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Article

Impact of Critical Factors on the Effectiveness of Online Learning

by
Rumpa Roy
1 and
Mujeeb Saif Mohsen Al-Absy
2,*
1
Administrative Science Department, College of Administrative and Financial Science, Gulf University, Sanad 26489, Bahrain
2
Accounting and Financial Science Department, College of Administrative and Financial Science, Gulf University, Sanad 26489, Bahrain
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(21), 14073; https://doi.org/10.3390/su142114073
Submission received: 30 August 2022 / Revised: 18 October 2022 / Accepted: 24 October 2022 / Published: 28 October 2022
Versions Notes

Abstract

:
Higher education institutions went through a radical transition from face to face to online learning due to the COVID-19 pandemic. The transition and success of online learning depends on various factors. This research aims to measure the impact of critical factors on the effectiveness of online learning. The theoretical framework of the study considers eight factors namely, IT infrastructure, learning platform, students’ characteristics, faculty support, active learning, course design, development and delivery, evaluation and assessment, and institutional support. The study is quantitative; a well-structured survey questionnaire was deployed to collect data from participants selected based on a purposive sampling technique. The results indicate that the majority of the respondents perceived online learning as effective, which is reflected by the average score of 4 on a Likert scale. A model summary of the multiple regression analysis illustrates that 39.2% influence on dependent variable ‘effectiveness of online learning’ is due to the above-mentioned eight independent variables. The coefficients of the model show that active learning and institutional support have significant positive impact on the effectiveness of online learning. The findings provide direction to educators in strengthening the pedagogy of active learning across courses and institutional support in terms of IT infrastructure, IT support and services, faculty development program, and management vision towards digitalization. The theoretical framework of the study has been confirmed by the results as reflected by the perception of the staff and students who participated in the survey. This guides university management in designing strategies to ensure effectiveness of online learning.

1. Introduction

The history of online education dates back to the 1970s with computer assisted learning, followed by computer-based training and multimedia during the 1980s, web-based education during the early 1990s and e-learning during last two decades, across higher education institutions worldwide [1]. With the advent of mobile learning and social networking, from in 2005 to date, the dynamics of online learning have changed dramatically with a diverse range of pedagogical practices. In this era of digitalization, online learning has expanded educational opportunities to the masses through blended, hybrid, or completely online delivery mode.
The COVID-19 pandemic led to a disruption in the educational system, and amidst lockdown measures higher education institutions went through the transition from face- to-face learning to online learning through communication platforms like, Zoom, Microsoft Teams, Google Classroom, ClassDojo, Google Meet, Web Ex, and so on. This not only ensured continuity in the student learning experience but also resulted in digital transformation in higher education. In other words, the pandemic enhanced the pace and scope of digital transformation of traditional universities to sustain their educational activities during lockdowns and in the new normal [2]. Such technology enabled learning is underpinned by the collaborative efforts of senior management, IT infrastructure and support, shared organizational vision and mission, students’ characteristics, faculty commitment, and active learning pedagogy [3].
The sudden transition to emergency remote learning had never been easy given poor internet connectivity, non-availability of electronic/mobile devices particularly in rural areas in developing countries, student readiness to learn in the new format, digital skill, the adaptability of course instructors, and so on. However, this transition opened alternative avenues for digital technologies and the extensive use of learning management systems for student engagement, accessing learning resources through synchronous and asynchronous sessions. Social media tools became useful during online learning to facilitate interaction between students and instructors, and among students, in a flexible manner without the barrier of time and distance [4].
COVID-19 as a global crisis had direct and indirect psychological impact on the common people resulting in depression, anxiety, and stress. Overcoming the disease was a challenge but adjusting to the new normal has not been smooth within the context of social distancing, self-isolation, quarantine, and related measures. Students and staff were forced to adapt to online learning without the luxury to choose. Within contextual differences, higher education institutions largely responded promptly to shift from face-to-face to online learning, however universities need to rediscover the learning environment such that digitalization complements social relation, along with expanding the scope of modular learning.
The quality of online learning depends on different critical success factors, which has captured the attention of researchers. A well-designed, learner-oriented, interactive, technology enabled, flexible, and inclusive online learning environment can culminate in enormous educational opportunities, with a focus on continuous quality enhancement. There are different factors, which can be broadly classified as technical factors, pedagogical factors, learner factors, institutional factors, and more. These further include sub-factors, which can be identified as impacting on online learning. The success factors are interrelated and cannot result in best educational practices independently [5].
Given this perspective, the research aims to measure the impact of critical factors on the effectiveness of online learning. Furthermore, the research intends to provide recommendations to enhance the effectiveness of online learning within the context of higher education institutions.
Hence, the following research questions have been developed, which have been addressed subsequently:
  • Is online learning effective within the context of higher education institutions?
  • What are the critical factors positively impacting online learning?

2. Background Literature

2.1. Online Learning

Traditional pedagogies and learning environments have undergone radical changes within the higher education landscape. Distance education was earlier restricted to correspondence courses, without having many opportunities for real-time interaction between the student and the instructor. However, the design of online education provides multiple choices to students, ranging from synchronous to asynchronous learning sessions using instructional technologies. Online learning has enabled interaction and enhanced engagement via electronic bulletin board, discussion forum, chat, wikis, and blogs in the learning management system (LMS). Thanks to the widespread use of communication technologies, distance education has increased the access of adult learners in non-traditional or non-formal programs [6].
Research in the context of a Chinese University reveals the role of computer and internet in online teaching for professional courses. The teaching platform, DingTalk, had been successfully implemented in a college in China and the on line teaching experience was surveyed in terms of class preparation, delivery or in-class teaching, and home assignment. Course design plays an important role by involving students in class activities, with digital tools like gamification. Students performed better amidst online learning; however practical aspects of learning are a challenge without innovative learning tools [7]. Interaction and communicative competence can be effectively enhanced through task-based language teaching in a synchronous computer mediated communication environment. The majority of language teacher candidates who were enrolled in the subject of applied linguistics at a university in Spain, and who participated in the survey, commented that online lectures still focused on traditional presentations. Learning activities and materials were not appropriately designed to match the context of the digital environment. Online education needs different teaching pedagogies compared to face-to-face learning. Adopting to digital tools and technologies ensures student engagement in digital platform and enhances their digital skills [8].
Online learning leads to more flexibility, accessibility for students in terms of location, as well as time, and provides opportunities for better learning resources and collaboration. Online learning prepares students as independent learners and self-regulated learning takes place through self-observation, self-judgement, and self-reaction. Academic achievement is a result of the conscious efforts of students through self-regulated learning strategies [9].
Marc Prensky coined the term ‘digital natives’, who are younger generations with different thinking and information processing skills than their predecessors, who speak the digital language of computer, internet, video games, and all other gadgets of the digital era. Digital immigrants, who belong to older generations, struggle to cope up with the new language and the accent of digital age. Educators face a challenge about how to deliver both legacy and future content in the language of digital natives [10]. Students, also known as digital natives, are blessed to have smart phones, high-speed internet, plenty of e-resources, and cost-effective internet packages to enjoy learning experiences in digital platform. The instructor has a critical role to adapt to multimedia presentations, instructional design material, and digital resources for active learning in the LMS. Online classes are equally demanding for students in terms of critical thinking and problem-solving skills, compared to real classes. The study comparing the experience of human resources management students in real vs. online classes at Gulf University identifies the following five factors contributing to the effectiveness of online learning: steady internet connection; peaceful ambience; active engagement; instructor support and encouragement; and smart devices [11].
Higher education institutions rely on blended learning with an emphasis on project-based learning, collaboration, case studies, simulation, and digital libraries. Learner-centered pedagogies bring meaningful results in terms of team building, time management, leadership, creativity, and innovation [12]. An online collaborative learning environment fosters social presence, ensures teaching presence, creates learning community, encourages cognitive presence, supports deep learning, and generates new knowledge and action through participative activities [13].
As per the research conducted by Gómez-Aguilar, and Hernández-García, visual analytics are useful tools to monitor student activity and engagement about their interaction with course content, peers and instructors in LMS. This opens up alternative arrangements for teaching, with learning and assessment leading to enhancement in the pedagogical approaches [14].
Social presence perception is critical in the context of online learning, which can be ensured through use of personal profiles, personalized messages and chat, video feedback, email communication, social media, and so on. A video-centered learning environment can undoubtedly enhance teachers’ social presence in the absence of real-time interaction that happens in a physical classroom [15]. E-learning has brought two major changes: flexibility in terms of time and space; and the opportunity to learn individually and collaboratively in parallel. Designing appropriate e-learning systems considers how the students can adjust with the new learning style, their motivation, learning objectives, and learning rhythm. Within the perspective of distance and asynchronous learning, the educational model focuses on flexibility, collaboration, and individualization [16].

2.2. Effectiveness of Online Learning

The research within the context of Sakarya University, Turkey, indicates that perceived usefulness, perceived playfulness, and multimedia content effectiveness, are critical factors determining satisfaction towards e-learning [17]. Students’ and instructors’ roles and characteristics contribute to the success of online learning. Students’ readiness, digital fluency, self-directed learning, self-control, motivation, and self-reliance, are the predominant factors impacting the satisfaction of students towards online learning [18].
A case study conducted in Chicago State University identified the factors influencing the faculty use of technology in online teaching. Faculty perceptions on online learning, prior experience with digital tools and technologies, specific experiences in higher education related to IT infrastructure, and training and workshop, are impacting faculty willingness to embed digital technologies in offered courses. Faculty opined to increase the quality and rigor of the online courses, compared to traditionally delivered courses [19]. Another case study within the context of Jordan indicates that the effectiveness of the e-learning system largely depends on the system interface and usability, IT infrastructure, technological acceptance form, training, and support to the users [20]. User experience is a strong predictor of brand meaning and user satisfaction within the perspective of online courses in digital platform. Brand equity and user satisfaction contribute significantly to the decision in undertaking e-learning [21].
The research conducted on the sample of graduate and post-graduate engineering students in the field of operations and production management, enrolled in multiple production management courses, posits that the appropriate mix of human technology factors should be followed to ensure the effectiveness of online learning. Interaction between the learners, along with the facilitating role of instructor, lead to the success of online learning. The institutions have a strategic role in helping traditional students to adapt to the emerging mode of online learning [22].
The study conducted in Georgetown University, Qatar, on student engagement in online and blended learning, summarized students’ perception and experiences of engagement in online learning along the community of inquiry framework and its social presence dimension. The perception of the Arabic as Foreign Language (AFL) learners and Arabic Heritage Learners (AHLs) in Arabic online courses shows that all of them are not competent enough in using digital technologies, and they did not get sufficient opportunity to engage socially during online sessions. Along with identifying challenges towards online learning, the learners/students provided a set of solutions such as: creating a welcoming environment; collaboration and engagement in class activities; conducting both synchronous and asynchronous sessions; interaction within classmates to develop a sense of belongingness; encouraging open communication and dialogue practice; and promoting group cohesion through interaction in chat rooms etc. [23].
The study conducted at a university in the US for 80 students enrolled in an online accounting course, revealed that students consider asynchronous learning as interesting and challenging. The following are the factors impacting the satisfaction of students in the digital format: instructors’ preparation and teaching strategies; the availability of the instructor to communicate with the students; grading criteria, complexity and challenging nature of assignment; and online resources. The approach of the instructor in designing the online course content, teaching and learning methods, interaction with the students during and after contact hours, and the ease of using digital tools and technologies, have a significant impact on students’ satisfaction from online courses [24].
Within the context of community college level online courses, students and instructors demonstrate positive perceptions towards the effectiveness of online courses. Teaching experience and digital skills are found to be crucial factors impacting online learning positively. Students with a language background other than English reflect low perception on the effectiveness of online learning. The need for faculty professional development and implementing social and cognitive strategies arises in order to cater to the diverse nature of students [25]. The use of online social technologies or social media is the new trend in education, which provides a better learning experience in terms of activities and interaction between students and instructors. Instructors need to develop well-planned social media integration strategies to use social media in course delivery [26].
The research conducted within the context of e-learning for the Department of Communication and Media at the University of Athens illustrates factors such as willingness to learn, and student participation and engagement in the session, which support the students to evaluate the online course and redesigning course material [27]. As an extension of this action research, critical reflections of the students on e-learning within the context of blended education had been targeted. The research addressed the questions, namely, how e-learning tackled the interest of the students in the digital platform; critical events affecting the whole online learning experience; and factors influencing students’ engagement during the delivered sessions [28].

2.3. Online Learning Amidst COVID-19 Pandemic

The COVID-19 pandemic has disrupted educational activities as perceived by different stakeholders. Adapting to emerging learning technologies is the obvious solution to cope up with the challenges of the pandemic. Resistance to change, lack of IT infrastructure and resources, inadequate training and support for staff and students, a lack of funding, and digital divide etc. are detrimental factors to the transition to online learning [29]. Research focusing on the psychological impact of the pandemic on academic performance of university students reveals that there is a correlation between the psychological well-being of students and the transition to online education, along with strict lockdown restrictions and social distancing. The learning experience in online learning did not replace traditional face-to-face learning completely. This is due to the inherent challenges of online education, such as IT infrastructure, engagement practices, semantic web technologies, lack of electricity, network issues, and limited access to labs and physical facilities. However, the use of digital learning tools for online education and the academic performance of the students during the pandemic are positively correlated. Additionally, the pandemic explores better opportunities for online learning and the use of educational technologies [30].
Faculty members of Gulf University, Bahrain, opined that transition to online learning should look beyond the emergency measure to cope with the challenges of pandemic. Online learning proves to be more effective for theoretical courses as knowledge transfer takes place through a wide range of online resources and material. However, delivering practical courses, developing social skills and belongingness amongst students, conducting fair assessments without appropriate proctoring tool, and the effectiveness of online learning is a matter of debate [31]. Within the context of the hybrid learning model experienced at Gulf University, there is a study which indicates that students’ awareness of accessing online resources, readiness to adapt to a hybrid mode of learning, and the instructors’ competence to offer extraordinary learning experience, are the vital factors determining the success of hybrid learning. The virtual learning environment, the learning management system, and the emotional bonding of students with the instructor and classmates contribute positively to the effectiveness of hybrid learning [32].
The case study conducted for the English language courses at Al Ain University, UAE, proposed that university management should: invest in online learning; provide advanced training to faculty; familiarize the faculty with synergy between pedagogy, technology, and content; provide both synchronous and asynchronous sessions; encourage instructor and students to communicate in multiple channels, boosting engagement; enhance the self-learning of students; and consider the social, emotional and psychological needs of the students [33].
Amidst the emergency response to pandemic, a study on college students at US universities revealed that students experienced lower motivation, self-efficacy, and cognitive engagement. Students developed a negative perception about online learning, leading to lower academic performance. This acts as a vicious cycle since lower performance and lack of learning, in turn, result in a lack of motivation and low self-confidence [34].
The research conducted within the context of Indonesia reflected the perception of the students studying in four different universities on online learning phenomenon, during the COVID-19 pandemic. Students considered online learning as an ineffective activity due to the lack of technology agility, and the inability of the instructors to redesign course content, teaching material and class activities. Students opined that online learning was an unpleasant activity due to lower level of social presence, less interaction with peers, and less opportunity for teamwork. Some students have difficulty in accessing the internet. Another perception towards online learning was limited self-actualization in learning. However, the students showed a diverse perception of the effective implementation of online learning. One group of students acknowledged that online learning is a fun activity, it makes them independent learners, and results in better academic performance [35]. Research on the digital divide and higher education challenges with emergency remote teaching in South African universities highlighted the role of a fourth industrial revolution, technological and digital inequalities, and the socio-economic and institutional barriers faced by the students. Students from marginalized universities had difficulties in accessing online education due to poor network coverage, a lack of digital devices, poor financial conditions, and the lack of digital competence. The problem of the digital divide became prominent as institutions were forced to use online teaching abruptly. Apart from technological barriers, environmental and situational barriers such as a non-conducive study environment, distraction, interruptions due to a socio economic context, heavy data consumption due to synchronous lecture and assessments, and technical challenges due to software and applications, contribute to digital inequality [36].

3. Methodology

The study contemplates descriptive research design in exploring the perception of students and faculty on the impact of critical factors towards the effectiveness of online learning in a higher education institution. Within the context of the higher education landscape in Bahrain, the survey was conducted to capture the responses of the students and faculty of Gulf University, since all the universities align their practices with the regulations from the Higher Education Council and the requirements of the Education and Training Quality Authority, Bahrain, towards online learning before and after the pandemic. Based on the literature review, a theoretical framework has been developed which considers eight independent variables namely: IT infrastructure, learning platform, students’ characteristics, faculty support, active learning, course design, development and delivery, evaluation and assessment, and institutional support. A well-structured questionnaire was developed based on the theoretical framework of the study which contains three sections (Supplementary Materials). Section I includes demographic information of the participants of the questionnaire/survey. Section II of the questionnaire focuses on the perception of the respondents on the effectiveness of online learning. Participants were asked to reveal their level of agreement on a Likert scale of 1 to 5 (with 1 for strongly disagree, 2 for disagree, … 5 for strongly agree) for each of the nine statements pertaining to the effectiveness of online learning. Section III includes eight sub-sections, where participants were asked to express their level of agreement on a Likert scale of 1 to 5 for the statements covered within each sub-section. Each sub-section includes the factors/independent variables considered in this study. The questionnaires were distributed during the fall semester of the academic year 2021–2022, which means the study refers to pivot online learning in the context of COVID-19 pandemic.
A total of 246 participants responded to the online survey out of which 218 are students from four programs offered at GU, 25 of them are faculty and three of the participants are administrative staff. Around 59% of the respondents are female and the remaining 41% are male. The majority (67%) of them are students within the age group 21–29 years. Around 42% of the respondents are from a business and management background, followed by 26% from an accounting and finance background, and 22% from a media and public relation specialization.
The responses of the participants had been analyzed using SPSS to identify multivariate outliers, and assess normality of the data. Further, a multicollinearity test, Cronbach’s alpha test for reliability analysis, and regression analysis were conducted to measure the effectiveness of online learning.

4. Results

This section presents an analysis of the results and how the result is interpreted to draw the conclusion of the research.
Descriptive statistics: the perception of the respondents on a Likert scale was identified to measure the effectiveness of online learning. Respondents revealed their level of agreement for each of the nine statements related to online learning and its effectiveness. The average score for each statement in the descriptive statistics varies between 3.80 and 4.15 on a scale of 1 to 5, reflecting the effectiveness of online learning within the context of Gulf University. The cumulative average was calculated as 4, which implies an overall effectiveness of online learning as perceived by the respondents of the survey.
In order to measure the impact of critical factors on the effectiveness of learning, inferential statistics were applied and the results were calculated using SPSS. The section below summarizes the analysis of the results in addressing the research questions.
Multivariate outliers: outlier cases occur due to variations in the measurement and indicate experimental inaccuracy [37]. The existence of an outlier can distort statistical results and make generalization impossible, except with the same type of outliers [38]. Therefore, this study identified and treated multivariate outliers based on the Mahalanobis’ measure (D2) [38]. This study treated only multivariate outliers as this takes care of the univariate ones [39].
The D2 is calculated by the distance of a data point from the calculated centroid of other cases in SPSS based on the nine variables of study. Chi-square table for 8 degrees of freedom with p < 0.001 [38] shows the value 26.64 as the benchmark. This implies that any case with a D2 value 26.64 and above is a multivariate outlier, which should be removed. Hence, seven cases were identified as outliers and removed from further analysis (66, 70, 80, 107, 192, 240, and 241).
Normality of the data: normality refers to the degree to which the distribution of the sample data corresponds to the scores on the variables clustered around the mean in a bell-shaped or normal curve [40]. The study deployed graphical method for assessing the normality of the data as with a large sample (200 and above) normality is better checked by graphical method rather than by skewness and kurtosis. A normal probability plot was examined to ensure that normality assumptions are met. The data for this study shows normal distribution since all the bars on the histogram are approximated to normal curve.
Multicollinearity Test: the existence of multicollinearity among the independent constructs can significantly misrepresent the estimates of regression coefficients and their statistical significance tests [39]. Hence, this study checks multicollinearity using variance inflated factor (VIF) and the tolerance level of the independent variables based on the 0.7 threshold [41]. Table 1 presents the results of collinearity diagnostic test.
Tolerance values below 0.100 and VIF values above 10 indicate high collinearity [39,41]. Table 1 presents the collinearity diagnostic test results, which indicates that tolerance level varies between 0.192 to 0.454 and VIF values lie between 2.202 and 5.204. Hence the result of the collinearity diagnostic test confirms that multicollinearity does not exist with the collected data under study.
Reliability Analysis: to check the reliability of the measured variables, Cronbach’s alpha was used. The Cronbach’s alpha coefficient indicates the extent to which the items measuring a particular construct are consistent. A Cronbach’s alpha coefficient of 0.60 is considered as average reliability, while a coefficient of 0.70 or higher indicates that the instrument achieved a high reliability standard [39,42,43]. Table 2 shows the result of the reliability test through the calculated value of Cronbach’s alpha coefficient.
Table 2 shows that Cronbach’s alpha for the variables of the study ranges between 0.742 and 0.932, implying that all the variables demonstrate reliability within the required threshold. In other words, since Cronbach’s alpha for all the variables are greater than 0.70, all are accepted for further study.
Multiple Regression Analysis: the model summary indicates how much independent variables influence on criterion variable. The model summary is presented in Table 3.
Table 3 illustrates that R-square is 0.392, which implies that 39.2% influence on dependent variable ‘effectiveness of online learning’ is due to the above mentioned eight independent variables. The value of adjusted R square is 0.370, which helps to control the overestimation of the model.
ANOVA describes the fitness of model that is included in the study. The result of ANOVA is presented in Table 4.
Table 4 shows F value which is calculated as (10.864/0.587 = 18.502) at p less and equal to 0.05. By using F table at (5%, 8, and 230) the result would be 1.97881, which is less than F = ±18.502. So, it is evident that the model is statistically significant.
The coefficients of the model is depicted in Table 5.
Table 5 shows that active learning has highest values with β = 0.655 with t-value = 7.212 at p-value = 0.00. This implies that for each unit increment in active learning, we can predict 0.655 gain in online learning. Institutional support has the second highest value with β = 0.203 with t-value= 1.971 at p-value = 0.50. This implies that for each unit increment in institutional support, we can predict 0.203 gain in online learning. Hence, it can be inferred that out of eight variables under study, only active learning and institutional support have significant positive impact on the effectiveness of online learning.

5. Discussion

The analysis of the results indicates that online learning has been considered as effective by the staff and students who responded in the survey, with regard to training and support, digital skills, flexibility, performance, engagement, delivery of courses and learning material. This is supported by the study conducted within the context of University of Borneo Tarakan, which shows online learning is effective in terms of students’ learning reactions, learning process, and behavior [44]. A study conducted in University of Technology and Education Ho Chi Minh City (HCMUTE), Vietnam, concluded that online education is an effective solution to cope up with the challenges of COVID-19 [45].
Around 71% of the respondents prefer to continue online learning even after the pandemic. This might be due to flexibility in attending sessions, saving time, energy, and the cost of commuting, and managing commitment at work (part time). Whether university will continue to deliver courses in online/hybrid mode or come back completely to traditional face-to-face learning is a matter of debate. This needs to be discussed at strategic level to develop the future direction of the university, along with the requirements of regulatory bodies.
Even though the ANOVA results show that the model is statistically significant, the coefficients of the model infer that only two variables, namely, active learning and institutional support, contribute significantly to the effectiveness of online learning. This provides insight to strengthen the pedagogy of active learning through consistent implementation across the courses supported by faculty professional development. Exposure to best practices in education around the world during and post pandemic will bring impactful results. For the engagement of the students in a digital platform, an active learning strategy with innovative and authentic tasks provides the best learning experience. Research conducted in a higher education institution in Singapore reveals that using digital tools in delivering online courses by the chemistry faculty during remote teaching, resulted in active learning, student engagement, and team teaching during pandemic [46].
As is evident from the result, institutional support plays a key role in providing a virtual learning environment in terms of IT infrastructure, IT support and services, and a faculty development program to enhance learning and teaching skills via an online format, management vision towards digitalization, and encouragement for online learning. The research conducted within the context of European universities further confirms that institutional support, trust in online education, and the perceived effectiveness of formative assessment directly contribute to the effectiveness of online learning [47]. However, the importance of student readiness, faculty expertise/motivation to deliver in digital platform, usability and the effectiveness of the learning management system, course content and material, and assessment methods and feedback, should not be overlooked to be responsive to the changes ushered in and impacting the higher education landscape. It is noteworthy that universities should not only focus on active learning and institutional support for the effectiveness of online learning, but should enable resources and facilities to redesign courses in the digital platform.
The research has been conducted with full respect to ethical issues and integrity. The respondents voluntarily participated in the survey and their responses were neither influenced nor intimidated by the researchers. The researchers understand the significance of misconduct in research and how it negatively affects the collaborative balance between science and society within the scientific enterprise [48].
The scope for further research lies in conducting a study on a similar theme but extending the participants of the survey to other higher education institutions in Bahrain, or a comparative analysis between the experience of private and public universities, amidst pandemics. Another future research would be to conduct a comparative study between the perception of staff and students in higher education institutions in Bahrain.

6. Limitations

The research has the following limitations: it involves survey participants from the Gulf University, Bahrain only; the use of a self-reporting scale is subject to honesty, introspective capability, and the subjective bias of the respondents.

7. Conclusions

The COVID-19 pandemic led to widespread disruption across the sectors in the world. For higher education institutions, the emergency shift to online learning ushered new opportunities for educators, students, and administrators, while being responsive to the changing needs of the stakeholders. The research examines the impact of critical factors on the effectiveness of online learning within the context of higher education institutions. The theoretical framework identified eight factors, such as: IT infrastructure, learning platform, students’ characteristics, faculty support, active learning, course design, development and delivery, evaluation and assessment, and institutional support, impacting the effectiveness of online learning. A quantitative analysis of the survey results, based on the perception of the students and staff who responded in the survey, illustrates that online learning has been regarded as effective thanks to the measures taken by the university as a response to the pandemic. Even though the ANOVA results show that the model is statistically significant, the coefficients of the model eventually lead to the fact that active learning and institutional support contribute significantly to the effectiveness of online learning. It is imperative to strengthen active learning strategies across programs and continue to get institutional support in terms of providing training, IT infrastructure and resources, and the endeavor of the university, towards digital transformation.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su142114073/s1. Questionnaire to identify the critical factors affecting online learning.

Author Contributions

The work of this paper was conducted by R.R. and M.S.M.A.-A. The first author contribution is higher than the co-author. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the section Research Ethics articulated in Research Policy and Conduct of Research Procedures at Gulf University, Bahrain and approved by the College Research Committee (GU-PR17CR-F08 and approved on 10-01-2022).

Informed Consent Statement

Informed consent was obtained from all participants involved in the study.

Data Availability Statement

Data will be provided upon request.

Conflicts of Interest

The authors declare no conflict of interest.

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Table
Table 1. Variance inflated factor and the tolerance level of the independent variables.
Table 1. Variance inflated factor and the tolerance level of the independent variables.
Independent VariablesToleranceVIF
Learning platform0.2543.940
Student characteristics0.3213.118
Faculty support0.2494.020
Active learning0.3203.120
Course design, development and delivery0.1925.204
Evaluation and assessment0.3113.214
Institutional support0.2484.029
IT infrastructure0.4542.202
Table
Table 2. Reliability of measurement.
Table 2. Reliability of measurement.
VariablesNumber of ItemsCronbach’s Alpha
Effectiveness of online learning90.932
Learning platform40.742
Student characteristics50.912
Faculty support40.824
Active learning40.886
Course design, development and delivery40.891
Evaluation and assessment40.876
Institutional support40.849
IT infrastructure30.911
Table
Table 3. Model summary.
Table 3. Model summary.
ModelRR SquareAdjusted R SquareStd. Error of the Estimate
10.626 a0.3920.3700.766
a. Predictors: (constant), learning platform, student characteristics, faculty support, active learning, course design, development and delivery, evaluation and assessment, institutional support, IT infrastructure.
Table
Table 4. ANOVA Results.
Table 4. ANOVA Results.
ModelSum of SquaresDegree of Freedom (df)Mean SquareFSig.
Regression
Residual
Total		86.915810.86418.5020.000 b
135.0582300.587
221.973238
b. Dependent variable: effectiveness of online learning.
Table
Table 5. Coefficients.
Table 5. Coefficients.
ModelUnstandardized CoefficientsStandardized Coefficients
BStd. ErrorBetatSig.
Constant1.4210.308 4.6080.000
Learning platform−0.0050.126−0.004−0.0390.969
Student characteristics0.0660.1110.0540.5910.555
Faculty support−0.1500.119−0.130−1.2650.207
Active learning0.7200.1000.6557.2120.000
Course design, development and delivery−0.0130.132−0.012−0.0990.921
Evaluation and assessment−0.1580.093−0.156−1.6930.092
Institutional support0.2140.1090.2031.9710.050
IT infrastructure−0.0510.092−0.042−0.5530.581
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Roy, R.; Al-Absy, M.S.M. Impact of Critical Factors on the Effectiveness of Online Learning. Sustainability 2022, 14, 14073. https://doi.org/10.3390/su142114073

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Roy R, Al-Absy MSM. Impact of Critical Factors on the Effectiveness of Online Learning. Sustainability. 2022; 14(21):14073. https://doi.org/10.3390/su142114073

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Roy, Rumpa, and Mujeeb Saif Mohsen Al-Absy. 2022. "Impact of Critical Factors on the Effectiveness of Online Learning" Sustainability 14, no. 21: 14073. https://doi.org/10.3390/su142114073

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