Examining Students’ Readiness toward Using Learning Management System at University of Ha’il: A Structural Equation Modelling Approach

Abstract

During the past two decades, many universities around the world have deployed various types of learning management systems (LMSs) to manage the learning and teaching process electronically. The rapid spread of the coronavirus disease at the start of the COVID-19 pandemic quickened educational systems’ shifting from face-to-face to online instruction. Blackboard is the most used LMS during this transition in Saudi Arabian universities, institutions, and higher education institutions. Although previous research has widely investigated students’ adoption of Blackboard, few studies have examined the factors affecting their readiness toward e-learning. The present study used the model of Student Online Learning Readiness (SOLR) as the primary framework for examining the factors that may affect students’ readiness toward using Blackboard. A survey was delivered to undergraduate and postgraduate students at the University of Ha’il to collect data, and a total of 196 responses were received. A two-step process in AMOS was applied to analyse the data. The study findings showed that technical, social, and communication competencies had a significant positive influence on the readiness of students toward using Blackboard LMS. The study outcomes will provide educators, designers, decision-makers, and practitioners with a deeper understanding of the factors that could influence students’ readiness toward using Blackboard and, thus, enhance the successful usage of the system.

1. Introduction

Following the dramatic spread of the coronavirus disease announcing the beginning of the COVID-19 pandemic in late 2019 and early 2020, many universities worldwide have rapidly shifted their systems from the traditional in-campus model to distance learning. Previous studies showed that utilising the Blackboard learning management system has many benefits for both faculty members and students, including organising and managing the course content, increasing student engagement, and reducing instructor planning time [1]. The most valuable advantage of integrating Blackboard into the teaching and learning process is the ability to create a teaching and learning environment for students and teaching staff without the restrictions of distance or time [2]. Despite all the benefits that Blackboard can offer, its adoption and usage by students in the higher education sector in Saudi Arabia continues to be concerning. According to a number of studies, some higher institutions in Saudi Arabia are integrating Blackboard LMS into their educational systems; however, usage of LMSs is not satisfying [3,4]. Most of the relevant studies have investigated the adoption and diffusion of LMS technologies among users. These studies used various types of models, including the Technology Acceptance Model (TAM) by [5], the Theory of Planned Behaviour (TPB) [6], the Theory of Acceptance and Use of Technology (UTAUT) model by [7], and the Diffusion of Innovation (DOI) theory [8]. These famous models focus on behavioural intention as the primary area to predict technology usage [9,10]. However, few studies have examined user readiness as a predictive variable [11,12]. No study has examined students’ readiness toward using Blackboard by applying the SOLR Model, especially in the Arab context. This study will contribute to the literature by applying the SOLR model to assess the factors that may affect students’ readiness toward using Blackboard LMS.

Literature Review

Dahlstrom et al. (2014) [13] described learning management systems (LMSs), such as Blackboard, as software technologies designed to support the implementation of learning and teaching processes on and off campus. Blackboard is defined as a web-based application software, which support the completion of e-education including learning and online teaching, institutional services, and campus communities. It can be integrated with other various pieces of administration software or work as a standalone application. There are several advantages of utilizing Blackboard such as manging and developing courses, supporting collaboration and communication activities, and delivering educational content [14]. According to [15], an LMS is “a key enabling technology for ‘anytime, anywhere’ access to learning content and administration” (p. 6). Considering all the previously mentioned advantages of incorporating LMSs into online teaching and learning environments, proper adoption of the system is needed to take advantage of its potential. Many studies have examined the adoption and diffusion of new technologies among LMS users. These studies employed various model types, including the Technology Acceptance Model (TAM) developed by [5], the Theory of Planned Behaviour (TPB) [6], the Diffusion of Innovation (DOI) theory [8], and the Theory of Acceptance and Use of Technology (UTAUT) model by [7]. These models have some limitations, as stated in the literature. For example, Lu et al. (2003) [16] indicated that TAM is too general and may not be able to capture in-depth information on users’ perceptions of a system. Additionally, DOI was criticised for not acknowledging the differences between users’ and institutions’ adoptions of different technologies [17]. Most studies used the previously mentioned models to examine the adoption of LMSs [9,10]. Few studies have utilised the SOLR model to examine students’ readiness toward using LMSs. Success and achievement in remote learning have been linked to students’ online learning readiness (SOLR) [18,19]. Liu and Roberts-Kaye (2016) [20] defined e-learning readiness as “cognitive awareness and maturity that a student develops for successful learning in a Web-based environment. It manifests in the attributes of recognizing the self-directed nature, formulating learning strategies, obtaining technology competencies, adjusting to digital etiquettes, and being open for help-seeking” (p. 242). Research suggests that it is essential to have adequate social support and to increase students’ sense of belonging in online learning to achieve higher retention rates and increase meaningful learning experiences [21].

In line with the highlighted importance of students’ readiness and social support to increase achievement in online courses, Yu and Richardson (2015) [22] showed that communication, social, and technical competencies significantly influence students’ satisfaction in online learning and, therefore, their academic achievement. Yu and Richardson (2015) [22] proposed the SOLR model in their study. SOLR consists of the following main competencies linked to students’ readiness for online learning environments: social, communication, and technical. Previous research has found a strong positive direct relationship between these constructs and user satisfaction and achievements in online courses [11,19].

Ref. [11] applied SOLR and used a structural equation model to examine students’ readiness toward MOOCs at institutions in Jordan. The study showed that students’ readiness to accept and use MOOCs for their learning is significantly correlated to all SOLR competency types: technical, social, communication, and self-learning management.

Another study by [12] examined students’ readiness level in educational institutions in Malaysia. The study applied the SOLR model as the main framework, focusing on the following six competencies: communication, technical, self-efficacy, social, self-directedness, and readiness. The study’s findings show that self-efficacy was significantly correlated with adult students’ readiness in MOOCs.

Similarly, Ranganathan et al. (2021) [23] conducted a cross-sectional study using SOLR constructs to assess the readiness levels towards e-learning of physiotherapy students in two private and two public universities in Malaysia. The findings showed that learners in these four educational institutions had moderate readiness levels towards these competencies, especially technical competencies, and gender did not have any significant influence on the readiness level.

So far, no study has examined students’ readiness toward using learning management systems by applying the SOLR Model, particularly in the Arab context. This study will contribute to the literature by applying the SOLR model to assess the possible factors affecting students’ readiness toward using Blackboard LMS. Understanding these factors is essential and could lead to the successful implementation and usage of Blackboard, resulting in significant benefits for users.

2. Research Model

In this study, the proposed theoretical model was adapted from a study by [22], who developed the Student Online Learning Readiness (SOLR) model based on the work of [24] student integration model (SIM). According to [22], SOLR consists of four main types of competencies believed to measure student readiness for learning in online environments. These competencies are social, communication, technical, and self-learning management competencies. The proposed research model is presented below in Figure 1.

3. Hypothesis

 Hypothesis 1 (H1):

Technical competency has a significant positive effect on students’ readiness toward using Blackboard LMS.

 Hypothesis 2 (H2):

Social competency has a significant positive effect on students’ readiness toward using Blackboard LMS.

 Hypothesis 3 (H3):

Communication competency has a significant positive effect on students’ readiness toward using Blackboard LMS.

4. Methods

4.1. Research Design

This study applied a quantitative approach, using an online survey to collect student data. According to [25], quantitative research is a method that is “explaining phenomena by collecting numerical data that are analysed using mathematically based methods (in particular statistics).” (p. 1). Quantitative data usually involves numerical measurements, uses structured and validated instruments for collecting data, and reports findings statistically with means comparisons, statistical significance, and correlations [26]. As this study aimed to examine the relationships between factors of the proposed model, a quantitative approach was considered the best method to apply.

4.2. Instrument

The survey used in this study consisted of two parts. The first section was self-designed to measure respondents’ demographics and descriptive information. In this part, students were requested to provide information about their gender, college, academic program, and the device used in their online education. The second part measured the constructs in the model and was adapted from a study of the SOLR model by [22] (Appendix A). The SOLR instrument has four constructs: technical competence (TC), social competence (SC), communication competence (CC), and student readiness (SR). All constructs were measured by 22 statement items using a 5-point Likert scale. Data were collected from respondents during the second semester of 2022 between April and June.

4.3. Participants

The participants (N = 175) were graduate and undergraduate students enrolled in online courses using a Blackboard LMS at the University of Ha’il in Saudi Arabia. The sample consisted of 135 female students and 40 male students.

4.4. Data Analysis

The data analysis applied two methods. In part A, SPSS was used for processing and analysing the descriptive statistics of respondents’ demographic information. In part B, a two-step approach in the analysis of moment structures (AMOS) was used. In the first step, confirmatory factor analysis (CFA) was used to assess the measurement model. In the second step, structural equation modelling (SEM) was applied to assess the relationship between factors and test the research hypothesis.

5. Data Analysis and Results

5.1. Analysing the Respondents’ Demographic Information

A total of 196 students responded to the survey. However, only 175 surveys were taken for further analysis due to missing values and incomplete responses.

Table 1. Demographic information of respondents.

		Frequency	Percent
Sex	Male	40	22.9
	Female	135	77.1
	Total	175	100.0
Academic Program	Bachelor	104	59.4
	Diploma	36	20.6
	Master	35	20.0
Colleges	Education	66	37.7
	Science	54	30.9
	Applied college	9	5.1
	Computer Science	14	8.0
	Art	26	14.9
	Engineering	2	1.1
	Health and Informatics	1	0.6
	Law	1	0.6
	Preparatory year	1	0.6
	Applied Health college	1	0.6
Used Device	PC	22	12.6
	Smart Phone	108	61.7
	Tablet	12	6.9
	Laptop	33	18.9
Training	Yes	73	41.7
	No	102	58.3

shows the respondents’ information in terms of sex, academic program, college, used technology device, and training experience. Regarding gender, 135 (77.1%) were female students, while 40 (22.9) were male. In terms of the academic program of respondents, there were various responses. Most of the participants (104) were enrolled in a bachelor’s degree program (59.4%), 36 students (20.6%) were enrolled in a diploma program, and 35 students (20.0%) were pursuing a master’s degree. In terms of colleges, the responses varied. Most students (66) were in a college of education (37.7%), followed by 54 students in colleges of science (30.9%), 26 students in colleges of art (14.9%), and 14 students studying computer science (8.0%). Most students (108 students) reported using smartphones to access Blackboard LMS (61.7%), followed by 33 students who used a laptop (18.9%). Other students (22) reported using a PC (12.6%), and the remaining 12 students (6.9%) used tablets. When questioned about LMS training, most students said they had not received any training on using Blackboard LMS (102 students, 58.3%). However, 73 students reported having attended training courses (41.7%). Table 1 shows the descriptive information of the respondents.

5.2. Applying Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM)

5.2.1. A-CFA

A pooled CFA has the power to consider the different correlations among constructs and address measurement errors, making it the most convenient technique for assessing a measurement model and validating its constructs [27]. This technique can also handle various latent constructs in the same time frame and treatment. Thus, a pooled CFA was applied to develop the measurement model. Additionally, the construct, convergent, and discriminant validities were examined [27]. The results of the first pooled CFA are presented in Figure 2.

Construct validity is achieved once all indices in the model meet the required and suggested value. The results of the first run, shown in

Table 2. The results of indices in the first run.

“Category Name”	“Index Name”	“Index Value”	“Acceptance Level”	“Decision”	Reference
“Absolute fit”	RMSEA	0.102	<0.1	Not Accepted	[28]
“Incremental fit”	CFI	0.829	>0.90	Not Accepted	[27]
	TLI	0.806	>0.90	Not Accepted	[27]
	GFI	0.754	>0.90	Not Accepted	[27]
	IFI	0.832	>0.90	Not Accepted	[27]
“Parsimonious fit”	Chisq/df	2.798	<3.0	Accepted	[27]

, indicated that some of the indices did not meet the required and suggested level.

Thus, some modification to the model was conducted to improve the model’s indices to achieve construct validity. For example, some items, such as those from TC3, SR7, SR5, CC2, SC5, and TC1, were deleted due to their low factor loading. The second run of the pooled CFA was then applied, the results of which are shown in Figure 3.

After the second run, all indices met the suggested and required level of acceptance. Thus, construct validity was achieved.

Table 3. The results of the model indices in the second run.

“Category Name”	“Index Name”	“Index Value”	“Acceptance Level”	“Decision”	Reference
“Absolute fit”	RMSEA	0.098	<0.1	Accepted	[28]
“Incremental fit”	CFI	0.904	>0.90	Accepted	[27]
	IFI	0.906	>0.90	Accepted	[27]
“Parsimonious fit”	Chisq/df	2.664	<3.0	Accepted	[27]

shows the results of the model indices.

Next, the convergent validity was checked. Convergent validity is met once the result of CR is above 0.60 and that of AVE is above 0.5 [29].

Table 4. Values of CR and AVE.

	“CR”	“AVE”
“SR”	0.867	0.569
“TC”	0.834	0.559
“SC”	0.862	0.613
“CC”	0.791	0.658

shows the value results of CR and AVE, which met the required level of acceptance (CR > 0.60, AVE > 0.50). Thus, convergent validity was achieved.

Discriminant validity was then examined to ensure that all factors in the proposed model differed. The bold value in the model (See

Table 5. Discriminant validity.

	SR	TC	SC	CC
SR	0.854
TC	0.848	0.878
SC	0.718	0.662	0.783
CC	0.495	0.403	0.449	0.811

) refers to the AVE square root, and other values point to correlations of constructs. Awang (2015) [27] stated that discriminant validity is achieved once all the values in bold are higher than all other values in their row and column. The results in Table 5 show that discriminant validity was met, as all bold values were higher than the other values in their row and column.

5.2.2. B-SEM: Standardised Estimate

Structural equation modelling (SEM) shows two outputs: standardised and unstandardised estimates. The standardised estimate is conducted to calculate the factor loading of construct items, the R square of the dependent constructs, and the strength of the relationships among the constructs. Conversely, the unstandardised estimate is applied to calculate the critical ratio and test the research hypothesis. The standardised estimate was run first, and Figure 4 shows the standardised estimate of the model.

The R square of the dependent construct (student readiness “SR”) was 0.78, meaning that 78% of the students’ readiness model was explained by the constructs of TC, SC, and CC. These results showed the model’s high explanatory power. Falk and Miller (1992) [30] stated that the R square value must be above 0.10 for the endogenous dependent construct in the model to be considered adequate. Cohen (1988) [31] also stated that an R square value below 0.12 refers to a weak explanatory power, a value between 0.13 and 0.25 refers to a medium explanatory power, while a value above 0.25 refers to a high explanatory power. Based on this explanation, the R square of the dependent construct in the proposed model (student readiness) was 0.78, demonstrating a high explanatory power for the proposed model.

5.3. Unstandardised Estimate

An unstandardised estimate was conducted to compute the critical ratio and test the research hypothesis. Figure 5 shows the output of the unstandardised estimate.

5.4. Results of the Hypotheses

The results demonstrated that the TC, SC, and CC factors had a significant positive effect on student readiness (SR); (β = 0.874, p < 0.05), (β = 0.260, p < 0.05), and (β = 0.147, p < 0.05). Thus, H1, H2 and H3 were supported.

Table 6. The hypothesis results (*** p < 0.001).

			Estimate	S.E.	C.R.	P	Results	Results of Hypothesis
SR	<---	TC	0.874	0.144	6.059	***	Significant	Supported
SR	<---	SC	0.260	0.092	2.820	0.005	Significant	Supported
SR	<---	CC	0.147	0.073	2.012	0.044	Significant	Supported

shows the results of the research hypothesis:

6. Discussion

The study’s findings showed that all the external factors in the model—namely, technical, social, and communication competencies—had a significant positive effect on students’ readiness toward using Blackboard LMS. Regarding technical competency, this finding was aligned with previous studies [11,32]. Technical competency is essential and could play a vital role in enhancing students’ readiness toward using Blackboard. Higher education institutes should provide students with a program to improve their technical competency, such as training that focuses on enhancing their ability to use technology and computers. Furthermore, students’ activities can be applied using technologies with the support of educational institutes and universities. Moreover, the Blackboard LMS facilitator should focus on increasing students’ competency by developing the LMS to make it as user-friendly as possible. They should also enhance their technical competencies to inspire students’ engagement in the LMS environment. A higher technical competency among students will lead to more readiness toward using Blackboard LMS.

However, this finding was inconsistent with a study by [12], who found that technical competency was not a significant factor contributing to MOOCS readiness. They explained that most respondents (above 95%) reported having access to various digital technologies, such as a laptop, PC, tablet, and smartphone. Additionally, about 85% of participants reported having a stable internet connection, and their ability to use data and files on the internet was high (77.1%), showing that they were familiar with digital technology and perceived technical competency as inherent competency.

Social and communication competencies had a significant positive effect on students’ readiness toward using Blackboard LMS. This study’s results were aligned with a study by [11]. Blackboard LMS is a collaborative learning environment that requires students’ participation and communication competencies. The social relationships with other students on Blackboard are one of the primary motivations of Blackboard users [33]. Thus, Willis (2013) [34] claimed that social and communication skills are vital for collaboration success in online learning environments where user communities exist and are considered a major factor. The lack of communication and social competencies among students is a concerning issue that can result in a poor collaboration environment [35]. This lack of skills is because the interactions in online environments are different from face-to-face communication; participants need a new competency, which is often not learned. Blackboard LMS developers and facilitators must increase students’ social presence while using Blackboard, thus facilitating and enhancing collaborative learning. Moreover, Blackboard developers and providers should focus on synchronous communication, which plays an essential role in enhancing the social presence of students. A high level of student social presence will enhance their communication and social competencies. Additionally, social and communication interactions among students could assist their learning [35]. Teachers and lecturers, especially those in higher education, should build excellent relationships with students inside and outside the classroom. These relationships will increase students’ social and communication competencies, enhancing their readiness to use and learn through Blackboard.

This study contributes theoretically and practically to the literature. Theoretically, this study can enhance the literature, specifically the SOLR literature. Practically, the study outcome can provide educators, designers, decision-makers, and practitioners with a deeper understanding of the factors that could influence students’ readiness toward using Blackboard and, thus, enhance the successful usage of the system.

7. Conclusions

This study aimed to examine the possible factors influencing students’ readiness toward using Blackboard LMS. To achieve the study’s goal, the SOLR model was applied. The proposed SOLR model examined the influence of three main competencies—technical, social, and communication—on students’ readiness toward using Blackboard LMS. The study’s findings showed that technical, social, and communication competencies had a significant positive effect on students’ readiness toward using Blackboard LMS. Thus, higher education institutes and universities should focus on improving these competencies to improve students’ readiness and abilities to use Blackboard LMS effectively

This study also had some limitations. Although the variance explained in the model was high, resulting in 78% of the students’ readiness constructs being explained by the three main constructs—technical, social and communication competencies—demonstrating a strong model, other factors could also contribute. Thus, future works should examine additional factors such as learners’ self-efficacy or motivation during online learning, which may enhance the strength of the model. Furthermore, this study relied on a quantitative study to investigate and examine the relationships among all latent constructs in the proposed model. Future studies could apply a mixed-method approach, including both quantitative and qualitative methods, to gain a deeper and clearer understanding of the readiness phenomena. Moreover, the data collected in this study were only from one university in the Kingdom of Saudi Arabia, which could influence the generalisability of this study. Future studies could collect larger samples from different universities to enhance generalisability.