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Article

Leveraging Technology Enhancement: The Well-Being Emotional Intelligence, Security Keys to the University Students’ Readiness in Digital Learning Ecosystem

by
Heru Susanto
1,2,3,
Desi Setiana
4,5,*,
Norainna Besar
4,
Muhamad Najib Ali
2,3,
Alifya Kayla Shafa Susanto
2,3,
Rozaidin Seruddin
3 and
Fahmi Ibrahim
3
1
Cyber Security Research Group, Center for International Research Collaboration of Graph Theory and Combinatorics, BRIN (National Research and Innovation Agency), Indonesia, UTB (University of Technology Brunei), Brunei Darussalam, ITB (Institute of Technology Bandung), Indonesia, UI (University of Indonesia), Indonesia, THU (Tunghai University), Bandar Seri Begawan BE1410, Brunei
2
Center for Innovative Engineering, University Technology Brunei, Bandar Seri Begawan BE1410, Brunei
3
School of Business, University Technology Brunei, Bandar Seri Begawan BE1410, Brunei
4
Digital Psychology Research Group, Institute of Brunei Studies, University Brunei Darussalam, Bandar Seri Begawan BE1410, Brunei
5
The Southern Correctional Center, BAPAS Jakarta Selatan, Ministry of Law and Human Rights, Jakarta 12940, Indonesia
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(9), 3765; https://doi.org/10.3390/su16093765
Submission received: 10 May 2023 / Revised: 10 July 2023 / Accepted: 26 July 2023 / Published: 30 April 2024
Browse Figures
Review Reports Versions Notes

Abstract

:
The principle of the digital learning ecosystem is the application of the educational process through digital learning environment platforms that help users learn more effectively. Educational institutions, teachers and students must evolve to adapt and be ready for the new world of learning. The individual’s emotional quality can influence their readiness in the digital learning ecosystem. This study aimed to examine whether there is a significant relationship between students’ readiness in the digital learning ecosystem and their emotional intelligence levels. This study utilized a correlational research method and multiple linear regression analysis. For data collection, the primary tools used were Trait Emotional Intelligence Scale-Short Form (TEIS-SF) and the Online Learning Readiness Scale (OLRS). The questionnaires were administered to 103 students enrolled in seven different higher institutions in Brunei. The results of the research showed that there was a link between students’ online learning readiness levels and their emotional intelligence levels. This study found that students with a high well-being sub-dimension of emotional intelligence had high online learning readiness levels.

1. Introduction

The development of Information Technology has been so rapid that almost every second, Information Technology products are created in all parts of the world to help human life. In the field of technology education, learning continues to experience development with the times. Teachers or students were previously accustomed to learning in a conventional setting. With the advancement of technology and the Internet, a new decade has been born to meet the demands of human learning. Digital learning is a new and innovative way of delivering education to people around the world [1]. The capacity to overcome the historical and geographic constraints of traditional teaching methods is one of the potential benefits of online learning [2]. However, learning in the twenty-first century is a different matter where new learning cultures and teaching styles have enabled the formation of learning environments in which nurturing and task execution are carried out through technology or digital tools [3]. Schools, institutions, teachers and students must evolve to adapt to the new world of learning. Educators have adapted and considered new teaching approaches as a result of adapting media to teaching processes, techniques, attitudes, knowledge, and understanding of changes in students [4]. Moreover, the teaching and learning process will run effectively and efficiently if it is supported by the availability of supportive and dynamic media.
Digitalization of education, specifically digital teaching and learning methods, can enhance or augment the learning experience, including instructional materials, data and assessment systems, learning platforms, online courses, adaptive software, and personal learning technologies, as well as online and blended learning [5]. Digital education technology can be used to acquire new skills and competencies through Information and Communication Technology for the Massive Open Online Course (MOOC) in the 21st century [6]. Students have the option to increase their engagement by exhibiting their work, which allows them to recognize their potential and develop skills such as information gathering, storytelling, data analysis and synthesis, communication, presentation, and idea. In addition, students can develop their skills in asking questions, participating, and increasing awareness of continuous learning. Changes in students’ abilities and experiences with various technologies produce a combination of skills, knowledge, and understanding that they must have in the world of digital learning [4].
The principle of the Digital Learning Ecosystem (DLE) is the application of the educational process through digital learning environment platforms that help users learn more effectively; for example, Learning Management Systems and Virtual Classroom Software, v. 2023 area collaboration of computer and network technology to create a virtual traditional classroom environment to strengthen and enhance students’ learning techniques and experiences [7]. The network in the form of the Internet is very important to connect users who use LMS application software, v. 2023 around the world. Lecturers, students, educational institutions and stakeholders can all benefit from the development of DLE by using various learning features, tools and media, as well as content that can be accessed and shared so as to encourage students to learn continuously [8]. Furthermore, the rapid development of technology has formed innovative learning methods; one of these innovations is blended learning. Blended learning is a method of instruction that builds on the successes of several delivery strategies and follows a learning model to accommodate different learning needs and preferences [9]. This process is carried out in an interactive learning environment that is meaningful between virtual learning and offline learning environments to achieve learning objectives. According to Besar’s [10] study, which involved 362 university-level students from Brunei, digital learning platforms such as Facebook have the potential to be complementary spaces for module learning because Facebook has features that can support the teaching and learning process. These features include content-sharing features, posting comments and status updates, uploading documents and videos to video-sharing sites, clicking the “like” button, reading friends’ comments, turning to an online chat/discussion group, checking friends’ status updates, creating a poll for friends online and following friends’ post.
Although DLE offers many advantages to users, there are also risks and challenges to data security. The cyber security risks associated with these systems include security threats to communications, messages, values, data, and work being damaged or deleted; user identity being known; personal information being stolen; and a socio-technical system being broken [11,12,13,14]. Therefore, it is essential to safeguard user security and safety in electronic systems. The main goal of information security is to implement effective policies without sacrificing an organization’s or institution’s productivity while balancing the protection of data availability, confidentiality, and integrity (also known as the “CIA triad”) [15,16,17,18]. Other concerns from DLE, such as not offering in-depth learning, poor quality of moral education such as values and beliefs, weak social and cultural aspects, online students being more prone to loneliness, technological aspects and the performance of teachers and students in an online environment [8,10,19,20,21]. This affects students’ emotional levels, such as self-confidence and motivation in DLE, so the readiness of the individuals involved in facing the challenges and risks of DLE is needed.
Readiness is a vital key that determines individual readiness to engage in an activity. In digital education, students must be ready online to benefit from a digital learning environment [22]. Digital or online learning readiness is defined as the mental or physical readiness of an institution to take advantage of e-learning experiences or activities [23]. Online readiness also acts as a competence to pursue opportunities that promote the use of DLE and its electronic resources, such as the Internet [24]. Therefore motivation in academia drives readiness for online learning programs. Motivation, interaction, and self-reflection are important elements for an effective learning process, as in traditional learning settings [25,26,27]. In addition, the interactive online environment enhances students’ ability to critically analyze and manage information. Thus, online communication is very important in the online learning process and allows students to develop while contributing positively [22]. An overview of the aforementioned elements involved in digital readiness reveals that the study mentions vital behaviors such as motivation, self-reflection, and communication skills. Individuals need to be mentally prepared for online learning experiences and the behaviors associated with their personality characteristics.
The study by Flood [28] defines learning as an individual’s emotional reaction to learning techniques, classes, and the learning environment. This statement suggests that an individual’s emotional quality can influence their readiness for online learning. The concept of emotional intelligence (EI) is defined as a method for defining and establishing individual emotional characteristics [22]. Emotional intelligence is the capacity to recognize one’s own and other people’s feelings and emotions [29]. Therefore, EI is an individual characteristic in the ability to use information to direct one’s thought processes and actions. Various previous studies have identified that emotions are a socio-cognitive tool for assessing achievement and an effective instrument in achieving desired goals [30,31]. EI is a valuable characteristic to develop because it helps in managing oneself and one’s environment, adaptability, empathy, dealing with challenging situations, and building beneficial social relationships [32,33]. Therefore, emotions are considered important internal states that incorporate psychological and psychometric elements of learner activity that significantly regulate or influence the nature of learning. Furthermore, EI is very important in the education sector because it is a process that supports the progressive and sustainable development of students [34]. According to Puertas-Molero [35], EI is becoming increasingly important because it promotes the psychological well-being of students and equips them with the necessary skills to cope with various situations that arise every day and allows them to better understand their environment. The study by Zayapragassarazan [36] shows that the use of online learning has exploited the digital ecosystem to ensure the long-term sustainability of the teaching-learning process. Therefore, if students are not prepared, it affects their performance by hindering the achievement of practical learning experiences. Furthermore, when readiness decreases, satisfaction from the learning experience also decreases [37].

1.1. Readiness in a Digital Learning Ecosystem

Previous research has looked at online learning readiness (OLR) from a variety of perspectives. OLR in three ways: student preference for online learning over face-to-face learning; students’ ability to use the internet and computer-assisted communication for learning; students’ ability to participate in independent learning [38]. The framework consists of five dimensions of OLR introduced and validated by Hung et al. [39]: Computer and Internet Self-efficacy (CIS); Self-directed Learning (SDL); Learner Control (LC); Motivation to Learn; and Online Communication Self-efficacy (OCS), see Figure 1. The next following paragraph discusses the five dimensions of the OLR.
Considering that online learning is carried out using digital devices and platforms, students must be prepared and competent in using computers and the internet. Previous research proposed an idea that combines computer and internet self-efficacy (CIS) [39]. The Social Cognitive Theory defines self-efficacy as an individual’s belief in their ability to complete tasks unrelated to their talents [40]. Self-efficacy is a psychological quality that affects academic motivation, perseverance, and success [41]. On the other hand, confidence in one’s ability to use a computer is computer self-efficacy [42]. One of the main needs of digital learning is knowing students’ confidence and competence in using computers [43]. Therefore, the level of students’ perceived self-efficacy affects the involvement and achievement of computer learning [44]. As a result, a student’s readiness in this field is very important. However, Internet self-efficacy can be different from computer self-efficacy in building, managing, and utilizing the internet [39]. Internet self-efficacy can influence students’ information-gathering skills and facilitate the use of these techniques in online-based learning environments [45].
Self-Directed Learning (SDL) is a learning method that allows students to take responsibility for their learning by identifying learning requirements, setting academic goals, developing learning methods, and assessing the quality and effectiveness of learning [46]. SDL emphasizes students’ ability to participate in the learning process and acquire knowledge [47]. Students who participate actively in the SDL process are better able to determine educational goals, tasks, interests, and efficacy and assume greater responsibility for their learning [48]. In this regard, SDL is an important feature in terms of online learning readiness (OLR), which will assess students’ readiness for the digital learning ecosystem.
Learner Control (LC) is students’ ability to direct their own understanding and learning processes [49]. LC refers to a student’s ability to decide what to learn, when to learn it, and how they want to learn it [50]. Online learning can present various obstacles for inexperienced students [51]. Students who can take advantage of online learning have more flexibility and personalized learning because they can choose which material to acquire and organize [52].
Learning motivation is another dimension of the online learning readiness scale used in the scope of this study. Motivation is well known to provide benefits in both offline and online educational settings and on student achievement [22]. The concept of utilizing digital learning ecosystems can be significantly self-directed. Online learning motivation is starting to grow more important in education and technology with decreasing accessibility for conventional learning settings [53]. Motivation in online learning is a significant aspect that can prevent poor academic achievement [54]. On the other hand, lecturers have considerable challenges appearing in the digital learning ecosystem to motivate students. Motivation for learning can be categorized into two main types: intrinsic and extrinsic motivation. Intrinsic motivation is influenced by a person’s mental, social, and physical growth, which has an impact on their interests and moves them towards self-assured preferences in life [55]. In contrast, extrinsic motivation is about the choice to achieve goals based on external compensations [55].
The OLR scale’s final dimension—online communication self-efficacy (OCS)—is covered in the context of this study. The development of technology has increased the use of social media and the Internet as communication tools in online learning environments [56]. The diversification of communication tools is increasing along with technology. As a result, it has presented some difficulties, particularly with regard to the difficulty of online teaching to support adequate interaction with the students. According to Chandra [19], effective educational activities, high-quality learning experiences, and student engagement are all prerequisites for online learning. According to a study by Stephenson [57], online interactive settings improve students’ responsibility, critical thinking, self-reflection, and information organization. Students’ communication self-efficacy is crucial in order to overcome this. Research on internet self-efficacy and anxiety, OCS, along with search self-efficacy, organizational efficacy, differentiation self-efficacy, and reactive/generative self-efficacy, is a more important principle of internet self-efficacy [58,59]. Since there was no in-person or physical interaction between lecturers and students during online learning, the students’ only means of communication with the lecturer and their fellow classmates was through the Internet. Thus, by asking questions and sharing their thoughts and opinions, students must participate in online communication to reflect on and synthesize what they have learned.

1.2. Emotional Intelligence

Emotional intelligence (EI) is widely used in management, psychology, and education. EI is a notion that is separate from individual personality characteristics but closely related to them [60]. EI was analyzed in terms of personality and measured using a valid self-assessment survey that evaluates habitual behavior [61,62]. In addition, Schulte [63] claims that personality traits such as empathy, cowardice, social skills, and kindness fall under the category of EI. Emotional intelligence (EI) is defined as the ability to identify and understand one’s feelings, relate emotionally to others, and manage one’s emotions to improve one’s livelihood [64]. On the other hand, Emotional intelligence is the ability to control emotions and express them in appropriate and useful ways. For instance, being able to comprehend and articulate feelings, control them for emotional growth, and mentor others [65,66].
EI is divided into two categories [67]: (1) EI as a trait and (2) EI as information processing. Moreover, despite embracing EI as an ability, they consider it a “personality-trait character attribute” because of EI’s strong association with vital elements of personality. The concept of “emotional self-efficacy” consists of imagined skills that do not exist in reality and can be assessed using personality assessments [68]. Individuals with high levels of emotional intelligence have an advantage over individuals with poor levels of emotional intelligence in managing emotions [62]. Empathy, self-awareness, stress management, decision-making, and preventing harmful emotions that interfere with an individual’s capacity to think effectively are some of the benefits of high-level emotional intelligence [69]. Individuals with high levels of emotional intelligence can cope with stress better, have wider social networks, and earn higher academic scores by reducing the negative impact of tension [70].
Trait emotional intelligence theory (Trait EI) is related to people’s passionate beliefs [67]. The definition of this theory is a set of emotional perceptions that can be measured using a questionnaire and rating scale [71]. This emotional perception refers to a four-dimensional structure and 15 components, see Appendix A: 1. Emotionality (trait empathy; emotion perception; emotion expression; relationships). 2. Sociability (emotion management (others); assertiveness; social awareness). 3. Well-being (self-esteem; trait optimism; trait happiness). 4. Self-control (emotion regulation; impulse control; stress management; adaptability; self-motivation. The previous study applied the EI scale, known as the Trait Emotional Intelligence Scale-Short Form (TEIS-SF), to utilize the questions of the tool that are more in line with research to study the relationship between the readiness of Turkish students and the level of EI [22].
Trait EI theory is the most relevant emotional intelligence theory for this study to measure the level of the students’ EI. The Trait Emotional Intelligence Scale-Short Form (TEIS-SF) can measure EI’s four primary traits: well-being, self-control, emotionality, and sociability, see Figure 2. Furthermore, the TEIS-SF will aid in achieving the aim of this study which wants to find a statistical relationship between EI levels and readiness for a DLE.

1.3. Readiness in a Digital Learning Ecosystem (DLE) and Emotional Intelligence (EI)

Previous studies have identified both advantages and disadvantages between EI and the DLE and their relationship. A study [72] investigated how EI and personality variables can predict Grade Point Average (GPA) in online learning among university students. A study [21] examined the role of EI in determining students’ preparation for online learning by focusing on their motivational and psychometric characteristics. According to a study by Chandra [19], students who encounter difficulties during online learning tend to become more burned out. They try to manage this problem by taking advantage of EI, turning their attention to various creative works, and taking courses that will help them develop new technical abilities. Several empirical studies have highlighted that a high level of EI is associated with high readiness for online learning [21,22,73,74], while [75] asserted that intrapersonal and general modes were better predictors of academic progress than interpersonal modes. Stress management and adaptability were not significant predictors.
The Bar-On Emotional Quotient Inventory (EQ-i) is used to assess emotional intelligence (EI) and its components in relation to predicting academic success [75,76]. On the other hand, Buzdaret al. [21] and Alenezi [73] used the EI scale (EIS)developed by Wong and Law [76], which can practically measure EI in leadership and management research. The aspects of this scale are emotional self-assessment, evaluation of other people’s emotions, emotional management, and emotional use. Another study uses a slightly different tool than the previous study, which can be measured using the Trait Emotional Intelligence Scale-Short Form (TEIS-SF) tool [22]. This tool has a measurement scale to assess the four significant aspects of EI: well-being, self-control, emotions, and sociability. Throughout the literature, there is increasing evidence that student performance in online learning is influenced by their level of EI, especially in Saudi Arabia, Iran, and the United States. However, limited research was conducted to comprehensively examine the relationship between EI and DLE in universities from Asian countries.
Therefore, the purpose of this research will be to fill this gap by finding out the relationship between EI and readiness in DLE for students in Brunei through utilizing the Trait Emotional Intelligence Scale-Short Form (TEIS-SF) and online learning readiness scale (OLRS). The following are the formed research objectives that will help this study reach its goal:
  • To investigate the relationship between the students’ EI levels and DLE readiness.
  • To determine whether implementing EI contributed to the DLE’s increased readiness.
The proposed research questions to be clarified in this study are as follows:
  • Is there a statistically significant relationship between the students’ levels of emotional intelligence and levels of readiness in the digital learning ecosystem?
  • Can emotional intelligence levels be used to forecast readiness in the digital learning ecosystem?
Benefits of this study for students: it provides information regarding EI and how it can influence students’ readiness to take advantage of the DLE, and this study will foster EI knowledge and skills to better prepare for online learning. The data collected can also assist students in improving practical and academic performance. For lectures: assisting lecturers in acquiring new knowledge about EI and DLE that enhances the effectiveness of their teaching on valuable components such as attitudes, skills, and performance. Next, develop teaching and learning strategies for lecturers to increase knowledge that best suits the will or motivation of students for better academic achievement. There is no research conducted in Brunei that uses the variables used in this study, especially in the context of higher education. Therefore, this study will also fill a gap in the literature. This study will promote the growth of online learning knowledge and emotional intelligence in Brunei institutions. In order to improve knowledge, skills, and attitudes, it is advisable to support novel approaches.
The research model is shown in Figure 3 below, where the “five dimensions of online learning readiness” and the “level of emotional intelligence” are labeled as independent and dependent variables, respectively. The trait emotional intelligence theory by Petrides and Furnham [67] is the foundation for the concept of emotional intelligence and its dimensions. The scales Hung et al. [39] developed to assess university students’ readiness for online learning serve as the foundation for readiness in digital learning. Therefore, based on the aforementioned theories that are consistent with the emotional intelligence and digital learning context, hypotheses were developed.
Numerous theories have been developed based on the online learning readiness dimensions developed by Hung et al. [39] and the trait emotional intelligence theory developed by Petrides and Furnham [67,68], respectively. The following hypotheses are suggested based on the justification and findings presented above:
Hypothesis 1 (H1). 
There is a significant relationship between the student’s emotional intelligence levels and their Computer/Internet Self-Efficacy (CIS) level.
Hypothesis 2 (H2). 
There is a significant relationship between the student’s emotional intelligence levels and their Self-Directed Learning (SDL) level.
Hypothesis 3 (H3). 
There is a significant relationship between the student’s emotional intelligence levels and their Learner Control (LC) level.
Hypothesis 4 (H4). 
There is a significant relationship between the student’s emotional intelligence levels and their motivation to learn level.
Hypothesis 5 (H5). 
There is a significant relationship between the student’s emotional intelligence levels and their online communication self-efficacy level.

2. Materials and Methods

2.1. Methodology

This study uses a quantitative approach and collects data using a survey questionnaire via Google Forms, distributed via email and WhatsApp. The Statistical Package for the Social Sciences (SPSS) version 25 was used to analyze the data collected from the survey of respondents. This study uses a correlational research method to examine the relationship between two or more variables [77]. This study chose this method to determine the relationship between the dimensions of students’ EI traits and the dimensions of online learning readiness in a digital learning ecosystem. Table 1 shows the results of this analysis. Correlation analysis seeks to quantify the strength of the linear relationship between the specified variables. According to Taylor [78], the primary objective of correlation analysis is to ascertain how closely the defined variables have linear relationships with one another and the first research question of the study: Is there a statistically significant relationship between the students’ levels of emotional intelligence and levels of readiness in the digital learning ecosystem?
Regression analysis can avoid the multicollinearity problem by showing that there is a relationship between variables. This study also conducted a multiple linear regression analysis to determine the predictive power of the level variables. The second research question of the study, “Can emotional intelligence levels be used to forecast readiness in the digital learning ecosystem?” was answered via a multiple linear regression analysis by testing all five hypotheses to determine whether there was a significant relationship or not between the independent and dependent variables.

2.2. Participants

The survey questionnaire was completed by 103 respondents in total, of whom 103 (61.2%) were female, and 40 (38.8%) were male. The majority (75.7%) of respondents were between the ages of 21 and 29, followed by those between the ages of 17 and 20 (19.4%), 30 and 39 (2.9%), and 40 and 49 (1.9%).
The business faculty accounted for the majority of them (59.2%), followed by the computing faculty (12.6%), the arts and social sciences (9.7%), engineering (7.8%), the applied sciences and mathematics (5.8%), design (2.9%), the communication center for teaching and learning (1.0%), and sharia and law (1.0%). Of the respondents, 84.5%identified as undergraduate students and 15.5% as graduate students.
The majority of the respondents were in their first year, semester two (30.1%), followed by the third year, semester six (23.3%), the fourth year, semester eight (23.3%), the second year, semester four (11.7%), the second year, semester three (4.9%), the first year, semester two (4.9%), and the third year, semester five (1.9%) (Table 1).

2.3. Research Tools and Procedures

The main tools used were a 30-item questionnaire developed by Petrides and Furnham [67] known as the Trait Emotional Intelligence Scale-Short Form (TEIS-SF) see Appendix C and 18-item online learning readiness scale (OLRS) developed by Hung et al. [39], see Appendix B. The study then entered these 48 items into a Google Form and other relevant questions, such as respondents’ demographic information.
The first part of the survey uses Hung et al.’s OLRS, which consists of five different dimensions known as self-directed learning (5 items), learning motivation (4 items), learner control (3 items), computer/internet self-efficacy (3 items), and online communication self-efficacy (3 items), see Appendix B [39]. The tool consists of a 5-point Likert scale, and respondents are assigned to rate themselves from 1 (strongly disagree) to 5 (strongly agree). In the study of [39], the reliability of the OLRS, as shown, ranges from 0.73 to 0.87. Furthermore, OLRS has been utilized in various online learning environments to measure students’ readiness for digital learning [79,80].
The second part of the survey used the Trait Emotional Intelligence Scale-Short Form (TEIS-SF) to assess four EI traits, see Appendix C: well-being (6 items), self-control (6 items), emotionality (8 items), and sociability (6 items). The remaining four items are added to the overall EI trait score unattached to a single factor, see Figure 2. The tool consists of a 7-point Likert scale, and respondents are assigned to rate themselves from 1 (strongly disagree) to 7 (strongly agree). TEIS-SF reliability and validity test by Stamatopoulou et al. [81] reported Cronbach’s alpha of 0.78, 0.60, 0.64, and 0.75, respectively. Reliability is the term used to describe the study’s indicator of the constructs’ internal consistency. Construct reliability testing was performed to validate the consistency and reliability of the results from the dimensions. Reliability defines a construct as reliable if its alpha value is 0.70 or higher [82]. Cronbach alpha values less than 0.70 may occasionally be acceptable, according to Bonett and Wright [83], depending on the type of application. Additionally, they advocate emphasizing population reliability values over sample reliability values. On the other hand, Streiner [84] suggested a maximum value of 0.90 because values higher than 0.90 reflect pointless duplication of content items instead of a desired internal consistency level.
It is also important to note that for this study, an Excel file named “TEIQue-SF results” was used as the scoring engine to perform the calculation of the respondents’ emotional intelligence score before transferring the data to SPSS. This Excel file is provided from the Petrides website known as https://psychometriclab.com (accessed on 14 May 2023).
The final part of the survey touches on the demographic information of the respondents to provide details on the background of the research subjects. Gender, age, name of institution, level of education, enrolled faculties, and current semester are some of the information collected, see Appendix D.

2.4. Pilot Study

Two experts in “educational psychology” were selected to assess the validity of the appearance and content of the survey questionnaire. Their comments and suggestions helped in improving the surveys in terms of questions, such as the Trait emotional intelligence theory as the main reference to be applied that came from domains of clinical, educational and organizational psychology that may connect with individual emotional perceptions, which refer to four sub-dimension structure as follows; (1) emotionality, (2) sociability, (4) well-being, and (4) self-control.
In addition, as part of a pilot study, 20 undergraduate students were selected to describe the difficulties they experienced reading, understanding, or filling out surveys. The students found the survey simple and easy to understand. The survey questionnaire was completed based on feedback from experts and students.

3. Results

3.1. Reliability Statistics

It was also critical for the current study to test its reliability within the Bruneian context to ensure accurate study results were obtained. As shown in Table 2, the values were 0.766 for CIS, 0.686 for SDL, 0.471 for LC, 0.811 for motivation for learning, and 0.725 for OCS. Cronbach alpha of the total variables was 0.872, which indicates the construct utilized in the current study can be used for further analysis. It was also critical for the current study to test its reliability within the Bruneian context to ensure accurate study results were obtained.
Similar to the circumstances of the OLRS, the Trait Emotional Intelligence Scale-Short Form (TEIS-SF) was also tested for internal consistency and reliability within the Bruneian context to ensure the attainability of accurate study results. As shown in Table 3, the values were 0.80 for well-being, 0.54 for self-control, 0.66 for emotionality, and 0.70 for sociability. The total Cronbach alpha value for the trait emotional intelligence variables was 0.89. Hence, the construct in the current study can be used for further analysis.

3.2. Mean and Standard Deviation

As shown in Table 4, the composite mean for each dimension ranged from 3.00 to 4.14 on a 5-point Likert scale. Computer/internet self-efficacy (4.14) had the highest composite mean score. The second highest mean score (3.56) was for online communication self-efficacy (OCS). The self-directed learning (SDL) dimension had the third-highest mean score (3.44). The fourth-highest mean score was for motivation to learn (3.43). The learner control (LC) dimension had the lowest mean score (3.00) across the board.
The mean scores and standard deviations for the respondents on each of the four dimensions of trait emotional intelligence areshown in Table 5. Higher trait emotional intelligence was indicated by higher mean scores. According to Table 5, the composite mean score for each dimension ranged from 3.80 to 4.53 on a scale of 1 to 7-point on the Likert scale.Well-being (4.53) had the highest composite mean score. Thesecond-highest mean score (4.33) was for emotionality. The self-control dimension had the third-highest mean score (4.00). The sociability dimension had the lowest mean score (3.80).

3.3. Correlation

A correlation analysis was performed based on the first research question, “Is there a statistically significant relationship between the students’ levels of emotional intelligence and levels of readiness in the digital learning ecosystem?”. In Table 6, it is first seen that there is a correlation between the trait emotional intelligence levels of well-being and the levels of readiness for online learning in terms of computer/internet self-efficacy (0.250), self-directed learning (0.317), learner control (0.152), motivation for learning (0.282), and online communication self-efficacy (0.223).
Secondly, there was an association between trait emotional intelligence levels of self-control and the readiness for online learning measures of computer/internet self-efficacy (0.073), self-directed learning (0.082), learner control (0.126), motivation for learning (0.191), and online communication self-efficacy (0.161).
Thirdly, there was a relationship between the trait emotional intelligence level of emotionality and the levels of computer/internet self-efficacy (0.058), self-directed learning (0.020), learner control (0.045), motivation for learning (0.193), and online communication self-efficacy (0.233) that indicate readiness for online learning.
Last but not least, there was a correlation between the trait emotional intelligence level of sociability and the levels of computer/internet self-efficacy (0.044), learner control (0.104), motivation for learning (0.165), and online communication self-efficacy (0.312) that indicate readiness for online learning.

3.4. Regression Analysis

Using a multiple linear regression analysis, the second research question of the study is—”Can emotional intelligence levels be used to forecast readiness in the digital learning ecosystem?” All five hypotheses were tested to see if there was a significant relationship between the independent and dependent variables. Table 7 presents the conclusions drawn from the analysis. Overall, all five hypotheses were confirmed.
Hypothesis 1 (H1): There is a significant relationship between the student’s emotional intelligence levels and their computer/Internet self-efficacy (CIS) level. For hypothesis 1 (H1), computer/Internet self-efficacy (CIS) level was a dependent factor, while the students’ emotional intelligence levels were the independent factor. The analysis gave a p-value of 0.00 (p < 0.05), which displays that there is a significant relationship between students’ CIS level and their emotional intelligence levels. Thus, hypothesis 1 (H1) was supported. The predictor variables accounted for R2 = 0.08 or 8% of the variance in CIS. The relative order of significance based on beta coefficients for emotional intelligence sub-dimensions on CIS was well-being (β = 0.34), emotionality (β = 0.01), self-control (β = −0.08), and sociability (β = −0.10). Furthermore, the assessment of t-test results on the significance of regression coefficients illustrated that emotional intelligence sub-dimension well-being (t = 2.72; p < 0.05) was a significant predictor of the online readiness CIS dimension.
Hypothesis 2 (H2): There is a significant relationship between the student’s emotional intelligence levels and their self-directed learning (SDL) level. For hypothesis 2 (H2), self-directed learning (SDL) level was a dependent factor, while the students’ emotional intelligence levels were the independent factor. Regression analysis gave a p-value of 0.00 (p < 0.05), which indicates the existence of a significant relationship between students’ SDL level and their emotional intelligence levels. Hence, hypothesis 2 (H2) was supported. The predictor variables accounted for R2 = 0.13 or 13% of the variance in SDL. The relative order of significance based on beta coefficients for emotional intelligence sub-dimensions on SDL was well-being (β = 0.46), emotionality (β = −0.05), self-control (β = −0.09), and sociability (β = −0.14). Furthermore, the assessment of t-test results on the significance of regression coefficients illustrated that emotional intelligence sub-dimension well-being (t = 3.79; p < 0.05) was a significant predictor of the online readiness SDL dimension.
Hypothesis 3 (H3): There is a significant relationship between the student’s emotional intelligence levels and their learner control (LC) level. For hypothesis 3 (H3), learner control (LC) level was a dependent factor, while the students’ emotional intelligence levels were the independent factor. Regression analysis gave a p-value of 0.00 (p < 0.05), which indicates the existence of a significant relationship between students’ LC level and their emotional intelligence levels. Hence, hypothesis 3 (H3) was supported. The predictor variables accounted for R2 = 0.03 or 3% of the variance in LC. The relative order of significance based on beta coefficients for emotional intelligence sub-dimensions on LC were well-being (β = 0.11), self-control (β = 0.07), sociability (β = 0.06), and emotionality (β = −0.07).
Hypothesis 4 (H4): There is a significant relationship between the student’s emotional intelligence levels and their motivation to learn level. For hypothesis 4 (H4), motivation for learning (MFL) level was a dependent factor, while the students’ emotional intelligence levels were the independent factor. Regression analysis gave a p-value of 0.00 (p < 0.05), which indicates the existence of a significant relationship between students’ MFL level and their emotional intelligence levels. Hence, hypothesis 4 (H4) was supported. The predictor variables accounted for R2 = 0.09 or 9% of the variance in MFL. The relative order of significance based on beta coefficients for emotional intelligence sub-dimensions on MFL was well-being (β = 0.24), emotionality (β = −0.09), self-control (β = −0.02), and sociability (β = −0.02).
Hypothesis 5 (H5): There is a significant relationship between the student’s emotional intelligence levels and their online communication self-efficacy level. For hypothesis 5 (H5), online communication self-efficacy (OCS) level was a dependent factor, while the students’ emotional intelligence levels were the independent factor. Regression analysis gave a p-value of 0.00 (p < 0.05), which indicates the existence of a significant relationship between students’ OCS level and their emotional intelligence levels. Hence, hypothesis 5 (H5) was supported. The predictor variables accounted for R2 = 0.11 or 11% of the variance in OCS. The relative order of significance based on beta coefficients for emotional intelligence sub-dimensions on OCS were sociability (β = 0.24), well-being (β = 0.08), emotionality (β = 0.06), and self-control (β = −0.01).

4. Discussion

The respondents’ mean scores and standard deviations on the five dimensions of online learning readiness: Computer/internet self-efficacy had the highest composite mean score. Therefore, it could be inferred that the students were generally comfortable using computers and the Internet to conduct information searches and carry out basic operations on Microsoft Office applications and other software for online learning. These skills are essential for learners to be better prepared for online learning [39]. Needless to say, today’s college students are often used to using technology due to their exposure to a digital environment [85].
The second-highest mean score was for online communication self-efficacy (OCS). This finding indicated that while the students were at ease using online tools for interpersonal communication and opinion expression, they hardly ever asked questions in discussion forums. According to research by Salaberry [86] and Hung et al. [39], students with higher OCS are more at ease expressing them in writing. The fact that the students in this study’s sample had a lower OCS mean score than CIS indicated that they were not yet fully prepared for online instruction. According to Chung et al. [87], lack of inquiry is a common occurrence even in a physical learning environment at a higher education facility. Additionally, even when a lecture’s topic is unclear to the students, they hardly ever ask questions.
The self-directed learning (SDL) dimension had the third-highest mean score. This finding showed that although the students learned their lessons online, they still sought assistance when they encountered problems. They also have high standards for their academic performance. However, they struggle with time management and hardly ever set goals for their online studies.
The fourth-highest mean score was for motivation to learn. The majority of students concurred that while studying online, they were open to fresh perspectives or recommendations. Additionally, the students loved learning from their past errors and how wonderful it was to share ideas with others. These findings are supported by Schunk and Usher [88], who emphasize the importance of learner motivation and how it affects what people learn, how they learn it, and when they decide to learn it, supporting these findings.
The Learner Control (LC) dimension had the lowest mean score across the board. It was discovered that the students acknowledged their ability to control their learning process while online and that they repeated the course materials based on their individual needs. However, while learning online, the students were primarily sidetracked by other online activities (WhatsApp, Instagram, etc.). As a result, it is one of the most challenging aspects of online learning for students. This was also the conclusion reached by Hung et al. [39]. According to Coates [89], the best way for students to avoid getting distracted while participating in other online activities is to develop a solid strategy.
The finding on the emotional intelligence dimension showed that the category of well-being had the highest composite mean score. Consequently, it became clear that the majority of the students thought they were confident, content, and inclined to see the positive side of things. According to studies by O’Boyle et al. [90] and Schutte and Loi [91], people who are happy perform better and are more satisfied. Consequently, stress and burnout will be decreased [92].
Furthermore, the majority of students felt that they could communicate their emotions to others, and they were aware of both their own and other people’s emotions, according to this study’s findings for the sub-dimension of emotionality. It was also discovered that the students agreed to some extent that they were able to maintain personal relationships and empathize with others. Due to the emphasis placed on understanding oneself and others, it can be inferred that emotionality can be crucial when making decisions in academic contexts. Studies have shown that emotionality has a significant impact on decisions about education and careers. As a result, it might be advantageous in terms of career-related decision-making and career adaptability [93,94].
The finding for the dimension of self-control was that the majority of the students reported that they could control their emotions, could handle pressure and stress, and were thoughtful about their impulses. This result was in line with Petrides et al. [34] findings. In contrast, out of all the emotional intelligence sub-dimensions, sociability had the lowest mean score. Only a few of the students wholeheartedly concurred that they considered themselves to be assertive, socially conscious, and able to control other people’s emotions. Students who performed poorly on the sociability scale believed that this made them ineffective networkers and negotiators because they could not affect other people’s emotions. They also do not know what to say or do in social situations. They appear to be reserved and quiet as a result [68]. Therefore, the results above show that the correlations are adequate among the variables. Hence, this indicates there is a relationship between them and also demonstrates that the emotional intelligence levels (independent variables) are not overly correlated, which helps to prevent the multicollinearity problem for regression analysis.
According to Martin and Kellermanns [95], there is no meaningful correlation between system accessibility and the perceived usability of the system when the measures do not distinguish between internal and external technology access but rather general access in general. The accessibility of internal and external technology, on the other hand, was disputed by Lee [96]; they defined internal technology as being provided by the organization and external technology as being equipment that is not provided by the organization. However, there was no proof provided that perceived usefulness and internal technology accessibility were related, and there was also no proof that perceived usability and internal technology accessibility were related.
The OLRS dimension of learner control (LC) and the trait emotional intelligence dimension of sociability should be given top priority in order to better prepare students for online learning. First, reward or compensation for participation. Lecturers should always encourage students to participate in online discussions by encouraging them to voice their opinions and pose questions more frequently. For this tactic to be effective, lecturers or instructors may need to develop some sort of rewards system or encourage feedback for students to communicate during online sessions. It is possible to implement it in a way that makes students’ online comments and inquiries part of a required module requirement and assessment. This will enable more online discussions by enabling other students to respond to the questions. Therefore, as they become more exposed to and receptive to online conversation, students will gain confidence [87]. Additionally, lecturers must commit to responding to, praising, and commenting on students’ posts in online learning. Secondly, breaking down long lectures. It is advised that lecturers use fairly distributed teaching by breaking up lengthy lectures into numerous online sessions, with short breaks in between, to prevent students from getting sidetracked while learning online [87]. Students should be made aware of the inclusion of brief tests during breaks or at the conclusion of each online session using tools such as Kahoot or Quizlet. Therefore, as students become more focused and disciplined while learning online, their levels of learner control may rise. Third, institutional guidance and support that is constant. Successful implementation of online learning, according to O’Doherty et al. [97], requires lecturers to teach students how to use challenging software or tools. Additionally, universities should hold more training sessions to give lecturers the knowledge and abilities they need to deliver online course materials more successfully.

5. Conclusions

There was a relationship between students’ online learning readiness and trait emotional intelligence levels. The five hypotheses were all verified. The current study’s findings show that EI was directly involved and was able to predict how students behave online. It was suggested that in order to improve students’ online learning readiness sub-dimensions for improved readiness in a digital learning ecosystem, incentives for participation, lecture breakup, and institutional support should be offered. According to this study, students who scored highly on the emotional intelligence sub-dimension of well-being were also highly prepared for online learning. Additionally, it was found that, when compared to other emotional intelligence sub-dimensions, the well-being emotional intelligence sub-dimension had the best ability to predict readiness for online learning and self-directed learning.
Although the empirical results are significant and have real-world applications, this study has a number of flaws that could be corrected in subsequent research. First, the study has a limited number of responses; only 103 students from seven different higher education institutions in Brunei responded to the study, which severely restricts the generalizability of the findings to other contexts. Second, the study primarily uses self-reported measures of emotional intelligence and readiness for online learning; however, these measurements are very prone to bias and inaccuracy. Therefore, in order to be generalized to the greater community, future research on comparable themes may seek to collect more responses. The qualitative technique may also be taken into account in future studies, and it can also be investigated in various contexts.

Author Contributions

Conceptualization, Software and Validation, Investigation, H.S.; Methodology, D.S.; Data Collection, Software and Formal Analysis, M.N.A.; Formal Analysis, N.B.; Investigation, A.K.S.S.; Resources, R.S.; Validation and Preparation, F.I. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Universiti Teknologi Brunei (UTB) Internal Grant. Under Center for Innovative Engineering UTB, grant number: UTB Internal Grant 03.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

Heru Susanto (H.S.) as Main Contributor and Lead Author. The remaining as contributors; Desi Setiana (D.S.), Muhamad Najib Ali (M.N.A.), Norainna Besar (N.B.), Alifya Kayla Shafa Susanto (A.K.S.S.), Rozaidin Serudin (R.S.), and Fahmi Ibrahim (F.I.). We would like to thank others that directly and indirectly supported this research through Center for International Research Collaboration, such as; Center for Innovative Engineering, Universiti Teknologi Brunei, Brunei Darussalam; Cyber Security Research Group, National Research and Innovation Agency, Indonesia; Information Management5G Security Technologies and, Tunghai University Taiwan. All authors have read, reviewed, and approved the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. The Trait EI Dimensions and Sampling Domain in Adults.
Table A1. The Trait EI Dimensions and Sampling Domain in Adults.
Trait EI DimensionsComponents/FacetsBrief Description of Components
EmotionalityTrait empathyConcerns individuals’ capability of taking someone else’s viewpoint or perspective.
Emotion perception (self and others)Concerns individuals’ perception of how clear their own and other people’s feelings are.
Emotion expressionConcerns individuals’ capability to communicate their feelings to others.
RelationshipsConcerns individuals’ capability to maintain satisfying personal relationships.
SociabilityEmotion management (others)Concerns individuals’ capability to influence other people’s feelings.
AssertivenessConcerns individuals’ honesty, fairness, and readiness to defend their rights.
Social awarenessConcerns individuals’ self-perceptions of how experienced networkers with excellent social skills they are.
Well-beingSelf-esteemConcerns individuals’ self-perceptions of their self-worth, such as self-confidence and success.
Trait optimismConcerns individuals’ optimism and confidence to look on the brighter side of events.
Trait happinessConcerns individuals’ happiness and satisfaction with their lives.
Self-controlEmotion regulationConcerns individuals’ capability to be able to keep their emotions under control.
Impulse controlConcerns individuals’ self-perceptions of how effectively they can control themselves from giving in to their desires or impulses.
Stress managementConcerns individuals’ capability to regulate stress and endure pressure.
-AdaptabilityConcerns individuals’ flexibility and willingness to adapt to new situations and people.
-Self-motivationConcerns individuals’ drive and motivation.

Appendix B. Online Learning Readiness of Digital Learning

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Figure A1. Computer/Internet Self-Efficacy.
Figure A1. Computer/Internet Self-Efficacy.
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Figure A2. Self Directed Learning.
Figure A2. Self Directed Learning.
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Figure A3. Learner Control.
Figure A3. Learner Control.
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Figure A4. Motivaation for Learning.
Figure A4. Motivaation for Learning.
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Figure A5. Online Communication self-efficacy.
Figure A5. Online Communication self-efficacy.
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Appendix C. Emotional Intelligence

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Figure A6. Trait Emotional Intelligence.
Figure A6. Trait Emotional Intelligence.
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Appendix D. Demographic

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Figure A7. Respondents Demographics Information.
Figure A7. Respondents Demographics Information.
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Figure 1. Online Learning Readiness (OLR) Framework [39].
Figure 1. Online Learning Readiness (OLR) Framework [39].
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Figure 2. The trait emotional intelligence scale consists of four dimensions and 15 components with reference to their corresponding dimension. It is worth noting that the components “adaptability” and “self-motivation” are not tied to any particular dimension but instead flow directly into the overall trait EI score.
Figure 2. The trait emotional intelligence scale consists of four dimensions and 15 components with reference to their corresponding dimension. It is worth noting that the components “adaptability” and “self-motivation” are not tied to any particular dimension but instead flow directly into the overall trait EI score.
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Figure 3. Research model.
Figure 3. Research model.
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Table 1. Demographic of Participants.
Table 1. Demographic of Participants.
FrequencyPercent
N = 103
GenderFemale6361.2%
Male4038.8%
Age21–297875.7%
17–202019.4%
30–3932.9%
40–4921.9%
FacultyBusiness6159.2%
Computing1312.6%
Arts and Social Sciences109.7%
Engineering87.8%
Applied Sciences and Mathematics65.8%
Design32.9%
Communication Center for Teaching and Learning11.0%
Shariah and Law11.0%
Education levelUndergraduate8784.5%
Postgraduate1615.5%
Year and semester1st Year, Semester 23130.1%
3rd Year, Semester 62423.3%
4th Year, Semester 82423.3%
2nd Year, Semester 41211.7%
1st Year, Semester 154.9%
2nd Year, Semester 354.9%
3rd Year, Semester 521.9%
Table 2. Reliabilities of Online Learning Readiness (OLRS) Dimensions.
Table 2. Reliabilities of Online Learning Readiness (OLRS) Dimensions.
Scale Cronbach’s Alpha
Computer/Internet self-efficacy (CIS) 0.766
Self-directed learning (SDL) 0.686
Learner control (LC) 0.471
Motivation for learning 0.811
Online communication self-efficacy (OCS) 0.725
Total (18 items)0.872
Table 3. Reliabilities of Trait Emotional Intelligence Dimensions.
Table 3. Reliabilities of Trait Emotional Intelligence Dimensions.
Scale Cronbach’s Alpha
Well-being 0.80
Self-control 0.54
Emotionality 0.66
Sociability 0.70
Total (30 items)0.89
Table 4. Mean and Standard Deviation of Online Learning Readiness Dimensions.
Table 4. Mean and Standard Deviation of Online Learning Readiness Dimensions.
DimensionsMeanStd. Deviation
Computer/Internet self-efficacy (CIS)4.140.618
Self-directed learning (SDL)3.440.655
Learner control (LC)3.000.654
Motivation for learning3.430.799
Online communication self-efficacy (OCS)3.560.839
Table 5. Mean and Standard Deviation of Emotional Intelligence Dimensions.
Table 5. Mean and Standard Deviation of Emotional Intelligence Dimensions.
DimensionsMeanStd. Deviation
Well-being4.531.079
Self-control4.000.782
Emotionality4.330.886
Sociability3.800.894
Table 6. Pearson’s Correlation Coefficient between the Students’ Levels of Emotional Intelligence and Online Learning Readiness Levels.
Table 6. Pearson’s Correlation Coefficient between the Students’ Levels of Emotional Intelligence and Online Learning Readiness Levels.
123456789
1. CIS1
2. SDL0.389 **1
3. LC0.292 **0.585 **1
4. MFL0.319 **0.551 **0.522 **1
5. OCS0.445 **0.348 **0.427 **0.506 **1
6. Well-being0.250 *0.317 **0.1520.282 **0.223 *1
7. Self-control0.0730.0820.1260.1910.1610.555 **1
8. Emotionality0.0580.0200.0450.1930.233 *0.424 **0.464 **1
9. Sociability0.0440.0220.1040.1650.312 **0.501 **0.409 **0.599 **1
**: Correlation is significant at the 0.01 level (2-tailed). *: Correlation is significant at the 0.05 level (2-tailed).
Table 7. Regression Analysis Between the Students’ Levels of Emotional Intelligence and Online Learning Readiness Levels.
Table 7. Regression Analysis Between the Students’ Levels of Emotional Intelligence and Online Learning Readiness Levels.
CISSDLLCMFLOCS
R = 0.275,
R2 = 0.076,
p = 0.000		R = 0.367,
R2 = 0.134,
p = 0.000		R = 0.171,
R2 = 0.029,
p = 0.000		R = 0.294,
R2 = 0.086,
p = 0.000		R = 0.325,
R2 = 0.105,
p = 0.000
Well-beingβ0.3400.4600.1120.2400.084
t2.7153.7930.8691.9230.682
p0.0080.0000.3870.0570.467
Self-controlβ−0.081−0.0930.0730.023−0.011
t−0.656−0.7820.5770.187−0.094
p0.5130.4360.5650.8520.925
Emotionalityβ0.011−0.047−0.0730.0940.060
t0.088−0.378−0.5620.7400.478
p0.9300.7060.5750.4610.633
Sociabilityβ−0.100−0.1430.062−0.0210.239
t−0.776−1.1450.468−0.1631.878
p0.4390.2550.6410.8710.063
Computer/Internet self-efficacy (CIS); Self-directed learning (SDL); Learner control (LC); Motivation for learning (MFL); Online communication self-efficacy (OCS).
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Susanto, H.; Setiana, D.; Besar, N.; Najib Ali, M.; Susanto, A.K.S.; Seruddin, R.; Ibrahim, F. Leveraging Technology Enhancement: The Well-Being Emotional Intelligence, Security Keys to the University Students’ Readiness in Digital Learning Ecosystem. Sustainability 2024, 16, 3765. https://doi.org/10.3390/su16093765

AMA Style

Susanto H, Setiana D, Besar N, Najib Ali M, Susanto AKS, Seruddin R, Ibrahim F. Leveraging Technology Enhancement: The Well-Being Emotional Intelligence, Security Keys to the University Students’ Readiness in Digital Learning Ecosystem. Sustainability. 2024; 16(9):3765. https://doi.org/10.3390/su16093765

Chicago/Turabian Style

Susanto, Heru, Desi Setiana, Norainna Besar, Muhamad Najib Ali, Alifya Kayla Shafa Susanto, Rozaidin Seruddin, and Fahmi Ibrahim. 2024. "Leveraging Technology Enhancement: The Well-Being Emotional Intelligence, Security Keys to the University Students’ Readiness in Digital Learning Ecosystem" Sustainability 16, no. 9: 3765. https://doi.org/10.3390/su16093765

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