Impact of ICR (Study Conducted by RIE + Testimonials)

8/3/2019 Impact of ICR (Study Conducted by RIE + Testimonials)

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Effect of Using Interactive Class Room (ICR) of NIIT in

Mathematics Learning of VI Standard Students

Motivation

Information and Communication Technology (ICT) has become an increasingly prevalent feature in manyaspects of life in contemporary societies, including education. Introducing ICT into the curriculum willensure that children will become more knowledgeable about information and are better to exploit theirpotential (DfEE, 1997b; DTI/DfEE, 2001, DfES, 2001)

Improving quality in education and learning is a critical issue, particularly at the time of educationalexpansion. ICTs enhance the quality of education in several ways: by increasing learner motivation andengagement, by facilitating the acquisition of basic skills, and by enhancing teacher training.

ICT plays a pivotal role in the educational paradigm shift. Wegreif (2002) says, ICT has often seen allowinglearners to reflect upon the learning process and so learning how to learn. But UNESCO document says,We believe that ICT will be a key factor in future positive change- provided they are in the possession of

the people who can use them creatively and for common good.

Students who enter in the school are communicative, curious and capable of learning things. They haveproved their ability by mastering mother tongue, physical motion, complicated games and many otherskills. However we believe that the traditional schools of 20 th century which is still very much with usdiminishes these abilities over a period over the period of learning. We need a new kind of schools for the20th century.

Regional Institute of Education (RIE) Mysore had conducted a study of the impact on the learners, whenMath Lab is incorporated into the teaching-learning learning environment.

Classroom Environment

The class room environment provides a structure to describe the setting in school within which learning isorganized and the role of teachers and students occur. Learning environment in school typically involvesone or more adult teacher connected with a number of students, usually in a well-defined physical settings.ICT integrated class room environment changes the traditional architect. The aim is to create learningenvironment centred on students as learners and a belief that they learn more from what they do and thinkabout rather than what they are told.

When we visualize with the help of computers, video camcorders and big screens high resolutionprojectors, we restructure a problem situation so that more of it can be processed by the pre consciouspart of our brain it is also possible to visualize a computer screen the spatial inter relations of elementarypredictors and consequently, to represent complex formulae of predicate logic. (Bederson & Shneiderman,

2003; Cand, MacKinaly & Shneiderman, 1990; Benzon, 1989, Rieber, 1995)Some studies revealed that there is a positive and significant relationship between class room learning andtheir cognitive and affective outcomes. (Goh & Fraser, 2000; Fraser & Chinonh, 2000; Wong &Fraser, 1996).


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ICT and Learning

Problem solving and creative thinking process such as reasoning and making connection are vital toincrease aptitude and effectiveness with ICT tools and should be integrated throughout all the pathways ofcurriculum, Students who require the ability to reason and communicate, to solve and to understand theuse of ICT for a variety of purposes (Moyle Kathryn, 2006).

Cuttance (2001) suggest that new way of thinking and solving problems in supportive class room

environment require a well-developed motivation, self-regulation strategies and meta cognitive capacitiesto engage students successfully.

When students use tool programmes for experimental activities, the learning changes from masteringprocedures to conceptual understanding, and the ability to interpret, analyze and access data in diagramand other statistical materials employed.

Approaches where ICT is used to support learning has been found out to benefit the affective learningdomain (attributes such as motivation, perseverance etc.) by inspiring students engagement to tasks(Jacobson, 2001), improving motivation and enthusiasm (Mandinash & Cline, 1996) and improving attitudeto learning (Ryser, Beeler & MaKenzie, 1995). Authors such as Cuttance (2001) and Schocter (1999)concluded that ICT will yield positive gains in students; achievement, and Scaidamalia & Berecta (1996)

reported, effective ICT use can support the depth of understanding and reflection.

Intrinsically motivated students were more goal oriented towards and pursuing learning with more effortand persistence. Students activity around and in a computer environment can make the true learningapproach of individual students. This means that the busyness of a student will automatically equate toactive or deep learning. Meaningful learning was often associated with project based learning wherestudents were encouraged to integrate content through inquiry based tasks (Gerge Neal, 2005).

There is evidence that thought has been given to integrate the use of ICT with the broad teaching andlearning goals of the school Te use of ICT has gone beyond simple presentation skills and simple researchtasks and is related to higher order thinking skills and visual activity. Students routinely use ICT across thecurriculum and understand the issue to do with its strengths and weaknesses in a variety of contexts

(Moyle Kathryn, 2006).

ICT and Mathematics Education

It is well known that the present Mathematics education suffers from serious problems. Prominentamong them is increasing difficulty to motivate students and maintain interest in the subject, which isalmost always present at a very young age, but which seems to demolish and often totally disappearas the years go by (Ambjorn, Naeve & Mikael Nilson, 2004). Other short coming that the traditionalMathematics architecture includes the inability to stimulate the interest, promote understanding,support personalization, facilitate transition between different layers, integrate abstraction withapplication and integrate mathematics with human culture.

Considering Mathematics as a language, where students are supposed to develop their adaptivereasoning, it is interesting to notice two studies by Ivarson (2002) and Wyndhamn (2001). Theyindicated that students cannot on their own, without the teachers support, create a meaning betweentheir everyday concepts and scientific discourses when they work together in front of a computerscreen.

A unique feature of the computer as a teaching toll is visualization. Dreyfus (1993) observing thatduring the last thirty years mathematics as an activity has become more experimental and more visual.The powerful visualization capacity of the computer is unprecedented and incomparable withtraditional teaching aids. Abstract concepts that have proved difficult for teachers to explain or forstudents to grasp can be made easy by using the powerful animation and graphical display capabilitiesof computers. With this, students reasoning and manipulative power are facilitated especially by

computer graphics. Not only will this increase the experimentation, exploration and understanding ofthe students, but it also increases the linkage of transferability of knowledge to real life settings(Dreyfus, 1993; Bransford et.al, 1999).


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There are many variables associated with success in the teaching of mathematics. However, Begle (1979)noted that the most of the variables that affect mathematics learning resides within the learners.

Attitude Towards Mathematics

There is no consensus on the exact meaning of the term attitude. However combining the commonelements of its various definitions, Aiken (2000) defined as a learned perception to respond positively ornegatively to a specific subject, situation, institution or person. There are three inter related components

that constitute and form attitude. These are cognitive component, affective component and the behaviouralor connotative components.

Attitude is regarded as the stumbling block for progress or otherwise is learning mathematics, it is believedthat a strong correlation with mathematics achievement (Aiken, 1970; Reyes, 1984).

Mathematics Aptitude:

There is always a need for the maximum utilization of both human and natural resources; this is dues tothe fact that the resources are always limited. In educational sectors, selection process for admission fromone level of education to the other is a routine that is prescribed globally. The selection is more rigorousfrom high school to university.

Aptitude is defined as the ... ability to profit readily from instruction, training or experience in a definedarea of performance (Bruno, 1986).

Attitude towards computers:

Given the pervasiveness of computers in all levels of educational system, it is likely the students will havedeveloped some attitude towards these machines.

In class room setting, studies have shown that students often experience reaction towards computer eitherpositively or negatively. This in turn either enhances or interferes with their developments of effectivecomputer skills (Green, White and Barr, 1998).

Shaft and Sharfman (1997) observed that: in the past computer anxiety and attitudes towards computer

have been seen as synonymous (ie., an individual who experiences high levels of computer anxiety) can besaid to have negative attitude towards computers (Mever, 1985) or as separate variable with commonantecedents (Igbaria and Parusuraman, 1989). However evidences suggest that computer anxiety is anintervening variable between variable such as demographic and attitude towards computer s(Igbria andParasuraman, 1989).

Studies have shown that attitude towards computers to influence not only acceptance of computers in classroom but also future behaviour such as using computers as a professional tool or introducing computerapplication into the class room or work place (Al-Badar, 1993).

Four types of attitudes towards computers have been identified by Loyd and Erressund (1984), these arecomputer anxiety, computer liking, computer confidence and computer usefulness. All the four were found

to have a significant effect on computer tasks.

It appears that, the use of computers in the teaching and learning of mathematics does influence theattitude of students towards computer and mathematics as well. Consequently there seems to be someimprovement in students understanding and achievement in mathematics.


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Review of Pertinent Researches

The use of computer is the teaching and learning of mathematics has been shown to have the potential ofpositively influencing students attitude towards mathematics. Studies have shown that technological aidssuch as calculators and computers have improvement effects on student attitude towards mathematics (C.fAiken, 1976; Collins, 1996). In research conducted by Kulik (1984) it was found that students attitudetowards mathematics were more positive in class room that used CAL than in class room without CAL,students from a class room that used CAL showed a slightly more positive attitude towards instruction from

class room without CAL (Collins,1996)

Ganguli (1992) investigated the effects of using computers as a teaching and in mathematics instruction onstudents attitude towards mathematics. He used computer as a supplement to normal class roominstruction. The results indicated that the attitude of the experimental group which was taught withcomputer aid were significantly changed in positive direction, whereas the control group that was taughtwithout computer aid failed to show a similar results.

Jianymin (2004) conducted a study to determine the causal ordering between attitude towardsmathematics and achievement in mathematics of secondary school students. Results showed that theachievement demonstrated causal predominance over attitude across the entire secondary school Genderdifference in this causal relationship was not found but elite status in mathematics moderated this causal

relationship.

Saha (2007) conducted a study Gender, attitude to mathematics cognitive styles and achievement inmathematics. It was found that all the three contributes to statistically significant difference in achievementin Mathematics.

As the National Council of Teachers of Mathematics stated in 1989, it is crucial for teachers to find tasksthat allow students to explore, to guess and even to make and correct errors so they can gain a confidencein their ability to solve complex problems (Bottage, 1999). It is the job of the teacher to help students withunderstanding the key ideas that are the foundation of Mathematics, instead of just focusing on simplyhaving the memorize rules and procedures (Butler, Bachingham a & Lauscher, 2005)

Teachers need to realize that mathematics is not only taught because it is useful, it should be a source ofdelight and wonder, offering pupils intellectual excitements and an appreciation of its essential creativity(Cross, 2004)

The present study is focusing on the impact of NIIT interactive Class Room solution in the Mathematicslearning of the class VI students of DMS, Mysore.

Statement of the problem:

The present study is entitled as: Effect of using NIIT NGuru Interactive Class Room (ICR) of NIIT inMathematics Learning of VI Standard Students.

Objectives:

To study the impact of ICR on the Mathematics learning of the students of class VI.

To study the impact of ICR on the Mathematics Aptitude of the students of Class VI.

To study the impact of ICR on the students believes towards Mathematics.

To study the impact of ICR on the problem solving ability of the Class VI students.


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Hypotheses:

1. ICR will have a positive impact on the Mathematics learning of the class VI students.

2. The impact of ICR on Mathematics learning of the students will differ between the experimentalgroup and control group.

3. The impact of ICR on Mathematics achievement will differ between the experimental and controlgroup.

4. The use of ICR will influence differently in the students believes towards Mathematics.

5. The impact of ICR on Problem Solving ability of the students will differ between theexperimental and control group.

Methodology:

The present study adopted a quasi-experimental, pre-test post-test design. The VI standard, section Bstudents of DMS, Mysore was taken as the experimental group, consisting of 35students. VI standardsection A students was taken as the control group for the study.

The tools for data collection were administered for both groups in order to collect the pre and postdata. Class room observations, assignments, discussions were made during the class room teachingprocess. The researcher had taken classes for the experimental group using NIIT ICR Mathematics for aperiod of three months. The topics covered include:

1. Ratio and Proportion.

2. Symmetry.

Variables:

Independent Variable: The interactive class room solution of NIIT

Dependent variables: Mathematics achievement, Believes towards Mathematics, Problem Solving

Ability, Mathematics Aptitude.

Sampling:

Purposive sampling technique will be used for the study. 35 students of standard VI.B will be selected asthe sample for experimental group. The other section of students, 35 in number will be selected as thesample for control group.

Tools for Data Collection:

1. Achievement test in Mathematics.

2. Test for Believes towards Mathematics (Developed by the researcher)

3. Mathematics Aptitude Test (Developed by the researcher).

4. Problem Solving Ability Test (Developed by the investigator)

Procedural details of the study:

The tools for data collection were administered before and after the intervention of ICR Mathematics. Theresearcher used the NIIT ICR for teaching Mathematics for about three months. During the implementationthe class room observations, assignments were given. The students were given scope for discussion andreinforcement was given by providing hands on experience with NIIT ICR in the learning lab.


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Statistical Analysis

Descriptive statistical techniques as well as inferential statistics such as t test; correlation etc. were usedfor verification of hypotheses and other analysis leading to interpretations and conclusions. Qualitativeanalysis of the structured interview as well as the daily observation made by the researcher on controlgroup will also be used to attain the meaningful result and thus interpretation.

Delimitations of the study

This study is restricted to a small group of students, pertaining to a single standard of DMS Mysore. The present

study is not considering the other computer aided learning environment in the school. The study focuses only on the

students learning NCERT Science syllabus.

Analysis:

Hypotheses:

1. ICR will have a positive impact on the Mathematics learning of the class VI students.

2. The impact of ICR on Mathematics learning of the students will differ between the experimental groupand control group.

3. The impact of ICR on Mathematics achievement will differ between the experimental and control group.

4. The use of ICR will influence differently in the students believes towards Mathematics.

5. The impact of ICR on Problem Solving ability of the students will differ between the experimentaland control group.

These hypotheses were restated and explored in detail for the statistical analysis in null form as given:

Hypothesis 1: H1,0

There is no significant difference between means of pre and post test score in verbal reasoning of theexperimental group

Alternate Hypothesis: H1

The experimental group shows significant difference in mean of the pre and post test score of verbalreasoning.

Paired Differences t df Sig. (2-tailed)

Mean

Std.Deviatio

n

Std.ErrorMean

95% ConfidenceInterval of the

Difference

Lower Upper

Pair 1 Verbal reasoning Pre-Exptl - verabalreasoning post expt

-1.49 1.976 .334 -2.16 -.81 -4.448 34 .000

Since the P value for t35,0.05 (=.000) is less than the critical value (=0.05) the null the null hypothesiscan be rejected and the alternate hypothesis can be accepted. That means the difference in means ofverbal reasoning of the experimental in pre and post-tests is statistically significant. Hence to conclude,the verbal reasoning of the experimental group between pre and post-tests are significantly differingafter the use of ICR.

Hypothesis 2: H2,0


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There is no significant difference between means of pre and post test score in numerical ability of theexperimental group


The experimental group shows significant difference in numerical ability pre and post test scores.

Paired Differences t dfSig. (2-tailed)

Mean

Std.Deviati

on

Std.ErrorMean

95% Confidence Intervalof the Difference

Lower Upper

Pair 1 Numercal ability PreExptl - numericalability post expt

-2.06 1.878 .317 -2.70 -1.41 -6.481 34 .000

Since the P value for t35,0.05 (=.000) is less than the critical value (=0.05) the null the null hypothesis

can be rejected and the alternate hypothesis can be accepted. That means the difference in means ofnumerical ability of the experimental in pre and post-tests is statistically significant. Hence to conclude,the numerical ability of the experimental group between pre and post-tests is significantly differingafter the use of ICR.

Hypothesis 3:H3,0

There is no significant difference between means of pre and post test score in number series of theexperimental group

Hypothesis 3:H3

The experimental group shows significant difference in number series pre and post-tests scores.


Mean

Std.Deviatio

n

Std.ErrorMean


Difference

Lower Upper

Pair 1 Number series Pre-

Exptl - number seriespost exptl

-1.91 3.109 .526 -2.98 -.85 -3.642 34 .001

Since the P value for t35,0.05 (=.001) is less than the critical value (=0.05) the null the null hypothesiscan be rejected and the alternate hypothesis can be accepted. That means the difference in means ofnumber series of the experimental in pre and post-tests is statistically significant. Hence to conclude,the number series of the experimental group between pre and post-tests is significantly differing afterthe use of ICR.


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Hypothesis 4: H4,0

There is no significant difference between means of pre and post test score in formulation of theexperimental group.


The experimental group shows significant difference in formulation pre and post-tests scores.


MeanStd.

DeviationStd. Error

Mean


Difference

Lower Upper

Pair 1 Formulation pre exp -formulation post exptl

-2.06 2.195 .371 -2.81 -1.30 -5.543 34 .000

Since the P value for t35,0.05 (=.000) is less than the critical value (=0.05) the null the null hypothesiscan be rejected and the alternate hypothesis can be accepted. That means the difference in means offormulation of the experimental in pre and post-tests is statistically significant. Hence to conclude, theformulation of the experimental group between pre and post-tests is significantly differing after theuse of ICR.

Hypothesis 5: H5,0

There is no significant difference in the mathematics aptitude of the experimental and control groupafter the use ICR.


The experimental and control group differs significantly on the mathematics aptitude after the use ofICR.

Levene's Testfor Equality of

Variances t-test for Equality of Means

F Sig. t dfSig. (2-tailed)

MeanDifference

Std.ErrorDifference


Difference

Lower Upper

mathematicsaptitude-post

Equalvariancesassumed

2.201 .143 -.356 68 .723 -.43 1.204 -2.832 1.974

Equalvariances notassumed

-.356 65.305 .723 -.43 1.204 -2.833 1.976

Since the p value for t68, 0.05 (=0.723) is greater than the critical value (0.05) the null hypothesis shouldbe retained and the alternate hypothesis can be rejected. Therefore the difference between the meansof mathematics aptitude of the experimental and control group is not statistically significant.

Hypothesis 6:H6,0


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There is no significant difference in the Problem Solving of the experimental and control group afterthe use ICR.


The experimental and control group differs significantly on the problem solving after the use of ICR.Levene's Testfor Equality of

Variances t-test for Equality of Means


MeanDifference

Std. ErrorDifference


Difference

Lower Upper

problemsolving -post

Equal variancesassumed .439 .510 -1.724 68 .089 -1.54 .895 -3.328 .243

Equal variancesnot assumed -1.724

65.576

.089 -1.54 .895 -3.329 .244

Since the p value for t68, 0.05 (=0.089) is greater than the critical value (0.05) the null hypothesis shouldbe retained and the alternate hypothesis can be rejected. Therefore the difference between the meansof problem solving of the experimental and control group is not statistically significant.

Hypothesis 7: H7,0

There is a significant difference in the mean of believes towards mathematics of the experimental andcontrol group after the use of ICR.


The experimental and control group significantly differs in the mean of believes towards mathematicsafter the use of ICR.

Levene's Test forEquality ofVariances t-test for Equality of Means


MeanDifferenc

e

Std. ErrorDifferenc

e95% Confidence Interval

of the Difference

Lower Upper

BelievesTowardsMathematics

Equalvariancesassumed

.019 .892 -5.025 68 .000 -12.31 2.451 -17.205 -7.424

Equalvariances notassumed

-5.025 67.834 .000 -12.31 2.451 -17.205 -7.424

Since the p value for t68, 0.05 (=0.000) is less than the critical value (0.05) the null hypothesis can berejected and the alternate hypothesis can be accepted. Therefore the difference between the means ofbelieves towards mathematics of the experimental and control group is statistically significant. That isthe experimental and control group significantly differs in the believes towards mathematics after theuse of ICR.

Hypothesis 8:H8,0

There is no significant relation between the achievement in mathematics and Problem solving ability ofthe students.



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There is a significant relation between achievement in mathematics and Problem solving ability.

achievementtest score

Problemsolving

Post exptl

achievement testscore

Pearson Correlation 1 .615(**)

Sig. (2-tailed) . .000

N 35 35

Problem solving Postexptl

Pearson Correlation .615(**) 1

Sig. (2-tailed) .000 .

N 35 35

** Correlation is significant at the 0.01 level (2-tailed).

The correlation coefficient for the achievement test and problem solving ability is positive andsignificant at the 0.01 level. The null hypothesis is not valid and hence the alternate hypothesis can beaccepted. That means there is a positive correlation between the mathematics achievement and theproblem solving ability of the students.

Hypothesis 9: H9,0

There is no relation between the mathematics achievement and mathematics aptitude of the students.


There is a relation between the achievement in mathematics and Mathematics aptitude

achievementtest score

Maths aptitudePost Exptl

achievement test score Pearson Correlation 1 .328

Sig. (2-tailed) . .054

N 35 35

Maths aptitude PostExptl

Pearson Correlation .328 1

Sig. (2-tailed) .054 .

N 35 35

The coefficient of correlation is found to be 0.328, which indicates that there is a positive correlationbetween achievement in mathematics and mathematics aptitude of the students.


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Summary of the Findings:

1. The verbal reasoning of the experimental group between pre and post-tests is significantly differingafter the use of ICR.

2. The numerical ability of the experimental group between pre and post-tests is significantly differingafter the use of ICR.

3. The number series scores of the experimental group between pre and post-tests is significantlydiffering after the use of ICR.

4. The formulation scores of the experimental group between pre and post-tests is significantlydiffering after the use of ICR.

5. There is no significant difference in the mathematics aptitude of the experimental and controlgroup after the use ICR.

6. The difference between the means of problem solving of the experimental and control group is notstatistically significant.

7. The experimental and control group significantly differs in believes towards mathematics after the

use of ICR.8. There is a positive correlation between the mathematics achievement and the problem solving

ability of the students.

9. There is a positive correlation between achievement in mathematics and mathematics aptitude ofthe students.

Conclusions:

It has been found that the use of NIIT ICR influenced the verbal reasoning, numerical ability, number

series and formulation of the class VI students significantly. The impact also show that the use of ICRhas influenced in students believes towards mathematics. The positive correlation is explicit about therelation between achievement in mathematics with the mathematics aptitude and problem solvingability of the students. Thus it is clear that, NIIT ICR is having a positive impact on the mathematicslearning of class VI students.


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