A STUDY ON EFFECTIVENESS OF GAMIFIED LEARNING AMONG ARTS & SCIENCE COLLEGE STUDENTS WITH SPECIAL REFERENCE TO CHENNAI CITY

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A STUDY ON EFFECTIVENESS OF GAMIFIED LEARNING AMONG ARTS & SCIENCE COLLEGE STUDENTS WITH SPECIAL REFERENCE

TO CHENNAI CITY

NIVEDDA MK, Dr. R. Angayarkanni,

Research Scholar, Research Supervisor, Assistant Professor, Associate Professor, Department of Commerce, Department of Commerce, Faculty of Science and Humanities, Faculty of Science and Humanities, SRM IST, Kattankulathur. SRM IST, Kattankulathur.

Abstract

PURPOSE: The purpose of this study is to find out the effectiveness of gamified learning among arts and science college students in Chennai city. The other purpose of this study is to analyze the factors that influence towards gamified learning.

METHODOLOGY: The data for the study is collected from 90 students in arts and science colleges in Chennai. Also, the study focuses only on 5 major departments in city colleges. Purposive sampling method has been used.

TOOLS AND TECHNIQUES: Demographic profile of the respondents is studied. ANOVA and Factor Analysis are used for the study.

FINDINGS: The study identifies that students feel easy to listen, understand and learn if the new teaching techniques like gamification are used in classrooms.

IMPLICATIONS: The results of the study indicates that, this is the right time for the educators to adapt new technology oriented techniques in classroom as most of the students in city colleges are already being benefited because of such techniques.

KEYWORDS: Gamification, learning, teaching and technology INTRODUCTION

Gamification refers to the application of game design elements to non-game activities and has been applied to a variety of contexts including education. Various elements have been used in gamification to increase user engagement. Examples of these elements include points, badges, leaderboards, and rewards.

Educational institutions are interested in gamification of education, where educators create gamified learning environments to enhance learner engagement and improve learning outcomes. Given the potential of gamification of education, we are interested in identifying game design elements that have been used to gamify education as well as the impact on learner outcomes

Computer and mobile games are increasingly part of the daily activities of students of all ages, and it’s been extremely burdensome and challenging to separate the students and games. Also, the expectation of echo boomers is more than that of just “chalk and talk” teaching. Hence, there is an absolute need for the teachers to think out of the box to motivate the students’ and make them learn. Many research have been conducted to improve students’ performance and learning in class room. In this study, the researcher try to connect the two elements i.e., games and learning. Also, the focus is given on Gamification, as a tool to improve students’ engagement in class room and learning. Gamification involves incorporating game elements such as quizzes, puzzles, smart classrooms into non-game contexts in order to take advantage of the motivation provided by a game environment. The findings of this study would be very useful for the Gen Y teachers to make the students involve themselves in the college or class room activities. Also, this tool will also detach the gap between the class room and students.

Similar to technological changes altering the way students learn, they can also be used by educators to alter the way subject content is delivered. This fact has long been recognized in academia and has seen the introduction of online learning, online discussion boards and smart classes, etc. in the classroom.

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Numerous research studies have been conducted into the effectiveness of computer games for instruction with mixed results reported (Kapp, 2012).

A recent trend in using games to influence behavior (not just learning behavior but any type of desirable behavior) is called “gamification.” A popular and broad definition of gamification is “the use of game design elements in non-game contexts” (Deterding et al., 2011). The term originated in the digital media industry and widespread adoption started to occur in the second half of 2010. To date, gamification has been used in a wide variety of industry categories such as: art, call center, commerce, education, entertainment, environment, design, government, health, life, marketing, market research, mobile, social good, web sites and work. The study focuses only on college level education sector. Many colleges in Chennai city are using this technique in class room to engage with students. For the study purpose, few departments of arts and science colleges in the city have been considered.

REVIEW OF LITERATURE

Gamification has been defined as the use of game design elements in non-game contexts such as education (Deterding, S., Dixon, D., Khaled, T., & Nacke, L (2011). Gamification has gained significant attention especially in educational contexts (Koivisto & Hamari, 2017; Seaborn & Fels, 2015). Gamifying education and learning has a long history (see e.g. Deterding, 2014) and an intuitively understandable background as game design and theories on learning draw heavily from same psychological theoretical backgrounds (Landers, 2014).

Gee, J. P. (2007) identified 36 learning principles that are present in the gamification process. These include critical learning, design, semiotic domains, meta level thinking, self-knowledge and achievement learning principles just to name a few. Nah, F. F,-H., Zeng, Q., Telaprolu, R., Ayyappa, A. P., &

Eschenbrenner, B. (2014) conducted a review of the literature on gamification in education. Their synthesis identified the design elements utilized to gamify education were the following: points, levels or stages, badges, leaderboards, prizes and rewards, progress bars, storyline, and feedback. Students experience motivation and engagement which enhances learning when these various game elements are used in a classroom situation (Lee, J. J., & Hammer, J. (2011).

Dicheva, D., Dichev, C., Agre, G., & Angelova, G. (2015) conducted a systematic mapping study of gamification in education from several research papers and revealed the categories of design principles included were: goals and challenges, personalization, rapid feedback, visible status, freedom of choice, freedom to fail, and social engagement. The most popular design principles were individual and group challenges where competition created social engagement and visible status were reported. It was noted that most of the gamification of education and reported research was conducted in science, engineering, information and computer technology at the college level (Nah, F. F,-H., Zeng, Q., Telaprolu, R., Ayyappa, A. P., & Eschenbrenner, B. (2014). Given such promising results and the grounding of theories and learning principles in the gamification process, more empirical research needs to be conducted. This study was carried out to discover the effectivess of gamification among college students.

OBJECTIVES

The current study is carried out to:

1. To study the demographic profile of the respondents

2. To analyze the factors that influence towards gamified learning 3. To analyze the effect of Gamification on students’ learning METHODOLOGY

COLLECTION OF DATA

The primary data of this study were collected from 90 arts and science college students in Chennai City with the help of well structured questionnaire. This study is developed to understand the effectiveness of gamified learning among respondents.

SAMPLING METHOD

Purposive sampling method was adopted to analyze the effectiveness of gamified learnings towards college students in Chennai city. The questionnaires were distributed to 95 arts and science college

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students. Out of which 80 questionnaires were used for the analysis. Secondary data have been collected from books, journals, internet, etc.

TOOLS USED FOR ANALYSIS

The primary data have been analyzed by using the statistical tools ANOVA and factor analysis.

DATA ANALYSIS AND INTERPRETATION

Table 1 - Demographic Profile of the Respondents

Factors Frequency Percentage

Gender Male 47 52.2

Female 43 47.8

Family Type Joint Family 45 50

Nuclear Family 45 50

Department Commerce 21 23.3

Computer Science 20 22.2

Management 16 17.8

Computer Application

14 15.6

Visual

Communication

19 21.1

Mode of

Transport

By Walk 22 24.4

Two Wheeler 27 30

Four Wheeler 19 21.1

College Bus 22 24.4

No. of Hours per day

5 hours 26 28.9

6 hours 28 31.1

7 hours 15 16.7

8 hours 21 23.3

Duration of an hour

45 Minutes 23 25.6

50 Minutes 28 31.1

55 Minutes 20 22.2

60 Minutes 19 21.1

Type of

Student

Day Scholar 49 54.4

Hosteller 41 45.6

Source: Primary Data Inference

The above table depicts the demographic factors and their distribution towards the study. Out of 90 respondents, 52.2% of respondents found to be male; 50% of the students come from a joint family background and 50% come from nuclear family background.

Out of the total respondents, Majority of the students belong to Commerce (23.3%), Computer Science (22.2%) and Visual Communication (21.1%). Also the other departments such as Management and Computer Application also hold an appropriate distribution of 17.8% and 15.6% respectively.

From the above table, it is clear that 30% of the students use two wheeler as a mode of transportation.

Also, it has been recorded that majority of the arts and science colleges in the city has 6 working hours per day (31.1%) and most of the colleges have 50 minutes duration for one working hour (31.1%).

54.4% of respondents turn out to be Day Scholars and 45.6% of them are Hostellers.

HYPOTHESIS

H0: There is no significant difference between gender and usefulness of gamified learning

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H1: There is a significant difference between gender and usefulness of gamified learning

Table 2 - ANOVA Analysis for Gender of Respondents and usefulness of gamified learning Sum of

Squares

df Mean Square F Sig.

Between Groups .327 1 .321 1.421 .241

Within Groups 10.603 88 .237

Total 11.210 89

Inference

The above ANOVA table shows that the significant value is 0.241 and it is higher than 0.05 so accept the null hypothesis. Hence there is no significance difference between the gender and the effectiveness of gamified learning.

HYPOTHESIS

H0: There is no significant difference between the Deparment and attitude towards gamified learning H1: There is a significant difference between the Deparment and attitude towards gamified learning

Table 3 - ANOVA Analysis for Department and attitude towards gamified learning Sum of

Squares

Between Groups .248 1 .293 .187 .658

Within Groups 78.723 88 1.690

Total 79.520 89

Inference

The above ANOVA table shows that the significant value is 0.658 and it is higher than 0.05 so accept the null hypothesis. Hence there is no significance difference in the Department of the student and the effectiveness of gamified learning.

HYPOTHESIS

H0: There is no significant difference between the Duration of an hour and impact of gamified learning on students

H1: There is a significant difference between the Duration of an hour and impact on students’ studies Table 4 - ANOVA Analysis for duration of an hour and its impact on students’ studies

Sum of Squares

Between Groups 5.689 1 5.982 2.865 .035

Within Groups 98.343 88 2.563

Total 104.281 89

Inference

The above ANOVA table shows that the significant value is 0.035 and it is lower than 0.05 so reject the null hypothesis. Hence there is a significance difference between the duration of an hour and their impact on students studies.

FACTOR ANALYSIS

Table – 5

KMO AND Bartlett’s Test

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Kaiser-Meyer-Olkin Measure of Sampling Adequacy .734

Barlett’s Test of Sphericity

Approx. Chi-Square 1654.773

Df 378

Sig. .000

Inference

After computing factor analysis for effectiveness of gamified learning among arts and science college students in Chennai city has been identified that KMO sampling acceptability is highly satisfied with the scale of measures. Based on KMO measure, values should be more than 0.60 to 0.70 while computing the result. As mentioned above, the Barlett’s test of Sphericity resulted with main aspects with approximate Chi-square value as 1654.773 as it is also considered as an constructive output. Degree of freedom implies the 378 as the freedom value and the final level of significance is 0.000 as it is less than 0.05 under the probability value. Hence, the study resulted in a valid output with greater significant value in order to deliver the concept in effective manner.

Inference

The term communalities is accumulated with two aspects namely initial value and extracted value. As per the communality terms, the majority of the results of initial value should be greater than 1 and after the extraction the value should be greater than 0.4 with higher level of significance. Based on the results delivered, the grouping factors were loaded with high significant values as extremely satisfied. Variables such as EG2, EG6, EG7, EG13, EG15, EG19 and EG20 are loaded with the value greater than 0.8 which clearly shows the significance of current study.

Table 7 - Total Variance Explained Compo

nent

Initial Eigenvalues Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Table – 6 Communalities

Initial Extraction

EG1 1.000 .682

EG 2 1.000 .877

EG 3 1.000 .751

EG 4 1.000 .610

EG 5 1.000 .648

EG 6 1.000 .979

EG 7 1.000 .819

EG 8 1.000 .764

EG 9 1.000 .779

EG 10 1.000 .677

EG 11 1.000 .669

EG 12 1.000 .730

EG 13 1.000 .979

EG 14 1.000 .778

EG 15 1.000 .979

EG 16 1.000 .682

EG 17 1.000 .764

EG 18 1.000 .748

EG 19 1.000 .979

EG 20 1.000 .979

EG 21 1.000 .779

Extraction Method: Principal Component Analysis.

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Total % of Variance

Cumulativ e %

1 2.547 21.227 21.227 10.594 37.836 37.836 4.091 14.610 14.610

2 1.877 15.640 36.867 2.512 8.971 46.808 3.871 13.827 28.437

3 1.562 13.015 49.882 1.963 7.012 53.820 2.831 10.109 38.546

4 1.373 11.439 61.320

5 .917 7.642 68.962

6 .867 7.221 76.183

7 .770 6.418 82.601

8 .602 5.017 87.618

9 .581 4.844 92.462

10 .438 3.649 96.111

11 .272 2.270 98.380

12 .270 .877 96.308

13 .267 .704 97.012

14 .264 .581 97.593

15 .252 .564 98.157

16 .243 .538 98.695

17 .241 .386 99.081

18 .238 .329 99.409

19 .228 .253 99.663

20 .221 .224 99.887

21 .194 1.620 100.000

Extraction Method: Principal Component Analysis.

Inference

The above table depicts the principal component analysis (PCA) methods which provides the relationship between the extracted factors and rotated factors with the variables used in this analysis. It is technically termed as factor loadings. The value of factor loadings indicate the relationships clearly. Third factor consists of higher variance i.e., 38,546.

Table – 8 ROTATED COMPONENT MATRIX COMPONENTS

1 2 3

EG3 .796

EG6 .717

EG9 .671

EG12 .612

EG15 .552

EG18 .501

EG21 .466

EG2 .832

EG4 .763

EG8 .711

EG14 .576

EG16 .567

EG20 .554

EG1 .526

EG5 .835

EG7 .792

EG10 .766

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Extraction Methods: Principal Component Analysis Rotation Method: Varimax with Kaiser Normalization Inference

Above tables explains the principal component analysis and rotated factor loading method is used to identify the factors. It has been observed that out of 21 variables, 3 factors namely Usefulness, Attitude and Impact on studies were identified by the rotation method.

GROUPING FACTOR

1. The “Usefulness factor” explains the 1^st component.

2. The “Attitude factor” explains the 2^nd component.

3. The “Impact on studies factor” explains the 3^rd component.

From the above table, it is very clear that 3^rd factor has been recorded with highest factor loading of 0.835.

FINDINGS AND SUGGESTIONS Findings based on Demographic Profile

 52.2 per cent of the respondents are male.

 50 per cent of the respondents come under Nuclear family size

 23.3 per cent of the respondents are from Commerce background

 30 per cent of the respondents uses two wheeler as their mode of transportation

 31.1 per cent of the respondents’ colleges have 6 hours per day

 31.1 per cent of the respondents’ college have 50 minutes for one hour

 54.4 per cent of the respondents are day scholars Findings based on ANOVA and Factor Analysis

Based on ANOVA, there is no significant difference between gender and usefulness of gamified learning;

department and attitude towards gamified learning. And also, there is a significant difference between duration of an hour and its impact on students’ studies.

Based on Factor Analysis, it is observed that out of 21 variables, 3 factors namely, Usefulness, Attitude and Impact on studies were identified by the rotation method. The highest factor loading is for “The online gamification functionality and interface is clear and understandable” with the loadings of .835.

From the above study, it is very clear that students find to use gamification techniques in class room and they find it useful for their studies. Hence, it is the right time for the educator to switch from old traditional way of teaching to using modern techniques in teaching.

LIMITATIONS

The study has been limited to only 90 students of arts and science colleges and that is also restricted to only 5 departments. Hence, the study has covered only minority of the population.

CONCLUSION

It is indicative from the above discussion that the students are ready to accept new techniques such as gamification in classroom. Based on the study carried out on some of the colleges in Chennai that are using this technique, it is evident that students find it very easy to listen and learn in classroom if new innovative methods are implemented rather than the old methods of lecturing. Also, the students expectation and exposure to technology is quite higher and hence using them positively in studies would make them interesting to learn the subjects.

REFERENCES:

1 Deterding, S., Dixon, D., Khaled, T., & Nacke, L. (2011). From game design elements to gamefullness: Defining “gamification”. In A. Lugmayr (Ed.), MindTrek 2011 (pp. 9-15).

Tampere, Finland: ACM.

EG11 .606

EG13 .631

EG17 .589

EG19 .576

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2 Koivisto, J., & Hamari, J. (2014). Demographic differences in perceived benefits from gamification. Computers in Human Behavior, 35, 179–188.

3 Deterding, S. (2014). The ambiguity of games: Histories and discourses of a gameful world. In S.

P. Walz & S. Deterding (eds.), The Gameful World: Approaches, Issues, Applications (pp. 23–

64). Cambridge, MA: MIT Press.

4 Landers, R. N. (2014). Developing a theory of gamified learning: Linking serious games and gamification of learning. Simulation & Gaming, 45(6), 752–768.

5 Gee, J. P. (2007). What video games have to teach us about learning and literacy. (Rev. Ed.) New York, NY: Palgrave Macmillan

6 Nah, F. F,-H., Zeng, Q., Telaprolu, R., Ayyappa, A. P., & Eschenbrenner, B. (2014).

Gamification of Education: A review of literature. In F. F.-H. Nah (Ed.) HCI in Business, (pp.

401-409). Heraklion, Greece: Springer.

7 Lee, J. J., & Hammer, J. (2011). Gamification in education: What, how, why bother? Academic Exchange Quarterly, 15(2), 1-5.

8 Dicheva, D., Dichev, C., Agre, G., & Angelova, G. (2015). Gamification in education: A systematic mapping study. Education Technology & Society 18(3), 1-10.