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Education & Educational Research
Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students’ readiness.
- e-learning readiness
- ADKAR factors
- machine learning
1. Introduction
The COVID-19 pandemic has brought about an unprecedented shift in teaching, learning, and assessment, leading to the rapid development of e-learning [1,2]. Digital transformation has become necessary in different fields, including education, due to information and communication technology being widespread [3]. Compared to other industries, however, university institutions need to be faster in adopting integrated digital transformation models to assess their level of maturity. This highlights the importance of e-learning readiness (ELR) in universities and the need to introduce administrative and educational adjustments regarding goals, plans, programs, practices, and means to improve digital readiness [4]. Implementing e-learning in universities has become increasingly important in the economies of countries, as it allows for using electronic mechanisms to support its advantages, such as its flexibility, accessibility, and interactivity.
Over the last decade, ELR has become an essential factor in the success of universities in achieving their educational goals [5,6]. ELR refers to the degree to which an institution is prepared to implement e-learning as a viable mode of education [7,8]. This readiness is based on various factors, such as organizational culture, technology infrastructure, faculty, and student readiness. The widespread utilization of the Internet and other digital communication systems has led to the emergence of e-learning, which provides numerous benefits such as accessibility, adaptability, and consistency [9]. Unlike face-to-face classes, virtual programs offer a more optimal learning environment and redundancy for those who can obtain their materials online [10]. The COVID-19 outbreak has underscored the significance of e-learning and digital preparedness in higher education. Educational technologies such as Desire to Learn (D2L), Massive Open Online Courses (MOOCs), Moodle, and Blackboard have been introduced to facilitate the educational process [11]. According to a systematic review conducted in [12], advanced programs have been developed to support student learning by providing performance monitoring and feedback. The aim is to create protocols and resources that enhance the appeal and accessibility of learning.
E-learning has become a crucial alternative solution due to rapid changes, with institutions requiring continuous changes to reach their goals in line with models. However, implementing online education poses challenges, including limited infrastructure, policies, financing, and awareness of human resources [13]. Accepting and implementing the required change in thinking and behavior is crucial [14]. Resistance to change can affect psychological state and performance. Managing change requires gradually and positively changing users’ attitudes, training and persuading them to enhance digital literacy, and integrating technologies into higher education institutions. Change management is crucial to implementing change processes in educational institutions, starting with building awareness of change and creating the desire to ensure the necessary capabilities and continuity [15].
Assessing ELR in universities in developing countries is crucial for successful implementation and can be achieved through various means such as surveys, interviews, and focus groups. ELR assessments can identify potential barriers to e-learning adoption, such as lack of infrastructure, technological resources, and instructor expertise [16]. Additionally, readiness assessments help identify opportunities for e-learning, such as reaching more students and offering flexible learning options. Ultimately, conducting ELR assessments can help ensure that universities in developing countries have the necessary resources and support to successfully implement e-learning programs, which can enhance access to education and improve student outcomes. Recently, there has been a growing number of studies evaluating universities’ readiness to implement e-learning in developing countries [17,18,19,20,21,22]. These studies seek to identify the factors that may contribute to the successful adoption of e-learning, such as the availability of technological infrastructure, the capacity of faculty and staff to deliver online courses, the level of support from university administration, and the perceptions of students and instructors toward e-learning. For instance, the study in [23] investigated the readiness of academic staff at the University of Ibadan in Nigeria to adopt e-learning. To this end, data were collected from 240 lecturers who expressed uncertainty about the readiness of students to engage in e-learning and their ability to integrate e-learning into their existing workload. However, the lecturers expressed confidence in the capacity of IT staff at their institutions. The results showed that several factors, including societal and public readiness, financial preparedness, training preparedness, ICT equipment preparedness, and availability of e-learning materials and contents, influenced Nigerian universities’ readiness toward e-learning adoption. The study conducted in [17] utilized a quantitative approach and an online questionnaire to assess the ELR of African engineering and IT students. Most participants were within the age range of 18 to 24, corresponding to the average age of first-year students who enroll in universities in South Africa. The findings indicated that the students exhibited proficiency in fundamental e-learning skills; however, they required further academic assistance, particularly in the areas of time management and problem-solving skills. In [24], Keramati et al. investigated the role of readiness factors in the relationship between e-learning factors and outcomes and proposed a conceptual model that categorizes readiness factors into technical, organizational, and social groups. The study was conducted with 96 high school teachers in Tehran using technology-based education. The data collected from a questionnaire were analyzed using hierarchical regression analysis, latent moderated structuring (LMS) technique, and MPLUS3 software. This revealed that readiness factors have a moderating role in the relationship between e-learning factors and outcomes, and organizational readiness factors significantly impact e-learning outcomes. The study in [25] examined the implementation of e-learning during the COVID-19 lockdown. To this end, questionnaires and interviews were conducted with students, teachers, management board members, and families. The findings emphasize the need for adaptable e-learning strategies that combine online and traditional teaching methods. Moreover, training is recommended to effectively integrate information and communication technologies (ICT) into classrooms. This analysis provided valuable insights for educational institutions and policymakers seeking to implement e-learning in similar circumstances. In [26], an assessment of remote learning in higher education in Poland was carried out considering the students’ perspective during the COVID-19 pandemic. The evaluation identified four dimensions: socio-emotional, developmental, time–financial, and negative attitude. Findings from a survey of 999 students highlight the benefits of remote learning, such as time-saving, location flexibility, work–study balance, and cost reduction. Conversely, disadvantages include the loss of social connections, technology fatigue, and increased distractions. The shape and duration of remote learning play a role in shaping students’ evaluations.
2. E-Learning Readiness Assessment
Recently, many studies have been conducted to evaluate the current status of ELR in universities and identify the factors that affect it [27,28]. These studies often use surveys and questionnaires to collect data from students, faculty, and staff and employ various analytical techniques, such as statistical analysis and machine learning, to identify the key factors that impact ELR. For instance, in [29], Laksitowening et al. present a methodology for measuring ELR at Telkom University in Indonesia using a multidimensional model. The assessment results were used to identify priority factors and areas for improvement. The evaluation was performed using surveys and data analysis, and the employed tool consists of a series of indicators for assessing the readiness for e-learning in five domains and thirteen factors. According to the evaluation outcomes, Telkom University has areas that require enhancement in terms of e-learning implementation. The top three priority factors are hardware, learning methods, and learning content. In [30], the aim was to assess the readiness for e-learning in two technological universities located in Yangon and Mandalay, Myanmar. This was achieved by investigating various characteristics, facilities, environments, and management practices pertaining to the universities and the students. To this end, the study surveyed 1024 students and found that while the universities were ready in characteristics and environmental dimensions, they were not ready in their facilities and management. The study suggested possible solutions for the universities to improve their readiness in these areas, such as having enough computers and a fast internet connection, better IT infrastructure, sufficient budgets and IT technicians, training, and tutorials, and preparatory knowledge sharing with national/international e-learning experts. In [31], Saekow et al. conducted a comparative analysis of higher-education ELR between Thailand and the USA. By assessing success factors across five dimensions, they concluded that institutional support from high-level administrators is a critical aspect of implementing successful online programs. Their findings also emphasized the significance of providing adequate resources for online programs, establishing clear project plans, launching initial program offerings, and conducting teacher training sessions. In [32], Irene et al. assessed ELR among educators and learners in selected Gauteng, South Africa schools. Utilizing a 29-item questionnaire, the study found that the expectations for ELR of educators and learners in the designated schools were met, but some areas required improvements, such as technology, content, and personnel. The STOPE (Strategy, Technology, Organization, People, and Environment) model was utilized to evaluate the level of readiness. The results showed a rating of 3.86 on the Likert scale, indicating that while the institution is generally considered “ready”, some improvements are still necessary.
Another study in [33] investigated critical factors that influence students’ preparedness to use e-learning systems in higher education, focusing on Wayamba University in Sri Lanka. The study results indicated that learners’ willingness to participate in e-learning activities significantly affects their readiness to use the system. Interestingly, the study revealed that the most crucial factors for adopting e-learning systems were e-learning confidence and training, rather than ELR. The study in [34] aimed to evaluate the ELR perspectives of medical students at the University of Fallujah in Iraq. A semi-structured self-administered questionnaire was utilized to collect data. The findings revealed that most medical students in the university were not adequately prepared for e-learning and lacked sufficient experience with ICT. Importantly, this study suggests that the university must invest in technology and provide formal training to students to facilitate e-learning as a viable learning approach. In [35], Samaneh et al. conducted a study to evaluate the level of ELR among Iranian students learning English as a foreign language and examine its correlation with their English language proficiency. The research involved 217 EFL students from Shiraz Azad University, who completed a self-assessment questionnaire on their ELR and the Test of English as a Foreign Language (TOEFL). The findings showed that the students exhibited a high level of preparedness for e-learning and demonstrated a positive relationship between their English language proficiency and readiness for e-learning. Ref. [36] considered the analysis of students’ readiness and facilitators’ perception toward e-learning in India based on machine learning algorithms. The survey results show that students have low satisfaction levels with e-learning systems and face data security and plagiarism issues. Teachers also had low satisfaction levels with e-learning and felt there was a lack of e-learning training and pedagogical models. In [37], Alotaibi et al. conducted a study to evaluate the readiness level among students at Shaqra University in Saudi Arabia to utilize an e-learning platform. The study utilized a questionnaire to measure five factors: study skills, technology skills, technology access, time management skills, and motivation. The findings revealed that the students enrolled at Shaqra University demonstrated an acceptable level of readiness for using the e-learning platform and possessed specific competencies that would aid in efficiently utilizing the platform. Furthermore, the study did not find any significant difference in readiness between male and female students, although female students exhibited higher levels of readiness in terms of technology and time management skills. In [38], Hung et al. construct and validate a multidimensional tool named the Online Learning Readiness Scale (OLRS) to assess college students’ readiness for online learning through confirmatory factor analysis. The OLRS encompasses five dimensions: learner-controlled and directed learning, computer/internet self-efficacy, online communication self-efficacy, motivation to learn, and online learning anxiety. In the study, data were gathered from 1051 undergraduate students in Taiwan. The findings showed that students had high readiness levels in computer/internet self-efficacy, motivation to learn, and online communication self-efficacy but low readiness levels in learner-controlled and directed learning. Furthermore, the study revealed that gender did not significantly influence the OLRS dimensions, while upper-grade students exhibited higher levels of readiness.
This entry is adapted from the peer-reviewed paper 10.3390/su15118924
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