Allana Management Journal of Research , 2015, Vol V, No 1,pp35-42, ISSN 2231-0290

Initiating the Computer Savvy Young Students into Research : Results of An Experiment with ICT Blended Approach

H. S. Acharya ,

Professor ,AIMS, PUNE

Abstract

Creating interest in research and initiating the young into research is a real challenge. When research methodologies are part of curriculum , one has to teach a bunch of students with various degrees of research aptitude. This heterogeneity complicates the matter further. However the reality that students now a days are more computer savvy , hence an intelligent use of ICT should help. This research effort identifies six factors , inherent in the target audience in general which need to be properly understood, properly measured and estimated , before the challenge can really be tackled. An experimental approach with planned ICT blended teaching methodology was adopted at Allana Institute of Management over last three years. The methodology is presented , and critically analyzed in this paper. An analysis of the tendency of students to use ICT in conduct of projects, use of analytics and report generation is also discussed . Two Selected graduate projects, one using e_survey, the other using experimental design are briefly discussed as independent case studies . These serve as evidences of effectiveness of the change in teaching methodology. The major limitation of the study is the absence of a control group of students. When the findings are interpreted this aspect must be kept in mind. Key words: ICT, Simulation,

Initiating the Computer Savvy Young Students into Research : Results of An Experiment with ICT Blended Approach

H. S. Acharya ,

Professor ,AIMS, PUNE

1. Introduction

Initiating the young IT graduates into Research , is a challenge. There are factors

which act as inherent deterrents in minds of students of MCA . These need to be countered

while initiating these students into research. The students of MCA are all computer savvy,

which is an aspect that needs to be put to use positively. They can learn things effortlessly if

ICT is brought intelligently into the learning process. ICT can make the approach Student

Centric, and make students learn more on their own,[1, 10] reducing the load teacher quite a bit.

The biggest advantage of such an approach is that students learn by doing and result is a better

understanding of the subject, and are likely to remember processes and concepts for a longer

period . A planned experiment was run for four consecutive years at the Allana Institute of

Management Sciences. Findings of the study are reported in this article.

Literature on how to teach Research methodology is voluminous [3][5][6][8][10][11]. One

of the interesting articles on the web is by An article by Mary Beth Hertz [ 7], where

experience of initiating school children into research is discussed and problems highlighted.

Cristina Leston-Bandeira [4] ,sums up the experiences of teaching Research methodology

quite comprehensively and arrives at ten useful tips for teachers, where emphasis is on use of

ICT happens to be one of the major tips. ResearchReady [1] is an online platform that could be

quite useful to teachers and students alike. The purpose this article is to suggest ways of

bringing in ICT effectively to resolve some of the issues in this connection.

2. The experimental setup and the design of experiment

The research approach was experimental. Four consecutive batches of MCA 3rd

Sem, were

subjected to a investigative plan through a designed experiment as under

Phase I : In the first phase , every batch was submitted to thorough question answer session to

determine their attitude towards research, their beliefs and myths regarding research.

Phase II: In the second phase , the students were exposed to a ICT blended learning plan ,

which is detailed below under the Phase named “ Pedagogy”.

Pedagogy : Face to Face (20%) with 80% blending of ICT

Table 1: The blend of Face-to-Face and ICT in the teaching method implemented.

Component Technolo

gy

% Remarks

Concepts

Explained

Face to

Face

5% Concepts were generally introduced with explanations,

relevance to real life. But were never defined ..

Concepts

defined

Internet 10% Classes were conducted with online facility to connect to

INTERNET. Students were given some time to search

for definitions and examples on each concept

introduced.

Examples ,

Problems (

numerical)

Face to

face

5% Examples and Statistical problems relevant to the

concepts were introduced , context explained, over view

of numerical process were given.

Actual Solution Software 20% Students were asked to fend for themselves, the

formulae, the algorithms and use them

Data creation

for Numerical

examples

Computer

Simulatio

n

20% Generally real data from text books are used in

traditional teaching. With ICT it is possible to generate

data satisfying near to real life conditions. Students were

made to generate data using simulation

Hypothesization Face to

Face

5% Hypothesis were laid wherever necessary with teacher’s

assistance

Inferencing

and

Documentation

Face to

Face

5% This was the most difficult part. Very few students could

do this on their own. Hence teachers Supervision was

necessary

Mini research

projects

Do-It-

Yourself

30% A. Online / Manual Survey OR Designed

Experiments OR Raw data from Internet

B. Followed by complete analytics on Spread Sheet

C. Followed by Report submission (PDF converted)

as soft copy

Total 100

%

The percentages in column three, are all basis of time spent in a class and can be very much the

choice of the teacher. The whole process of teaching was planned in such a way that the

teachers role is to help students in discovering and learning rather than delivering and

teaching.

Phase III : Evaluation

No learning methodology can be complete without an evaluation scheme. Evaluation

plan also included high component of ICT.

Table 2. Evaluation plan with high percentage of ICT.

Item Stage (week) Description Weightage Remarks

Test 1 3rd

/4th

Fully online (Objective + small

computations)

20 Marks Best two out of

three attempts

were considered

to grade the

student

Test 2 7th

/8th

Partly online + Spread Sheet to be

submitted online

20 Marks

Test 3 12th

/13th

Partly online + Spread Sheet to be

submitted online

20 Marks

Project 14th

Own Problem selection + Independent

data collection + Analytics + Inference

+ Report writing

30 Marks Projects were

group projects

Total 70 Marks

All the online tests were conducted using MOODLE. The spread sheet part of the test

contained Problems with inbuilt randomized data creation facility , which ensured that each

student had different data set to deal with.

Target group ( basic experimental units): The experiment was replicated on Four batches

of students with varying batch sizes.

Table 3 Target group ( Students of MT31:Research Methodologies, MCA, 3rd

SEM )

Year/Batch No of students Remarks

2011-12 110 Only direct admissions

2012-13 110 Only direct admissions

2013-14 225 A mix of almost 50% direct and 50% lateral entry into

2nd

year

2014-15 169 A mix of 5% direct and 95% lateral entry into 2nd

year

Reason for large variation in the batch sizes is that , in 2013-14, and 2014-15 sizable no of

students were admitted under the lateral- entry ( direct entry to 3rd

Sem).

Brief Description of Major ICT Components and Processes:

Classes were conducted with provision of individual computer access for 80% of the time ,

and in rest of the classes (20%) a single computer for the whole class. Use of INTERNET

during the class was encouraged. Once a concept was introduced in the class students were

allowed to quickly gather information on the concept using the net for about five minutes.

Through an intensive question answer session the definitions, examples , derivations were

thoroughly discussed for a considerable time. Level of active participation students was very

high since they had access to the learning material on their systems. Every concept was followed

immediately by a few numerical exercises which the students would solve on their own with the

help of spread sheet functions. Once a problem was formulated large datasets for the

computational part was allowed to be simulated.

Use of Digital Simulation ( learning stage):

Students were taught how to use the random number generators RAND() and

RANDBWETWEEN(), to create large simulated data sets to match the requirements of the

problem formulated. In most cases this took around five to six minutes.

Use of Digital Scenario:

Each problem was represented as a SCENARIO, with an exclusive WORKBOOK devoted to

the exercise, and a collection of NAMED SPREAD SHEETS controlled by a simple

DASHBOARD.

Use of Online Survey ( project stage):

Use of Google forms was allowed. Groups mostly designed their questionnaire on their own,

after they received a few initial responses they were allowed to improve the questionnaire and

proceed with the actual survey. This was not mandatory. Few groups used hard copy

questionnaire to survey.

Use of Pivot Tables : Data handling and organization was mainly through spread sheets.

Students were allowed to experience the problems of cross tabulation without software

assistance first. Then they were asked to try “pivot tables”. They were left to discover the ease

of cross tabulation with help of OLAP operations on their own.

Moodle online tests: Online tests were designed on the open source Learning Management

System MOODLE and a good mix of small computational question and large problems to be

solved on Spread Sheets were set. Ease of conduct of examination, automated evaluation of

small questions, surprise element incorporated by the tool in randomization of small questions

were huge benefits.

Statistical Problems for Online Tests: Test questions mainly constituted of statistical

problems to be solved on spread sheets, and required online uploading of the answers under

invigilated conduct of examination. Exclusive code based random generation of data sets was

built into the system so that each student had a different set of observations to deal with.

3. Experimental Observations and their Critical Analysis ;

Observations from Phase I: Identifying the major deterrents to learning of RM by students of MCA.

was the objective of the first phase. Following factors were identified and the findings remained

almost same over the four years.

.

Factor identified Description Possible control/ ICT

control

Relevance Students find no direct relevance to computer

applications

Mini Projects related to

IT can help

Raw data

collection

/gathering

Students of IT have a misconception that raw

data collection and handling is not their

responsibility. Their responsibility is limited to

creating tools.

Need to be told how

“Requirement gathering”

is a similar process

Interpretation They think it is too much of application of

discrete mathematics and statistics

Need to be trained in

formal theories.

Use of software to reduce

computation and focus

on interpretation can help

Analytical

Capacity

Good, but their ways thinking is a mix of logic

and experience ( hands on )

Literature survey More comfortable with INTERNET Proper key search

Team Work Confused about benefits of working in team Mini Projects related to

IT can help

Observations from Phase II : Response of Students to Use of Major ICT Components

and Processes:

Use of INTERNET for search was naturally accepted and was effectively put to use

by students. Since the use was during the class and the discussions focused on concepts

introduced by the teacher focus was sustained. Responses to rest of the ICT components were

as under:

Use of Digital Simulation ( learning stage): Response was spontaneous and excellent. Use

and relevance of RAND(), and RANDBETWEEN() were quickly understood by students.

The opportunity to create data quickly and solve multiple problems were fully utilized. The

discussion sessions became more lively and quite interactive. Unlike the text book examples

used in traditional teaching, which use very small set of data and where the computational

complexities would be underplayed, in this case , the student handled large set of data , almost

real life situation with all built in complexities incorporated.

Use of Electronic Scenario: Response to Use of SCENARIOs and DASHBOARDs was

limited. There were many who could not complete the work in allotted time in the class.

Students showed a general lack of interest in putting up cases in to well knit scenario.

Use of Online Survey ( project stage): Five groups in the year 2014 used this technique in

their projects. In earlier years no group used online survey. Every year few groups used hard

copy questionnaire to survey. Acceptance of e-survey is increasing.

Use of Pivot Tables : This was one of the most accepted features of OLAP, the students were

found to naturally adopt and use.

Moodle online tests: Online tests were designed on Moodle and a good mix of small

computational question and large problems to be solved on Spread Sheets were set. Students

naturally accept this mode of testing. Ease of conduct of examination, automated evaluation of

small questions, surprise element incorporated by the tool in randomization of small questions

were huge benefits. Students have to toggle between spread sheets and the exam application

during the test. With very few exceptions most of the students were comfortable.

Statistical Problems for Online Tests: Students were comfortable and naturally accepted the

process. This was expected.

Observations from Phase III

Student’s response (Table 4-6 ) to Mini Projects are summed up in following tables.

Table 4 : Willingness to work in groups : Frequency distribution of project-group sizes

GroupSize one two Three or Four

Five to eight

> eight Total projects

Total Students

Batch

2014 2 11 10 16 3 43 169

2013 9 36 42 0 0 87 225

2012 2 2 8 0 0 35 110

2011 2 2 8 0 0 34 110

Table 5 : Methodology wise distribution of projects opted by students

Sr No

Year Exlusive e-surveys

Manual Surveys

Internet Data Designed Experiment

Total projects

1 2014 5 6 31 3 45

2 2013 0 37 45 5 87

3 2012 0 11 22 2 35

4 2011 0 11 21 2 34

Table 6 : Domain wise distribution of projects opted by students

Sr No

Year Information technology

Industrial Problems

Social Problems

Sports Business and Trading

Auto Mobiles

Total

1 2014 10 3 7 3 19 3 45

2 2013 27 8 15 10 22 5 87

3 2012 9 2 6 4 11 3 35

4 2011 9 2 5 4 11 3 34

Table7 : Distribution of concepts and tools used by students in their projects.

Sr No

Year OLAP operations

Google Forms

Testing of Hypothesis Chi Square-tests

Testing of Hypothesis T-tests

ANOVA Curve-fitting (regression analysis)

1 2014 45 6 30 12 2 6

2 2013 87 0 62 18 4 10

3 2012 35 0 28 9 2 3

4 2011 34 0 26 9 2 3

In all above tables the numbers indicate the frequency of projects. Simple chi-square tests indicated

that there is no association between the column classifications and the years (rows) at a level of

significance of 0.05. Which essentially means that the patterns observed remained the same across the

years. Which is an indication of consistency of patterns.

4. Sample student projects:

Two selected projects are discussed in brief here as typical cases. One is a designed experiment and

the other is an e-survey.

4.1 Title: “Monitoring of College Wi-Fi Connection” ( [9]) Method: Designed Experiment Metrics : Signal strength, Download speed, Upload speed. Factors considered: Time of the day, Distance from the Access Point Group size : Two students Time spent : 3 days for experimentation , 2 days for tabulation and Analytics Analytical techniques used : Pivot tables ( OLAP), Curve fitting

Abstract: A designed experiment in which the signal strength was measured at various times

and various radial distances from the Wi-Fi Access Point of the IT facility. The experimental

observations were subjected to curve fitting to determine the relationship between the signal

strength and the distance from the Wi-Fi Source.

4.2 Title: “Survey of Future Plans of Students of MBA “ ([2])

Method: E-Survey ( google forms) Total respondents: 149 Survey on :Willingness to take up Independent Business, Current info about family businessand related aspects Group size : Ten students Time spent : 3 days for Google form design , 7 days for actual e-survey,

10 days for tabulation and Analytics, 7 days documentation Analytical techniques used : Pivot tables ( OLAP), Decision Trees, Simple charts, Ch-Square Tests

Abstract: This report examines the findings from a study of 149 respondents who are current

students of MBA in various colleges of Pune. Method used was a google-forms survey.

Decision trees were drawn so that the opinion of students could be properly represented. OLAP

tools as provided in Pivot Table of Spread Sheet were used to determine basic frequencies. Chi-

square tests were used to determine association between student profiles and their opinions.

5. Significant Findings

Response to Digital Simulation of Data was immediate and excellent. Students got

used to handling large set of data. Response to Use of SCENARIOs and DASHBOARDs was

limited. There were many who could not complete the work in allotted time in the class. Once

the problems were solved , students showed a general lack of interest in organizing the

information. Use of Online Survey ( project stage): This aspect was a grand success. Most of

the surveys in 2014 were online and very quick.. Acceptance of e-survey is increasing .

Use of Pivot Tables : This was one of the most accepted features of analytics. Understanding

of Online analytic processing (OLAP) increased and at least to the extent of use of OLAP for

cross table analysis increased.

Moodle online tests: There was tremendous amount of saving of time and better

organization of test. Students naturally accepted these . Statistical Problems for Online Tests:

Students were comfortable and naturally accepted the process. This was expected.

The results show that , computer savvy students adopt to e-surveys naturally. If they

are allowed to use OLAP tools they organize data and perform cross table analysis effortlessly

and they get very easily imitated into analytics. If they are taught simulation of data they get

opportunity to experiment with data and fear of analytics is considerably reduced. Freedom to

choose domain of their interest and do-it-yourself approach helps in building confidence in

them and they start believing that these methods can be used universally and research can be

relevant to any field of specialization.

References

[1] Anonymous, (2014) ResearchReady (http://www.researchready.com)

[2] Altaf Khan, Akshay Chauhan, Chittaranjan Sharma , ZoyebMansuri, Shoaib Shaikh ,

Samdani Shaikh , MaheenSayyed , PradnyaPoojari , TarannumChougle and

NafeesaLangde (2014) , Survey of Future Plans of Students of MBA ,A TECHNICAL

REPORT , Submitted to Dept of MCA, AIMS, Pune , under Course MT31 , Research

Methodologies

[3] Cooper D. R., and Schindler P. S., (2006 ),Business Research Methods, TMH, Ltd

[4] Cristina Leston-Bandeira (2013),Ten tips to develop engaging undergraduate research

methods teaching, (http://www.psa.ac.uk/political-insight/blog/ten-tips-develop-engaging-

undergraduate-research-methods-teaching)on 5 June 2013

[5] Kothari C. R. ( 2011 )Research Methodology, New Age Publication, p 401

[6] Krishnaswamy K. N , Sivakumar A. I. and Mathirajan M ( 2009), Management Research

Methodology, Pub PearsonEducation, India Ltd,

[7] Mary Beth Hertz , ( 2014),(http://www.edutopia.org/blog/elementary-research-mary-beth-

hertz

[8] McBurney, D. H., ( 2006 ) Research Methods, 5th Edition, Thomson Learning, ISBN:81-

315-0047-0

[9] SAYYED MOHD SHOAIB JAFFER and KHAN MUDASSAR ASHFAQUE (2014),

“Monitoring College Wi-Fi Connection” A TECHNICAL REPORT , Submitted to Dept of

MCA, AIMS, Pune , under Course MT31 , Research Methodologies

[10]Tony Bates A.W. Technology, (2005), e-Learning and Distance Education, New York

[11]Zikmund W. G, ( 2003)Business Research Methods , Pub. Thomson South-Western, p748