A modified technology acceptance model for digital learning in Chinese universities

Theoretical foundations of TAM and its adaptations

The Technology Acceptance Model (TAM), first introduced by Davis (1989), has been a dominant theoretical framework for understanding technology adoption across various domains, including education. TAM posits that two key constructs—Perceived Usefulness (PU) and Perceived Ease of Use (PEOU)—are crucial in shaping users’ attitudes toward a given technology, ultimately influencing their intentions and actual usage. Over the years, TAM has been extended to incorporate other variables to better suit different contexts, particularly in digital learning environments.

Recent studies have expanded TAM by integrating factors such as perceived enjoyment, satisfaction, and self-efficacy. For instance, Estriegana et al. (2019) added perceived enjoyment to understand its effect on DL acceptance, while Al Kurdi et al. (2020) included social influence and cultural factors to explore technology acceptance in Jordanian universities. Such extensions highlight the need to adapt TAM to the cultural and institutional contexts in which it is applied, particularly in non-Western educational systems.

Institutional and cultural factors in technology adoption

In addition to PU and PEOU, institutional policies and interpersonal interactions have been identified as critical factors influencing DL technology adoption. Tarhini et al. (2016) emphasized the role of cultural dimensions such as power distance and uncertainty avoidance in moderating the relationships between PU, PEOU, and technology acceptance. In Chinese universities, institutional policies related to DL infrastructure, training, and technical support significantly impact user adoption (Xue et al., 2021). Thus, sociocultural factors are particularly influential in the context of Chinese higher education.

Moreover, interpersonal interactions—particularly between students and teachers—play a pivotal role in shaping the perceived value of DL technologies. Research by Mehall (2020) highlights that effective teacher-student communication fosters a positive DL experience, which in turn enhances technology adoption. Given the importance of these factors in the Chinese educational context, this study integrates “Policy” and “Perceived Interpersonal Interaction” (PII) into the TAM framework to provide a more comprehensive understanding of DL technology acceptance.

Gaps in existing research

While there has been considerable research on DL technology adoption, most studies focus on specific platforms (Al-Adwan, 2020; Rahmawati, 2019), tools (Mehall, 2020; Mulenga and Marbán, 2020; Saeed Al-Maroof et al., 2020), or regions (Al-Adwan, 2020; Lazim et al., 2021; Mehall, 2020; Mulenga and Marbán, 2020; Rahmawati, 2019; Saeed Al-Maroof et al., 2020). Few studies (He and Zhu, 2017; Ling and Ze, 2011) examine the broader context of DL technology adoption across different types of universities within China. Some researchers, who focus on students’ perspectives, believe that student satisfaction is the key factor affecting the effectiveness of DL implementation (Wong and Chapman, 2023). Conversely, other researchers argue that interpersonal interaction between teachers and students has a positive impact on students’ learning outcomes (Tian and Shen, 2023). This study addresses these gaps by examining both students’ and teachers’ experiences with DL technologies in Chinese universities and considering a wider range of influencing factors, including institutional policy and interpersonal dynamics.

Research model

The customized TAM includes seven variables: Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude, Actual Usage, Intention to Continue Usage, Policy, and Perceived Interpersonal Interaction (PII). The proposed TAM for this study builds upon the traditional TAM (Davis, 1989) by integrating additional factors relevant to the Chinese educational context. The model is designed to capture the multifaceted nature of DL technology acceptance, considering cultural and institutional influences unique to Chinese universities. Perceived Ease of Use (PEOU) measures the extent to which users believe that using DL technology will be free of effort, hypothesizing that higher perceived ease of use will positively influence both perceived usefulness and users’ attitudes towards DL technology. Perceived Usefulness (PU) assesses the degree to which users believe that DL technology will enhance their learning or teaching performance, with higher perceived usefulness expected to directly improve users' attitudes towards DL technology and their intention to continue using it. Attitude represents users’ overall affective reactions to using DL technology, with positive attitudes predicted to lead to higher actual usage and stronger intentions to continue using DL technology. Actual Usage measures the frequency and extent to which DL technology is used by students and teachers, serving both as an outcome of the model and a predictor of future behavior. Intention to Continue Usage captures users' plans to keep using DL technology in the future, a critical indicator of long-term acceptance and integration of DL technology. Policy evaluates the influence of institutional policies and support on DL technology adoption, with effective policies expected to facilitate greater usage and positive attitudes towards DL technology. Perceived Interpersonal Interaction (PII) uniquely measures the perceived impact of DL technology on the interaction between students and teachers, with positive perceptions of interpersonal interaction hypothesized to enhance overall acceptance and actual usage of DL technology.

As shown in Figure 1, there are 10 hypotheses, labeled H1-H10, within the entire model. In the figure, directional arrows are used to indicate the correlation between connected variables. These hypotheses will be tested through subsequent correlation analysis of the collected data.

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Figure 1.

Research model.

The TAM framework relies on correlation analysis to evaluate the hypothesized relationships among the model’s variables. Each hypothesis (H1-H10) posits specific correlations between variables within the TAM framework, capturing both direct and indirect influences on DL technology acceptance. For example, PEOU and PU are hypothesized to directly influence Attitude toward DL technology, while Attitude is expected to correlate positively with Actual Usage and Intention to Continue Usage. By testing these correlations, the study aims to validate how each TAM component interrelates, thereby identifying key factors in DL acceptance and use.

Thus, through correlation analysis, the customized TAM framework not only tests the traditional TAM constructs but also provides insights into how educational policy and interpersonal factors contribute to digital learning acceptance in Chinese universities.

Case selection and questionnaire design

This study used questionnaires and interviews to obtain qualitative and quantitative data. The two universities chosen for this study are both representative and typical comprehensive universities in China. They differ in terms of geographical location, academic programs, and levels of policy support, providing a certain level of diversity for sample selection. Yunnan University, one of the universities involved in the study, is a comprehensive university, while Fujian Normal University belongs to the category of normal universities (teacher-training oriented). Both types of comprehensive universities are common in China, and selecting these two universities allows for consideration of potential differences in the survey results between different types of universities.

Moreover, when distributing survey questionnaires and selecting interview subjects, factors such as participants’ gender, age, grade or position, and academic backgrounds will be considered. Measures such as stratified sampling and random sampling will be employed to ensure that the selected samples are diverse and representative of various identity backgrounds.

The questionnaire’s question design was based on the studies of Davis (1989), Dixson (2015), Bhattarai and Maharjan (2020). Their questionnaire designs have been validated, indicating good reliability. The survey is divided into nine sections, comprising a total of 30 questions. These questions are designed based on research hypotheses and aim to collect quantitative data on both the usage of DL technology by selected university teachers and students, as well as the factors influencing their adoption and usage of DL technology.

Firstly, in the “Respondent Information” section, consisting of five questions designed to categorize respondents based on various criteria, participants are asked about their grade or position, gender, age groups, and more. This classification aligns with the categorization standards for academic qualifications and teaching positions in Chinese universities and will help the researcher explore the impact of educational backgrounds on the acceptance and usage of DL technology.

Moving on to the “Digital Learning Usage” section includes questions regarding whether participants have used DL tools, types of DL tools commonly used, usual methods and platforms for DL usage, and the frequency of DL tools usage. These questions aim to understand the basic usage patterns of DL technology within Chinese universities.

Following that, in the “PEOU” section, which includes questions such as “Do you find the interfaces of the digital learning tools you use easy to operate?” and “Do you often need to search for functions or make multiple attempts when using these tools?” These questions quantitatively assess participants’ perceptions of the usability of currently used DL technology from different angles.

Moreover, in the “PU” section, the researcher asks questions like “Has the use of digital learning tools improved your learning outcomes?” and “Do you find that the use of digital learning tools helps with your learning progress?” to understand participants’ evaluations of how much DL technology has helped them.

Furthermore, in the “Attitude” section, respondents are asked about their overall impressions of DL tools and whether they would be willing to incorporate DL tools as a regular part of their learning or teaching. This section also allows participants to select aspects of DL tools they are primarily concerned about.

The “Perceived Interactivity” section collects participants’ evaluations of interpersonal interactivity during DL technology usage. Questions include whether they feel there are more opportunities for human interaction, the frequency of communication interactions, and the impact of teacher-student interaction on DL outcomes.

Moreover, in the “Policy Influence” section, questions assess the impact of university policies and guidelines on DL, as well as whether participants believe more involvement of teachers and students is needed in policy formulation.

Finally, in the “Intention to Continue Usage” sections, the researcher will measure the extent to which participants are willing to continue using DL technology through specific questions.

In summary, this research survey covers the scope of all research hypotheses, and through the design of these questions, the researcher will be able to obtain the quantitative data needed for the study.

Interview design

To collect qualitative data that may not be easily obtained through surveys, the researcher has decided to conduct semi-structured interviews with both teachers and students to explore their experiences, challenges encountered while using DL technology, and their suggestions and views on the future development of this technology.

According to the plan, six students and four teachers from each of the Arts, Sciences, and Engineering disciplines at each university will be selected as interview participants. The selection process will take into consideration the diversity of participants in terms of grade or position to ensure the representativeness of the interviewees. In total, there will be 60 interviewees from both universities combined.

The interviews will be conducted through online channels, and participants in the survey will have the chance to voluntarily participate in the interviews. The interviews will be guided by an interview outline and may delve into specific topics based on the individual responses and experiences of the participants.

The interview outline will cover various aspects, including the participants’ basic information such as gender, age, grade or position, and field of study. It will also explore their past experiences with similar technologies and how these experiences have influenced their usage of DL technology. Additionally, the interviews will investigate the participants’ perceptions of DL technology, the conveniences and challenges they face while using DL products, their motivations for using DL both internally and externally, and their preferences regarding types of technology.

By conducting these interviews, the researcher aims to gain deeper insights into the qualitative aspects of DL technology usage and gather valuable feedback from participants to enhance the understanding of their experiences and perspectives.

Ethical and confidentiality considerations

In this research, both interviews and survey questionnaires will strictly adhere to the principles of securing informed consent and upholding the confidentiality of the participants. Throughout the data collection process, ensuring that participants willingly provide their consent and safeguarding the confidentiality of their responses will be of paramount importance. Moreover, in the subsequent data analysis and the final reports, meticulous measures will be taken to prevent drawing conclusions directly linked to individuals, thereby protecting their privacy and anonymity. Survey participants will retain the option to refrain from responding to any questions they prefer not to answer and may choose to withdraw from the study at their own discretion. The design of interviews, survey questionnaires, and the research procedures will align with established academic ethical standards.

Questionnaire data

As shown in the table above, out of the participants in this survey, 175 people are students, accounting for 85.4%, while an additional 30 people are teachers, accounting for 14.6% (Table 1).

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Table 1.

Distribution of students and teachers.

		Student/Teacher
		Frequency	Percent	Valid percent	Cumulative percent
Valid	Student	175	85.4	85.4	85.4
	Teacher	30	14.6	14.6	100.0
	Total	205	100.0	100.0

In terms of grade and position distribution, master’s students represent the largest proportion among the students, totalling 84 individuals, accounting for 41% of the entire sample size. Following them are undergraduate students, comprising 40% of the entire sample with a total of 82 individuals. Doctoral candidates have the lowest representation among the students, with only 11 individuals (Table 2).

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Table 2.

Distribution of grade and position.

		Grade/Position
		Frequency	Percent	Valid percent	Cumulative percent
Valid	Bachelor student	82	40.0	40.0	40.0
	Master student	84	41.0	41.0	81.0
	PhD. Candidate	11	5.4	5.4	86.3
	Junior position	3	1.5	1.5	87.8
	Intermediate position	15	7.3	7.3	95.1
	Senior position	10	4.9	4.9	100.0
	Total	205	100.0	100.0

Among the teachers, intermediate-level professionals are the most numerous, constituting 7.3% of the total sample size. Next are the senior-level professionals, totaling 10 individuals and making up 4.9% of the sample. Teachers with junior-level positions are the least represented, with only three individuals, making them the smallest group within all categories.

Through this analysis, it is evident that the sample covers students from various educational stages and teachers of all position levels within the Chinese higher education system, demonstrating a good level of sample diversity and representativeness.

The gender distribution of the sample is fairly balanced, with 99 males, accounting for 48.3%, and 106 females, comprising 51.7% (Table 3).

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Table 3.

Distribution of gender.

		Sex
		Frequency	Percent	Valid percent	Cumulative percent
Valid	Male	99	48.3	48.3	48.3
	Female	106	51.7	51.7	100.0
	Total	205	100.0	100.0

The sample is generally skewed towards a younger demographic, with 91.7% of participants falling within the age range of 18–44 years. There are an additional 17 individuals whose ages fall between 45 and 59 years, accounting for 8.3% of the sample, and there are no participants aged 60 or older (Table 4).

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Table 4.

Distribution of age group.

		Age group
		Frequency	Percent	Valid percent	Cumulative percent
Valid	18–44 years old	188	91.7	91.7	91.7
	45–59 years old	17	8.3	8.3	100.0
	Total	205	100.0	100.0

Terms of academic disciplines, the Arts have the highest representation, with a total of 118 individuals, exceeding half of the sample size. The number of individuals in the science and engineering fields is approximately equal, with 40 and 43 individuals, respectively. Additionally, there are four individuals with a focus on sports-related disciplines (Table 5).

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Table 5.

Distribution of academic disciplines.

		Field of study
		Frequency	Percent	Valid percent	Cumulative percent
Valid	Arts	118	57.6	57.6	57.6
	Science	40	19.5	19.5	77.1
	Engineering	43	21.0	21.0	98.0
	Others	4	2.0	2.0	2.0
	Total	205	100.0	100.0

The researcher calculated the average values for all evaluation types under the categories of PEOU, PU, attitude impact, motivation for use, usage, perceived interpersonal interaction, policy impact, and willingness to continue using. These average values were used for correlation analysis as the values for the respective factors.

Through the correlation analysis, the table below presents some correlations in the data, providing clear access to the correlation coefficients and significance levels associated with all hypotheses (Table 6).

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Table 6.

Results of correlation analysis.

	Pearson correlation	Sig. (2-Tailed)
PEOU-PU	0.543	<0.001
PEOU-attitude	0.551	<0.001
PU-attitude	0.680	<0.001
Attitude-actual usage	0.012	0.864
Attitude-PII	0.482	<0.001
Attitude-policy	0.360	<0.001
Attitude-intention to continue usage	0.463	<0.001
Actual usage-policy	0.171	0.014
PII-intention to continue usage	0.427	<0.001
Policy-intention to continue usage	0.532	<0.001

The analysis reveals notable correlations among factors in the research model shown in Figure 1. Perceived Ease of Use (PEOU) is significantly positively correlated with Perceived Usefulness (PU) (r = 0.543, p < .01) and with attitude (r = 0.551, p < .01). PU also shows a significant positive correlation with attitude (r = 0.680, p < .01). Furthermore, attitude is significantly positively correlated with Perceived Individual Influence (PII) (r = 0.482, p < .01), policy (r = 0.360, p < .01), and the intention to continue usage (r = 0.463, p < .01). Additionally, PII is significantly positively correlated with both policy and the intention to continue usage (r = 0.427, p < .01). Lastly, policy shows a significant positive correlation with the intention to continue usage (r = 0.532, p < .01). In addition to these significant relationships, some correlations between factors were found to be non-significant.

Interview data

This study collected a series of opinions and perspectives on the usage of DL technology in universities by interviewing multiple respondents. A total of 32 respondents were interviewed, representing students and teachers from various backgrounds and experiences. The following summarizes their views, including primary viewpoints and some unique perspectives.

The answer to the question “When did you start using digital learning, and did you have any prior experience with similar technologies? Do you think the experience you gained from using technology tools before has a significant impact on your current proficiency in using digital learning tools?” shows that the use of DL technology in higher education is gradually becoming more widespread. Twenty-six people mentioned that they began using these technologies during their undergraduate or postgraduate studies, indicating that DL technology has become a common tool in the educational process. They all mentioned that “prior experience has a significant impact on proficiency in using DL,” and they noted that their prior experience with other technologies, such as computers, was helpful in quickly mastering the use of digital learning tools.

Regarding the question, “Did you start using digital learning technology voluntarily due to its advantages or was it solely to meet the university’s requirements?”, 20 respondents indicated that they started using DL technology spontaneously after recognizing its advantages. However, six respondents initially used DL to meet university requirements. The remaining participants mentioned that they used DL tools for both meeting the university’s requirements and fulfilling their own needs. Although the two universities surveyed had their own DL platforms, few respondents were familiar with them. Most respondents preferred external platforms over the university’s platforms, citing richer resources and more comprehensive functionality.

The answer to the question “How frequently did you use digital learning technology before the pandemic? Do you agree that the pandemic has accelerated the adoption of related technologies? Are you willing to continue using this technology after the pandemic, or do you lean toward returning to traditional learning and teaching methods?” shows that before the COVID-19 pandemic, the usage of DL technology was relatively low, with traditional face-to-face teaching being the primary mode of instruction in universities. However, the pandemic had a profound impact on the adoption of this technology, a sentiment widely echoed in the interviews. Respondents generally believed that the pandemic accelerated the widespread use of DL technology because it restricted face-to-face teaching. Twenty-one individuals mentioned that they “will continue to use DL technology”. Schools and teachers quickly embraced online education tools to continue their educational activities. DL technology became a necessary alternative and was widely accepted and used to meet students’ learning needs.

However, 10 respondents also expressed the desire to return to some traditional learning methods after the pandemic, especially face-to-face teaching. They believed that traditional teaching methods “still have irreplaceable value”, including teacher-student interaction, in-person communication, and hands-on experiments. They hoped to find a balance between DL and traditional teaching to best meet the diverse needs and learning styles of students.

Therefore, the frequency of DL technology usage was relatively low before the COVID-19 pandemic, but the pandemic drove its widespread application. In the future, DL technology may continue to evolve to meet the diverse needs of students and teachers, ensuring that they can benefit fully. This viewpoint reflects the diversity and complexity extracted from the interview data.

In response to the question, “What features of digital learning applications do you find convenient or inconvenient to use?” respondents generally believed that DL technology offered several advantages to higher education.

First, two individuals pointed out that it significantly enhanced teacher-student interaction, introducing more interactivity into the educational process, and making students more actively engaged in learning. DL tools such as Treenity and Rain Classroom were seen as effective in promoting communication and interaction between teachers and students, providing more learning opportunities.

Second, 13 individuals mentioned that DL technology-enriched learning resources by offering various multimedia materials, including videos, audio, and online content. This was beneficial for students as it allowed them to engage with course content in multiple ways, making learning more dynamic and interesting. Rich learning resources also cater to students with different learning styles and needs.

Third, two individuals highlighted the “on-demand accessibility” of DL tools, which made learning more flexible. Students could choose when and where they wanted to study based on their schedules and personal needs, especially benefiting those juggling work, family, or other obligations.

Finally, four individuals pointed out that this technology was viewed as a tool to promote educational equity. Through online education platforms, students can access educational resources globally, regardless of geographical location, economic status, or other limitations. This helped narrow the educational gap between different students, providing more equal learning opportunities. This was particularly significant during the pandemic as it allowed students to continue learning despite geographical restrictions and campus closures.

When it came to the issues and shortcomings of DL technology, respondents raised several points. First, five participants believed that current DL materials lacked systematic organization and needed more integration to meet learning needs. Second, one respondent pointed out the need for improved search functionality to cater to personalized needs and provide more precise learning resources. Additionally, 10 respondents expressed dissatisfaction with certain features of online learning platforms, such as the lack of note-taking capabilities while viewing course materials, which was inconvenient for post-class review. At the same time, most DL tools lacked offline functionality, making it inconvenient to use without an internet connection. Finally, six participants mentioned that DL tools were complex and required payment, which posed some inconvenience, particularly for financially constrained students.

DL technology had multifaceted effects on higher education, and respondents’ views were diverse. In response to the question posed to students, “Do you think digital learning has made significant changes to your study habits? Are there any differences in your study methods after using digital learning?”, 14 students believed that DL had changed their learning methods, granting them more autonomy and flexibility to study at times and places of their choice. Additionally, DL provided diverse learning resources, including videos, audio, and online materials, catering to various learning needs and styles. Thus, four students also pointed out that DL encouraged self-directed learning, fostering time management and autonomy. However, they believed that DL could be distracting and challenging to maintain focus. Moreover, eight respondents felt that DL had “little impact” on their learning and teaching habits, and they continued to adopt traditional methods.

For teachers, the researcher asked, “Has the use of digital learning technology caused you to change your teaching and lesson planning methods to better adapt to the changes brought by new technology? What changes have you made?”. Six teachers stated that DL required them to adjust their teaching methods, providing more interaction and personalized education, as well as increasing interaction and real-time feedback with students. In summary, the impact of DL technology varies due to individual differences, and in the future, more personalized support and resources may be needed to better meet the diverse needs of students and teachers to ensure they benefit fully.

Respondents were optimistic about the future of DL technology, hoping that future technology would provide richer and more systematic learning resources covering various subjects and domains. Additionally, they expected future technology to focus on increasing interactive features to promote closer interaction between teachers and students, as well as student discussions and collaborations, making learning more engaging. Personalized services were also highly anticipated, with DL technology expected to cater to the individual needs of different types of learners to better serve a wide range of students. Respondents also suggested that future technology should emphasize timely content updates to ensure that learning materials are synchronized with the latest knowledge and trends. Finally, they hoped that different DL platforms could integrate more effectively, making it more convenient for students to access a variety of learning resources.

During the interview process, some respondents provided perspectives beyond the scope of the interview outline, but these perspectives were equally important. One respondent believed that the future of DL technology should focus on cultivating students’ abilities to question, inquire, and utilize tools, rather than just providing information. Another respondent mentioned that technology could be integrated with real-life practices to make learning more dynamic and understandable, hoping that future technology would provide more of these learning experiences. One respondent emphasized that digital learning technology should enhance the interactive experience to meet the demand for personalized services and provide more interactivity. Some respondents hoped to retain some traditional learning methods, especially face-to-face teaching, after the pandemic to maintain interpersonal interactions. Many respondents expressed that DL technology changed their learning methods, making learning more convenient, but also raised issues and needs regarding digital learning.

These additional insights reflect the diversity and complexity of DL technology applications and are valuable references for improving digital learning technology and educational policies. They offer invaluable references for optimizing digital learning technologies and informing the development of effective educational policies. By revealing the diversity and complexity of its applications, they provide essential guidance for enhancing digital learning tools and crafting innovative educational policies.

Recommendations for universities

Universities should continue to actively promote the application of DL technology by investing in modern online learning platforms and resources, ensuring that they provide more flexible, diverse, and high-quality online courses. This will help meet the diverse needs of students, particularly those requiring remote learning. Moreover, universities can offer tailored training for teachers and students to assist them in better addressing DL challenges and enhancing DL efficiency. Universities should also encourage and support research and innovation to continually improve the digital learning experience and invest in research projects to understand the impact of DL technology on student learning and teacher teaching, in order to better meet future demands. Through interviews, researchers found that most participants were not familiar with their own school’s DL learning platform, and the majority of participants stated that they spent more time using external platforms. Therefore, universities should reconsider the positioning and necessity of self-built platforms to avoid wasting resources. At this stage, universities should not completely transition away from traditional teaching methods in favor of DL.

Recommendations for administrative bodies

Administrative bodies, including government education departments, play a crucial role in promoting the application of digital learning technology. They should provide financial support to help schools and educational institutions improve their digital learning infrastructure and reduce disparities in DL infrastructure among different regions and types of universities, ensuring that all students have access to high-quality online learning resources. Additionally, they should formulate policies and regulations that encourage schools to adopt digital learning technology while strengthening policy oversight to ensure data privacy and network security. Legal frameworks for digital learning should be established to protect the rights of students and teachers. Administrative bodies should also promote research and development of digital learning technology, supporting innovative projects and interdisciplinary collaboration to continually enhance the online learning experience.

Recommendations for digital learning developers

Digital learning developers play a critical role as their work directly impacts the experiences of students and teachers. Thus, developers should focus on creating user-friendly and navigable DL tools to ensure that students and teachers can easily access and use these tools. They should prioritize diversity and personalization by offering diverse learning resources and teaching tools to cater to different learning styles and needs. Future products can improve features that receive high user feedback, such as note-taking functionality, and add offline access capabilities. Developers should also focus on data security and privacy protection to safeguard students’ personal information while providing clear privacy policies and terms of use.

Recommendations for teachers and students

Teachers and students, as the ultimate users of digital learning, play a crucial role in its success. Teachers should actively engage in professional development and training to enhance their teaching skills in a digital learning environment. This includes proficiency in using online educational tools and facilitating online interaction. Students should fully utilize digital learning resources, actively participate in online courses, and develop independent learning skills. Efficient use of DL requires high levels of self-directed learning, enabling students to plan their learning, set goals, and actively engage in online discussions and activities. Both teachers and students should focus on managing their attention in a digital learning environment to minimize distractions and improve focus. Schools and educational institutions should encourage positive interactions between teachers and students to enhance educational quality and student satisfaction. It is recommended that students actively interact with teachers, ask questions, seek assistance, participate in discussions, and collaborate on group projects.

These recommendations aim to assist all stakeholders in adapting better to the digital learning environment to meet future learning needs effectively.