The Relationship Between Internet Self-Efficacy, Self-Directed Learning, and Motivation for Learning towards Technology Acceptance in Digital Learning among Indigenous Society in
Malaysia
Nor Intan Adha Hafit1*, Azilah Anis1, Nur Lyana Syamimie Shuhaime1, Md Murad Miah1
1 Faculty of Business and Management, Universiti Teknologi MARA, Selangor Campus, Malaysia
*Corresponding Author: intan520@uitm.edu.my
Accepted: 15 March 2021 | Published: 1 April 2021
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Abstract: With the development of the Internet and new technologies, digital education has ended up a promising solution for the society which are currently in an environment of intense change. Orang Asli community is unendingly given support by the government for their development so as not to expand the digital differences with other advanced races in Malaysia.
IT knowledge and access to internet and computers appear as one of the attainable choices for the young people to support the information that society value. The purpose of this study was twofold: 1) To examine the level of technology acceptance of digital learning among indigenous society. 2) To examine the relationship between internet self-efficacy, self-directed learning, motivation for learning, and technology acceptance. Hope this contribution of study can help this society to emerge. Therefore, the proposed study is to identify the factors contributing to acceptance of digital learning among indigenous society in Cameron Highland.
A preliminary focus group interview will be conducted to understand the issues, problems and factors affecting the acceptance of digital learning. Next, 200 questionnaires are expected to be distributed to indigenous society. The questionnaires will be analyzed both descriptive and structured equation modelling (SEM), applying SmartPLS software version 28, and hoped the output of the research can contribute to their development for a better life.
Keywords: Internet self-efficacy, Self-directed learning, Motivation for learning, Technology acceptance, Digital learning, Indigenous society in Malaysia
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1. Introduction
Digital learning was introduced first by Jay Cross in 1999 (Lin & Chen, 2017). With the development and enhancement of technological instruments, it seemed distinct clarifications and phrasing, for example, distance learning, online learning, network learning or web-based training, Internet-based training. Digital learning as the convenience of digital media (e.g, text, images) over the Internet, and the relevance of teaching materials and incentive methods to improve student learning and showing individual progress of knowledge and capabilities (Thoma, Turnquist, Zaver, Hall & Chan, 2019). Essentially, computer and network technologies have been applied to environments, including synchronous and asynchronous network learning, to capture media time, space, and time constraints to achieve learning focused on individual learning (Grand-Clement, 2017). Although the knowledge and flow of information are quick, the applications of digital learning cover a broad scope of disciplines
and industries. The definition is different in light of a particular situation. However, the definition suggested by the American Society for Training and Education (ASTD) is the most representative. It portrays digital learning as the pattern of students using or making use of the digital media to learn (Lin & Chen, 2017). Digital media include the Internet, corporate network, PCs or computers, satellite telecom, audiotapes, tapes, intelligent TV, and CD. The implementation involves network-based learning, computer-based learning, virtual study halls, and digital participation. Anttila, Hatonen, Luukkaala, and Kaila (2012) is considered as a digital learning tool for the acquisition of digital training information for the learning activity online or offline through networks and wireless (Hockly, 2012). Besides, digital learning approves customize and personal education, developing models that let students to develop as impartial peer individual. The idea converges—and covers—with ideas comprehensive of customizable learning, separated guidance, competency-based instruction, and analytics (Pardo, 2018). It individualizes and customizes learning to assurance for all students to reach their ability to succeed in a business and career. It focuses on excessive-quality instruction and gives admittance to challenging content, feedback through developmental evaluation and learning opportunities for learning each time and any place (Thoma et al., 2019).
Online readiness used to define the organization's capacity to implement digital media education of a convenient, efficient way (Martin, Stamper, & Flowers, 2020). Readiness is a necessary concept for the studies of online learning. However, technology development, digital learning has emerged as an effective solution for the society that is currently in an excessive change of environment (Shahid, 2018). In Malaysia perspective, the Orang Asli community is ceaselessly given consideration by the public authority for their development so as now not to broaden the digital differences with other progressed races in Malaysia. IT education and IT access give off an impression of being one of the respectable potential choices for the young people to support the information society values. For example, the Malaysian Communications and Multimedia Commission (MCMC), had organised the enhancement of holistic and infrastructure for the Orang Asli in 2023 (New Straits Times, 2019). To gain this objective, the National Fiberisation and Connectivity Plan (NFCP) started to equip the Orang Asli in the village with broadband access facilities to narrow down the digital differences (New Straits Times, 2019). Also, MCMC has planned for NFCP 4, which includes upgrading broadband access and cellular coverage in 151 Orang Asli villages nationwide, beginning from 2020 (New Straits Time, 2019). Therefore, an increasing variety of media, researchers, professionals’
adverse reviews connected to Orang Asli attitudes and actions in the media as well as a substantial response in research publication.
2. Literature Review
Internet self-efficacy
The definition of Internet self-efficacy is a person’s capability to consider his or her Internet utilization as properly as the capability to effectively use the Internet on their own (Chiu, Liang, Mao, & Tsai, 2016). Some researchers in the past had observed that Internet self-efficacy ought to foresee the learners’ effects as well as assisting to maintain their Internet-based learning activities (Wang, Shannon & Ross, 2013; Lin & Chen, 2017). Besides, greater high-quality learning beliefs (Chang, Liu, Sung, Lin, Chen, & Cheng, 2014), more classic learning techniques (Chuang, Lin & Tsai, 2015) and more advantageous searching approaches (Vanderlinde, Aesaert, & Van Braak, 2014) can be developed and can be anticipated as a result of having greater Internet self-efficacy. Chuang et al. (2015) detailed that achievement of a higher comprehension learners of Internet self-efficacy in online learning settings can likewise be higher than learners with a lower comprehension of Internet self-efficacy. For instance, Tsai
et al. (2011) revealed that high certainty learners in their ability to utilize the Internet were more prominent beneficial in the utilization of searching information in a Web-based learning task than the low certainty learners. Essentially, people who see themselves as unimaginably effectual in performing online task may also be additionally ready to utilize new Internet programs or applications (for example; wikis, blogs, search engines) to overcome the issues they discover (Kim, and Glassman, 2013). A few studies verify the connection between students' Internet self-efficacy and their attitudes and motivations toward web-based persevering with learning (Chen, & Tseng, 2012; Yilmaz, 2017).
In addition, Internet self-efficacy or belief in the ability of people to alter and behaviour the procedures of internet movements are indispensable to reveal particular skills which play an essential role in their efforts to use e-services. Moreover, self-efficacy shows how a good deal effort an individual will make for an exercise and it impacts individual’ alternatives in the learning process (Hsu & Chiu, 2004). Kim and Glassman constructed up an internet self- efficacy scale (ISS) and utilized factor analysis to represent that there are five measurements of the net self-efficacy: (a) reliance about a person’s capability to discuss online with others (correspondence self-efficacy); (b) reliance about a person’s capability to look for information on the internet (search self-efficacy); (c) reliance about a person’s ability to find out and differentiate the massive wide variety of online records (association self-efficacy); (d) reliance about a person’s capability to separate among on-line information based on pleasant (separation self-efficacy); and (e) reliance about a person's potential to reply to data dispensed online via others (i.e., receptive capacity) and create instructively important information (generative capacity) to add to the online data building method (all in all named as reactive/generative self- efficacy).
Furthermore, Internet self‐efficacy emphasises around what an individual accepts the person can achieve online now or later on (Sun, Yu, Lin, & Tseng, 2016). It does no longer allude to an individual's capability at performing particular Internet‐related works, for example, composing HTML, utilising a program, or transferring archives or files, for instance.
Relatively, it surveys a person’s judgment of their potential to make use of their Internet abilities in a moreover encompassing mode, for example, looking for information or troubleshooting search issues. The Internet requires improvement of a further association of skills that, to the beginner user, in any event, would possibly be overwhelming. These consist of building up and sustained a steady Internet connection, figuring out how to explore on the Internet, and scanning it for significant data. Internet self‐efficacy might be known from computer self‐efficacy as an individual’s belief that he or she can effectively operate a particular arrangement of practices wanted to set up, keep up and utilise efficaciously the Internet properly beyond quintessential primary personal computer abilities. However, Internet self-efficacy depends on several factors; previous computer experience, spending time online and bodily obstacles that are not below the direct control of the social community site owners and marketers, but through sites designed to be convenient to navigate, can affect the level of believing (Gangadharbatla, 2008). Also, excessive internet self-efficacy increases individuals’
behavioural, operational and metacognitive strategies to reach information in web-based environments and allows their learning accordingly. The use and adoption of web technologies rely on how much the individuals believe in their potential to efficiently recognise, handle and assess the online content. (Torun, 2019). The internet is used for purposes such as to get entry to information and sharing, games, communication, socialization and education.
Self-directed learning
Self-directed learning (SDL) can be defined as an extend in performance, information or capabilities sought after an individual utilizes anything, whenever, anyplace, and initiative in order to meet with his or her needs without the assistance of others (Herlo, 2017). This is a proof that independence in learning is a fundamental ability that an individual need to adjust to new conditions and environment to get sources and research instantly in handling issues, work or new conditions they face (Tan et. al., 2011). SDL is a reliance had through somebody to endeavour to clear up the issues they are facing to improve higher result (Brockett and Hiemstra, 2018). SDL can be seen as a procedure, purpose and technique (Nasri and Mansor, 2016). SDL is considered as a way in which controlling their own learning is the learners’
responsibility themselves, SDL is viewed as an objective that centres around the learners’
desire and disposition for self-direction. SDL has criteria such as: a) responsibility for is its obligation in distinguishing learning differences and defining learning objectives, b) self- administration and self-supervision is the progressing cycle of administrating assignments, managing time and information to enhance in making a move to meet the needs of learning, c) expansion of learning is creating an association between logical orders, connections among formal and informal learning and interests in and out of institutions(Khiat,2017).
Likewise, self-directed learning in digital learning settings is an enhance situation connected with both learning cycle process and learners’ attributes (Curran, Gustafson, Simmons, and Lannon, 2019). Study on self-directed learning in a digital age is very lacking, nonetheless, there is an improving utilization of these advanced technologies as a modality to encourage self-directed, casual, and accidental learning (Sumuer, 2018). The advantages of information, intuitiveness of technological offering, and capacity to talk about the ideas with partners in our current technologic world has overcome the majority of what can be made in normal digital education (Cervero and Daley, 2016).
Self-directed learning permitted a high self-efficacy individual and naturally inspired individual. Learners can likewise examine the needs of their learning, define a person’s objectives, choose a suitable strategy to accomplish those objectives, assess themselves, and are not scared of confronting new difficulties (Sukardjo & Salam, 2020). Self-directed learning (SDL) refers to the psychology of learners that intentionally manage themselves to pick up information and observe how to handle issues they facing (Lemmetty & Collin, 2020).). Self- directed students normally more effectively take part in learning assignments, for example, reading web-based learning information, finishing classroom tasks, arranging and assessing achievements of learning. High level of self-administration is significant in SDL and students to need to embraced various methodologies in managing different issues (Lee & Teo, 2010).
The differences among SDL and self-controlled learning allocates in their fundamental abilities. The works of SDL are at a huge scope level or also called as macro-level, and the advancement of self-directed learning belongs with the micro-level (Jossberger, Brand- Gruwel, Boshuizen, and Wiel, 2010). Self-directed learners will in general look on the web or Internet for information. Study on self-directed learning with development (SDLT) (Timothy et al., 2010) indicated the learners’ perspective on aggregate learning can improve learners' SDL. Learners of the SDL process help with the usage of Internet correspondence advancement for aggregate learning (Lee, Tsai, Chai, and Koh, 2014).
Motivation for learning
Learning motivation is portrayed as the reason for students' exercises and interests which drives students to conduct and hold their learning exercises and is additionally the internal drive that
causes learning exercises to move towards meeting objectives set-up by instructors (Lin and Chen, 2017). By definition, learning motivation would help in students' learning activities and accomplish learning needs set up by utilizing instructors. In Educational Psychology, learning capacity and learning motivation have been viewed as two subjective specifications to understand the learners. The learners' learning motivation could influence their learning results straightforwardly (Puteri, 2018). Learning motivation is an intermediary among response and stimulation. Alternatively stated, learning motivation is a learner’s personal judgement about rookies and affairs would current different information acquisition wishes because of different conclusions. Shabani (2012) viewed learning motivation as the natural trust to manage learner's learning objective, bring about learning practices to create a consistent attempt, strengthen comprehension history, and improve as well as upgrade the learning result. Elevating motivation to learn is one of the prevalent standards for productive education (Yovkov, 2020).
Besides that, motivation to learn displays that a learner needs to take a stage in, and examine from, a preparation action (Harandi, 2015). Study motivation in the factors of the theory of planned behaviour (TPB) indicates a behavioural variable. Learner motivation is regularly separated into two kinds: internal motivation and external motivation. Yovkov (2020) insist that learners would look forward to get motivators from others for the practices; for this situation, learning used to be purposive, nonetheless, could be changed from external into internal motivation. Despite the fact that learners would perhaps not, at this point be self- governing, the acquisition of some satisfaction motivation or the change into the self- development in the learning technique would be an appropriate motivation internalization cycle (Law, Geng, & Li, 2019). Ones with internal learning motivation did not need assistant or encouragement, ought to make decision independently, have fun and feeling of success in the process. On the other hand, external motivation was the learning motivation that comes through others’ punishment or rewards and identification to certain behavioural value. Internal motivation may be more self-sustaining and determine with excessive value, but external factors should additionally effect the motivation that is encouraging and exterior aid were fundamental (Tas, Brown, Esen-Danaci, Lysaker, & Brüne, 2012).). Lin and Chen (2017) stated that learning motivation is the learners’ objective or wish to take part in and make attempts to learn, which was performed on student desire of unique learning endeavour and the efforts on such activity.
Technology Acceptance
Technology is spread across almost all sectors of society. At least two trends can be seen when considering education: First, global education systems are integrating digital capabilities into curriculum and assessment (Granic & Marangunic, 2019). Second, intructors and educators are recommended to practice technology into their teaching and learning to facilitate planning or as a constructive assessment tool (Teo & Zhou, 2017). Among them, technology brings new issues, challenges and pressures in educational institutions (Scherer, Siddiq, & Tondeur, 2019).
The speed at which technology has evolved is astonishing. Today, teachers in many countries globally engage with "digital parents" who are evolving with advanced technologies to become an integral part of their lives (Aypay, Celik, Aypay, & Sever, 2012). Technology gives us chance to keep, store, and use the knowledge and information. It allows us to connect with people and resources globally. Research shows that the incorporation of technology is a complex process of educational transformation, and the range of technological use in organizations is still very diverse (Scherer et al., 2019). The use of emerging educational technology in education has increased in recent years, but the acceptance and use of technology remains an issue for educational institutions (Dumpit, & Fernandez, 2017). The literature repeatedly asks the question of which variables determine the integration of technologies in
education. The objective of education is to help students become a digitally wise individual and able to cope with the complexities and dynamics of modern society (Fraillon, Ainley, Schulz, Friedman & Gebhardt, 2014). However, this enhancement requires significant integration of technology into teaching and learning contexts (Pena-Lopez, 2016). The abundant literature examines the factors associated with this inclusion with a focus on digital learning (Straub, 2009).
However, there is one model that has dominated the field of research - the Technology Acceptance Model (TAM). The TAM includes few variables that directly or indirectly explain behavioural intentions and the use of technology (e.g., perceived utility, perceived ease of use, attitudes towards technology), and has been extended to external variables such as self- efficacy, subjective norms and ease of use of technology (Schepers & Wetzels, 2007). TAM has gained significant notoriety, in part for its portability in different contexts and models, its ability to explain differences in the intent or use of technology, and its ease of specification in modelling structural equations (Aypay et al., 2012).
3. Methodology
The current study will be using convenience sampling method to collect data. Through the help of contact persons in the department of Orang Asli in Cameron Highland. 200 questionnaires are expected to be distributed to society right after the focus group interview. The questionnaires will be analysed both descriptive and structured equation modelling (SEM), applying SmartPLS software version 28. The survey questionnaire is divided into 3 parts. Part A is concerned with the respondents’ demographic information including gender, age, ethnicity, marital status, working position and Part B is concerned with the literature review questions which involved:
Table 1: Variables and selected items
Variables Items
Internet self-efficiency Kim and Glassman developed an internet self-efficacy scale (ISS) and used factor analysis to illustrate that internet self-efficacy has five dimensions with 16 items.
Self-directed learning Carson, (2012) with 12-items Self-directed Learning Inventory.
Motivation for learning Items from Pintrich, R. R., & DeGroot, E. V. (1990), 22 items.
Technology Acceptance Items from Anchalee Ngampornchai & Jonathan Adams, 2016, 18 items.
4. Conclusion
The work discussed the meaning of acceptance of digital learning in indigenous society.
Therefore, it has contributed to the literature by proposing a conceptual framework based on the technology acceptance model. The framework suggests the direct effects of Internet self- efficacy, self-directed learning, motivation for learning, and technology acceptance. However, this conceptual framework is still based on an in-depth literature review and has not been empirically tested, which is an opportunity for future research. Finally, this survey can serve as a basis for future research to analyze key factors for better innovation and competitive advantage.
References
Anttila, M., Välimäki, M., Hätönen, H., Luukkaala, T., & Kaila, M. (2012). Use of web-based patient education sessions on psychiatric wards. International Journal of Medical Informatics, 81(6), 424-433.
Aesaert, K., & Van Braak, J. (2014). Institutionalised ICT use in primary education: A multilevel analysis. Computers & Education, 72, 1-10.
Aypay, A., Celik, H. C., Aypay, A., & Sever, M. (2012). Technology Acceptance in Education:
A Study of Pre-Service Teachers in Turkey. Turkish Online Journal of Educational Technology-TOJET, 11(4), 264-272.
Brockett, R. G., & Hiemstra, R. (2018). Self-direction in adult learning: Perspectives on theory, research and practice. Routledge.
Chiu, Y. L., Liang, J. C., Mao, P. C. M., & Tsai, C. C. (2016). Improving health care providers' capacity for self-regulated learning in online continuing pharmacy education: the role of internet self-efficacy. Journal of Continuing Education in the Health Professions, 36(2), 89-95.
Chuang, S. C., Lin, F. M., & Tsai, C. C. (2015). An exploration of the relationship between Internet self-efficacy and sources of Internet self-efficacy among Taiwanese university students. Computers in Human Behavior, 48, 147-155.
Chang, C. S., Liu, E. Z. F., Sung, H. Y., Lin, C. H., Chen, N. S., & Cheng, S. S. (2014). Effects of online college student’s Internet self-efficacy on learning motivation and performance. Innovations in education and teaching international, 51(4), 366-377.
Curran, V., Gustafson, D. L., Simmons, K., Lannon, H., Wang, C., Garmsiri, M., ... & Wetsch, L. (2019). Adult learners’ perceptions of self-directed learning and digital technology usage in continuing professional education: An update for the digital age. Journal of Adult and Continuing Education, 25(1), 74-93.
Cervero, R. M., & Daley, B. J. (2016). Continuing professional education: A contested space. New directions for adult and continuing education, 2016(151), 9-18.
Carson, E.H. (2012). Self-directed learning and academic achievement in secondary online students. The University of Tennessee at Chattanooga, Chattanooga. Dumpit, D. Z., &
Fernandez, C. J. (2017). Analysis of the use of social media in Higher Education Institutions (HEIs) using the Technology Acceptance Model. International Journal of Educational Technology in Higher Education, 14(1), 5.
Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Gebhardt, E. (2014). Preparing for life in a digital age: The IEA International Computer and Information Literacy Study international report (p. 308). Springer Nature.
Grand-Clement, S. (2017). Digital Learning: Education and Skills in the Digital Age. RAND Europe.
Gangadharbatla, H. (2008). Facebook me: Collective self-esteem, need to belong, and internet self-efficacy as predictors of the iGeneration’s attitudes toward social networking sites. Journal of interactive advertising, 8(2), 5-15.
Granic, A., & Marangunic, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572- 2593.
Hockly, N. (2012). Substitute or redefine. Modern English Teacher, 21(3), 40-42.
Hsu, M. H., & Chiu, C. M. (2004). Internet self-efficacy and electronic service acceptance. Decision support systems, 38(3), 369-381.
Herlo, D. (2017). Self-directed learning on teacher training studies programs. Educația Plus, 18(2), 7-17.
Harandi, S. R. (2015). Effects of e-learning on Students’ Motivation. Procedia-Social and Behavioral Sciences, 181(2015), 423-430.
Jossberger, H., Brand‐Gruwel, S., Boshuizen, H., & Van de Wiel, M. (2010). The challenge of self‐directed and self‐regulated learning in vocational education: A theoretical analysis and synthesis of requirements. Journal of vocational education and training, 62(4), 415-440.
Kim, Y., & Glassman, M. (2013). Beyond search and communication: Development and validation of the internet self-efficacy scale (ISS). Computers in Human Behaviour Journal
Khiat, H. (2017). Academic performance and the practice of self-directed learning: The adult student perspective. Journal of further and Higher Education, 41(1), 44-59.
Lin, M. H., & Chen, H. G. (2017). A study of the effects of digital learning on learning motivation and learning outcome. Eurasia Journal of Mathematics, Science and Technology Education, 13(7), 3553-3564.
Lemmetty, S., & Collin, K. (2020). Self-directed learning as a practice of workplace learning:
interpretative repertoires of self-directed learning in ICT work. Vocations and Learning, 13(1), 47-70.
Lee, K., Tsai, P. S., Chai, C. S., & Koh, J. H. L. (2014). Students' perceptions of self‐directed learning and collaborative learning with and without technology. Journal of Computer Assisted Learning, 30(5), 425-437.
Law, K. M., Geng, S., & Li, T. (2019). Student enrollment, motivation and learning performance in a blended learning environment: The mediating effects of social, teaching, and cognitive presence. Computers & Education, 136, 1-12.
Martin, F., Stamper, B., & Flowers, C. (2020). Examining Student Perception of Readiness for Online Learning: Importance and Confidence. Online Learning, 24(2), 38-58.
Nasri, N. M., & Mansor, A. N. (2016). Teacher educators’ perspectives on the sociocultural dimensions of self-directed learning. Creative Education, 7(18), 2755.
New Straits Times (2019). NFCP to bridge Orang Asli digital gap. Retrieved From:
https://www.nst.com.my/news/government-public-policy/2019/10/530784/nfcp- bridge-orang-asli-digital-gap.
Pardo, A. (2018). A feedback model for data-rich learning experiences. Assessment &
Evaluation in Higher Education, 43(3), 428–438.
https://doi.org/10.1080/02602938.2017.1356905
Puteri, L. H. (2018). The apperception approach for stimulating student learning motivation. International Journal of Education, Training and Learning, 2(1), 7-12.
Pena-Lopez, I. (2016). Innovating Education and Educating for Innovation. The Power of Digital Technologies and Skills.
Pintrich, R. R., & DeGroot, E. V. (1990). Motivational and Self-Regulated Learning.Components of Classroom Academic Performance. Journal of Educational Psychology.
Sun, J. C. Y., Yu, S. J., Lin, S. S., & Tseng, S. S. (2016). The mediating effect of anti-phishing self-efficacy between college students’ internet self-efficacy and anti-phishing behavior and gender difference. Computers in Human Behavior, 59, 249-257.
Sumuer, E. (2018). Factors related to college students’ self-directed learning with technology. Australasian Journal of Educational Technology, 34(4).
Sukardjo, M., & Salam, M. (2020). Effect of Concept Attainment Models and Self-Directed Learning (SDL) on Mathematics Learning Outcomes. International Journal of Instruction, 13(3), 275-292.
Shabani, K. (2012). Dynamic assessment of L2 learners’ reading comprehension processes: A Vygotskian perspective. Procedia-Social and Behavioral Sciences, 32, 321-328.
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13-35.
Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of educational research, 79(2), 625-649.
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model:
Investigating subjective norm and moderation effects. Information &
management, 44(1), 90-103.
Thoma, B., Turnquist, A., Zaver, F., Hall, A. K., & Chan, T. M. (2019). Communication, learning and assessment: Exploring the dimensions of the digital learning environment. Medical teacher, 41(4), 385-390.
Tsai, C. C., Chuang, S. C., Liang, J. C., & Tsai, M. J. (2011). Self-efficacy in Internet-based learning environments: A literature review. Journal of Educational Technology &
Society, 14(4), 222-240.
Torun, E. D. (2019). University students’ innovative personality and internet self- efficacy. Current Debates in Film & Media Studies, 67.
Teo, T., & Lee, C. (2010). Fostering self-directed learning with ICT. ICT for Self-Directed and Collaborative Learning, 39-51.
Teo, T., & Zhou, M. (2017). The influence of teachers’ conceptions of teaching and learning on their technology acceptance. Interactive Learning Environments, 25(4), 513-527.
Timothy, T., Chee, T. S., Beng, L. C., Sing, C. C., Ling, K. J. H., Li, C. W., & Mun, C. H.
(2010). The self-directed learning with technology scale (SDLTS) for young students:
An initial development and validation. Computers & Education, 55(4), 1764-1771.
Tas, C., Brown, E. C., Esen-Danaci, A., Lysaker, P. H., & Brüne, M. (2012). Intrinsic motivation and metacognition as predictors of learning potential in patients with remitted schizophrenia. Journal of Psychiatric Research, 46(8), 1086-1092.
Wang, C. H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34(3), 302-323.
Yilmaz, R. (2017). Exploring the role of e-learning readiness on student satisfaction and motivation in flipped classroom. Computers in Human Behavior, 70, 251-260.
Yovkov, l. (2020). Motivation for traditional study and e-learning of desired and undesired specialties. Anthropological Researches and Studies, 1(10), 123-130.
