Comparative Study Of Learning Analytics Tools of classrooms, and maximizing alumni donations.Yet despite the limpid the reason why for adopting/using analytics, the di versity of conditions

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

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@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


Volume 03, Issue 06; June – 2017 [Online ISSN 2455–1392]


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.”




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:




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:




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.


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


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.




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.



Latest version available is 0.9.1

Has GNU General Public License, Common Development and Distribution License

Platform independent software.

Supported by Java, OpenGL


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.




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.



Latest reading available is WEKA 3.6.11.

Has GNU of the whole national permission.

Platform voluntary software.

Supported by Java


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,




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




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.




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.




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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Documents

Comparative Study Of Learning Analytics Tools of classrooms, and maximizing alumni donations.Yet despite the limpid the reason why for adopting/using analytics, the di versity of conditions