for prototype evaluation using gibbet validation and exempt from
arbitrary control validation sets.
More than 1,500methods for facts integration, facts metamorphosis, dissection and,
modelling as well as visualization – no other discontinuance on the emporium offers more
procedures and therefore more possibilities of defining the optimal dissection processes.
Offers made up of many procedures, especially in the sphere of ascribe pick and for outlier
finding out, which no other discontinuance offers
Limitations
is the data mining software package that is most suited for people who are accustomed to
working with database files, such as in academic settings or in business settings.
The reason for this is that the software requires the ability to manipulate SQL statements and
files.
International Journal of Current Trends in Engineering & Research (IJCTER)
Volume 03, Issue 06; June – 2017 [Online ISSN 2455–1392]
@IJCTER-2017, All rights Reserved 43
Fig 2.: THE Rapidminer TOOL
3.THE Gephi TOOL:
Gephi is an free-spring reticulated or decussated work decomposition and visualization software
bundle written in Java on the NetBeans platform, initially developed by students of the Universal
school of Technology of Compiègne (UTC) in France.
Technical Specification:
First released in 2008.
Latest version available is 0.9.1
Has GNU General Public License, Common Development and Distribution License
Platform independent software.
Supported by Java, OpenGL
General specification:
New redeveloped core
New Appearance module
Timestamp support
GEXF 1.3 support
Multiple files import
Multi-graph support
New workspace selection UI
Java and MacOS Compatibility
Strengths:
Gephi is a implement for facts analysts and scientists zealous to examine and comprehend
graphs. Like Photoshop™ but for graph facts, the user interacts with the show, work or
operate with the hands the structures, shapes and standard to betray concealed patterns.
International Journal of Current Trends in Engineering & Research (IJCTER)
Volume 03, Issue 06; June – 2017 [Online ISSN 2455–1392]
@IJCTER-2017, All rights Reserved 44
The limit is to help facts analysts to make theory, by intuition exhibit patterns, dissociate
conformation singularities or faults during facts sourcing. It is a complementary implement to
traditive statistics, as of the sight or vision rational with interactive interfaces is now
recognized to make easy argument.
This is a software for Exploratory Facts Resolution, an example appeared in the Of the sight
or vision Analytics tract of land of examination.
Limitations
The visualization is limited in greatness, by an incapable of being seen box, that turns to frontier
graph expanding and cause extend issues. The question has no workaround except reducing the
greatness of the graph.
Fig 3.: THE Gephi TOOL
4. THE Weka TOOL:
Waikato Environment for Knowledge Analysis. Weka is a buildup of machine learning algorithms
for data mining tasks. These algorithms can either be applied directly to a data set or can be called
from your own Java code. The Weka (pronounced Weh-Kuh) workbench contains a adding together
of several tools for visualization and algorithms for analytics of data and predictive modeling,
together once graphical enthusiast interfaces for easy admission to this functionality.
Technical Specification:
First released in 1997.
Latest reading available is WEKA 3.6.11.
Has GNU of the whole national permission.
Platform voluntary software.
Supported by Java
General specification:
Weka is a Java based free spring instrument given conditions burrowing instrument which is
a group of many given conditions burrowing and engine acquisition of knowledge
algorithms, including pre-processing on given conditions, arrangement, clustering, and
connection control drawing out
Weka provides three graphical user interfaces i.e. the Explorer for exploratory given
conditions resolution to shore up pre-processing, refer preference, acquisition of knowledge,
International Journal of Current Trends in Engineering & Research (IJCTER)
Volume 03, Issue 06; June – 2017 [Online ISSN 2455–1392]
@IJCTER-2017, All rights Reserved 45
visualization, the Experimenter that provides empirical environment for testing and
evaluating engine acquisition of knowledge algorithms, and the Understanding Roll on for
new continued movement original inspired interface for optical project of KDD train. A
uncombined Require-streak explorer which is a uncombined interface for typing commands
is also provided by weka.
Strengths:
It is also fitting for developing new engine acquisition of knowledge schemes.
Weka loads facts toothed or threaded tool in formats of ARFF, CSV, C4.5, dual. Though it is
free beginning, At liberty, Extendible, Can be integrated into other java packages.
Limitations:
It lacks special and fitting documentations and suffers from “Kitchen Fall Syndrome” where
systems are updated constantly.
Worse connectivity to Beat spread sheet and non-Java based databases.
CSV reader not as athletic as in Fleet Miner.
Not as smooth.
Weka is much weaker in classical statistics.
Does not have the ease to keep parameters for scaling to put or place upon to coming time
datasets.
Does not have self-moving ease for Parameter optimization of engine acquisition of
knowledge/statistical methods
Fig 4.:THE Weka TOOL
V. DATA COLLECTION METHODS
The every second data accretion methods discussed widely throughout the various literature put in
the received questionnaire creature prepared and circulated and collecting data based regarding the
responses from the various people. The data adding happening method is to use the existing learning
International Journal of Current Trends in Engineering & Research (IJCTER)
Volume 03, Issue 06; June – 2017 [Online ISSN 2455–1392]
@IJCTER-2017, All rights Reserved 46
dispensation system in the merged education institution. Also Log maintained in the LMS system
will declaration the analyzer and scholarly a propos various parameters. Overall, the various research
actions secure the keep amused of these three ways of collecting the data.
1. Questionnaire
2. Learning Management System
3. Log details
The various issues concerned surrounded by data put in is due to the nature of data as well as,
Structured data, nebulous data and semi structured data. Structured data refers to the data from Web
analytics, data warehouses, and different data-incline tools. Unstructured data refers to the data from
documents, emails, answers, responses, and new online discussions. Semi structured data is a
assimilation of structured and formless data.
VI. COMPARISION OF TOOLS
The four of available learning analytics tools were chosen and analytical study was made.
Table 1: Technical Overview of tools
The table shown gives the technical overview of the tools which includes name of tool and
description of release date, latest version release date, licence, operating system, language and
official website.
International Journal of Current Trends in Engineering & Research (IJCTER)
Volume 03, Issue 06; June – 2017 [Online ISSN 2455–1392]
@IJCTER-2017, All rights Reserved 47
Table 2: comparative study of tools on basis of features and functionality:
The table shown gives the comparative overview of the tools which includes functionality of tool and
Features of tool.
International Journal of Current Trends in Engineering & Research (IJCTER)
Volume 03, Issue 06; June – 2017 [Online ISSN 2455–1392]
@IJCTER-2017, All rights Reserved 48
Table 3: comparative study of tools on basis of its Types, Advantages and Diasdvantages
The given table enumerates the type of tool, advantages and limitation of each tool separately
VII. CONCLUSION
The examination shows that the usage of learning analytics is highly limited to Higher education
institutions in India. In many cases, Higher education institutions in India are not occurring to date of
the courses needed by the students. Knowledge from the data mining should be brought out to far
afield-off ahead education institutions therefore that courses could be structured based regarding the
infatuation. The literature review shows that the various research behaviour are concerned mainly
upon students after joining into a particular course. This proves to be detrimental if the student has
not chosen a course properly.
Education is the basic compulsion for the developing countries following India. To bump the number
of students continuing well ahead education, the unfriendly research discharge duty is towards the
design of a system for students to pick courses in the Indian universities using Learning Analytics.
An efficient course deterrent system can count the student skirmish. Such course advisory system
minimizes the slip outs in future education due to gross course selection. Course reproving systems
in addition to yet to be occurring the cold education institutions to plot the education system by
knowing the demand for each course in an efficient showing off. Course dread systems by now
occurring the teachers to release their duty by knowing the students strengths and added capabilities.
Employability can be enhanced if the requirements from the industry are incorporated into the
education system. This paper provides a detailed review upon various Learning Analytics tools used
to design and fabricate Learning Analytic systems.
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International Journal of Current Trends in Engineering & Research (IJCTER)
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@IJCTER-2017, All rights Reserved 49
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