Adoption and Use of Learning Management Systems Among Teachers and Students in Higher Educational Institutions in Greater Noida

doi:10.21203/rs.3.rs-3861402/v1

This research investigates the adoption and usage patterns of learning management systems (LMSs) in private higher education institutions, namely,Sharda University and Galgotias University, in Greater Noida, India. This study, guided by the unified theory of acceptance and use of technology (UTAUT) model, employs a quantitative approach through surveys and statistical analyses. The data collected from 76 teachers and 106 students provideinsights into LMS adoption.

Among the students, there was a moderate level of acceptance of the LMS, with Google Classroom being the preferred platform. While social influence and facilitating conditions varied, perceived usefulness, ease of use, and behavioral intentions remained consistent across demographic factors. Among the teachers, a generally positive attitude toward LMS adoption was observed, with Google Classroom being the predominant attitude. Demographic factors, such as age, gender, and teaching experience, influence teachers' perceptions. Regression analysis revealed a significant relationship between perceived aspects and teachers' behavioral intentions, highlighting the importance of these factors in shaping attitudes toward LMS adoption.

In summary, the study provides valuable insights into LMS adoption dynamics in private higher education in Greater Noida. The findings have implications for educational institutions aiming to improve their digital infrastructure and promote effective LMS utilization, offering potential applications for policymakers, educators, and stakeholders to enhance decision-making and educational quality in the region. Top of Form

Educational Philosophy and Theory

LMS Usage

Level of LMS Adoption

Learning Management Systems

Higher Educational Institutions

UTAUT

The landscape of higher education has undergone a significant transformation due to the widespread use of the internet and rapid technological advancements, marking the era of digitalization. This shift has considerably impacted teaching and learning methods, necessitating educators to acquire skills for effective technology integration in the classroom. The COVID-19 pandemic further highlighted educators' resilience in adapting instructional strategies to meet evolving needs, with learning management systems (LMSs) emerging as essential tools.

LMS, which allows curriculum development, student tracking, and progress assessment in online settings, has a history dating back to 1924 but has undergone significant advancements over the years. In the context of India, which boasts a high concentration of higher education facilities, especially in Uttar Pradesh, the adoption of LMS has become crucial for collaborative learning and skill development. Despite the recognition of the value of information and communication technology (ICT) in education, barriers such as a lack of technological knowledge persist among teachers.

The COVID-19 outbreak accentuated existing disparities in technology utilization, particularly across socioeconomic, geographic, and gender levels. To enhance educational practices, and learning outcomes and ensure effective technology integration, understanding the level of LMS adoption among teachers and students becomes imperative. While numerous studies have explored LMS adoption in higher education institutions, none have specifically focused on Greater Noida, recognized as the knowledge hub of India.

This study, titled "The Level of Adoption and Use of Learning Management Systems among Teachers and Students of Higher Educational Institutions in Greater Noida," aims to fill this gap. Employing the UTAUT model as the theoretical framework, this research seeks to contribute to improving educational quality improvement in Greater Noida Province, providing valuable insights for policymakers and partners involved in educational reform, especially those at the higher education levels.

A. Perceptions and Problems toward the Adoption of LMSs

Recent advancements in ICTs have had a profound impact on systems that exhibit flexibility in terms of hardware, software, human resources, and finances. The choice of a Learning Management System (LMS) in Higher Education Organizations (HEOs) is influenced by factors such as the institution's size, including the number of students, range of specialties, and budget allocation (Dobre, 2015). Fathema, Shannon & Ross (2015) identified external variables, including system quality, perceived self-efficacy, and facilitation conditions, as significant predictors of faculty attitudes toward LMSs.

Maina and Nzuki (2016) emphasized that the adoption of educational management systems (EMSs) in higher learning institutions is influenced by factors such as expected performance, supporting infrastructures, institutional policies, training assistance, leadership, and ease of use. Gamede, Ajani & Afolabi (2022) explored the use of LMSs during the COVID-19 lockdown in South African higher education institutions, highlighting Moodle as an effective platform. However, obstacles similar to those in other African universities hinder its widespread adoption.

Yakubu et al. (2020) identified social influence, conducive settings, system quality, perceived ease of use, and perceived utility as significant factors influencing students' behavioral intention to use LMSs. Alias & Zainuddin (2005) found unexpected positivity among Institute of Education professors in embracing educational change. Wichadee (2015) reported varying levels of LMS utilization among faculty members.

Fındık-Coşkunçay et al. (2018) revealed that students' behavioral intention to use LMSs is predicted by their perceptions of utility and usability. Alharbi & Drew (2014) conducted a study predicting students' behavioral intentions to use LMSs in Saudi Arabia, highlighting the impact of individual characteristics on behavioral intent.

Fathema & Sutton (2013) emphasized the role of perceived self-efficacy and system quality in faculty usage of LMSs. Liu & Geertshuis (2021) studied the relationship between professional identity and LMS adoption. Reid (2019) found positive attitudes and satisfaction among faculty members and students toward LMSs in adult and higher education institutions.

Alshammari (2015) observed positive opinions but limited integration of LMSs in Saudi universities. Alshalan (2019) examined faculty characteristics, organizational support, and barriers to LMS use in Saudi universities. Spelke (2011) explored LMS selection processes and the influence of intellectual property rights on decision-making among IT leaders.

B. Use and Effects of LMS in Teaching and Learning

Alghamdi & Bayaga (2016) conducted research on LMS use and attitudes in Saudi Arabian universities. They discovered that older generations, particularly those older than 40 years, displayed a greater preference for utilizing LMS in teaching activities more than did their younger counterparts. Despite the availability of LMSs, the majority of educational activities did not actively incorporate this technology. The organization of resources used by LMSs showed a statistically significant correlation with limitation level. Moreover, the study highlighted differences in attitudes toward online exams and social media among faculty members who use LMS in pedagogy compared to those who do not. Evaluations of LMS tool use and attitudes were found to be influenced by both institutional and personal experiences.

Coates, James, and Baldwin (2005) provided a broader perspective, defining LMSs as enterprise-wide, internet-based systems such as WebCT and Blackboard. Drawing on Australian experience, they outlined how LMSs evolved in response to internet and multimedia advancements in the 1990s, emphasizing their impact on instructional strategies, student involvement, academic work, and the management of academic information. Muries & Masele (2017) explored electronic learning management systems (ELMSs) in Tanzanian higher education. Their findings highlighted the favorable impact of "perceived importance," "perceived ease of use," and "organizational management support" on ELMS continuing usage intentions. Notably, the study indicated that while "facilitating conditions" were advantageous, they did not significantly alter users' intentions to continue using ELMSs.

In contrast, Alshehri, Rutter, & Smith (2019) focused on the crucial role of technical support in determining the acceptance and use of e-learning systems at Saudi Arabian tertiary institutions utilizing the unified theory of acceptance and use of technology (UTAUT) model.

Shifting the focus to Tanzania, Ngeze's (2016) study on learning management systems at higher learning institutions identified key challenges such as lack of system proficiency and network problems. Students preferred accessing the system via personal laptops and mobile devices instead of via the campus local area network (LAN). The recommendations included enhancing the university network, providing user instructions, adding distance learning courses, and addressing power outages.

Al-Fraihat, Joy & Sinclair (2020) concentrated on evaluating e-learning system success. Their study identified technical system quality, information quality, service quality, support system quality, learner quality, instructor quality, and perceived usefulness as factors contributing to perceived satisfaction in e-learning. These factors collectively accounted for a significant portion of the variance in perceived satisfaction. Additionally, the study determined the drivers of perceived usefulness and the factors influencing the use of e-learning.

Rahrouh, Taleb & Mohamed (2018) explored the value of e-learning management system delivery in higher education, with respondents unanimously acknowledging the effectiveness, reliability, usability, manageability, and efficiency of LMSs. The respondents expressed enthusiasm for utilizing the LMS and allocating specialized trainers based on user demands. Almarashdeh (2016) highlighted that perceived utility and service quality had the greatest influence on instructor satisfaction in the context of LMS use.

The literature review revealed several factors influencing LMS adoption, including system quality, perceived self-efficacy, external variables, and technical support. Additionally, studies have explored the use and effects of LMSs in teaching and learning, highlighting issues such as generational differences, varying levels of utilization among faculty members, and challenges such as lack of system proficiency and network problems. However, there are research gaps in understanding the specific challenges faced by private institutions in Greater Noida and the intricate dynamics influencing behavioral intentions in this context. However, further research is needed to address these gaps and provide a more nuanced understanding of LMS adoption in the specific regions.

Theoretical Framework

The unified theory of acceptance and use of technology (UTAUT) model, developed by Venkatesh, Morris, Davis, and Davis in 2003, integrates four existing models-the theory of reasoned action, the technology acceptance model, the motivation model, and the theory of planned behavior. The UTAUT model comprises four main construct-performance expectancy, effort expectancy, social influence, and facilitating conditions-each incorporating key elements from related models. Performance expectancy focuses on users' belief that technology enhances job performance, drawing from various theoretical concepts. Effort expectancy assesses users' perceived ease of technology use, influenced by TAM, MPCU, and IDT, with diminishing effects over prolonged use.

Social influence measures the impact of significant others' expectations on users' technology adoption, drawing parallels with subjective norms, social variables, and image constructs in related models. Facilitating conditions gauge users' belief in having access to necessary resources and organizational support, integrating constructs from the TPB, CTAMTPB, MPCU, and IDT. The model also includes four moderating factors—gender, age, experience, and voluntariness of use—that influence the relationship between the core constructs and users' technology acceptance.

UTAUT has found widespread application in studying technology acceptance across various contexts, including social media, e-learning platforms, and mobile banking. The model has been adapted for specific domains such as healthcare and education and expanded to include additional components such as trust and anxiety. Its versatility and applicability make it a valuable framework for understanding and predicting users' intentions to accept and use technology.

The study seeks to address the following objectives:

1. To identify LMS use by teachers and students of higher educational private institutions in Greater Noida

2. To explore the level of adoption of LMS by teachers and students in private institutions of higher education in Greater Noida

3. To determine the PE, EE, SI, FC and behavioral intentions of higher education teachers and students toward the adoption of LMSs in private institutions of higher education in Greater Noida with respect to gender, age, experience and level of education

The following null hypotheses are formulated for the study.

1. There is no significant difference in the usage of learning management systems (LMSs) between teachers and students in higher-educational private institutions in Greater Noida.

2. There is no significant difference in the level of adoption of learning management systems (LMSs) between teachers and students in private institutions of higher education in Greater Noida.

3. There are no significant relationships between the perceived ease of use (PE), perceived usefulness (PU), social influence (SI), facilitating conditions (FC), or behavioral intentions (BI) of higher education teachers and students toward the adoption of LMS in private institutions of higher education in Greater Noida.

4. There is no significant variation in the perceived ease of use (PE), perceived usefulness (PU), social influence (SI), facilitating conditions (FC), or behavioral intentions (BI) toward LMS adoption with respect to gender, age, experience, or level of education among higher education teachers and students in private institutions in Greater Noida.

This study employed a cross-sectional research design utilizing a survey approach to collect data on learning management system (LMS) usage, adoption levels, and perceptions among teachers and students in private higher educational institutions in Greater Noida. The setting included two prominent institutions, Sharda University and Galgotias University, with participants consisting of male and female students and faculty members from diverse academic fields. The target population comprised 76 teachers and 106 students selected through stratified random sampling. Structured questionnaires were used for data collection, they included demographic details, the UTAUT tool for LMS adoption determinants, and data on LMS functions. The collected data were subjected to quantitative analysis, including descriptive statistics, hypothesis testing, t tests, ANOVA, and regression analysis.

In terms of variables, the dependent variables included LMS usage, level of LMS adoption, perceived ease of use, perceived usefulness, social influence, facilitating conditions, and behavioral intentions. The independent variables included sex, age, experience, and education. Ethical considerations were followed, with approval from the institutional review board, informed consent, confidentiality, and voluntary participation. The study's significance lies in providing valuable insights into the current state of LMS usage in private higher educational institutions, aiming to inform decision-makers, educators, and administrators for better implementation and improvement strategies.

A. STUDENTS’ ADOPTION AND USE OF LMS

Table 1: Overview of Students’ Adoption and Use of Learning Management Systems

Variable	N	Mean	Std. Deviation	Std. Error Mean
Perceived Usefulness (PU)	106	12.11	2.512	0.244
Perceived Ease of Use (PE)	106	16.32	2.850	0.277
Social Influence (SI)	106	15.24	2.978	0.289
Facilitating Condition (FC)	106	15.37	2.768	0.269
Behavioral Intention (BI)	106	11.31	2.474	0.240
Age Group	106	1.48	0.693	-
Gender	106	1.65	0.479	-
Programme Enrolled	106	2.75	0.536	-
LMS use in their institutions	106	1.04	0.191	-

Table 1 presents a thorough overview of key variables pertaining to students' adoption and utilization of learning management systems (LMSs). In terms of perceived usefulness (PU), respondents had an average score of 12.11, indicating a moderate level of variability (std. deviation = 2.512). The small standard error mean (0.244) suggests a precise estimate of the population mean. On average, regarding perceived ease of use (PE), students, perceive the LMS to be user friendly, with a mean score of 16.32, accompanied by a moderately variable response (std. deviation = 2.850) and a reliable sample mean estimate (std. error mean = 0.277). Social influence (SI) is reflected by an average score of 15.24, indicating a moderate degree of variability (std. deviation = 2.978), with a relatively small sampling variability (std. error mean = 0.289).

The facilitating condition (FC) yields a mean score of 15.37, signifying the extent to which external conditions support LMS adoption. A moderate standard deviation (2.768) implies variability, while a small standard error (0.269) suggests a precise estimate of the population mean. In terms of behavioral intention (BI), students give an average score of 11.31, indicating willingness to engage with the LMS, with variability (std. deviation = 2.474) and a reliable sample mean estimate (error mean = 0.240). The demographic variables, such as age group, sex, enrollment status, and LMS use in the Institutions, are presented with mean scores and standard deviations, denoting their categorical nature. Notably, the absence of standard errors for these variables underscores their non-continuous nature.

I. STUDENTS’ USE OF LMS

Objective 1: To identify LMS use by students at higher educational private institutions in Greater Noida.

Usage of LMSs: Google Classroom is the primary LMS used (96.2%). Moodle is used by a smaller percentage (3.8%).

Ho1: There was no significant difference in LMS usage among students in higher education private institutions in Greater Noida.

Students' Use of LMS: Mean: 1.04 (indicating low usage).

Table 2: Student Use of LMS-One -Sample Statistics:

Variable	Frequency	Mean	Std. Deviation	Std. Error Mean
LMS use in your institution	106	1.04	0.191	0.019

II. STUDENTS’ ADOPTION LEVEL OF LMS

Objective 2: To explore the level of adoption of LMSs by the students in private institutions of higher education in Greater Noida

The mean scores for students' adoption factors were as follows: Perceived Usefulness (PU): 12.11, Perceived Ease of Use (PE): 16.32, Social Influence (SI): 15.24, Facilitating Condition (FC): 15.37, and Behavioral Intention (BI): 11.31

Ho2: There is no significant difference in the level of adoption of LMS by students in private institutions of higher education in Greater Noida.

Table 3a: Students’ adoption of LMS (Ho2)

	Variable		Frequency	Mean		Std. Deviation		Std. Error Mean
	Perceived Usefulness (PU)		106	12.11		2.512		0.244
	Perceived Ease of Use (PE)		106	16.32		2.850		0.277
	Social Influence (SI)		106	15.24		2.978		0.289
	Facilitating Condition (FC)		106	15.37		2.768		0.269
	Behavioral Intention (BI)		106	11.31		2.474		0.240
Table 3b: ANOVA
					Sum of Squares		df		Mean Square	F	Sig.
Perceived Usefulness (PU)		Between Groups			4.310		1		4.310	.681	.411
		Within Groups			658.331		104		6.330
		Total			662.642		105
Perceived Ease of Use (PE)		Between Groups			14.781		1		14.781	1.834	.179
		Within Groups			838.313		104		8.061
		Total			853.094		105
Social Influence (SI)		Between Groups			39.199		1		39.199	4.571	.035
		Within Groups			891.904		104		8.576
		Total			931.104		105
Facilitating Condition (FC)		Between Groups			49.744		1		49.744	6.853	.010
		Within Groups			754.907		104		7.259
		Total			804.651		105
Behavioral Intention (BI)		Between Groups			.011		1		.011	.002	.966
		Within Groups			642.715		104		6.180
		Total			642.726		105

Table 3 presents the results of an analysis aimed at testing Hypothesis 2 (Ho2), which posits no significant difference in the level of adoption of Learning Management Systems (LMS) by students in private institutions of higher education in Greater Noida. The table provides descriptive statistics, including the frequency, mean, standard deviation, and standard error mean, for key variables related to LMS adoption: Perceived Usefulness (PU), Perceived Ease of Use (PE), Social Influence (SI), Facilitating Condition (FC), and Behavioral Intention (BI). The means offer insights into the average perceptions and intentions of students regarding LMS adoption.

To evaluate the statistical significance of these differences, an analysis of variance (ANOVA) was conducted for each variable. The ANOVA results are summarized in terms of the sum of squares, degrees of freedom, mean square, F-statistic, and significance level (Sig.). For social influence (SI) and facilitating condition (FC), the ANOVA results indicated statistically significant differences between groups, as evidenced by the F-statistic (4.571 for SI and 6.853 for FC) and the corresponding significance levels (0.035 for SI and 0.010 for FC). This finding suggested that there are variations in social influence and facilitating conditions influencing students' LMS adoption. However, for perceived usefulness (PU), perceived ease of use (PE), and Behavioral intention (BI), the ANOVA results do not significantly differ between groups, as indicated by non-significant F-statistics and higher p values (e.g., 0.681 for PU, 1.834 for PE, and 0.966 for BI). Therefore, there is insufficient evidence to reject Ho2 for these variables, implying that there is no significant difference in the level of adoption based on perceived usefulness, ease of use, or behavioral intention among students in private institutions of higher education in Greater Noida.

Objective 3: To determine the PE, EE, SI, FC, and behavioral intentions of higher education students toward the adoption of LMSs with respect to sex, age, and program enrollment.

Ho3: There is no significant relationship between the PE, PU, SI, FC, or BI of higher education students and LMS adoption.

Table 4: Students’ adoption of the LMS

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	F Change	Sig. F Change
1	0.487	0.237	0.207	2.204	7.839	0.000

Model	Sum of Squares	df	Mean Square	F	Sig.
1	152.268	4	38.067	7.839	0.000

Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
1	(Constant)	4.306	1.377	3.128
1	Perceived Usefulness (PU)	-0.017	-0.018	-0.155
1	Perceived Ease of Use (PE)	0.040	0.046	0.353
1	Social Influence (SI)	0.212	0.255	1.893
1	Facilitating Condition (FC)	0.216	0.242	1.723

Regression analysis was conducted to explore the relationships between key variables—perceived ease of use (PE), perceived usefulness (PU), social influence (SI), facilitating conditions (FCs), and behavioral intentions (BIs)—in the context of learning management system (LMS) adoption among higher education teachers and students. The overall model fit is statistically significant, with an F change of 7.839 (p < 0.001), indicating that at least one of the predictors significantly helps explain the variance in BI. The model, encompassing PE, PU, SI, and FC, collectively accounts for 23.7% of the variability in behavioral intentions, as denoted by the R Square. The adjusted R square, which adjusts for the number of predictors in the model, is 20.7%, reinforcing the model's explanatory power.

Examining individual predictors, the unstandardized coefficients provide insights into the direction and magnitude of their impact on BI. Perceived usefulness (PU) exhibited a negligible negative association with BI, though it was statistically insignificant (t = -0.155, p > 0.05). Perceived ease of use (PE) showed a positive association with BI, but again, this relationship lacked statistical significance (t = 0.353, p > 0.05). Social influence (SI) emerges as a crucial factor with a positive and statistically significant association with BI (t = 1.893, p < 0.05). In contrast, the facilitating condition (FC) demonstrated a positive association with BI, but this association was not statistically significant (t = 1.723, p > 0.05). Given these findings, the null hypothesis (Ho3), positing no significant relationship between PE, PU, SI, FC, or BI among higher education students toward LMS adoption, is partially supported, highlighting the importance of social influence in shaping behavioral intentions in the context of LMS adoption in higher education.

Ho4: There is no significant variation in PE, PU, SI, FC, or BI with respect to LMS adoption with respect to sex, age, experience, or education level.

TABLE 5: PERCEIVED USEFULNESS (PU)

Model Summary	R	R Square	Adj. R Square	Std Error	Change Stats
Model 1	.124a	.015	-014	2.529	.531

ANOVA	Sum of square	df	Mean Square	F	Sig. F
Regression	10.181	3	3.394	.531	.662b
Residual	652.460	102	6.397	-	-
Total	662.642	105		-	-

Coefficients	Unstand. Coef.	Std. Coef.	t	Sig.
Constant	10.632		6.249	.000
1. Age Group	.352	.097	.953	.343
2. Gender	.503	.096	.945	.347
3. Programmed enrolled	.047	.010	.098	.922

Residuals Statistics	Minimum Predicted	Maximum Pred	Mean Predicted	Std. Deviation	N
Predicted Value	11.58	12.79	12.11	.311	106
Residual	-8.483	4.371	.000	2.493	106
Std. Predicted Value	-1.708	2.168	.000	1.000	106
Std. Residual	-3.354	1.728	.000	.986	106

TABLE 6: PERCEIVED EASE OF USE (PE)

Model Summary	R	R Square	Adj. R Square	Std. Error	Change Stats
Model 1	.154a	.024	-.005	2.858	.823

ANOVA	Sum of Squares	df	Mean Square	F	Sig. F
Regression	20.160	3	6.720	.823	.484b
Residual	832.934	102	8.166	-	-
Total	853.094	105	-	-	-

Coefficients	Unstand. Coef.\| Std. Coef.	Std. Coef.	t	Sig.
Constant	15.287		7.953	.000
1. Age Group	.212	.052	.508	.612
2. Gender	.903	.152	1.503	.136
3. Programme enrolled	-.281	-.053	-.517	.606

Residuals Statistics	Minimum Predicted	Maximum Pred.	Mean Predicted	Std. Deviation	N
Predicted Value	15.56	17.17	16.32	.438	106
Residual	-12.052	4.441	.000	2.817	106
Std. Predicted Value	-1.738	1.933	.000	1.000	106
Std. Residual	-4.218	1.554	.000	.986	106

TABLE 7: SOCIAL INFLUENCE (SI)

Model Summary	R	R Square	Adj. R Square	Std. Error	Change Stats
Model 1	.269a	.072	.045	2.910	.072

ANOVA	Sum of Squares	df	Mean Square	F	Sig. F
Regression	67.469	3	22.490	2.656	.052b
Residual	863.635	102	8.467	-	-
Total	931.104	105	-	-	-

Coefficients	Unstand. Coef	Std. Coef.	t	Sig.
Constant	10.841		5.538	.000
1. Age Group	.768	.179	1.807	.074
2. Gender	1.393	.224	2.276	.025
3. Programme enrolled	.349	.063	.631	.529

Residuals Statistics	Minimum Predicted	Maximum Pred.	Mean Predicted	Std. Deviation	N
Predicted Value	13.70	16.63	15.24	.802	106
Residual	-8.441	5.951	.000	2.868	106
Std. Predicted Value	-1.916	1.737	.000	1.000	106
Std. Residual	-2.901	2.045	.000	.986	106

TABLE 8: FACILITATING CONDITION (FC)

Model Summary	R	R Square	Adj. R Square	Std. Error	Change Stats
Model 1	.296a	.088	.061	2.683	.088

ANOVA	Sum of Squares	df	Mean Square	F	Sig. F
Regression	70.625	3	23.542	3.271	.024b
Residual	734.026	102	7.196	-	-
Total	804.651	105	-	-	-

Coefficients	Unstand. Coef.	Std. Coef.	t	Sig.
Constant	10.515		5.827	.000
1. Age Group	.238	.060	.608	.545
2. Gender	1.300	.225	2.305	.023
3. Programme enrolled	.858	.166	1.681	.096

Residuals Statistics	Minimum Predicted	Maximum Pred.	Mean Predicted	Std. Deviation	N
Predicted Value	13.63	17.02	15.37	.820	106
Residual	-6.626	5.994	.000	2.644	106
Std. Predicted Value	-2.125	2.016	.000	1.000	106
Std. Residual	-2.470	2.234	.000	.986	106

TABLE 9: BEHAVIORAL INTENTION (BI)

Model Summary	R	R Square	Adj. R Square	Std. Error	Change Stats
Model 1	.173a	.030	.001	2.472	.030

ANOVA	Sum of Squares	df	Mean Square	F	Sig. F
Regression	19.208	3	6.403	1.047	.375b
Residual	623.519	102	6.113	-	-
Total	642.726	105	-	-	-

Coefficients	Unstand. Coef.	Std. Coef.	t	Sig.
Constant	9.202		5.533	.000
1. Age Group	.617	.173	1.708	.091
2. Gender	.092	.018	.178	.859
3. Programme enrolled	.380	.082	.808	.421

Residuals Statistics	Minimum Predicted	Maximum Pred.	Mean Predicted	Std. Deviation	N
Predicted Value	10.67	12.90	11.31	.428	106
Residual	-8.144	3.949	.000	2.437	106
Std. Predicted Value	-1.496	3.718	.000	1.000	106
Std. Residual	-3.294	1.597	.000	.986	106

The presented analysis corresponds to Hypothesis 4 (Ho4), which asserts that there is no significant variation in the perceived ease of use (PE), perceived usefulness (PU), social influence (SI), facilitating conditions (FC), or behavioral intentions (BI) toward learning management system (LMS) adoption concerning gender, age, or program enrollment among higher education students in private institutions in Greater Noida.

For perceived usefulness (PU), the model summary indicated that the predictors (age group, sex, and program enrolled) collectively explained a small proportion of the variance (R squared = 0.015). The ANOVA results suggest that the overall model is not statistically significant (F = 0.531, p > 0.05), supporting Ho4. The coefficients for age group, sex, and program enrolled are not statistically significant, indicating that these factors do not significantly influence perceived usefulness.

Similarly, for perceived ease of use (PE), social influence (SI), facilitating conditions (FCs), and behavioral intentions (BI), the model summaries and ANOVA results show limited explanatory power and lack statistical significance. The predictors, including age group, sex, and program enrolled, did not collectively contribute significantly to explaining the variances in these dependent variables. The coefficients for the predictors are not statistically significant in each case, supporting the notion that there is no substantial variation in PE, SI, FC, or BI concerning gender, age, or level of education among higher education students in private institutions in Greater Noida.

In summary, the findings support Hypothesis 4, suggesting that gender, age, and program enrollment do not significantly influence the perceived ease of use, perceived usefulness, social influence, facilitating conditions, or behavioral intentions of higher education students toward LMS adoption in private institutions in Greater Noida.

B. TEACHERS’ ADOPTION AND USE OF LMS

Table 1: Overview of Teachers’ Adoption and Use of Learning Management Systems

Variable	N	Mean	Std. Deviation	Std. Error Mean
Perceived Usefulness (PU)	76	12.30	2.791	0.320
Perceived Ease of Use (PE)	76	16.08	3.106	0.356
Social Influence (SI)	76	15.45	3.376	0.387
Facilitating Condition (FC)	76	15.01	2.845	0.326
Behavioral Intention (BI)	76	11.21	2.739	0.314
Age Group	76	2.43	1.124	0.129
Gender	76	1.51	0.503	0.058
Teaching Experience in year(s)	76	3.05	1.305	0.150
LMS Usage	76	1.29	0.629	0.072

This study focused on examining various aspects of learning management system (LMS) adoption in higher educational private institutions in Greater Noida and aimed to accomplish specific objectives through the formulation of hypotheses.

For the first objective and Hypothesis 1, which sought to identify LMS usage by teachers, the study conducted one-sample t tests on multiple variables. The results revealed significant differences (p < 0.05) in perceived aspects and characteristics related to LMS usage among teachers, contradicting the null hypothesis that there is no significant difference in LMS usage between these two groups.

The second objective was to explore the level of LMS adoption among teachers, with Hypothesis 2 positing no significant difference. However, the one-sample t tests indicated significant differences across variables related to adoption, suggesting distinct perceptions and attitudes toward LMS adoption among teachers and students.

Objective 3 aimed to determine the perceived ease of use, perceived usefulness, social influence, facilitating conditions, and behavioral intentions of higher education teachers concerning LMS adoption, considering demographic factors. Hypothesis 3, asserting no significant relationship with demographic factors, was refuted because the one-sample t tests demonstrated significant differences. This implies that demographic factors, including age, gender, and teaching experience, have a substantial impact on the perceived aspects of LMS adoption.

Finally, Hypothesis 4 suggested no significant variation in the perceived aspects of LMS adoption with respect to gender, age, or experience among higher education teachers. The one-sample t- tests for each variable related to these demographic factors revealed significant differences, leading to the rejection of Hypothesis 4. Consequently, the study's overall findings provide evidence against the null hypotheses, indicating noteworthy differences and variations in perceptions of LMS usage, adoption, and related factors among teachers, especially concerning demographic variables.

I. USAGE OF LMS BY TEACHERS

Ho1: There is no significant difference in the usage of learning management systems (LMSs) by teachers in higher-educational private institutions in Greater Noida.

Table 2: Learning Management Systems Used by Teachers in Higher Educational Private Institutions

LMS Platform	Frequency	Percent	Valid Percent	Cumulative Percent
Google Classroom	61	80.3	80.3	80.3
Moodle	8	10.5	10.5	90.8
Blackboard	7	9.2	9.2	100.0
Total	76	100.0	100.0	100.0

A survey of teachers at higher educational private institutions in Greater Noida aimed to determine the usage of learning management systems (LMSs) at these institutions. Among the respondents, 80.3% reported using Google Classroom, 10.5% used Moodle, and 9.2% utilized Blackboard. The mean LMS usage score was 1.29, with a standard deviation of 0.629, suggesting a moderate average level of utilization.

To assess the statistical significance of these findings, a one-sample t test was conducted with a test value of 0. The results yielded a high t value of 17.884 with 75 degrees of freedom and a p value of .000, indicating a significant difference in LMS usage. The mean difference was 1.289, and the 95% confidence interval (1.15 to 1.43) further supported the robustness of the results. Overall, the survey revealed a distinct preference for Google Classroom among teachers in the sampled institutions, and the statistical analysis underscored the significance of this observed difference in LMS usage patterns. Top of Form

II. TEACHERS’ ADOPTION OF LMS

Ho2: There is no significant difference in the level of adoption of learning management systems (LMSs) between teachers and students in private institutions of higher education in Greater Noida.

Table 3: Teachers’ Adoption of LMS—One-Sample Statistics

Dimension	N	Mean	Std. Deviation	Std. Error Mean
Perceived Usefulness (PU)	76	12.30	2.791	0.320
Perceived Ease of Use (PE)	76	16.08	3.106	0.356
Social Influence (SI)	76	15.45	3.376	0.387
Facilitating Condition (FC)	76	15.01	2.845	0.326
Behavioral Intention (BI)	76	11.21	2.739	0.314

Table 3 presents one-sample statistics, offering a comprehensive overview of teachers' perceptions across various dimensions of LMS adoption. The mean scores indicate the average level of agreement or disagreement with statements related to perceived usefulnessfulness (PU), perceived ease of use (PE), social influence (SI), facilitating condition (FC), and behavioral intention (BI). Notably, teachers expressed positive perceptions, with mean scores ranging from 11.21 to 16.08, suggesting a generally favorable attitude toward LMS adoption. Standard deviations and standard errors provide insights into the variability and precision of these mean scores.

Table 4: Teachers’ Adoption of LMS—One-Sample Test

Dimension	Test Value	df	Sig. (2-tailed)	Mean Difference	95% CI of the Difference
Perceived Usefulness (PU)	0	75	.000	12.303	[11.66, 12.94]
Perceived Ease of Use (PE)	0	75	.000	16.079	[15.37, 16.79]
Social Influence (SI)	0	75	.000	15.447	[14.68, 16.22]
Facilitating Condition (FC)	0	75	.000	15.013	[14.36, 15.66]
Behavioral Intention (BI)	0	75	.000	11.211	[10.58, 11.84]

Table 2 delves into the significance of these mean differences through one-sample tests. A test value of 0 represents a neutral stance where there is no difference. The highly significant p values (p < .001) across all dimensions indicate that teachers' perceptions significantly deviate from a neutral position, suggesting a substantial positive inclination toward LMS adoption. The mean differences, along with their associated confidence intervals, provide additional context to the magnitude and reliability of these deviations, highlighting the robustness of the observed positive perceptions.

Table 5: Teachers’ Adoption of LMS—ANOVA

Dimension	Sum of Squares (Between Groups)	df (Between Groups)	Mean Square (Between Groups)	F	Sig.
Perceived Usefulness (PU)	0.538	1	0.538	0.068	0.795
Perceived Ease of Use (PE)	0.449	1	0.449	0.046	0.831
Social Influence (SI)	0.315	1	0.315	0.027	0.869
Facilitating Condition (FC)	0.326	1	0.326	0.040	0.843
Behavioral Intention (BI)	13.840	1	13.840	1.866	0.176

Table 3 further investigates between-group differences in teachers' adoption of LMSs using ANOVA. The dimensions include Perceived Usefulness (PU), Perceived Ease of Use (PE), Social Influence (SI), Facilitating Condition (FC), and Behavioral Intention (BI). While the ANOVA results indicate non-significant F values in most dimensions, the behavioral intention (BI) dimension exhibits a marginally significant F value (p = 0.176). Despite this, the overall pattern suggests that teachers in private institutions of higher education in Greater Noida share relatively consistent perceptions regarding the adoption of LMSs. These findings collectively reject the null hypothesis and underscore a positive and uniform inclination toward the adoption of LMS among the surveyed teachers.

Ho3. There are no significant relationships between the perceived ease of use (PE), perceived usefulness (PU), social influence (SI), facilitating conditions (FC), or behavioral intentions (BI) of higher education teachers toward the adoption of LMS in private institutions of higher education in Greater Noida.

ANOVA
Model		Sum of Squares		df	Mean Square		F		Sig.
1	Regression	207.211		4	51.803		10.348		.000^b
	Residual	355.421		71	5.006
	Total	562.632		75
a. Dependent Variable: Behavioral Intention (BI)
b. Predictors: (Constant), Facilitating Condition (FC), Perceived Usefulness (PU), Perceived Ease of Use (PE), Social Influence (SI)
Coefficients
Model			Unstandardized Coefficients			Standardized Coefficients				t		Sig.

	(Constant)		2.199					-			1.409		.163
	Perceived Usefulness (PU)		.084					0.085			.552		.583
	Perceived Ease of Use (PE)		.156					0.176			1.135		.260
	Social Influence (SI)		.267					0.329			1.997		.050
	Facilitating Condition (FC)		.090					0.094			.768		.445
Dependent Variable: Behavioral Intention (BI)

The analysis employed a multiple regression model, incorporating predictors such as facilitating conditions, perceived usefulnessfulness, perceived ease of use, and social influence.

The model summary revealed a good fit, with an R-square of 36.8%, indicating that approximately 36.8% of the variability in behavioral intentions could be explained by the included predictors. The adjusted R-square, accounting for the number of predictors, was 33.3%, and the standard error of the estimate was 2.237, providing a measure of the model's accuracy.

The ANOVA results revealed a statistically significant relationship (F = 10.348, p < 0.001) between the predictors (FC, PU, PE, SI) and behavioral intentions, rejecting the null hypothesis (Ho3). The coefficient analysis elucidated the individual contributions of the predictors, with social influence (SI) emerging as a significant predictor (beta = 0.329, p = 0.050), implying a positive impact on behavioral intentions.

Residuals statistics indicated that the model's predictions were, on average, accurate, as the mean residual was close to zero. The standard deviation of the residuals (2.177) gauged the dispersion of the actual values around the predicted values. In conclusion, the findings underscore the significant relationship between the explored predictors and the behavioral intentions of higher education teachers toward LMS adoption, highlighting the influential role of social influence in shaping their intentions.

Ho4: There is no significant variation in the perceived ease of use (PE), perceived usefulness (PU), social influence (SI), facilitating conditions (FC), or behavioral intentions (BI) toward LMS adoption with respect to gender, age, and experience among higher education teachers in private institutions in Greater Noida.

ANOVA (Age Group)
		Sum of Squares	df	Mean Square	F	Sig.
Perceived Usefulness (PU)	Between Groups	42.377	3	14.126	1.878	.141
	Within Groups	541.663	72	7.523
	Total	584.039	75
Perceived Ease of Use (PE)	Between Groups	17.555	3	5.852	.597	.619
	Within Groups	705.971	72	9.805
	Total	723.526	75
Social Influence (SI)	Between Groups	34.062	3	11.354	.996	.400
	Within Groups	820.727	72	11.399
	Total	854.789	75
Facilitating Condition (FC)	Between Groups	48.984	3	16.328	2.107	.107
	Within Groups	558.003	72	7.750
	Total	606.987	75
Behavioral Intention (BI)	Between Groups	43.590	3	14.530	2.016	.119
	Within Groups	519.042	72	7.209
	Total	562.632	75

ANOVA (Gender)
		Sum of Squares	df	Mean Square	F	Sig.
Perceived Usefulness (PU)	Between Groups	.538	1	.538	.068	.795
	Within Groups	583.501	74	7.885
	Total	584.039	75
Perceived Ease of Use (PE)	Between Groups	.449	1	.449	.046	.831
	Within Groups	723.077	74	9.771
	Total	723.526	75
Social Influence (SI)	Between Groups	.315	1	.315	.027	.869
	Within Groups	854.474	74	11.547
	Total	854.789	75
Facilitating Condition (FC)	Between Groups	.326	1	.326	.040	.843
	Within Groups	606.661	74	8.198
	Total	606.987	75
Behavioral Intention (BI)	Between Groups	13.840	1	13.840	1.866	.176
	Within Groups	548.791	74	7.416
	Total	562.632	75

ANOVA Teaching Experience)
		Sum of Squares	df	Mean Square	F	Sig.
Perceived Usefulness (PU)	Between Groups	51.316	4	12.829	1.710	.157
	Within Groups	532.723	71	7.503
	Total	584.039	75
Perceived Ease of Use (PE)	Between Groups	33.704	4	8.426	.867	.488
	Within Groups	689.822	71	9.716
	Total	723.526	75
Social Influence (SI)	Between Groups	58.746	4	14.686	1.310	.275
	Within Groups	796.044	71	11.212
	Total	854.789	75
Facilitating Condition (FC)	Between Groups	38.779	4	9.695	1.211	.314
	Within Groups	568.208	71	8.003
	Total	606.987	75
Behavioral Intention (BI)	Between Groups	42.276	4	10.569	1.442	.229
	Within Groups	520.356	71	7.329
	Total	562.632	75

The results of the analysis, conducted through ANOVA, provide insights into the variation in the dimensions of interest with respect to different demographic factors. For age group, sex, and teaching experience, the ANOVA results showed that there were no statistically significant differences in perceived usefulness (PU), perceived ease of use (PE), social influence (SI), facilitating conditions (FC), or behavioral intentions (BI) among the different groups. The p values for each factor exceeded the conventional significance threshold of 0.05, suggesting that the observed differences in means between groups were not statistically significant.

In summary, the results support Hypothesis 4, indicating that there is no significant variation in the perceived ease of use, perceived usefulness, social influence, facilitating conditions, or behavioral intentions toward LMS adoption with respect to gender, age, or teaching experience among higher education teachers in private institutions in Greater Noida. This implies a consistent pattern of perceptions and intentions across different demographic groups, highlighting the robustness of these factors in influencing the adoption of LMS in the context of the study.

In conclusion, this research focused on the adoption of learning management systems (LMSs) among teachers and students in higher educational institutions in Greater Noida, India, with particular attention given to the challenges posed by the COVID-19 pandemic. Utilizing the unified theory of acceptance and use of technology (UTAUT) model, this study addresses gaps in the literature by exploring the factors influencing LMS adoption and its multifaceted effects on teaching and learning. Notably, this study emphasizes the specific challenges faced by private institutions in Greater Noida and the nuanced dynamics influencing behavioral intentions, aiming to provide valuable insights for policymakers, educators, and stakeholders.

The research methodology, anchored in the UTAUT model, involved a cross-sectional design with participants from Sharda University and Galgotias University. Ethical considerations ensure participant confidentiality and voluntary participation. The significance of this study lies in its potential to guide decision makers and educators in enhancing strategies for LMS implementation and improvement.

The findings reveal nuanced insights into adoption and usage patterns among students and teachers. Students exhibit moderate levels of acceptance of LMS, with Google Classroom being the preferred platform. Social influence and facilitating conditions vary among student groups, while perceived usefulness, ease of use, and behavioral intention remain consistent across demographics.

Teachers, on the other hand, generally display a positive attitude toward LMS adoption, particularly toward Google Classroom. Their perceptions significantly deviate from neutrality, indicating a strong inclination toward LMS adoption. Demographic factors, including age, sex, and teaching experience, influence teachers' perceptions, with regression analysis establishing a significant relationship between perceived aspects and behavioral intentions, underscoring the role of these factors in shaping attitudes toward LMS adoption.

Overall, the research contributes comprehensive insights into the dynamics of LMS adoption, offering practical implications for educational institutions seeking to enhance digital infrastructure and promote effective LMS utilization in Greater Noida.

RECOMMENDATIONS

Based on the conclusions drawn from the research on the adoption of learning management systems (LMSs) in Greater Noida, India, the following research is recommended:

Tailored Strategies for Private Institutions: Given the specific challenges faced by private institutions in Greater Noida, it is recommended that policymakers and educational leaders develop tailored strategies to address the unique circumstances of these institutions. This may involve targeted support, resource allocation, and policy adjustments to enhance the implementation of LMSs in private higher education settings.

Promotion of the Google Classroom Platform: Considering that the Google Classroom has emerged as the preferred LMS platform among students and teachers, educational institutions should consider promoting and providing additional training for this platform. This can include workshops, tutorials, and resource materials to maximize the effective use of Google Classroom, capitalizing on the positive attitudes and acceptance levels observed in the study.

Age-Specific Training Programs: Recognizing the influence of demographic factors, especially age, on teachers' perceptions and behavioral intentions, it is advisable to implement age-specific training programs. These programs can cater to the varying needs and preferences of different age groups, ensuring that educators of all generations feel equipped and confident in utilizing LMS effectively in their teaching practices.

Continuous Evaluation and Improvement: This study highlights the need for continuous evaluation and improvement of LMS implementation. Educational institutions should establish mechanisms for ongoing assessment of the effectiveness of LMSs, taking into account feedback from both students and teachers. This iterative process can inform adjustments to strategies, address evolving challenges and maximize the benefits of digital learning platforms.

Collaboration and Knowledge Sharing: Given the multifaceted effects of LMS adoption on teaching and learning, fostering a culture of collaboration and knowledge sharing among educational institutions can be beneficial. Platforms for sharing best practices, challenges, and innovative solutions can facilitate collective learning and contribute to the overall improvement of LMS utilization in the Greater Noida region.

These recommendations aim to guide educational institutions, policymakers, and stakeholders in refining their approaches to LMS adoption, promoting effective digital learning, and addressing the specific contextual nuances observed in Greater Noida.

AVALBILITY OF DATA AND MATERIALS

The data and materials supporting the findings of this research are available upon request. Requests for access to the dataset or any supplementary materials should be addressed to Paul Dominic Jarry, Jr., at [email protected]. Please provide a detailed description of the intended use of the data or materials at your request.

I am committed to fostering transparency and reproducibility in scientific research, and I welcome inquiries from fellow researchers, scholars, and institutions interested in further examining or building upon the results presented in this study.

FUNDING

No external funding was received for conducting this research. The authors would like to explicitly state that the study was self-funded and that no financial support was provided by any external organizations or individuals. The research was independently performed, with the author assuming full responsibility for the design, execution, and analysis of the study.

ACKNOWLEDGEMENTS

The writer expresses gratitude to the students, educators, and administrative personnel of both Sharda University and Galgotias University in Greater Noida for actively engaging in the research and creating a conducive environment for data collection. A particular acknowledgment is extended to Prof. Kritiki Gosain from the School of Education at Sharda University for her valuable supervision.

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The authors declare no competing interests.

Adoption and Use of Learning Management Systems Among Teachers and Students in Higher Educational Institutions in Greater Noida

Status:

Version 1

Abstract

Figures

I. INTRODUCTION

II. LITERATURE REVIEW

III. Research Objectives

IV. Hypotheses of the Study

V. METHODOLOGY OF THE STUDY

VI. RESULTS

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1