FACTOR AFFECTING THE KNOWLEDGE QUALITY AND STUDENT’S LEARNING OUTCOMES THROUGH E- LEARNING IN INDORE CITY
Mrs. Ranjita Das Soni Assistant Professor, IMIRC, Indore
Dr. Priyanka Sharma
Faculty, Department of Management Studies, GACC, Indore
Abstract:- The first focus of this study is however we are able to produce quality Knowledge through the e-learning surroundings that is absolutely associated with students’
perceptions of their learning outcomes. This includes the investigation of the probable relationship between the management of Knowledge quality and student learning outcomes.
This relationship includes many aspects of Knowledge quality and student learning outcomes. A literature review provided the idea for the event of the analysis model. The model known four explicit aspects of Knowledge quality (soundness, dependableness, usefulness, and usability) and also the student learning outcomes. This intrigued the researcher, and, given the experience and practical problems faced, the researcher therefore submitted an initial proposal that addressed the factor affecting the Knowledge Quality and Student’s Learning Outcomes through E- Learning in Indore City
Keywords: Factors, E-learning, Students, Knowledge Quality.
1. INTRODUCTION
Many HEIs and programmers have with success tailored and used a succession of technological advances in recent decades, as well as technology-assisted open universities, non-classroom based mostly modes of tutorial delivery, and pc modeling and simulation as tutorial tools. “Blended” instruction within which room time is increased through internet- based student-faculty interaction or student-to-student networking is currently the norm in several HEIs and programmes. Yet, analysis suggests that these steps square measure solely early innovations within the transformation of each instruction and learning which bigger potential will be complete through the combination of technology (Norway Open Universities, 2011; Johnson et al., 2012).
More exactly speaking incorporating IT to proportion Knowledge comes appears ineluctable (Lee and Choi, 2003). The simplest resolution regarding applying IT to Knowledge is to own the attention regarding the boundaries of IT which will provide most optimum results for Knowledge. Another truth value mentioning here is that any IT readying won't be utterly eminent till in the middle of cultural amendment towards Knowledge values. Therefore, technology support is critical for Knowledge in a company.
Knowledge comes seemingly to succeed once a complicated technology infrastructure is adopted. IT infrastructure includes Email, document management, knowledge storage, progress code; call web etc. Knowledge is useless for competitive functions till communication and application system supports the varied business operations. Moreover, Knowledge creation, seeking and dissemination square measure improved by IT and it's additionally a crucial help for storing and sharing structure Knowledge.
2. LITERATURE REVIEW
Ivergard & Hunt (2005) argued that poor quality Knowledge created through the e-learning surroundings “gave users a sense of being stressed and badly treated by the system” and caused students to feel pissed off and eventually stop learning. In addition, Knowledge created ought to be tailored to the requirements of the learners: it ought to be simple to use and students ought to have quick access to steering and knowledge (Howell et al., 2003;
James Gordon et al., 2003). What is more, poor usability of an internet course can inhibit the learner’s ability to accumulate Knowledge (Smulders, 2003). In short, Knowledge created through e-learning surroundings ought to be simple to use and are available with careful steering and ultimately be appropriate for all learners. Thus, making quality Knowledge through the e-learning surroundings to suit learners appears to be a troublesome task, not to mention up student learning outcomes.
2.1 Purpose of the Study
This understanding and empirical analysis would facilitate decision-makers to figure on weak processes to deal with and strength others for any enhancements. Moreover, in line with the orientations of the India’s Ministry of Higher Education and University Grant Commission concerning the educational performance improvement; this study tries to shed light-weight on problems regarding the appliance of Knowledge in pedagogy in India to beat the barriers obstruction the improvement of educational performance. The simplest resolution regarding applying IT to Knowledge is to own the attention regarding the boundaries of IT which will provide most optimum results for Knowledge. Another truth value mentioning here is that any IT readying won't be utterly eminent till in the middle of cultural amendment towards Knowledge values. Modern technology has introduced new ways of teaching, and e-learning is a rising technique in this era. The present study determines the factor affecting the Knowledge Quality and Student’s Learning Outcomes through E- Learning in Indore City
2.2 Objectives of the Study
1. To analysis the students’ understanding of e-learning environment and its benefits to institutes and themselves.
2. To analysis the factor affecting the Knowledge quality and student’s learning outcomes.
2.3 Hypothesis
H01: Knowledge quality as presented on the e-learning environment is not significant as per the Student’s Perception in Indore City.
H02: There is no significant impact of e-learning environment on students learning outcomes in Indore City.
3. METHODOLOGY
The present research study is descriptive and explorative in nature. The research was conjointly created use of quantitative values in describing the phenomena of the study, thereby creating it a mixed method approach. In general, researchers like probabilistic or sampling ways over non-probabilistic ones, and take into account them to be more accurate and rigorous. The probabilistic sampling technique is adopted since the sampling area and sample size are large and a more overly the targeted group i.e., vulnerable size is not precise. A well structured questionnaire with 5 point scale is used to collect the responses using scheduling method for the respondents. The sample size selected for the analysis and inference was 400 respondents. SPSS [17] The universe for the study is management institutes (MBA) of Indore. It includes private college, university teaching department and private university as well as B-Schools. To study the factor affecting the Knowledge quality and student’s learning outcomes is done by using factors analysis. For testing the hypothesis, ANOVA and t-test is used.
Table-01
Cronbach's Alpha Cronbach's Alpha Based on Standardized Items
0.797 0.734
(Source: Researcher’s Computation)
Table -02: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .723 Bartlett's Test of Sphericity Approx. Chi-Square 4.9888
df 126
Sig. .000
It may be noted that the value of KMO statistics is greater than 0.5, indicating that factor analysis could be used for the given set of data. Further, Bartlett’s test of sphericity testing for the significance of the correlation matrix of the variables
4. RESULT OF HYPOTHESIS
H01: Knowledge quality as presented on the e-learning environment is not significant as per the Student’s Perception.
Table -03: Model Summary in case of Student’s Perception on Knowledge quality as presented on the e-learning environment
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .855a .783 .714 .57663
a. Predictors: (Constant), The up to date of information, The clarity and reliability of the information, The comprehension of Information system, The benefits of the information in operation, User’s demand response of the information, Categorization of the information, The systematic and ordering information presentation, The rapidity of the searching engine, Accessibility of information, Simplicity of the information, Satisfaction on system overview, Convenience of information using, Rapidity of using, Accuracy of the system, The comprehension of system, Consulting and solution providing
From the above table, it is found that the R square is 0.783. This indicates that the determination power of the regression equation is about 78.3 percent. Hence 78.3 percent variation in the knowledge quality is explained by the independent variables. The rest of 21.7 percent of knowledge quality is unexplained in the model. The standard error of the estimates is 0.57663.
The F ratio (ANOVA) is 18.463, which is significant at 5 percent level of significance.
Therefore, the model is acceptable. The regression model is estimated by enter method.
Table -04: ANOVAb in case of Student’s Perception on Knowledge quality as presented on the e-learning environment
Table -05: Coefficients in case of Student’s Perception on Knowledge quality as presented on the e-learning environment
Model Unstandardized
Coefficients Standardized
Coefficients t Sig.
B Std. Error Beta
1 (Constant) 5.608 .478 11.738 .000
VAR01 -.874 .086 -.726 -10.174 .000
VAR02 .855 .105 .960 8.135 .000
VAR03 -.884 .107 -.800 -8.294 .000
VAR04 -.396 .052 -.568 -7.681 .000
VAR05 .128 .058 .180 2.212 .028
VAR06 .926 .113 1.010 8.228 .000
VAR07 -.095 .112 -.091 -.844 .399
VAR08 .609 .079 .592 7.673 .000
VAR09 -.307 .073 -.386 -4.211 .000
VAR10 -.787 .101 -.825 -7.823 .000
VAR11 -.026 .085 -.025 -.310 .757
VAR12 -.531 .099 -.490 -5.373 .000
VAR13 .297 .043 .442 6.898 .000
VAR14 -.549 .056 -.854 -9.888 .000
Model Sum of
Squares df Mean Square F Sig.
1 Regression 97144 16 6.009 18.463 .000a Residual 132.446 383 .322
Total 291.590 399
VAR15 1.436 .112 1.259 12.814 .000
VAR16 -.285 .046 -.363 -6.141 .000
a. Dependent Variable: knowledge quality
From the above, it is clear that the intercept is 5.608 and statistically significant.
This implies that Knowledge quality as presented on the e-learning environment is significant as per the Student’s Perception in Indore city. The coefficients are positive and statistically significant at 5 percent level of significance. The coefficients of User’s demand response of the information, The systematic and ordering information presentation, Satisfaction on system overview is negative, hence it is showing negative Knowledge quality as presented on the e-learning environment is not significant as per the Student’s Perception in Indore city as it is statistically insignificant.
Thus in case of Knowledge quality, it can be concluded that, Knowledge quality as presented on the e-learning environment is significant as per the Student’s Perception but this is not significant in the case of User’s demand response of the information, The systematic and ordering information presentation, Satisfaction on system overview.
H02: There is no significant impact of e-learning environment on students learning outcomes.
Table -06: Model Summary in case of impact of e-learning environment on students learning outcomes
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 .985a .981 .921 .20676
From the above, it is found that the R square is 0.981. This indicates that the determination power of the regression equation is about 98.1 percent. Hence 98.1 percent variation in the e-learning environment is explained by the independent variables. The rest of 1.9 percent of e-learning environment is unexplained in the model. The standard error of the estimates is 0. 20676.
Table -07: Communalities
Variable Initial Extraction
1 The up to date of information 1.000 0.69
2 The clarity and reliability of the information
1.000 0.73 3 The comprehension of Information
system
1.000 0.79 4 The benefits of the information in
operation
1.000 0.89 5 User’s demand response of the
information
1.000 0.68 6 Categorization of the information 1.000 0.87
7 The systematic and ordering
information presentation
1.000 0.77 8 The rapidity of the searching engine. 1.000 0.84
9 Accessibility of information 1.000 0.83
10 Simplicity of the information 1.000 0.74
11 Satisfaction on system overview 1.000 0.77 12 Convenience of information using 1.000 0.79
13 Rapidity of using 1.000 0.86
14 Accuracy of the system 1.000 0.88
15 The comprehension of system 1.000 0.81
16 Consulting and solution providing 1.000 0.44
17 Overall knowledge quality 1.000 0.76
5. FACTOR ANALYSIS
Table -08: Total Variance Explained Co
mp one nt
Initial Eigenvalues Extraction Sums of Squared
Loadings Rotation Sums of Squared
Loadings Total % of
Variance Cumulati
ve % Total % of
Variance Cumulativ
e % Total % of
Variance Cumula tive % 1 5.298 31.164 31.164 5.298 31.164 31.164 3.801 22.359 22.359 2 2.071 12.184 43.348 2.071 12.184 43.348 1.966 11.563 33.921 3 1.753 10.312 53.660 1.753 10.312 53.660 1.827 10.749 44.670 4 1.394 8.201 61.862 1.394 8.201 61.862 1.794 10.553 55.223 5 1.365 8.030 69.892 1.365 8.030 69.892 1.791 10.536 65.759 6 1.030 6.059 75.950 1.030 6.059 75.950 1.733 10.191 75.950
7 .820 4.824 80.774
8 .683 4.019 84.794
9 .559 3.289 88.083
10 .505 2.970 91.053 11 .466 2.741 93.794 12 .341 2.007 95.801 13 .204 1.197 96.998
14 .161 .947 97.945
15 .131 .771 98.716
16 .114 .670 99.386
17 .104 .614 100.000
Extraction Method: Principal Component Analysis.
Source: Researcher’s Calculation from Primary Data
As depicted from table no.08 there are six variables which have more than 1.000 Eigen value. The cumulative variance explained by these three components is 75.950%.
Eigen values and associated components can further be studied through Cattell’s Scree Plot (figure 1).
Figure 1: Scree Plot
The graph clearly demonstrates that there are six components which are more crucial for the respondents regarding Knowledge quality and student’s learning outcomes.
The remaining variables also have exerted influence on the respondents but that is on a limited scale. The result of principal component analysis has further been analyzed through factor loading. To identify substantive loadings, the present research suppresses loadings having value less than 0.40.
Table -09: Rotated Component Matrixa Component
1 2 3 4 5 6
VAR00001 .736
VAR00002 .679
VAR00003 .558
VAR00004 .887
VAR00005 .751 VAR00006 .879
VAR00007 .483
VAR00008 .711
VAR00009 .795
VAR00010 .777 .
VAR00011 .722
VAR00012 826
VAR00013 .890
VAR00014 .899
VAR00015 .882
VAR00016 .580
VAR00017 -.638
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 8 iterations.
Rotated component matrix reveals that out of total 17 variables four variables load highly onto one factor and remaining thirteen variables load on two or more factors. The entire rotation process has been converged in eight iterations and has resulted into six factors. These factors may be summarized as follows:
5.1 The Six Factors
The component transformational matrix of these components may be shown as follows (table -10)
Table -10: Component Transformation Matrix
Component 1 2 3 4 5 6
1 .781 .388 -.084 .338 .195 .283
2 -.354 .316 .573 .253 .618 -.014
3 .170 -.113 .743 -.158 -.461 .411
4 .194 .295 .257 .160 -.362 -.808
5 .043 -.666 .074 .739 -.007 -.060
6 -.443 .454 -.201 .475 -.486 .308
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
(Source: Researcher’s Calculation from Primary Data)
The scores of factor analysis may further be utilized to have regression analysis and ANOVA which has been discussed in next paragraph.
5.5 Regression Analysis and ANOVA
The study employs regression analysis and ANOVA to test the strength of relationship of overall knowledge quality (dependent variable) with RGER factor scores (independent variables). The results of this analysis may be studied through table no.5.24 and 5.25.
Table -11: Results of Regression analysis and ANOVA Model R R
Squa re
Adjusted
R Square Std.
Error of the Estimat e
Change Statistics R
Square Change
F Chan ge
df1 df2 Sig. F Chang e
1 .830 .765 .764 .30770 .765 481.1
04 17 40
0 .00 Table -12: ANOVA
Model Sum of Squares df Mean Square F Sig.
1 Regression 471.231 17 35.068 571.104 .000
Residual 70.380 400 .043
Total 531.581 417
The above table apparently exhibits that the regression model developed is significant at 5% level of significance (as the value of F 0.00< 0.05). The value of R square is 0.765 (i.e.>0.40) which is satisfactory for defining the positive strength of relationship between overall knowledge quality and other independent variables.
6. CONCLUSION & SUGGESTIONS
There has been so a paradigm shifts in management education in India. The new breed of management professionals have to be compelled to be economical to tackle issues from cross practical, cultural and moral views and equipped with skills to bench mark for world leadership positions. There has been a crying have to be compelled to commence a high quality movement and to benchmark identical with world standards. ICT can play a key role in creating a culture and an infrastructure for promoting and supporting access to and sharing of knowledge in the management institutions. Management institutes should enhance its Management of Business Environments, Internal-External Connectivity, Innovation and Organizational Learning and intellectual networks around the world between education and industry, education and professional bodies, education and governmental and non governmental institutions. There are different advantages of e- learning such as convenience, cost effectiveness, in-depth learning, diverse learning and freedom of speech. One of the conditions concerns how increasing the quantity and quality of instruction would increase student learning. Instruction knowledge can be easily created through the e-learning system. However, how are we going to ensure that the instruction knowledge created through the e-learning system is of high quality so that students indeed acquire the requisite knowledge and skill.
REFERENCES
1. Alavi, M., & Leidner, D. E. (2001). Knowledge management and knowledge management systems:
conceptual foundations and research issues. Management Information Systems Quarterly, 25(1), 107- 136.
2. Delen, D., Al-Hawamdeh, S., 2009, “A Holistic Framework for Knowledge Discovery and Management”, Communications of the ACM June 2009, vol. 52, No. 6, pp. 141-145.
3. Duffy, N., 1999, ”Benchmarking Knowledge Strategy”, Leveraging Knowledge for Business Performance1999: Knowledge in Action WITS Business School, Johannesburg.
4. Eppler, M., 2002, Glossary definition: Knowledge Management.Net Academy: www.knowledgemedia.org.
5. Frappaolo, C., 2006, “Knowledge Management”, West Sussex: Capstone Publishing Ltd. 127.
6. Gavin P. Levett, Marin D. Guenov, (2000) "A methodology for knowledge management implementation", Journal of Knowledge Management, Vol. 4 Issue: 3, pp.258-270, https://doi.org/10.1108/13673270010350066.
7. Kevin, C.D., Evaristo, J.B., 2004, “Managing Knowledge in Distributed Projects”, Communications of the ACM, Vol. 47, No. 4 pp. 87-9.
8. Nakkiran, N.S., Sewry, D.A., 2002, “A Theoretical Framework for Knowledge Management Implementation”, Proceedings of SAICSIT, pp. 235-245.
9. Petrides, L.A., 2004, “Knowledge Management, Information Systems and Organizations”, EDUCAUSE Center for Applied Research, Research Bulletin.
10. Ranjan, J., Khalil, S., 2007, “Application of Knowledge Management in Management Education : A Conceptual Framework”, Journal of Theoretical and Applied Information Technology, pp. 15-25
11. Tiwana, A., 2000, The Knowledge Management Toolkit: Practical Techniques for Building a Knowledge Management System, Prentice Hall, New Jersey.
12. Tseng, S. (2008). The effects of information technology on knowledge management systems. Expert Systems with applications, 35(1&2), pp150-160.
13. Wiig, K.M., 1996, “On the Management of Knowledge”, available at http://www.km- forum.org/what_is.htm.
14. Yeh, C.M.Y., 2005, “The Implementation of Knowledge Management System in Taiwan's Higher Education”, Journal of College Teaching and Learning, Vol.2, No.9, pp.35-41.
15. Yu, T, Lu, T. & Liu, T. (2010). Exploring factors that influence knowledge sharing behavior via web logs, Computers in human behavior, 26(1), pp.32-41.
16. Zack, M.H., 1999, “Managing Codified Knowledge”, Sloan Management Review, vol. 40, no. 4, pp.45-59.
17. Ziggy, HUI King-Chung Knowledge Management to be needed in on-line education. Press conference at MIT (2001).
