Indian Journal of Educational Technology Volume 6, Issue 2, July 2024 196 Research Article Exploring Users’ Satisfaction towards University’s Learning Management System Nidhi Bansal Associate Professor, Department of Commerce, Atma Ram Sanatan Dharma College, University of Delhi Email: nbansal@arsd,du.ac.in Abstract Post Covid-19, there has been paradigm shift in the way students and teachers interact with the institutions for their daily tasks. It’s a shift from traditional manual interaction to web aided interaction. For this, it is made mandatory by UGC for all institutions to have Learning Management systems (LMS) also referred to as websites in some cases. However, the users’ perception of the effectiveness of the LMS depends on how well it is updated and maintained. In Indian context not much literature review is available on satisfaction as regards LMS. Even few studies that have been conducted they focus on students. Teachers and Administrators/Head of institution viewpoint is ignored. Based on literature review, the study identified web architecture, understandability, effectiveness, efficiency, portability, security as s parameters for assessing satisfaction of students, teachers and administrators. The study observed that there is lot of scope of improvement as regards reliability, portability and security aspect. Further analysis revealed that policy makers need to work upon training of not so young teachers, as these people don’t find themselves so comfortable with the usage and knowledge of LMS. Only if teachers are apt with e-initiatives usage, they can motivate and make the students use these initiatives. Keywords: Web architecture, understandability, effectiveness, efficiency, portability, security, LMS. Introduction Universities now a days, provide majority services through electronic means. Electronic interface i.e., website is not one way interaction, that is just providing downloadable and informative services. It refers to two-way interaction between the management and all the stakeholders. The LMS is not restricted to completing office procedures. LMS also can help students/teachers gain knowledge beyond books. They can access online journals, e-books and research articles. Students/ teachers/administrators can also do collaborative learning if the system is fully interactive and offers facilities like video conferencing. But this is possible only if the users perceive the system to be effective and beneficial. The more satisfied user is the greater will be the acceptance of the system. Literature Review Post Covid-19, learning management systems have become part of education system. Assessing and understanding students’ satisfaction of learning management system has been objective of many recent researches. (Balkaya & Akkucuk, 2021;  Mehrolia et al., 2021;  Camilleri & Camilleri, 2021; Alturki & Aldraiweesh, 2021; Salem Al-Mamary, 2022). Pham et al. (2019) reiterated that student’s satisfaction is supreme, students become the customers of Indian Journal of Educational Technology Volume 6, Issue 2, July 2024 197 higher education institution. To assess the satisfaction of users of LMS many researches have been conducted worldwide. Mouakket and Bettayeb (2015) researched on factors affecting adoption of LMS by faculty and found perceived ease of use, web design, training and technical assistance as determinants. Ease of use, high-speed access to information, reliability, , attractive features, security and user- friendliness were identified as success factors for LMS ( Hassanzadeh et  al., 2012; Ramírez-Correa et al., 2017; Costa et al., 2020; Kurdi et al., 2020). A number of studies have used Technology Acceptance Model (TAM) (Davis, 1989). TAM model focusses on perceived usefulness and perceived ease of use as the main determinants. This model has been criticised by a number of researchers. It was contended though both ease of use and usefulness are determinants of success but are not equivalent to success, other factors also play a role (Petter et al., 2008; Sukendro et al., 2020). In this context, the present research uses a theoretical model which though is comparatively new but is extensive in elaborating the factors affecting the satisfaction of LMS, which is  Nguyen’s (2021). Bifurcating the factors identified (Nguyen, 2021) in Indian context we get parameters as web architecture, understandability, effectiveness, efficiency, portability and security. In India not much of the research work has been done on assessing satisfaction of students and particularly of teachers and administrators. The type of research is necessary to add value to LMS which has not been discarded after Covid-19 rather has become indispensable part of education system. Objectives The research paper intends to achieve the under said objectives: 1. To assess the users’ satisfaction towards Learning Management System in the University. 2. To offer suggestions to improve the Learning Management System. 3. Hypothesis H0:1: There is no significant difference between different users as regards Web Architecture. H0:2: There is no significant difference between different users as regards Understandability. H0:3: There is no significant difference between different users as regards Effectiveness. H0:4: There is no significant difference between different users as regards Efficiency. H0:5: There is no significant difference between different users as regards Reliability. H0:6: There is no significant difference between different users as regards Portability H0:7: There is no significant difference between different users as regards security Sampling Design Population y Administrators (Head of Institution and Vice-Principal) of Non-Technical (General) and technical colleges of Delhi and NCR at the undergraduate level. y Faculty in Non-Technical (General) and technical colleges of Delhi and NCR at the undergraduate level. y Students studying in Non-Technical (General) and technical colleges of Delhi and NCR at the undergraduate level. Indian Journal of Educational Technology Volume 6, Issue 2, July 2024 198 Sample Size Table-1: Number of Non-Technical (General) and Technical Colleges in Delhi (NCT) Uttar Pradesh, Rajasthan and Haryana in 2020-21 States/NCT Non-Technical (General) Colleges Technical Colleges Delhi 93 15 Haryana 554 74 Rajasthan 2282 71 Uttar Pradesh 5449 114 Source: All India Survey on Higher Education Report 2020-2021 Table-2: Number of Colleges Sector Wise in Delhi (NCT) Uttar Pradesh, Rajasthan and Haryana 2020-2021 States/NCT Private Colleges Government Colleges Total Delhi 75 98 173 Haryana 823 258 1081 Rajasthan 2217 722 3339 Uttar Pradesh 6315 812 7127 Source: All India Survey on Higher Educa- tion Report 2020-21. As can be seen from the tables above, number of general colleges are more than the number of technology colleges. In Delhi NCR more of higher education institutions are in the private sector. Data is available for different categories of institutions as per specialization and ownership for the entire state as a whole. No segregate data is available for number of colleges in individual districts of states. The entire Haryana, Rajasthan and Uttar Pradesh is not part of NCR. Further for sample size determination specific formula (Cochran, 1977) could not be used as the average enrolment ratio is different in all three states and Delhi. So non-probability convenience sampling method is used to select general and technical colleges from both the public and private sector. Table-3: Number of Institutions Selected as Sample for the Study States/NCT Non-Technical (General) Colleges Technical Colleges Total Private Government Private Government Delhi and NCR 7 7 7 7 28 Indian Journal of Educational Technology Volume 6, Issue 2, July 2024 199 Table-4: Sampling Unit, Sample Size, Sampling Method, Total Sample Size Unit Size Sampling Technique No. of institutions chosen Total Sample Size Students 10 Non-probability Judgement Sampling 28 280 Teaching Staff 5 Non-probability Judgement Sampling 28 140 Administrator 2 Non-probability Judgement Sampling 28 56 Total 17 Total 476 Data Set The researcher has collected inputs from both primary and secondary sources. Primary data was collected from the stakeholders on quality parameters identified on 5-point Likert scale. For the research stakeholders are divided into three categories: administrator, teachers, students. Ambit of Study Data collection has been done from Delhi NCT and Delhi NCR regions. Data Testing Data Analysis is carried out using SPSS 22. First normality of data is checked. Kruskal Wallis also referred to as H-test is applied to check the hypothesis. Normality Test Foremost assumption for the application of any parametric test is the normality of the data for all categories of independent variable. Table-5: Normality Test for Students, Teachers and Administrators on Various Quality Parameters Quality Parameter Category of Respondent N Kolmogorov-Smirnov Statistic Df Sig. Web Architecture Student 280 .321 280 .000 Teacher 140 .221 140 .000 Administrator 56 .254 56 .000 Understandability Student 280 .332 280 .000 Teacher 140 .334 140 .000 Administrator 56 .289 56 .000 Effectiveness Student 280 .301 280 .000 Teacher 140 .358 140 .000 Administrator 56 .360 56 .000 Indian Journal of Educational Technology Volume 6, Issue 2, July 2024 200 Quality Parameter Category of Respondent N Kolmogorov-Smirnov Statistic Df Sig. Efficiency Student 280 .310 280 .000 Teacher 140 .288 140 .000 Administrator 56 .260 56 .000 Reliability Student 280 .332 280 .000 Teacher 140 .289 140 .000 Administrator 56 .332 56 .000 Portability Student 280 .306 280 .000 Teacher 140 .198 140 .000 Administrator 56 .404 56 .000 Security Student 280 .270 280 .000 Teacher 140 .333 140 .000 Administrator 56 .341 56 .000 As the above table reflects p value is 0.000, which means that null hypothesis cannot be accepted. This indicates it is normally distributed. In case of violation of normality condition, parametric test cannot be applied. Hence, in this case, Kruskal Wallis also referred to as H-test is used. Kruskal Wallis Test is a non-parametric test based on ranks. It is also known as one-way ANOVA on ranks. The table below gives he ranks on the various quality parameters. Table-6: Ranks as Per Kruskal Wallis Category of Respondent N Mean Rank Web Architecture Student 280 245.13 Teacher 140 229.26 Administrator 56 228.43 Total 476 Understandability Student 280 233.69 Teacher 140 240.18 Administrator 56 258.35 Total 476 Effectiveness Student 280 235.21 Teacher 140 227.85 Administrator 56 281.58 Total 476 Indian Journal of Educational Technology Volume 6, Issue 2, July 2024 201 Category of Respondent N Mean Rank Efficiency Student 280 247.70 Teacher 140 216.14 Administrator 56 248.41 Total 476 Reliability Student 280 206.98 Teacher 140 278.38 Administrator 56 296.38 Total 476 Portability Student 280 275.59 Teacher 140 167.75 Administrator 56 229.94 Total 476 Security Student 280 270.30 Teacher 140 181.32 Administrator 56 222.47 Total 476 Table-7: Kruskal Wallis Test Statistics Web architecture Under- standability Effective- ness Efficiency Reliability Portability Security Chi-Square 1.867 1.970 8.269 5.896 41.280 64.746 45.072 Df 2 2 2 2 2 2 2 Asymp. Sig. .393 .373 .016 .052 .000 .000 .000 The above table depicts that for Web Architecture, Understandability and Efficiency dimensions there is no significant difference in quality perception among different users while it varies on other quality parameters. Post-Hoc Analysis Further, to find out which category of independent variable differs significantly from which other category Post-Hoc analysis is carried out. The table below lists the post-hoc analysis for each of the quality parameters. (a) Web Architecture Table-8: Web Architecture Aspect Hypothesis Test Abstract Null Hypothesis H0:1 Test Significant Value Result The Dispersal of web architecture is alike across all groups of Respondent Independent Samples Kruskal- Wallis Test .393 Accept null hypothesis Indian Journal of Educational Technology Volume 6, Issue 2, July 2024 203 Each node shows the sample average rank of Category of Respondent. Sample1-Sample2 Test Statistic Std. Error Std. Test Statistic Sig. Adj.Sig Teacher-Student 7.364 12.617 .584 .559 1.000 Teacher-Administrator -53.734 19.273 -2.788 .005 .016 Student-Administrator -46.370 17.843 -2.599 .009 .028 Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same. Asymptotic significances (2-sided tests) are displayed. The significance level is. 05. As on effectiveness parameter, pairwise comparison reveals that there is not much difference between teacher and student perception on effectiveness of the system used to extend e-Governance initiatives. However, both teacher-administrator and student-administrator differ in their opinion on the effectiveness of the system. The administrators have ranked the effectiveness parameter higher than both teachers and students. i.e. administrators believe that system is meeting the expectations of the users but teachers and students feel otherwise. (d) Efficiency Table-12: Efficiency Aspect Hypothesis Test Abstract Null Hypothesis H0:4 Test Significant Value Result The Dispersal of Efficiency is alike across all groups of Respondent Independ- ent Samples Kruskal-Wallis Test .052 Accept null hypothesis As regards Null Hypothesis H0:4, significant value 0.052 (p>.05), indicates null hypothesis is not rejected i.e. there is no statistical difference between three groups. All the three students, teachers and administrators have almost given similar rank. (e) Reliability Table-13: Reliability Aspect Hypothesis Test Abstract Null Hypothesis H0:5 Test Significant Value Result The Dispersal of Reliabilty is alike across all groups of Respondent Independ- ent Samples Kruskal-Wallis Test .000 Discard the null hypothesis As regards Null Hypothesis H0:5, significant value 0.000 (p<.05), indicates null hypothesis is rejected i.e. statistical disimilarity between mean scores of the three groups is observed. Indian Journal of Educational Technology Volume 6, Issue 2, July 2024 207 they fear the least about security aspect. They are aware that online systems are completely safe. Though teachers are quite apprehensive and fear a lot about leakage/misuse of information. However, their fears seem to be unfounded. Suggestions 1. Bandwidth must be increased to support increased users which will ensure more reliability, 2. To address security issues, gate walls need to be installed. 3. Different passwords should be allotted to teachers and stu- dents to identify potential mis- creants. 4. Periodical induction programs, hands on training sessions must be organised to train and up- date all the stakeholders. 5. A proper feedback mechanism needs to be developed so that grievances and queries of the users are addressed timely. References Al Kurdi, B., Alshurideh, M., Salloum, S., Obeidat, Z., & Al-dweeri, R. (2020). 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